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DOE OSTI2022

Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics

In this Letter, we present a framework that combines machine learning potential (MLP) and metadynamics to investigate solid-solid phase transition. Here, based on the spectral descriptors and neural networks regression, we develop a scalable MLP model to warrant an accurate interpolation of the energy surface where two phases coexist. Applying it to the simulation of B4–B1 phase transition of GaN under 50 GPa with different model sizes, we observe sequential change of the phase transition mechanism from collective modes to nucleation and growths. When the size is at or below 128 000 atoms, the nucleation and growth appear to follow a preferred direction. At larger sizes, the nuclei occur at multiple sites simultaneously and grow to microstructures by passing the critical size. The observed change of the atomistic mechanism manifests the importance of statistical sampling with large system size in phase transition modeling.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Mechanisms of dissolution from gibbsite step edges elucidated by ab initio molecular dynamics with enhanced sampling

Surface reactions underpin our collective understanding of mineral dissolution. This impacts a range of predictive geochemical models (for weathering, the fate and transport of metals) as well as industrial utilization of minerals. Yet atomistic details are rarely known due to the complex mineral/fluid interfacial environment. There is significant need to understand the mechanistic details of dissolution reactions and how they depend on surface morphology and solution conditions. This work utilizes surface pit models in conjunction with changes to solution composition that mimic pH to explore surface detachment of aluminate from gibbsite, which is a primary source of Al in soils and within the industrial processing of aluminum. Ab initio molecular dynamics simulations with enhanced sampling has been used to explore the detailed process of the detachment, the results of which indicate two potential pathways that are differentiated based upon the extent of water hydration. Here, the heights of the energy barriers depend upon the local morphology which influence the number of bridges (quasi-)simultaneously broken (1 or 2) or the Al-O coordination of the neighboring aluminum atoms (5 or 6) at the armchair edge. pH effects are significant, with a nearly 50% reduction in barrier height under alkaline conditions that are relevant to geothermal fluids and Al extraction from minerals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Performance efficient macromolecular mechanics via sub-nanometer shape based coarse graining

Dimensionality reduction via coarse grain modeling is a valuable tool in biomolecular research. For large assemblies, ultra coarse models are often knowledge-based, relying on a priori information to parameterize models thus hindering general predictive capability. Here, we present substantial advances to the shape based coarse graining (SBCG) method, which we refer to as SBCG2. SBCG2 utilizes a revitalized formulation of the topology representing network which makes high-granularity modeling possible, preserving atomistic details that maintain assembly characteristics. Further, we present a method of granularity selection based on charge density Fourier Shell Correlation and have additionally developed a refinement method to optimize, adjust and validate high-granularity models. We demonstrate our approach with the conical HIV-1 capsid and heteromultimeric cofilin-2 bound actin filaments. Our approach is available in the Visual Molecular Dynamics (VMD) software suite, and employs a CHARMM-compatible Hamiltonian that enables high-performance simulation in the GPU-resident NAMD3 molecular dynamics engine.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Simulations of nonradiative processes in semiconductor nanocrystals

The description of carrier dynamics in spatially confined semiconductor nanocrystals (NCs), which have enhanced electron–hole and exciton–phonon interactions, is a great challenge for modern computational science. These NCs typically contain thousands of atoms and tens of thousands of valence electrons with discrete spectra at low excitation energies, similar to atoms and molecules, that converge to the continuum bulk limit at higher energies. Computational methods developed for molecules are limited to very small nanoclusters, and methods for bulk systems with periodic boundary conditions are not suitable due to the lack of translational symmetry in NCs. This perspective focuses on our recent efforts in developing a unified atomistic model based on the semiempirical pseudopotential approach, which is parameterized by first-principle calculations and validated against experimental measurements, to describe two of the main nonradiative relaxation processes of quantum confined excitons: exciton cooling and Auger recombination. Here, we focus on the description of both electron–hole and exciton–phonon interactions in our approach and discuss the role of size, shape, and interfacing on the electronic properties and dynamics for II–VI and III–V semiconductor NCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Defect-driven anomalous transport in fast-ion conducting solid electrolytes

We report solid-state ionic conduction is a key enabler of electrochemical energy storage and conversion. The mechanistic connections between material processing, defect chemistry, transport dynamics, and practical performance are of considerable importance, but remain incomplete. Here, inspired by studies of fluids and biophysical systems, we re-examine anomalous diffusion in the iconic two-dimensional fast-ion conductors, the β- and β"-aluminas. Using large-scale simulations, we reproduce the frequency dependence of alternating-current ionic conductivity data. We show how the distribution of charge-compensating defects, modulated by processing, drives static and dynamic disorder, which lead to persistent sub-diffusive ion transport at macroscopic timescales. We deconvolute the effects of repulsions between mobile ions, the attraction between the mobile ions and charge-compensating defects, and geometric crowding on ionic conductivity. Our quantitative framework based on these model solid electrolytes connects their atomistic defect chemistry to macroscopic performance with minimal assumptions and enables mechanism-driven 'atoms-to-device' optimization of fast-ion conductors.

25 ENERGY STORAGE↗

DOE OSTI2020

Enhanced liquid metal wetting on oxide surfaces via patterned particles

Liquid metals typically do not intrinsically wet oxide surfaces due to chemical dissimilarity, preventing the formation of well-bonded interfaces in many applications. This paper investigates how the wetting and spreading of liquid silver (Ag) droplets on oxide surfaces can be enhanced via patterned nickel (Ni) particles using multiscale modeling. First, a force field (FF) for an Ag/yttria-stabilized-zirconia system was developed by fitting the interfacial binding energy to density-functional theory (DFT) calculations. Then, molecular dynamics (MD) simulations using these FF parameters revealed that the good wetting of Ag on Ni caused Ag droplets to be attracted to the Ni particles, promoting spreading. Thus, the optimal hexagonal Ni pattern was shown to enhance the wetting area by 224% compared to a bare oxide surface. Second, an analytical model predicting the maximum possible enhancement in wetting area as a function of the intrinsic wetting angle, size of the Ag droplet, and size of the Ni particles was derived and validated with MD simulations, to generalize this method to other substrates. Here, both the analytical model and MD results suggested that the ability of Ni patterns to enhance the Ag wetting area becomes more significant as the wettability of substrate becomes worse.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Nanosecond Laser Drying in Wafer Foundries for Significant Energy Efficiency Improvement

Surface drying is a critical process in microelectronics wafer fabrication. In order to reduce and eliminate watermarks, many surface drying processes have been studied and used. Most of the wafer foundries currently use drying processes based on Isopropyl Alcohol (IPA), which causes serious safety (fire), health, environmental, and energy efficiency issues. The long-term goal of this project is to develop a fast, effective, chemical-free, and extremely energy efficient wafer drying technology that employs nanosecond laser-induced sub-surface evaporation and explosion. The objectives of this project are to 1) experimentally develop and verify the laser surface drying method, explore and identify how and to what extent various physical parameters influence the drying efficiency and effectiveness; and 2) understand and optimize the underlying thermodynamics in the proposed laser surface drying, including micro/nanoscale sub-surface superheating, evaporation, explosion, and photo/thermochemical bond breaking via atomistic modeling and experimental characterization. Toward these objectives, five tasks have been accomplished successfully, including 1) develop in-lab laser surface drying setup and study the effect of varied laser parameters, 2) use multiscale hybrid-modeling to study and understand the physics of water behavior during drying toward process optimization, 3) investigate the effect of drying conditions on the drying effectiveness, 4) develop a large-scale laser drying scanning system with automatic scanning, and 5) conduct large-scale simulation to study the effect of dopant level and develop strategies for system development toward industry deployment.

42 ENGINEERING↗

DOE OSTI2022

Modeling Heterogeneity in UO 2 Nanoparticles Using X‐ray Absorption Spectroscopy

Abstract EXAFS provides the capability to interrogate nanoparticle (NP) structure in atomistic detail without relying on long‐range crystallinity. There is a limitation in that EXAFS provides averaged structural information, making it difficult to separate a small amount of heterogeneous structure from bulk. In this work, models were developed to extract surface‐specific information from conventional EXAFS measurements collected on UO 2 NPs of varying size. Specifically, the surface terminating species of UO 2 NPs was determined from comparison of coordination numbers with geometric models while the origin of static disorder was interrogated from user‐defined simulations. Results show that the degree of oxygenation on the NP surface does not significantly deviate from bulk surface and that static disorder is highly enhanced in NP surface layers but cannot be attributed to surface relaxation effects alone. The approach described herein has the potential to be adapted to a range of inorganic NP systems to interrogate surface structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Understanding copper diffusion in CuInSe 2 with first-principles based atomistic and continuum models

We investigate the diffusion of copper in CuInSe 2 using thermodynamic and kinetic models based on density functional theory calculations, attempting to reconcile large differences in reported experimental diffusivities. We find that observations of rapid chemical diffusion can be explained by large thermodynamic factors, which we calculate using a compositionally constrained model of intrinsic point defect formation. We further characterize how copper diffusion coefficients depend on material synthesis conditions and exhibit their variation across the CuInSe 2 secondary phase diagram. In doing so, we identify stable off-stoichiometries that are dominated by either vacancy- or interstitial-mediated diffusion mechanisms. These results are employed in the development of a continuum reaction–diffusion model, which we use to simulate experimental depth profiles.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Atomistic structure of the SARS-CoV-2 pseudoknot in solution from SAXS-driven molecular dynamics

SARS-CoV-2 depends on -1 programmed ribosomal frameshifting (-1 PRF) to express proteins essential for its replication. The RNA pseudoknot stimulating -1 PRF is thus an attractive drug target. However, the structural models of this pseudoknot obtained from cryo-EM and crystallography differ in some important features, leaving the pseudoknot structure unclear. We measured the solution structure of the pseudoknot using small-angle X-ray scattering (SAXS). The measured profile did not agree with profiles computed from the previously solved structures. Beginning with each of these solved structures, we used the SAXS data to direct all atom molecular dynamics (MD) simulations to improve the agreement in profiles. In all cases, this refinement resulted in a bent conformation that more closely resembled the cryo-EM structures than the crystal structure. Applying the same approach to a point mutant abolishing -1 PRF revealed a notably more bent structure with reoriented helices. This work clarifies the dynamic structures of the SARS-CoV-2 pseudoknot in solution.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Atomistic learning in the electronically grand-canonical ensemble

Abstract A strategy is presented for the machine-learning emulation of electronic structure calculations carried out in the electronically grand-canonical ensemble. The approach relies upon a dual-learning scheme, where both the system charge and the system energy are predicted for each image. The scheme is shown to be capable of emulating basic electrochemical reactions at a range of potentials, and coupling it with a bootstrap-ensemble approach gives reasonable estimates of the prediction uncertainty. The method is also demonstrated to accelerate saddle-point searches, and to extrapolate to systems with one to five water layers. We anticipate that this method will allow for larger length- and time-scale simulations necessary for electrochemical simulations.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Permeability of the HIV-1 capsid to metabolites modulates viral DNA synthesis

Reverse transcription, an essential event in the HIV-1 life cycle, requires deoxynucleotide triphosphates (dNTPs) to fuel DNA synthesis, thus requiring penetration of dNTPs into the viral capsid. The central cavity of the capsid protein (CA) hexamer reveals itself as a plausible channel that allows the passage of dNTPs into assembled capsids. Nevertheless, the molecular mechanism of nucleotide import into the capsid remains unknown. Employing all-atom molecular dynamics (MD) simulations, we established that cooperative binding between nucleotides inside a CA hexamer cavity results in energetically favorable conditions for passive translocation of dNTPs into the HIV-1 capsid. Furthermore, binding of the host cell metabolite inositol hexakisphosphate (IP 6 ) enhances dNTP import, while binding of synthesized molecules like benzenehexacarboxylic acid (BHC) inhibits it. The enhancing effect on reverse transcription by IP 6 and the consequences of interactions between CA and nucleotides were corroborated using atomic force microscopy, transmission electron microscopy, and virological assays. Collectively, our results provide an atomistic description of the permeability of the HIV-1 capsid to small molecules and reveal a novel mechanism for the involvement of metabolites in HIV-1 capsid stabilization, nucleotide import, and reverse transcription.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Modeling Single-Crystal Battery Materials: From Fundamental Understanding to Performance Evaluation

The performance of rechargeable batteries is fundamentally influenced by the physicochemical properties and microstructural features of their key material components. Recent experimental advancements have highlighted the potential of single-crystal (SC) morphologies to address inherent limitations of polycrystalline (PC) electrodes and solid-state electrolytes, offering tunable charge transport kinetics and improved cell cycling performance. Here, this review examines how state-of-the-art computational modeling, from atomistic and mesoscale to continuum-level approaches, including machine learning methodologies, has been utilized to investigate the critical factors governing the electrochemical behavior of SC battery materials. We explore how predictive modeling can elucidate the processing–structure–property–performance relationships of SC cathodes, anodes, and solid-state electrolytes, with a focus on unique SC characteristics such as crystallographic anisotropy, size effects, and facet-dependent properties. Additionally, we identify limitations in commonly used modeling techniques and discuss strategies to address these challenges. By integrating high-fidelity simulations with experimental insights, this review aims to outline a clear path for the rational design and optimization of SC battery components, paving the way for accelerated advancements in energy storage technologies.

Materials science↗

DOE OSTI2020

Low friction in bcc metals via grain boundary sliding

In this work, low friction is demonstrated with pure polycrystalline tantalum sliding contacts in both molecular dynamics simulations and ultrahigh vacuum experiments. This phenomenon is shown to be correlated with deformation occurring primarily through grain boundary sliding and can be explained using a recently developed predictive model for the shear strength of metals. Specifically, low friction is associated with grain sizes at the interface being smaller than a critical, material-dependent value, where a crossover from dislocation mediated plasticity to grain-boundary sliding occurs. Low friction is therefore associated with inverse Hall-Petch behavior and softening of the interface. Direct quantitative comparisons between experiments and atomistic calculations are used to illustrate the accuracy of the predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Solvation Thermodynamics of Solutes in Water and Ionic Liquids Using the Multiscale Solvation-Layer Interface Condition Continuum Model

We report molecular assembly processes are generally driven by thermodynamic properties in solutions. Atomistic modeling can be very helpful in designing and understanding complex systems, except that bulk solvent is very inefficient to treat explicitly as discrete molecules. The SLIC/CDC multiscale model combines continuum solvent electrostatics based on the solvent layer interface condition (SLIC) with statistical thermodynamic models for hydrogen bonding and nonpolar modes: cavity formation, dispersion interactions, combinatorial mixing (CDC). The SLIC/CDC model predicts Gibbs energies of solvation for a database of 500 solutes in water with average accuracy better than 1 kcal/mol both for experimental measurements and for explicit-solvent molecular dynamics simulations. The separate SLIC/CDC energy mode values agree quantitatively with those computed from explicit-solvent molecular dynamics. The SLIC/SASA multiscale model combines the SLIC continuum electrostatic model with the solvent accessible surface area (SASA) nonpolar energy mode. The SLIC/SASA model predicts Gibbs energies of solvation with better than 1.4 kcal/mol average accuracy in aqueous systems and better than 1.6 kcal/mol average accuracy in ionic liquids. Both models predict solvation entropies, and are the first implicit-solvation models capable of predicting solvation heat capacities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Revealing hidden defects through stored energy measurements of radiation damage

With full knowledge of a material’s atomistic structure, it is possible to predict any macroscopic property of interest. In practice, this is hindered by limitations of the chosen characterization techniques. For example, electron microscopy is unable to detect the smallest and most numerous defects in irradiated materials. Instead of spatial characterization, we propose to detect and quantify defects through their excess energy. Differential scanning calorimetry of irradiated Ti measures defect densities five times greater than those determined using transmission electron microscopy. Our experiments also reveal two energetically distinct processes where the established annealing model predicts one. Molecular dynamics simulations discover the defects responsible and inform a new mechanism for the recovery of irradiation-induced defects. The combination of annealing experiments and simulations can reveal defects hidden to other characterization techniques and has the potential to uncover new mechanisms behind the evolution of defects in materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Effect of Solvent Composition on Non-DLVO Forces and Oriented Attachment of Zinc Oxide Nanoparticles

Oriented attachment (OA) occurs when nanoparticles in solution align their crystallographic axes prior to colliding and subsequently fuse into single crystals. Traditional colloidal theories such as DLVO provide a framework for evaluating OA but fail to capture key particle interactions due to the atomistic details of both the crystal structure and the interfacial solution structure. Using zinc oxide as a model system, we investigated the effect of the solvent on short-ranged and long-ranged particle interactions and the resulting OA mechanism. In situ TEM imaging showed that ZnO nanocrystals in toluene undergo long-range attraction comparable to 1kT at separations of 10 nm and 3kT near particle contact. These observations were rationalized by considering non-DLVO interactions, namely dipole-dipole forces and torques between the polar ZnO nanocrystals. Langevin dynamics simulations showed stronger interactions in toluene compared to methanol solvents, consistent with the experimental results. Concurrently, we performed atomic force microscopy measurements using ZnO-coated probes for the short-ranged interaction. Our data provided valuable insights into another type of non-DLVO interaction, namely the repulsive solvation force. Specifically, the solvation force was stronger in water compared to ethanol and methanol, due to the stronger hydrogen bonding and denser packing of water molecules at the interface. In conclusion, our results highlight the importance of non-DLVO forces in a general framework for understanding and predicting particle aggregation and attachment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Evolution of dislocation loops in irradiated α-Uranium: An atomistically-informed cluster dynamics investigation

An atomistically-informed mean field cluster dynamics model has been developed to investigate the nucleation and growth of prismatic loops in irradiated a-Uranium. TEM analysis of neutron irradiated a-Uranium shows the evolution of self-interstitial atom and vacancy loops on (010) and (100) crystallographic planes, respectively, resulting in an anisotropic lattice swelling of its face-centered orthorhombic crystal. To provide model parameters, the crystallography of loops and the binding energy of point defects to these loops were studied using an angular dependent EAM potential and classical molecular dynamics (MD) simulations. Furthermore, using the bond-boost hyperdynamics method, the anisotropic diffusion of interstitials and vacancies in a-Uranium was investigated. Here, the mechanisms of point defect diffusion and the associated migration energies were reported and compared with previous DFT studies. The energetics and kinetic quantities mentioned above were used in the cluster dynamics model to predict the cluster density as a function of dose rate, dose and temperature and the results were compared to the reported neutron irradiation experiments. The model predictions reveal an accumulation of small sized vacancy loops along with a population of large and growing self-interstitial loops, which closely corresponds to the TEM observations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗