Computational Materials, Chemistry, and Biochemistry: From Bold Initiatives to the Last Mile

The generalized valence bond (GVB) method was developed by William A. Goddard III in the latter half of the 1960s. The GVB wavefunction addresses the most serious deficiencies of the Hartree–Fock (HF) wavefunction and its early application led to a number of important new concepts. Unfortunately, the complexity of the GVB equations limited its application to small atoms and molecules. This limitation was overcome by the development of the GVB(PP/SO) method in the early 1970s by William J. Hunt, P. Jeffrey Hay, and W. A. Goddard III. The GVB(PP/SO) wavefunction combines the perfect pairing (PP) spin function with orthogonality between orbitals in different electron pairs (SO), leading to a greatly simplified energy expression, but also some loss of generality. The GVB(PP/SO) and associated CI methods were used to elucidate the essential features of a broad range of molecular problems in the 1970s through the early 1990s. In the late 1990s, spurred by the developments in Joseph Gerratt’s group, the full GVB method enjoyed a renaissance. The studies by Gerratt and coworkers and Dunning and coworkers have, once again, shown the importance of GVB theory as a sound theoretical foundation for understanding molecules and molecular processes. In addition, the GVB wavefunction, when cast in its natural orbital (NO) form, is a compact, yet efficient zero-order wavefunction for the treatment of dynamical correlation.

The two-phase thermodynamic (2PT) model provides an efficient route for calculating the absolute entropy values from a typical trajectory of molecular dynamic simulations. The method calculates the entropy based on the vibrational density of state (DOS), which is considered to be the superposition of contributions from a gas-like and a solid-like component. A fluidicity parameter is used to determine the fraction of the gas-like component, which is considered as a hard-sphere gas. The DOS of the hard-sphere gas was estimated using a delta memory function. Recently Desjarlais suggested to use a Gaussian memory function for better description of the gas-like DOS, in particular in the high-frequency region. However, a systematic but tedious approach involving the evaluation of moments of the DOS was required for evaluating the fluidicity. In this work, we propose a new approach to determine the fluidicity which can be easily implemented in a computer code. We validated this new approach by calculating the entropy of Lennard–Jones fluids in the gas, liquid, solid, and supercritical regions. It is found that the entropy determined from using the Gaussian memory function for the gas-like component (denoted as 2PT-GMF) is always lower than that determined from that using delta memory function (denoted as 2PT-δMF). Furthermore, 2PT-GMF is more accurate (with an absolute average relative error of 1% compared to the results from MBWR EOS) than 2PT-δMF (AARD = 2%) in the liquid and supercritical regions, whereas their performances are comparable in other regions (AARD = 1%).

Understanding the electronic dynamics on surfaces of materials is fundamentally important for applications including nanoelectronics, inhomogeneous catalysis, and photovoltaics. Time-dependent density-functional theory (TDDFT) has been successfully applied to predict excited-state properties of isolated and periodic systems. However, it cannot address a system coupled to an environment or whose number of electrons is not conserved. To tackle these problems, TDDFT needs to be extended to accommodate open systems. This chapter provides a comprehensive account of TDDFT for open systems (TDDFT-OS), including both theoretical and practical aspects. The practicality and accuracy of TDDFT-OS method are demonstrated with two numerical examples: the time-dependent electron transport through a series of quasi-one-dimensional atomic chains and the real-time electronic dynamics on a two-dimensional graphene surface.

Uberuaga, B. P. Perez, D. Voter, A. F. Many important processes in materials and chemical systems are intrinsically atomistic in nature but involve timescales that span many orders of magnitude, thus exceeding what can be directly simulated using molecular dynamics. In this paper, dedicated to William A. Goddard III, we give a brief introduction to the accelerated molecular dynamics (AMD) methodology, which is aimed at this problem. AMD methods exploit the infrequent-event nature of the activated processes that typically comprise this long-time evolution. In favorable cases, these methods can predict state-to-state evolution that approximates what would result from an extremely long molecular dynamics simulation, and the most accurate of the methods can do this to arbitrary accuracy. We present some examples of applications of these methods and discuss the greatest ongoing challenge for the methods.

The ReaxFF method has been successfully applied to study a wide range of chemistries in the fields of materials and biomolecular science, e.g., hydrocarbons, metal/metal oxides, combustion, catalysis, atomic layer deposition, DNA oxidation, and hydrolysis. In this work, we focus on current and potential future applications of ReaxFF to biological systems using various ReaxFF force field parameterizations specifically developed for the study of common reactive processes in biochemistry, including (1) RNA/DNA cleavage, (2) protonation/deprotonation of imidazole-Zn-ligand complexes, and (3) Cu-catalyzed and noncatalyzed peptide bond hydrolysis. First, ReaxFF reproduced the cleavage mechanism for phosphodiester linkage of RNA and DNA with the atomic level picture. Cleaving RNA yields a 2′-OH,3′-phosphate and a 5′-OH nucleoside via 3′,5′-cyclic phosphate intermediate. Also, due to the absence of a 2′-hydroxyl group in DNA, DNA is cleaved by the nucleophilic attack of water on the phosphorus center, producing a 5′-OH nucleoside and a nucleotide. Second, the interactions between Zn(II) metal and various ligands were successfully compared between ReaxFF and the earlier DFT work, which the ligand dissociation energies and proton affinities are systematically examined in Zn-ligand (L) complexes (L = NH2, NH3, NHCH2, NCH2, and NCH2CH3 functional groups, imidazole, and H2O). Using this force field, we performed a reactive MD simulation for the formation of Zn(Im)n(H2O)m in aqueous at 300 K. The results show that the mixed ligand Zn(Im)n(H2O)m complexes are allowed to have various coordination numbers, due to the dynamic nature of Zn(II) coordination. One of the common and important ligands in metalloprotein is imidazole in a histidine residue. The role of imidazole as a proton donor and acceptor is very essential for the proton transfer occurring at enzyme active sites. We showed that ReaxFF can describe the pH-dependent protonation state of imidazole by investigating the energy barriers for the protonation and performing a reactive MD simulation for the formation of imidazolium cations at low pH. Finally, we compared ReaxFF results for catalyzed and uncatalyzed peptide bond hydrolysis mechanisms. It is found that the transition state is stabilized significantly by the formation of a Cu(II)-complex. The ReaxFF potential energy profiles along the reaction pathway for both catalyzed and uncatalyzed hydrolysis reactions are in excellent agreement with the DFT. The role of imidazole as a bridge for the proton transfer and its biological significance in the Ser-His-Asp catalytic triad were further discussed.

Noncovalent interactions (NCIs) play crucial roles in supramolecular chemistries; however, they are difficult to measure and compute. Currently, reliable computational methods are being pursued to meet this challenge, but the accuracy of calculations based on low levels of theory is not satisfactory and calculations based on high levels of theory are often too costly. Accordingly, to reduce the cost and increase the accuracy of low-level theoretical calculations to describe NCIs, an efficient approach is proposed to correct NCI calculations based on the benchmark databases S22, S66, and X40. In this approach, machine learning methods, general regression neural network (GRNN), and support vector machine (SVM) are used to perform the correction for DFT methods on the basis of DFT calculations. Various DFT methods, including M06-2X, B3LYP, B3LYP-D3, PBE, PBE-D3, and ωB97XD, with two small basis sets (i.e., 6-31G* and 6-31+G*) were investigated. Moreover, the conductor-like polarizable continuum model (C-PCM) with two types of solvents (water and pentylamine) was considered in some DFT calculations. With the correction, the root mean square errors (RMSEs) of all DFT calculations were improved by at least 70%. Relative to CCSD(T)/CBS benchmark values (used as experimental NCI values because of its high accuracy), the mean absolute error (MAE) of the best GRNN result was 0.33 kcal/mol, which is comparable to high-level ab initio methods or DFT methods with fairly large basis sets. Notably, this level of accuracy is achieved within a fraction of the time required by other methods. Additionally, SVM is applied on datasets in the gas phase, which gave similar correction accuracy as GRNN. For all of the correction models based on various DFT approaches, the validation parameters according to OECD principles (i.e., the correlation coefficient R, the predictive squared correlation coefficient q2 and q cv 2 from cross-validation) were greater than 0.92, which suggests that the correction model has good stability, robustness, and predictive power. The correction can be added following DFT calculations. With the obtained molecular descriptors, the NCIs produced by DFT methods can be improved to achieve high-level accuracy. Moreover, only one parameter is introduced into the GRNN correction model, which makes it easily applicable. Overall, this work demonstrates that the machine learning correction model may be an alternative to the traditional means of correcting for NCIs.

Toomey, Bridget Han, Xuchen Dan Dong, Chen Edward, Verne Kaminski, Jakub W. Shankar, SadasivanMaterial design and optimization of properties of known compounds strongly depends on understanding the structural parameters and atomic arrangements of the underlying crystal structures and their interfaces. In this work, we propose a novel approach to characterize the morphology and orientation of polycrystalline and complex micro-structured material systems. Based on a lattice reduction algorithm, we are able to discover the fundamental lattice type, orientation, and boundaries of each polycrystalline region. Our algorithm identifies the Bravais lattice types of all crystalline regions in any given sample, within certain size limits. Our method can be applied to characterize materials with ideal crystals, structures with imperfections and amorphous fractions, and material interfaces with complex grain morphologies. We foresee applications of our methodology, based on computational crystallography, to areas of materials design research in nanoscale transistors and other functional devices, where crystallography and morphology from both experiments and simulations need to be characterized.

In this chapter, I will describe how I have found the concepts of the valence bond description of chemical bonding essential in several materials chemistry research programs I have participated in. I learned these ideas from Bill Goddard early on in my scientific education, and they have been constant intellectual companions. An intuition about how electronic structures change as atoms move is a great boon to a synthesis chemist when designing and exploiting new chemical reactions.

Polycyclic saturated hydrocarbons (PSHs) are attractive candidates as synthetic jet fuels. Accurate calculations of their heats of formation (HOFs), although important in evaluating their performance as hydrocarbon propellants, remain a great challenge in computational chemistry. In this study, we have examined the reliability of the XYG3 type of doubly hybrid functionals for such a purpose. In comparison with the results from B3LYP, MP2 and the G4 theory against a set of 36 PSHs, it is demonstrated here that the XYG3 type of doubly hybrid functionals, in particular, XYGJ-OS, yield accurate HOFs in good agreement with those from experiments or the G4 theory. The present success holds the promise for the future study on other larger potentially important hydrocarbons as candidates of high performance fuels.

The synergy between experimental and computational modeling has yielded great benefits for both fields, in particular in the areas of material screening and applications, and the process design and optimization.

Negre, Christian F. A. Niklasson, Anders M. N. Redondo, AntonioIn this chapter we describe a method that combines three different approaches to achieve practical, large-scale, quantum-based, Born-Oppenheimer molecular dynamics simulations. This particular combination of methods provides a very powerful and unified framework to next-generation quantum molecular dynamics simulations that can be applied to problems of industrial interest.

We report a first principles Quantum Mechanics (QM) study to screen chemical functionalities in polymer-based chemical sensing materials to detect sulfur dioxide (SO2) and elemental mercury (Hg) vapors. The screening methodology involves evaluating the performance of various chemical functionalities in polymers based on their binding energy scores of the target molecules (SO2 and Hg). The QM results were validated by comparing the actual sensor response trends with the calculated binding energy values, by performing experiments using polymer-carbon composite sensors made from the polymers with the recommended chemical functionalities. A good correlation is found between the experimental sensor responses (strong or weak) to SO2 and Hg and the calculated binding energy values (strong or weak). The sensors were successfully used in the Third Generation JPL Electronic Nose (ENose) Technology Demonstration Experiment on the International Space Station (ISS).

This chapter describes the application of computational chemistry methods to help solve a suite of industry relevant problems, from materials manufacturing, to water oil extraction and personalized medicine on Earth and in space. It then expands into previously uncharted areas such as hypersonic reentry, thermal protection materials and space manufacturing to transform the engineering status quo and connect these disciplines into an innovation pipeline for commercialization.

Gamble, John King Nielsen, Erik Baczewski, Andrew Moussa, Jonathan E. Gao, Xujiao Salinger, Andrew G. Muller, Richard P.This paper describes our work over the past few years to use tools from quantum chemistry to describe electronic structure of nanoelectronic devices. These devices, dubbed “artificial atoms,” comprise a few electrons, confined by semiconductor heterostructures, impurities, and patterned electrodes, and are of intense interest due to potential applications in quantum information processing, quantum sensing, and extreme-scale classical logic. We detail two approaches we have employed: finite-element and Gaussian basis sets, exploring the interesting complications that arise when techniques that were intended to apply to atomic systems are instead used for artificial, solid-state devices.

Pramanik, Debabrata Kanchi, Subbarao Ayappa, K. G. Maiti, Prabal K.Dendrimers are essentially hyperbranched polymers synthesized in a step-wise manner and show very interesting material properties because of their globular, compact structure and tunable functionality.

We present equilibrium and non-equilibrium molecular dynamics approaches to determine thermalÇağın, Tahir Haskins, Justin B. Kınacı, Alper Sevik, Cemtransport properties in particular Helfand formulation and its modifications due to finite size encountered in nanoscale systems. Applications on carbon nanotubes, graphene and graphene nanoribbons, the influence of topological defects, and isotopic effect are discussed.

Single-walled carbon nanotubes (SWNT) [1] have extraordinary material properties [2–6] that make them ideal replacements for silicon as conduction channels in integrated electronic circuits (IC) for high-performance logic applications [7–11]. One of the most critical problems barring utilization of SWNTs in high-performance ICs is the arrangement of dense (~1 SWNT/10 nm scale) arrays of semiconducting carbon nanotubes at precise locations on IC substrates with geometric patterns suitable for nanoscale circuit fabrication [11, 12]. Here, we will review DNA nanotechnology-based methods that offer scalable routes for self-assembly of highly structured, nanometer scale, IC compatible SWNT patterns and arrays [13–15], discuss the challenges for incorporating such methods into high-performance IC fabrication and offer perspective on future directions needed to overcome these challenges.

The adsorption of mixed surfactants on solid substrates is important in a wide range of technological and industrial applications. Surfactant flooding in oil reservoirs is one of the most successful processes employed in enhanced oil recovery (EOR), where surfactant aqueous solutions are injected in the reservoir in order to decrease oil/water interfacial tension, leading to an increase in oil production. However, it suffers from a severe economic drawback due to the loss of large amounts of surfactant, which end up adsorbed on the rocks surface. This problem can be overcome with the use of systems of surfactant-modified silica particles that would be able to permeate through the rocks pores and either deliver the surfactants at the oil/water interface or act themselves as amphiphilic nanoagents, reducing the interfacial tension. For this purpose, we investigated the adsorption behavior of a nonylphenylethoxylate (NP10) on silica nanoparticles, mediated by the co-adsorption of a cationic surfactant (cetyltrimethylammonium bromide, CTAB). While NP10 alone shows meager adsorption, because of the lack of electrostatic interactions, CTAB adsorbs significantly on the oppositely charged silica surface. The adsorption isotherms of the binary surfactant mixtures at different molar ratios showed a sharp increase of NP10 adsorption on the silica/water interface in the presence of CTAB. Saturation values are reached at concentrations of NP10 higher than the critical micelle concentration, indicating significant adsorption on the nanoparticles. The adsorbed CTAB molecules would be probably acting as nucleating sites to form mixed aggregates with NP10 on the silica surface, through hydrophobic interactions with the CTAB hydrocarbon chain and would be responsible for the marked adsorption synergy between the two surfactants. The oil/water interfacial tension results suggest that the studied systems present a good potential as surfactant nanocarriers for EOR.

Sun, Bin Blood, Ryan Atalay, Selcuk Colli, Dylan Rankin, Stephen E. Knutson, Barbara L. Kekenes-Huskey, Peter M.Mesoporous silica films offer exciting potential for the delivery of molecular cargo, detection of molecular agents and as environment-dependent ‘nanoreactors’ in biological systems. Fundamentally important to realizing this potential are quantitative models for how material topology, surface chemistry and surface/solution interfaces govern molecular transport (via diffusion). Partial differential equation (PDE)-based approaches are particularly well suited for reaction–diffusion processes in materials, given the ability to incorporate into the important simulation details including material morphology, surface chemistry and charge. However, two challenges that hinder the application of reaction–diffusion partial differential equations (PDEs) to structurally realistic models of materials are (1) burdensome post-processing and annotation of microscopy data needed for PDE solutions and (2) challenges in extrapolating model predictions determined at the nanoscale to heterogeneous materials. To address this gap, we developed a new workflow for simulating ion reaction–adsorption–diffusion in nanoporous silica-based materials that are resolved through electron microscopy. Firstly, we propose a matched filtering procedure to identify and segment unique porous regions of the material that will be subject to PDE simulation. Secondly, we perform reaction–adsorption–diffusion PDE simulations on representative material regions that are then applied to characterize the entire microscopy-resolved film surface. Using this model, we examine the capacity of a recently synthesized mesoporous film to tune small molecule permeation through modulating the material permeability, surface chemistry including buffering and adsorption, as well as electrolyte composition. Specifically, we find that our proposed matched filtering approach reliably discriminates hexagonal close-packed (HCP) porous regions (bulk) from characterized defect regions in transmission electron microscopy data for nanoporous silica films. Further, based on our implementation of a pH-/surface-chemistry-dependent Poisson–Nernst–Planck (PNP) model that is consistent with existing experimental measurements of KCl and CaCl $$_2$$ 2 conductance, we characterize ion and 5(6)-Carboxyfluorescein (CF) dye permeability in silica-based nanoporous materials over a broad range of ionic strengths, pHs and surface chemistries. Using this protocol, we probe conditions for selectively tuning small molecule permeability based on mesoporous film pore size, surface charge, ionic strength and surface reactions in the rapid equilibrium limit. Altogether, this framework provides means to utilize and validate high-resolution microscopy data of mesoporous materials, from which spatially heterogeneous transport parameters can be estimated. As such, the protocol will have significance for characterization of new materials for wide-ranging applications.

Supercapacitors are electrochemical energy storage devices known for their large power densities and long lifetimes yet limited energy densities. A conventional understanding of supercapacitors relates the high power to fast ion accumulation at the polarized electrode interface, forming the so-called electric double layer (EDL), and the low energy to limited electrode surface area (SA). As such, carbon-based nanomaterials have been extensively explored as high SA electrode materials. Interestingly, anomalous and nonlinear relationships between observed capacitances and SAs have recently emerged. These observations suggest that a gap exists in our fundamental understanding of charge storage mechanisms due to the introduction of low-dimensional materials. In this chapter, we review new physical insights from both quantum mechanical calculations and atomistic simulations of two broad types of carbon-based nanomaterials. First, the study of graphene-derived materials has highlighted the importance of competing contributions from the electrode and EDL capacitance. Furthermore, the study of nanoporous carbons has elucidated the importance of charging dynamics. Taken together, these findings can be generalized to establish design principles for future electrode materials.

Direct graphene growth—without physical transfer—by C molecular beam epitaxy has been reported on two incommensurate polar oxide substrates: Co3O4(111) and MgO(111). Recent experimental and theoretical work regarding graphene growth on Co3O4(111) indicates that growth proceeds via the initial formation of a graphene oxide buffer layer formed by C covalent bond formation to oxygen sites on the cobalt oxide substrate. This finding motivates a reinterpretation of previously reported results for graphene growth on MgO(111). This reinterpretation indicates that formation of a non-planar graphene oxide buffer layer occurs for growth on MgO(111), similar to that on Co3O4(111). Results suggest that the formation of a graphene oxide buffer layer on MgO(111), rather than reconstruction of the MgO(111) surface, is what leads to the observed C3v symmetry and substantial band gap for the C overlayer on that substrate. Prospects for direct growth on other polar oxide surfaces and implications for device applications are discussed.

Ion-enhanced dry etch methods inflict “etch process damage” through surface ion bombardment. These inherent limitations in conventional dry etch methods create potential roadblocks to achieving device properties necessary for scaling below 10 nm. We describe an alternative dry etch method in which electrons with energies below about 100 eV stimulate precision etching of features as small as 5 nm without damage. This Low Energy Electron Enhanced Etching (LE4) method also gives atomically smooth etched surfaces, very high selectivity between materials, and maintains stoichiometry of compound materials. LE4 etches low K dielectric materials with no loss of carbon and gives Line Width Roughness (LWR) values dramatically smaller than achieved by ion-enhanced etching. In addition, LE4 is used to modify substrates in a variety of applications, like low-temperature surface cleaning and the modification of the surface architecture on multiple length scales for biotechnology applications. We have developed the electron force field (eFF) method to describe electron dynamics in highly excited electronic states and use it to show preferential bond breaking and product desorption after electronic excitation of the sample surface.

Bone, nacre, and other biological materials exhibit unique hierarchical structures at different length scales, and thereby achieve versatile material functions. The excellent performance of bioinspired designs in materials science has attracted the interest of engineers and scientists to expand this bioinspiration to many other materials, including graphene. Among the different designs that draw significant attention, butterfly wings are particularly noteworthy, whose iridescent colors arise from the interaction of light with highly precise multiscale structures that in some cases correspond to a 3D shape of gyroid geometries. Especially, the combination of gyroid designs with graphene can produce mechanical and thermal functions for carbon porous materials that exhibit superior properties such as strength 10 times higher than mild steel, with only around 4.6% the density of this material, featuring also density-insensitive thermal stability, as well as an outstanding impact energy absorption capability as high as 269 MJ/m3. Based on molecular modeling, the physics and mechanics of graphene-based gyroid structures, as well as their performance in the context of thermal conduction and impact energy absorption are included in our current short review.

Dye molecules are the key component of dye-sensitized solar cells (DSSCs), which are a promising alternative to conventional solid-state photovoltaic devices. In principle, an unlimited number of dyes can be designed; however, the dyes reported to date represent only a tiny fraction of the possible structures. Here, we demonstrate a computational approach that can quantitatively predict the photovoltaic efficiency of a DSSC by simply providing a dye structure. In our approach, the photovoltaic efficiency was obtained using a macroscale model with most parameters inherited from microscale and mesoscale simulations. This approach was validated by comparing simulations of experiments using similar dyes. The application of this approach led to the experimental identification of a zinc-porphyrin dye that exhibits a 25% enhancement in cell efficiency over that of YD2-o-C8 under the same conditions. This approach provides the non-experiential quantitatively model for the large-scale screening of dyes for DSSCs.

High-efficient photovoltaic devices based on organic-conjugated polymers have attracted tremendous attentions in the past few decades thanks to their low-cost and flexibility. Theoretical and experimental understandings of the structure–property relationship are needed to further enhance the power conversion efficiency of devices. Herein, a multiscale simulation method, combining dissipative particles dynamics and graph theory, is adopted to simulate the morphology evolution of bulk heterojunction active layer and correlate an efficiency indicator to characterize the performance of polymer solar cells by graph theory. The effects of molecular weight, side chain length, annealing temperature, solvents, and additives are investigated in the poly(3-hexylthiophene-2,5-diyl) (P3HT) and [6,6]-phenyl C61 butyric acid methyl (PCBM) system. Our simulation results indicate that the mixture of P3HT with a molecular weight of 24 K g/mol shows the optimal morphology and efficiency indicator when annealing temperature is at 153 °C. It is found that our multiscale simulation results agree well with the experimental observation, providing a quantitative and qualitative description of the relationship between morphology and performance.

We describe a newly developed multiscale computational method, combining quantum mechanics with classical electrodynamics for simulations of photovoltaic devices. In this quantum mechanics/electromagnetics (QM/EM) method, the regions of the system where charge excitation and migration processes take place are treated quantum mechanically, while the surroundings are described by Maxwell’s equations coupled with a semiclassical drift-diffusion model. The QM model and the EM model are solved, respectively, in different regions of the system in a self-consistent manner. Potential distributions and current densities at the interface between QM and EM regions are employed as the boundary conditions for the quantum mechanical and electromagnetic simulations, respectively. In this chapter, we first demonstrate the method by studying the plasmonic scattering and light trapping effects in silicon nanowire array solar cells. Our results show that there exists an optimal nanowire number density in terms of optical confinement. The method is then applied to study a tandem solar cell where the subcells are treated quantum mechanically. The QM/EM simulation results demonstrate that a significant enhancement of open-circuit voltage is achieved by using the tandem architecture.

Reducing Pt loading in hydrogen evolution reaction (HER) catalysts is critical to developing widespread electrochemical water splitting systems. Transition metal carbide (TMC) catalysts have been shown to allow for reduced Pt loading by serving as substrates. Computational studies of potential HER catalysts have focused mainly on identifying materials with hydrogen binding energies (HBEs) similar to that of pure Pt. However, HER activity is governed by many other factors in addition to the HBE. Using first-principles quantum mechanics calculations, we perform a thorough verification of our previous prediction that monolayer Pt on a Mo2C substrate could serve as an effective replacement for pure Pt as an HER catalyst. We first determine that the HBEs of Pt/Mo2C and Pt are almost identical. We next show that the electronic structure of Pt/Mo2C exhibits the qualities desired in an HER catalyst: a d-band that spans the Fermi level and a strong overlap between the catalyst dd-band and hydrogen 1s band. Crystal orbital overlap population analyses reveal that the bonding and antibonding characteristics of Pt/Mo2C are as balanced as they are in Pt. Finally, our calculation of the double-layer capacitance (DLC) shows that the Pt overlayer, in addition to improving the bonding characteristics between the substrate and hydrogen, reduces the DLC relative to pure Mo2C. This work demonstrates that it is unnecessary that the substrate BE valence isoelectronic to Pt to serve as an effective catalyst support, in contrast to previous explanations for the success of Pt/TMC hybrid systems. Thus, in addition to demonstrating that Pt/Mo2C is well-suited for acting as an HER catalyst, this work provides an example of a more rigorous methodology for screening materials for their suitability as HER catalysts.

The mechanism of the commercially significant oxidation and ammoxidation of propylene to acrylonitrile using heterogeneous bismuth molybdate catalysts has been extensively studied and is believed to occur via a Mo π-allylic intermediate formed in the rate determining step, followed by nitrogen insertion via reversible formation of σ-allylic species. The active surface site is likely composed of two Mo and one Bi centers, where Bi is required for H-abstraction, and Mo for O or N-insertion. Valuable insights have been provided by the computational chemistry of Goddard including the favorable thermodynamics associated with the “spectator oxo” effect, which provides a driving force for the collapse of the initially formed π-allyllic species onto a Mo-dioxo species. It also provides a plausible explanation for the N-insertion to be favored over O-insertion by reaction with a Mo-oxo-imido species, thus leading to acrylonitrile in the presence of ammonia. Key functions of H-abstraction/O-insertion and re-oxidation can be applied to other selective (amm) oxidation catalysts. Bi-Mo-O catalysts are also useful for selective (amm) oxidation of other allylic/benzylic hydrocarbons.

One of the major goals set by our humanity is to make breakthroughs in key technologies pertaining to energy, environment, and water. The main purpose of this chapter is to give an overview over sustainable CO2 conversion technologies that could be demonstrated from high-performance materials both design and also the realization, so that they could prevent simultaneously environmental problems such as global warming.

For many years, the development of new methods and catalytic processes for the functionalization of C–H bonds has been among the primary challenges in the field of synthetic chemistry. The selective functionalization of light alkanes offers the possibility of expanded and more efficient use of natural gas, as the functionalization of larger hydrocarbons (e.g., long chain alkanes or arenes) could afford less expensive processes that are more environmentally benign as well as access to entirely new compounds, and the selective C–H functionalization of more complex organic compounds offers strategies for improved routes to prepare high-value fine chemicals.

Dedicated to Professor William A. Goddard III at the occasion of his 80th Birthday. A scholar, whose boundless enthusiasm and willingness to always constructively interact with his peers is legendary.

Interest in biological markers (biomarkers) for personalized medicine and public health has recently increased, because they provide a cost-effective and objective means of screening patients for various disease processes in a timely, accurate, and reproducible fashion. In addition, biomarkers are increasingly utilized for their ability to provide diagnostic information to help guide the management of acute disease. Despite significant advances in the diagnosis and treatment of acute brain injuries, the early diagnosis and evaluation of prognosis and treatment efficacy remains challenging. In patients who experience ischemic stroke or hemorrhagic stroke from subarachnoid hemorrhage (SAH), high-quality biomarkers in the blood and cerebrospinal fluid are gaining increasing evidence-based traction for use in monitoring brain injury evolution and response to therapy. In addition to a comprehensive analysis of the current evidence supporting biomarkers for SAH and stroke, we discuss the persisting need for additional reliable biomarkers of neurological disease, the process of biomarker and point-of-care test development, and the importance of continued research in the area of biomarkers, particularly as it applies to cerebrovascular pathology.

To provide some insight on odorant recognition, we partitioned the 398 human olfactory receptors (OR) into six subclasses based on the phylogenetic tree and predicted the 3D structure and binding site for the family head of each subclass. We used the GPCR Ensemble of Structures in Membrane BiLayer Environment (GEnSeMBLE) method that samples 10 trillion combinations of helix tilts and rotations to select an ensemble of the 25 most stable 7-helix packings. We found that the ensembles for the OR in all 6 subclasses exhibit the TM1-2-7 coupling characteristic of Class A G protein-coupled receptors (GPCRs). In many cases of tetrapod subclasses, the conserved R3.50:E6.30 TM3-TM6 coupling in the inactive GPCRs is replaced with D(E)3.39:H6.40 and D3.49:R(K)6.30 interactions. We found a different pattern for the fish-like class I, where D3.49 couples with residues in TM4 (R4.44 for O52D1-I and Q4.41/K4.38 for O52B6-I) instead of TM6. A variety of residues lining the binding site are involved depending on the ligand: Q1033.28 and N2827.39 for O52D1-I, Y912.53 for O11H6-II, Q973.28 for OR4Q2-III, S2727.35 for OR7D4-IV, S2065.43 for OR2J3-V, and T2496.45 for OR5P3-VI. Here, we provide the structures for the top25 for all 11 ORs, including the structures with ligands.

In this chapter we discuss the interaction between theory and single molecule experiments on a biological motor, F1-ATPase. In particular, we consider an interplay between the experiment, analytical theory, and computer simulations. The complementarity of the millisecond and microsecond experiments is noted. For example, the limited experiments on the latter indicate that the ATP binding in one β subunit precedes an ADP release in another β subunit, whereas the millisecond experiments do not time-resolve the two steps.

Essentially, all new disruptive discoveries/technologies begin with a predictable period of disbelief and rejection. In some cases, progress or delay of acceptance is determined by access to special experimental instrumentation or emerging new characterization methodologies for unequivocal confirmation. More often than not, acceptance is simply determined by a handful of unique individuals who possess the curiosity, technical insights and skill sets to obtain the compelling results that overcome even the most severe critics. Such was the case for the discovery of dendrimers/dendritic polymers. Collaborating experimentalists such as: Prof. W. A. Goddard III, (Cal. Tech.), Profs. J. M. J. Fréchet/C. Hawker (then at Cornell Univ.), Prof. E. W. Meijer (then at DSM), Prof. N. Turro (Columbia Univ.), Prof. P.-G. de Gennes (College de France) and various Dow Chemical Co. scientific staff (i.e., Drs. D. M. Hedstrand, L. Wilson, S. Martin, P. Smith, G. Kallos, etc.) were among a handful of individuals who deserve special recognition for contributing critical results that garnered early acceptance and understanding of dendrimers and the “dendritic state.”

Su, Julius T.Quantifying how assemblies of molecules coordinate their motions is key to understanding the operation of biological machines and other modular nanosystems. We explore the question of how tightly coupled the motions of adjacent units should be to optimize the transmission of signals, in an environment beset by thermal fluctuations. In this paper, we consider an allosteric dimer consisting of two ligand-binding domains and study how variations of ligand binding at one site are translated to similar variations in ligand affinity at the adjacent site. We compute the dynamics using a simple energy landscape model, and using mathematical methods used to study phenomenon of stochastic resonance, quantify the fraction of signal that propagates through the allosteric system as a transmission coefficient. We find that the transmission coefficient is optimized for an intermediate coupling between domains. The intermediate coupling is a compromise—when it is too low, the motion of the domains become uncorrelated, and fidelity suffers, but when it is too high, the system must surmount multiple transition barriers to change conformation, and communication slows down. There are two consequences. First, for a wide range of energy landscape parameters, the intermediate coupling corresponds to stepwise switching of domains via high energy (1.4 kcal/mol) lowly populated (<10% occupancy) intermediates. Second, the compromise between fidelity and rate caps the transmission coefficient at 40%, so that the majority of signal is degraded, even at the most perfectly formed allosteric interface. We propose that the stepwise switching observed in many allosteric proteins does not arise from an inherent limit in the mechanical couplings within the protein, but is a consequence of optimizing signal transmission; and that in general, if signals are to be communicated between domains via conformational change, a certain looseness of coupling is preferred, to provide a useful freedom of motion.

As a graduate student (GS), I had worked out that the GVB wavefunction for CH4 could lead to localized pairs of VB-like orbitals along each bond but the full wavefunction would still have Td symmetry. Also, I had shown that the GVB wavefunctions could dissociate properly as a bond is broken, going to atomic limits. However, I had not yet learned to program a computer, and indeed, I avoided reading any papers trying to do QM on materials because I had learned that they were completely useless. After joining the chemistry department in Nov. 1964 I learned how to program and over the next few years learned about bonding for main group and for bonds to metals. We also learned about mechanisms for reactions based on Valence Bond concepts. Ideas about resonance were worked out. For H2 and He2 we were able to get a VB view of all excited states. These ideas were extended later to compounds involving other main group elements and transition metal. However, for metallic materials such as Fe and brass, our methods are still deficient.

Initially, my goal as a GS was to extract from QM unique new concepts into how chemical bonding determines the remarkable varieties of structures, properties, and reactivity. However the new GVB wave functions required more sophisticated methods to optimize the wave functions. We describe here some of these advances.

In the 1960s, no serious calculations were done on transition metals or even the Si row of the periodic table because the core electrons had to be present, adding substantially to the cost (state-of-the-art computers were about 0.1 mips and memory was 36 K words, including operating system). To get around this problem, the concept of a pseudopotential that cancels the deep potential in the core region was introduced by Phillips, Kleinman, Cohen, Heine, and Ziman as a way to think about bonding in metals such as Li. As formulated, their pseudopotential had serious problems. 1. The pseudopotential was not unique; there are an infinite number of different pseudopotentials each of which leads to a different pseudo-orbitals, but with the same E. 2. Second, the new Hamiltonian is not Hermitian, leading to complications when considering scattering. 3. Third, the pseudopotential was an integral operator; not a local potential, causing problems when considering scattering. The Goddard group (Carl Melius and Luis Kahn) solved this problem by introducing the Effective Core Potential (ECP). Adding in core orbital character to make the valence orbital nodeless while going smoothly to zero as the distance from the nucleus goes to 0 led a pseudo-orbital that leads to a unique ECP (or pseudopotential). Most importantly, the ECP depended dramatically on symmetry. It was very different for s states than p states then d states, etc. This led to the concept of writing the ECP in terms of angular momentum projection operators. We showed that the excited states of each symmetry are well described with the same ECP. This was completed by 1974. The final refinement (1977) was restricting the form of the smooth pseudo-orbital so that the long range size was not changed (norm conserving). This concept of ECP with angular momentum projection operators extracted from ab initio atomic wavefunctions has been the basis of the enormous progress in the last 50 years for treating materials for the full periodic table.

Materials at extreme conditions in which high temperature and pressure are fluctuating rapidly with molecule, atoms, ions and electrons (e.g., plasma) all interacting dynamically has been a no-man’s land for atomistic simulations. It is clearly foolhardy to attack this problem with our current tools and methods. Even so Su and Goddard (SG) jumped headlong into the problem, developing the eFF method to describe the non-adiabatic evolution of large-scale quantum systems.

The practical size and time scale for QM based MD (QM-MD or AIMD) is 200 atoms for 50 ps. But we want to do MD on systems with 100,000 to 3 million atoms for 100’s of ns. Thus the forces from QM must be replaced for analytic functions that depend directly on distances and angles. The parameters could be trained with QM but better is to develop rules that lead to an acceptable accuracy for wide classes of materials. Thus we developed DREIDING for main group elements (columns 14–17) and later UFF for all elements (up to Lr, Z=103) aimed at inorganic materials. These are aimed at structures but not bond breaking. For bond breaking we developed the ReaxFF and RexPoN FF described in chapter 47.

MD requires charges, but the only way to assign charges was to do QM, which is totally impractical for most materials problems. For proteins and DNA, one can limit the calculation to the monomers (or better trimers). Tony Rappe and I solved this problem in 1991. Saber Naserifar and I extended to polarization in 2017.

A major breakthrough for multiscale reactive simulations is the ReaxFF reactive force field [#471] developed by van Duin and me in 2001. ReaxFF has enabled the simulations of wide-ranging reactive systems, including shock impact on an energetic materials polymer composites (model for a plastic bonded explosive, PBX) with 3.7 million atoms for 60 ns (see [#951]), the simulation of the effect of hydration on the strength of concrete [#978], elucidation of the mechanism of complex ammoxidation catalysts [#934], and the product distributions from hypervelocity impact with molecular clusters [1004]. Recently, we have developed a new generation, RexPoN that may provide even higher accuracy.

It is straightforward to predict electronic energy from QM and the potential energy from FF-based MD and more recently from QM-based MD. However, extracting entropy and free energy has been problematic. Generally, the accepted methodology, going back to Jack Kirkwood and Richard Tolman, is thermodynamic integration theory or free energy perturbation theory. These methods are rigorous for obtaining free energy differences if the perturbations are sufficiently slow that the system remains in equilibrium as system A morphs into B. This generally requires repeated equilibrium calculations during the MD, which makes it very expensive for large-scale (100,000 atom) systems. A major advance here is the validation of FEP technology by Bill Jorgensen and its implementation into an automatic module by Schodinger. To make entropy and free energy calculations practical for nanosecond reactive simulations of large systems with up to millions of atoms, Lin, Blanco, and I (LBG) developed the two-phase thermodynamics (2PT) method that is generally 1000’s of time faster than thermodynamic integration (TI) but equally accurate.

A valuable aspect of reactive MD is to provide rate constants of complex reactions. We give some examples for cases of detonation and pyrolysis. For the general case we need a good machine learning model to collect together the reactions at any one temperature so that we can extract rate constants. Since a reactive force field such as ReaxFF, describes all possible reactions of a system, it could be the basis for general approaches to extract the fundamental kinetic parameters to describe combustion (say of diesel full, gasoline, or JP-10 Hydrocarbon Jet Fuel) or shock-induced decompositions. Although great progress has been achieved in first principles modeling of low-pressure gas-phase reactions, it has been difficult to provide first principles predictions of Combustion and Shock processes for condensed phases. We will discuss several activities we have been pursuing to accomplish this.

Including solvation is very important for systems with polar solvents. This motivated development of a number of methods for describing solvent implicitly. Our more recent methods do this at constant potential.

In the early 1970s, advances in experiments were giving the first clues about surface reconstruction. Our GVB calculations on clusters representing semiconductor and metal surfaces were very useful in predicting and interpreting the experiment.

My group got interested in designing nanoscale systems that could self-organize in the 1980s. This led to papers on molecular machines, where we carried out MD for systems designed by others. Usually they did not work so well because the atom sizes are discrete. One of my graduate students (Ching-Hwa Kiang) discovered the first single wall nanotube, which led to papers characterizing them and the mechanism by which they formed. We also had some collaborations with the Atwater group.

When I joined Pol Duwez’s group in Sept. 1960, I was interested in understanding how the interaction of electrons between atoms controlled the crystal structures of metal alloys. I wanted to derive this understanding from first principles. This led eventually to my development of the GVB method and then joining the chemistry faculty. This chapter summarizes the insights we have obtained about metal and their alloys, but we have still not succeeded at my original goals. Hopefully we can succeed over the next 20 years.

This chapter brings together various studies on materials that happen to be ceramics. This includes Boron Carbide, (C11B) CBC or B4C, where we seek alloys or other changes to make it ductile, The BaTiO3 ferroelectric where we examined 90 and 180 degree domain wall boundaries, and Silica glass Applications to BaZrO3 electrolytes are in the fuel cell chapter.

Fraser Stoddart and I have interacted closely since 2004 (monthly and sometimes weekly when he was still at UCLA) and we have published no fewer than 46 papers together. This has been a great collaboration because Fraser relies on advice from computations for nearly all of the revolutionary materials utilizing the mechanical bond that he has been developing. We have used a variety of theory and methods: quantum mechanics (QM), molecular dynamics (MD), Monte Carlo, or QM tunneling, which we have extended to problems necessary to deal with the complex mechanically constrained systems Stoddart has been designing, synthesizing, and characterizing.

My group has been involved with several projects involving solar cells: The CIGS (Cu-In-Ga-Se Solar Cells), Dye-Sensitized Solar Cells, and organic PbI3 solar cells. Also we have been involved with surface plasmon generation of hot carriers near interfaces.

The current state of development in battery technology is not adequate for modern needs in renewable energy. Li-ion batteries provide the greatest capacity, but Li metal anodes can lead to dendrite growth and catastrophic failure. We can reduce this by pulse charging, but this slows the charging rate [#1056, 1117, 1133]. To reduce the danger of dendrite growth, we can use polymer [#1016] or ionic liquid electrolytes, but this decreases the Li mobility. We can replace Li anode with Na, but this reduces capacity (see [#1168]). We can use other electrodes but this generally reduces capacity [1154, 1098, 1046, 1068]. These are all areas that we have modeled at the atomistic level with some success in understanding some of the issues. The most important area for the theory is to understand may be the nature of the solid–electrolyte interface (SEI). The Li-ions and Li metal electrode react with the electrolyte to produce the SEI, which must play an essential role in charging and discharging since the ions must be transported through this layer and then must be desolvated as they are deposited on the Li metal surface. We have used QM to characterize the SEI for the Li electrode-ionic liquid interface.

Our studies on thermoelectrics have been primarily motivated by the innovative developments by Jeff Snyder mostly in collaboration with Guodong Li of Wuhan University in China, but we did one paper with Jim Heath.

In the early 2000s, there was a great deal of interest in finding materials that could store H2 at high density and small volume for use in H2 fuel cells, particularly for transportation. The concern was that pressured H2 gas would be too dangerous. DOE set standards but no experimental system came close. It seems like the industry has decided that pressured H2 is OK. In 2004 we designed a series of systems that met DOE standards for H2 and CH4 storage, but generally did not find experimentalists to test our designs. Then in 2006 I learned about the MOF’s being developed by my friend Omar Yaghi at UCLA that I thought would be ideal for storing H2 and CH4. We did a series of papers, sometimes in collaboration with Omar, showing designs of MOFs and later COFs that would satisfy DOE requirements.

With the development of ReaxFF, an important application is toward impact induced reactive decompositions of large-scale polymer-energetic material (EM) composites. Here we were able to simulate the steps leading to decomposition and reactions for systems with 3.7 million atoms. This allowed us to explain the origin of hot spots in EM and to suggest ways to eliminate them. We also used ReaxFF to characterize the CJ state for steady detonation waves in these systems (discussed in Chap. 45 ).

Although not a prime interest in my group (and never funded), we got heavily involved in high Tc materials. After a false start where we assumed incorrectly that the doping leads to hole in the CuO2 plane, we eventually showed that the hole is out of the plane, next to the dopant. It leads to a degenerate state with 3 electrons between two degenerate e levels, leading one hole that become conducting above 5% doping and couple to antiferromagnetic Cu at the boundary. This explains most strange properties of Cuprates. We have not been able to predict Tc, but we showed how to order the dopants to double Tc.

The Goddard group has been a leading force in developing new generations of organometallic catalysts, working with experimentalists like Brent Gunnoe, Jay Groves, Roy Periana, and Alan Goldman to design the ligands in advance of experiments, greatly accelerating the development of improved catalysts. In these studies, the Goddard group showed tremendous insight and intuition about reaction mechanisms combined with their deep understanding of how to include high levels of correlation and solvation in quantum mechanics [QM] calculations of complex reaction mechanisms in various solvents has been critical. Their predictions nearly always preceded the experiments, often eliminating many potential catalysts that would have to be much less active or selective, and were nearly always correct. Our only real mistake was waiting for the experimental confirmation before publishing. This made the theory easier to publish in good journals, but the result is that the experimental community does not have a way to judge the accuracy in advance of experimental validation.

The Goddard group was the first to apply ab initio QM to determining the mechanisms of complex heterogeneous catalysts. Particular emphasis has been on activation of propane, propene, and butane also ammoxiation to acrylonitral and N2 reducton to NH3. Since then we have used QM to discover the reaction mechanisms for many practical heterogeneous catalysts for butane to maleic anhydride, ammoxidation, and N2 reduction (Haber Bosch).

Our studies on hydrogen PEM fuel cells have focused on the Oxygen Reduction Reaction (ORR), which is currently the biggest obstacle to wide spread application, and the role of the Nafion electrolyte in proton conduction and degradation. The big breakthrough here was full solvent (5 layers) solvation, which required ReaxFF to pre-equilibrate the system.

Artificial photosynthesis (AP) using solar energy to convert H2O into H2 (a fuel) and O2 is a most promising approach to a carbon-neutral cycle and scalable energy storage. Electrocatalysis provides an attractive candidate route to AP, which could extend to all intermittent renewable energy resources. A major challenge in renewable energy technology is water splitting, which uses solar radiation to photoelectrochemically convert water molecules into H2 (a fuel) and O2. Here both the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) present challenges for the catalysts. The detailed reaction mechanisms had not yet been established for either one. We did the first mechanisms under electrochemical conditions and including free energy reaction barriers for the transition states for both systems. Here we separately consider the two electrochemical half-reactions, HER and OER, which require drastically different catalysts for optimal performance.

In recent years, the Goddard group has turned to electrocatalysts as ideal systems for catalyst design where one can combine anode and cathode electrodes, with electrolytes, and applied fields to provide the flexibility to control complex reactions. In addition to the oxygen reduction reaction (ORR) at the cathode of the hydrogen fuel cell discussed in Chap. 64 and the water-splitting reactions, HER and OER discussed in Chap. 65 , the other the main applications relate to CO2 reduction (CO2R) to form fuels or hydrocarbon products. These studies validate the level of DFT theory and the level of explicit solvent that can be used for the design of improved electrocatalysts.

Our group has been a leader in building realistic models of polymers using force fields based upon quantum chemical calculations. We have carried out large-scale molecular dynamics simulations of these systems to elucidate fundamental materials properties, using the molecular simulation technology described above. Highlights are the first simulations of the Tomalia dendrimers, the Percec dendritic system and the Frechet systems. We also carried out the first simulations on polyethylene and nylon. Later we carried out the first realistic simulations on the Nafion electrolyte for fuel cells. More recent work is on PEO polymer electrolytes for Li batteries. Along the way, we developed efficient methodologies to predict the structures of amorphous polymers using thermal and volume quenching and finally scaled effective solvent (SES) methodology. We extended these ideas to network polymers and nonlinear optical materials.

In 1998 Vaidehi Nagarajan and I initiated a project to predict the structures of G protein-coupled receptors (GPCR) from first principles (there were no crystal structures at the time). Our early methods were successful in predicting fairly accurate structures for several GPCRs, and we published the first GPCR crystal structures. First for olfactory receptors and then in 2004, the dopamine D2 and adrenergic b2 structures. The first experimental X-ray crystallography structure was for bovine rhodopsin and then for b2 AR in 2005, which showed that our 1st principle structure was rather accurate. Later we used the templates of transmembrane tilt from experiment to develop methods for predicting the low energy packing of GPCRs sufficiently stable to bind to ligands, with a number of successes. More recently, we have focused on the mechanism by which the GPCR attached to a Gprotein activates the G protein after binding an agonis.

We carried out the first simulations of Ned Seeman’s DNA crossover structures that laid out how to do self-assembly with DNA [#594, 651, 707, 1182]. Tod Pascal studied the branched three-way junction [#995] where he found that the stable structure is strongly affected by salt concentration and by the ratio of MgCl2 to NaCl [995]. He also applied 2PT method to calculate the entropy and free energy changes during the rearrangement showing that the barrier is entropic. While he was a materials science graduate student, Si-Ping Han used DNA origami to self-organize carbon nanotubes (CNT) into field-effect transistors. In addition he used DNA to organize CNTs into parallel bundles. But, the major breakthrough in nucleic acid devices was Si-Ping Han’s idea to develop a dual double helix of RNA we refer to a conditional siRNA (cond-siRNA). This incorporates a sensor element targeted to say HIV or AML that in turn serves as a logic device that only in the cells expressing proteins for this virus disassembles to release a 23 RNA fragment that incorporates into the dicer element of the ribosome to kill the cell by preventing expression of a protein vital to the cell. This promises to enable very selectively killing of diseased cells while not affecting adjacent cells that do not have the virus.