arXiv subjects

William A. Goddard III

Publications and source records attributed to William A. Goddard III.

At least 19 recordsLinked to original sources

arXiv2026

Beyond DFT electronic band structures via dual-space orbital learning

Accurate electronic band structures are essential for predicting material properties, but high-fidelity calculations are computationally demanding. We introduce O-kRystal, a dual-space orbital-learning framework that uses a lower-cost Perdew-Burke-Ernzerhof (PBE) calculation as both an energy baseline and a source of structured orbital information. Combining PBE band energies with real- and reciprocal-space orbital matrices, O-kRystal predicts higher-level corrections for individual electronic states as a function of crystal momentum and spin. For states within 5 eV of the band-edge midpoint, it predicts Heyd-Scuseria-Ernzerhof (HSE06) band energies with a mean absolute error of $0.171 \pm 0.009$ eV across nearly 6,000 three-dimensional materials spanning insulating, metallic, magnetic, and nonmagnetic systems. On a separate benchmark of six strongly correlated materials, it reduces the corresponding PBE band-energy MAE by 68.7% and resolves orbital-dependent changes in the relative placement of transition-metal d and ligand p states. Despite training only at high-symmetry points, it maintains comparable accuracy at unseen points along band paths and reconstructs HSE06 densities of states from dense Brillouin-zone predictions. Finally, training O-kRystal on $G_0W_0$ quasiparticle energies for two-dimensional materials demonstrates its applicability across levels of theory and the potential of orbital learning for higher-fidelity, momentum-resolved electronic-structure prediction beyond density functional theory.

cs.LG↗

arXiv2026

OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systems

We introduce OrbitAll, a geometry- and physics-informed deep learning framework that encodes any molecular system with arbitrary charges, spins, and environmental effects using electronic structure information. It utilizes spin-polarized orbital features from the underlying quantum mechanical method and combines them with SE(3)-equivariant graph neural networks. OrbitAll demonstrates superior performance and generalization in predicting charged, open-shell, and solvated molecules, and robustly extrapolates to molecules significantly larger than the training data. OrbitAll achieves chemical accuracy using 10 times fewer training data than competing AI models, with approximately $10^3$ - $10^4$ speedup compared to density functional theory. Trained on a chemically diverse dataset, OrbitAll performs robustly on challenging molecular systems, and outperforms the foundational machine-learned interatomic potential, UMA, for highly charged species, despite using 35 times less molecular data and a 50-times-smaller model. After learning solvent effects, it accurately predicts solvent-dependent reaction pathways at about 100 times lower cost than explicit-solvation simulations using UMA.

cs.LG↗

arXiv2026

Proton-electron coupled catalyst for ionomer-free electrochemical energy conversion

Efficient electrochemical energy devices are vital to renewable energy technology, yet coordinating the effective flow of electrons, ions, and chemical species continues to be a major challenge. In conventional proton-exchange membrane fuel cell (PEMFC) catalyst layers, proton and electron transport are supplied separately through percolating carbon networks and ionomer binders, rendering the catalyst largely passive and imposing fundamental trade-offs between reactant accessibility, ionic conductivity, and catalyst activity. Here, we introduce a one-dimensional proton-electron coupled catalyst (PECC) design, a transport-integrated electrocatalyst architecture in which the catalyst itself simultaneously supplies electronic and protonic transport to catalyst active sites. Using this PECC, PEMFCs can have an ionomer-free cathode catalyst layer (CCL), resulting in a dramatic 95% reduction in non-Fickian oxygen transport and boosting power density by 34% and 85% compared to traditional CCLs, with cathode Pt loadings of approximately 0.090 mg/cm^2 and 0.037 mg/cm^2, respectively. Meanwhile, PECC retains 65% of its mass activity and exhibits 32% higher power density than its ionomer-based CCL counterpart after 30k accelerated stressed test. Similar mass transport improvements have been observed in the electrochemical hydrogen pump (EHP) using PECC in the catalyst layers. Molecular dynamics simulations show the PECC's proton conductivity is 249% higher than Nafion. This PECC catalyst structure addresses core transport problems in PEMFCs, leading to almost 20% improvement in fuel efficiency and opens up new possibilities for designing high-performance, cost-effective electrochemical devices.

cond-mat.mtrl-sci↗

arXiv2026

Material-Property-Field-based Deep Neural Network in Hopfield Framework

Current deep neural networks (DNNs) used in materials modeling often lack explicit physical structure and clear analytical formulations tailored to material systems, which can limit their interpretability. In this work, we integrate Material Property Fields (MPF) with the Hopfield network architecture and propose an analytically structured DNN framework named mPFDNN. MPF provides a unified framework that represents physical properties of materials as an analytical field built upon pairwise interactions, rigorously respecting fundamental symmetries, while also enabling a physically legitimate decomposition of property distributions at the atomic level. Although the Hopfield model was originally developed for Ising-like systems, we show that its dynamical evolution strategy can be naturally extended to the MPF framework. By reformulating nonlinear interatomic interactions as "hidden neurons", MPF can be extended into a deep yet analytically tractable DNN architecture that progressively captures an increasingly connected interaction landscape. This framework also provides a unified perspective that connects linear expansions and nonlinear DNN architectures within a common interaction-based formulation. Extensive validation across diverse systems, including inorganic crystals, organic molecules, and aqueous solutions, and across multiple target properties, shows that mPFDNN achieves competitive predictive accuracy while offering a physically motivated framework for structure-property mapping in chemistry, physics, and materials science.

cond-mat.mtrl-sci↗

arXiv2026

Nanoconfined Grain Boundaries Increase Conductivity of Polycrystalline Molecular Crystals

Soft-solid molecular crystals consist of crystalline grains and fluid grain boundaries (GB) that enhance grain binding and transport of Li+ ions between the grains. The total ionic conductivity consists of ion migration in both the grains and GBs. To unravel these contributions in adiponitrile (Adpn)-LiPF6 molecular crystals, the GB volume fraction was varied by changing the size of the crystals and the Adpn-LiPF6 molar ratio. Molecular dynamic (MD) simulations indicate that ion motion was sub-diffusive in the grains and well-diffusive in the GBs, with GBs characterized as disordered nano-confined regions of higher charge carrier concentration (1M) than in saturated Adpn-LiPF6 solutions (0.04M), and Li+ ions predominantly solvated by cyano groups with few contact ion pairs. The diffusivity in the GBs is at least an order of magnitude higher than in the crystalline grains. The emergent picture is the grains as a reservoir of ions that migrate to the faster-conducting GBs.

cond-mat.mtrl-sci↗

arXiv2025

Neural Scaling Laws Surpass Chemical Accuracy for the Many-Electron Schrödinger Equation

We demonstrate, for the first time, that neural scaling laws can deliver near-exact solutions to the many-electron Schrödinger equation across a broad range of realistic molecules. This progress is enabled by the Lookahead Variational Algorithm (LAVA), an effective optimization scheme that systematically translates increased model size and computational resources into greatly improved energy accuracy for neural network wavefunctions. Across all tested cases, including benzene, the absolute energy error exhibits a systematic power-law decay with respect to model capacity and computation resources. The resulting energies not only surpass the 1 kcal/mol "chemical-accuracy" threshold but also achieve 1 kJ/mol subchemical accuracy. Beyond energies, the scaled-up neural network also yields better wavefunctions with improved physical symmetries, alongside accurate electron densities, dipole moments, and other important properties. Our approach offers a promising way forward to addressing many long-standing challenges in quantum chemistry. For instance, we improve energetic properties for systems such as the potential energy curve of nitrogen dimer as dissociation is approached and the cyclobutadiene automerization reaction barrier, producing definitive benchmarks, particularly in regimes where experimental data are sparse or highly uncertain. We also shed light on the decades-old puzzle of the cyclic ozone stability with highly accurate calculations for the cyclic-to-open ozone barrier. These results provide near-exact reference calculations with unprecedented accuracy, universal reliability and practical applicability, establishing a foundation for AI-driven quantum chemistry.

physics.chem-ph↗

arXiv2025

Can electrostatic stresses affect charged water structures in weakly ionized plasmas?

This theoretical and numerical study investigates the impact of electrostatic stresses on the shape of charged water structures (grains) in weakly ionized plasmas. We developed an analytic model to predict the conditions under which a grain in a plasma is deformed. We find that electrostatic stresses can overcome the opposing surface tension stresses on nanometer-scale grains, causing initially spherical clusters to elongate and become ellipsoidal. The exact size limit of the grain for which electrostatic stress will dominate depends on the floating potential, surface tension, and local radius of curvature. Clusters larger than this limit are not affected by electrostatic stresses due to an insufficient number of electrons on the surface. The model is compared to Molecular Dynamics (MD) simulations performed with a calculated solvated electron potential on initially spherical grains of 2.5 nm radius charged with 0.5 to 1% electrons. We find excellent agreement between MD simulations and the analytic theory. We also carried out Quantum Mechanics (QM) computations showing that the surface tension increases with decreasing size of the water molecule cluster and increases even more with the addition of solvated electrons. This increase in surface tension can hinder the elongation of the grains. Our QM computations also show that on the nanosecond time scale, the binding force of electrons to water molecule clusters is stronger than the electrostatic repulsion between adjacent electrons, and thus the cluster behaves as an insulator. However, consideration of the very small conductivity of ice shows that on time scales of a fraction of a second, ice clusters behave as conductors, so their surface may be considered to be at an equipotential.

physics.plasm-ph↗

arXiv2022

Multivalent optical cycling centers in polyatomic molecules

Optical control of polyatomic molecules promises new opportunities in precision metrology, fundamental chemistry, quantum information, and many-body science. Contemporary experimental and theoretical efforts have mostly focused on cycling photons via excitation of a single electron localized to an alkaline earth (group 2)-like metal center. In this manuscript, we consider pathways towards optical cycling in polyatomic molecules with multi-electron degrees of freedom, which arise from two or more cycling electrons localized to $p$-block post-transition metal and metalloid (group 13, 14, and 15) centers. We characterize the electronic structure and rovibrational branching of several prototypical candidates using ab initio quantum chemical methods. Despite increased internal complexity and challenging design parameters, we find several molecules possessing quasi-closed photon cycling schemes with highly diagonal, visible and near-infrared transitions. Furthermore, we identify new heuristics for engineering optically controllable and laser-coolable polyatomic molecules with multi-electron cycling centers. Our results help elucidate the interplay between hybridization, repulsion, and ionicity in optically active species and provide a first step towards using polyatomic molecules with complex electronic structure as a resource for quantum science and measurement.

physics.atom-ph↗

arXiv2021

ReaxFF Reactive Force Field Development for Cu/Si Systems and application to Copper Cluster Formation During Cu Diffusion Inside Silicon

Transition metal impurities such as nickel, copper, and iron, in solid-state materials like silicon have a significant impact on the electrical performance of integrated circuits and solar cells. To study the impact of copper impurities inside bulk silicon on the electrical properties of the material, one needs to understand the configurational space of copper atoms incorporated inside the silicon lattice. In this work, we performed ReaxFF reactive force field based molecular dynamics simulations, studying different configurations of individual and crystalline copper atoms inside bulk silicon by looking at the diffusional behavior of copper in silicon. The ReaxFF Cu/Si parameter set was developed by training against DFT data, including the energy barrier for an individual Cu-atom inside a silicon lattice. We found that the diffusion of copper atoms has a direct relationship with the temperature. Moreover, it is also shown that individual copper atoms start to clusterize inside bulk silicon at elevated temperatures. Our simulation results provide a comprehensive picture of the effects of temperature and copper concentration on the crystallization of individual copper inside silicon lattice. Finally, the stress-strain relationship of Cu/Si compounds under uniaxial tensile loading have been obtained. Our results indicate a decrease in the elastic modulus with increasing level of Cu-impurity concentration. We observe spontaneous microcracking of the Si during the stress-strain tests as a consequence of the formation of a small Cu clusters adjacent to the Si surface.

cond-mat.mtrl-sci↗

arXiv2020

Convexification of Charge Equilibrium within the Dendrites of Rechargeable Batteries

The amorphous propagation of microstructures during the electrochemical charging of a battery is the main reason for the capacity decay and short circuit. The charge distribution across the micro-structure is the result of both local and global equilibrium and is non-convex problem merely due to stochastic placement of the atoms. As such, obtaining the charge equilibrium (QEq) is a critical factor, since the amount of charge determines the success rate of the bond formation for the ionic species approaching the microstructure and consequently the ultimate morphology of the electrochemical deposits. Herein we develop a computationally-affordable method for determining the charge allocation within such microstructures. The cost function and the span of the charge distribution correlates very closely with the trivial method as well as a conventional method, albeit having significantly less computational cost. The method can be used for optimization in non-convex environments, specially those of stochastic nature.

physics.chem-ph↗

arXiv2020

Control of Dendrites in Rechargeable Batteries using Smart Charging

In this paper we develop a feed-back control framework for the real-time minimization of microstructures grown within the rechargeable battery. Due to quickening nature of the branched evolution, we identify the critical ramified peaks in the early stages and based on the state we compute the relaxation time for the concentration in those branching fingers. The control parameter is a function of the maximum curvature (i.e. minimum radius) of the branched microstructure, where the higher rate dendritic evolution would lead to the more critical state to be controlled. The charging time is minimized for generating the most packed microstructures and obtained results correlate closely with those of considerably higher charging time periods. The developed framework could be utilized as a smart charging protocol for the safe and sustainable operation the rechargeable batteries, where the branching of the microstructures could be correlated to the sudden variation in the current/voltage.

physics.chem-ph↗

arXiv2019

Finite Pulse Waves for Efficient Suppression of Evolving Mesoscale Dendrites in Rechargeable Batteries

The ramified and stochastic evolution of dendritic microstructures has been a major issue on the safety and longevity of rechargeable batteries, particularly for the utilization high-energy metallic electrodes. We analytically develop criteria for the pulse characteristics leading to the effective halting of the ramified electrodeposits grown during extensive time scales beyond inter-ionic collisions. Our framework is based on the competitive interplay between diffusion and electromigration and tracks the gradient of ionic concentration throughout the entire cycle of pulse-rest as a critical measure for heterogeneous evolution. In particular, the framework incorporates the Brownian motion of the ions and investigates the role of the geometry of the electrodeposition interface. Our novel experimental observations verify the analytical developments, where the the dimension-free developments allows the application to the electrochemical systems of various scales.

physics.app-ph↗

arXiv2019

Transport of hot carriers in plasmonic nanostructures

Plasmonic hot carrier devices extract excited carriers from metal nanostructures before equilibration, and have the potential to surpass semiconductor light absorbers. However their efficiencies have so far remained well below theoretical limits, which necessitates quantitative prediction of carrier transport and energy loss in plasmonic structures to identify and overcome bottlenecks in carrier harvesting. Here, we present a theoretical and computational framework, Non-Equilibrium Scattering in Space and Energy (NESSE), to predict the spatial evolution of carrier energy distributions that combines the best features of phase-space (Boltzmann) and particle-based (Monte Carlo) methods. Within the NESSE framework, we bridge first-principles electronic structure predictions of plasmon decay and carrier collision integrals at the atomic scale, with electromagnetic field simulations at the nano- to mesoscale. Finally, we apply NESSE to predict spatially-resolved energy distributions of photo-excited carriers that impact the surface of experimentally realizable plasmonic nanostructures at length scales ranging from tens to several hundreds of nanometers, enabling first-principles design of hot carrier devices.

physics.comp-ph↗

arXiv2019

Constriction Percolation Model for Coupled Diffusion-Reaction Corrosion of Zirconium in PWR

Percolation phenomena are pervasive in nature, ranging from capillary flow, crack propagation, ionic transport, fluid permeation, etc. Modeling percolation in highly-branched media requires the use of numerical solutions, as problems can quickly become intractable due to the number of pathways available. This becomes even more challenging in dynamic scenarios where the generation of pathways can quickly become a combinatorial problem. In this work, we develop a new constriction percolation paradigm, using cellular automata to predict the transport of oxygen through a stochastically cracked Zr oxide layer within a coupled diffusion-reaction framework. We simulate such branching trees by generating a series porosity-controlled media. Additionally, we develop an analytical criterion based on compressive yielding for bridging the transition state in corrosion regime, where the percolation threshold has been achieved. Our model extends Dijkstras shortest path method to constriction pathways and predicts the arrival rate of oxygen ions at the oxide interface. This is a critical parameter to predict oxide growth in the so-called post-transition regime, when bulk diffusion is no longer the rate-limiting phenomenon.

physics.chem-ph↗

arXiv2019

Stability and flow fields structure for interfacial dynamics with interfacial mass flux

We analyze from a far field the evolution of an interface that separates ideal incompressible fluids of different densities and has an interfacial mass flux. We develop and apply the general matrix method to rigorously solve the boundary value problem involving the governing equations in the fluid bulk and the boundary conditions at the interface and at the outside boundaries of the domain. We find the fundamental solutions for the linearized system of equations, and analyze the interplay of interface stability with flow fields structure, by directly linking rigorous mathematical attributes to physical observables. New mechanisms are identified of the interface stabilization and destabilization. We find that interfacial dynamics is stable when it conserves the fluxes of mass, momentum and energy. The stabilization is due to inertial effects causing small oscillations of the interface velocity. In the classic Landau dynamics, the postulate of perfect constancy of the interface velocity leads to the development of the Landau-Darrieus instability. This destabilization is also associated with the imbalance of the perturbed energy at the interface, in full consistency with the classic results. We identify extreme sensitivity of the interface dynamics to the interfacial boundary conditions, including formal properties of fundamental solutions and qualitative and quantitative properties of the flow fields. This provides new opportunities for studies, diagnostics, and control of multiphase flows in a broad range of processes in nature and technology.

physics.plasm-ph↗

arXiv2018

The Chemical Reactions in Electrosprays of Water Do Not Always Correspond to Those at the Pristine Air-Water Interface

The recent application of electrosprays to characterize the air-water interface, along with the reports on dramatically accelerated chemical reactions in aqueous electrosprays, have sparked a broad interest. Herein, we report on complementary laboratory and in silico experiments tracking the oligomerization of isoprene, an important biogenic gas, in electrosprays and isoprene-water emulsions to differentiate the contributions of interfacial effects from those of high voltages leading to charge-separation and concentration of reactants in the electrosprays. To this end, we employed electrospray ionization mass spectrometry, proton nuclear magnetic resonance, and quantum mechanical simulations. We found that the oligomerization of isoprene in aqueous electrosprays involved minimally hydrated and highly reactive hydronium ions. Those conditions, however, are non-existent at pristine air-water interfaces and oil-water emulsions under normal temperature and pressure. Thus, electrosprays should be complemented with surface-specific platforms and theoretical methods to reliably investigate chemistries at the pristine air-water interface.

physics.chem-ph↗

arXiv2018

Free energy landscape of sodium solvation into graphite

Na is known to deliver very low energy capacity for sodium intercalation compared to Lithium. In this study, we use quantum mechanics based metadynamics simulations to obtain the free energy landscape for sodium ion intercalation from Dimethyl sulfoxide (DMSO) solvent into graphite. We find that the lowest free energy minima from the metadynamics are associated with sodium solvated by 3 or 4 DMSO. The free energy minima of these states are activated by a free energy of solvation computed to be 0.17 eV, which in turn are the most thermodynamically stable. We observe weak interactions of sodium with graphite sheets during the unbiased and biased molecular dynamics simulations. Our simulations results indicate that solvent plays an important role in stabilizing the sodium intercalation into graphite through shielding of the sodium while modulating the interaction of the solvent with the graphite sheets. In order to facilitate this intercalation, we propose solvents with negatively charged groups and aromatic cores (e.g., cyclic ethers) that could allow a greater rate of anion exchange to increase Na+ mobility.

physics.chem-ph↗

arXiv2018

Shear driven formation of nano-diamonds at sub-gigapascals and 300 K

The transformation pathways of carbon at high pressures are of broad interest for synthesis of novel materials and for revealing the Earth's geological history. We have applied large plastic shear on graphite in rotational anvils to form hexagonal and nanocrystalline cubic diamond at extremely low pressures of 0.4 and 0.7 GPa, which are 50 and 100 times lower than the transformation pressures under hydrostatic compression and well below the phase equilibrium. Large shearing accompanied with pressure elevation to 3 GPa also leads to formation of a new orthorhombic diamond phase. Our results demonstrate new mechanisms and new means for plastic shear-controlled material synthesis at drastically reduced pressures, enabling new technologies for material synthesis. The results indicate that the micro-diamonds found in the low pressure-temperature crust could have formed during a large shear producing event, such as tectonic rifting and continued plate collision, without the need to postulate subduction to the mantle.

cond-mat.mtrl-sci↗