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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Ultra-fast interpretable machine-learning potentials

Abstract All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate the exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Resonant Landau-Zener conversion in multi-axion systems

Multiple axions may emerge in the low-energy effective theory of Nature. Generically, the potentials describing these axion fields are non-diagonal, leading to mass mixing between axion states which can be temperature-dependent due to QCD instanton effects. As the temperature of the Universe drops, level crossing can occur, causing resonant conversion between axion states. In this work, we present an analytic study of the cosmological evolution of multi-axion systems including adiabatic and non-adiabatic resonant conversion from one axion state into another during the misalignment process. We show how the Landau-Zener formalism accurately captures the non-adiabatic resonant conversion, permitting an analytic description of the relic abundances of each axion field for nearly any arbitrary two-state axion mass matrix. As an application, we study the mixing of a QCD axion with an axion-like-particle for specific potentials to identify the predictions for haloscope experiments. We conclude that the detection of an axion off the expected QCD mass-coupling line predicts other haloscope targets if it mixes with the QCD axion.

Axions and ALPs↗
DOE OSTI2025

Machine learning-accelerated path integral molecular dynamics simulations of reactive organic electrolytes

Hydrogen bonded electrolytes that exhibit accelerated proton transport via sequential reactive hops have drawn interest for their promise in clean energy applications. Molecular dynamics simulations of these electrolytes offer the opportunity to uncover microscopic mechanistic details that could be used to design and tune the properties of candidate electrolyte technologies. However, accurately modeling the proton transfer reactions and transport properties that give rise to high charge conductivites in these electrolytes proves computationally challenging because of the need to perform lengthy condensed phase simulations, treating both the electronic and nuclear degrees of freedom quantum mechanically. In this paper, we demonstrate that such a modeling task can be efficiently achieved with the use of density functional theory (DFT)-trained machine learning potentials (MLP) to accelerate path integral molecular dynamics (PIMD) simulations. We highlight the practical utility of this approach by using it to benchmark how closely PIMD simulations employing different DFT exchange–correlation functionals reproduce the composition-dependent densities, diffusion coefficients, and electrical conductivities of mixtures consisting of imidazole and levulinic acid. Even with the speedup afforded by our MLPs, PIMD simulations remain quite expensive. Furthermore, in order to render PIMD more computationally tractable, we introduce and benchmark the accuracy of a ring polymer contraction approach that leverages a computationally efficient short-range MLP to accelerate our PIMD simulations by an additional factor of four.

Chemical bonding↗
DOE OSTI2023

Simulations with machine learning potentials identify the ion conduction mechanism mediating non-Arrhenius behavior in LGPS

Abstract Li 10 Ge(PS 6 ) 2 (LGPS) is a highly concentrated solid electrolyte, in which Coulombic repulsion between neighboring cations is hypothesized as the underlying reason for concerted ion hopping, a mechanism common among superionic conductors such as Li 7 La 3 Zr 2 O 12 (LLZO) and Li 1.3 Al 0.3 Ti 1.7 (PO 4 ) 3 (LATP). While first principles simulations using molecular dynamics (MD) provide insight into the Li + transport mechanism, historically, there has been a gap in the temperature ranges studied in simulations and experiments. Here, we used a neural network potential trained on density functional theory (DFT) simulations, to run up to 40-nanosecond long MD simulations at DFT-like accuracy to characterize the ion conduction mechanisms across a range of temperatures that includes previous simulations and experimental studies. We have confirmed a Li + sublattice phase transition in LGPS around 400 K, below which the ab -plane diffusivity D a b ∗ is drastically reduced. Concomitant with the sublattice phase transition near 400 K, there is less cation-cation (cross) correlation, as characterized by Haven ratios closer to 1, and the vibrations in the system are more harmonic at lower temperature. Intuitively, at high temperature, the collection of vibrational modes may be sufficient to drive concerted ion hops. However, near room temperature, the vibrational modes available may be insufficient to overcome electrostatic repulsion, thus resulting in less correlated ion motion and comparatively slower ion conduction. Such phenomena of a sublattice phase transition, below which concerted hopping plays a less significant role, may be extended to other highly concentrated solid electrolytes such as LLZO and LATP.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Nonlinear Sigma model amplitudes to all loop orders are contained in the Tr ( Φ 3 ) theory

Scattering amplitudes for the simplest theory of colored scalar particles—the Tr ( Φ 3 ) theory—have recently been the subject of active investigations. In this work we describe an unanticipated wider implication of this work: the Tr ( Φ 3 ) theory secretly contains nonlinear sigma model (NLSM) amplitudes to all loop orders. The NLSM amplitudes are obtained from Tr ( Φ 3 ) amplitudes by a unique shift of kinematic variables. We show that this shifted kinematics produces amplitudes for a cubic theory with a linear term in the potential, with extrema spontaneously breaking U ( N ) → U ( N − k ) × U ( k ) . The Goldstone amplitudes for this theory coincide with those of pions in the U ( N ) × U ( N ) → U ( N ) chiral Lagrangian to all orders in the planar limit. We also give a purely on-shell understanding of this correspondence, showing that integrands defined by the kinematic shifts have the correct residues on poles and appropriately produce the Adler zero. Finally, we discuss how similar kinematic shifts produce certain infinite classes of mixed amplitudes of pions and Tr ( Φ 3 ) scalars, most of which are not interpretable from the Lagrangian description. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Monolayer TiS 2 Nanosheets on Au(111)–Structural Characterization and Effect of Edge Stability for Shape Control

Transition metal dichalcogenides are promising alternatives to noble metal catalysts, e.g., for (photo-)activation of greenhouse gases or hydrogenations. Herein, a direct synthetic route for 2D TiS 2 nanosheets on Au(111) by titanium deposition in the presence of a mild, organic, non-oxidizing sulfur source is presented. High-resolution scanning tunneling microscopy (STM) is used to gain atomic-level insights into the TiS 2 nanosheet morphology. In contrast to the literature, this protocol gains mostly hexagonal and truncated triangular nanosheets with an increased edge contrast in STM, analog to metallic edge states in MoS 2 . Synchrotron-based photoelectron spectroscopy allows insights into compositional details, specifically to distinguish different S sites on the TiS 2 sheets and other S species on the sample. Further, a minimum size is identified (9 S atoms side length), which underlines the importance of moiré reconstructions for stress relief. The TiS 2 sheets coexist with [Au]Ti 1 S 3 clusters, in which a single gold atom is alloyed into the surface and capped by three S atoms. Together with the finding of a critical sheet size, this points toward on-surface Ostwald ripening as a relevant process in the sheet formation. Ab-initio calculations (density functional theory) underscore that the chemical potential of S is an essential descriptor to maintain shape control.

2D nanomaterials↗
DOE OSTI2023

Exploring structural transitions at grain boundaries in Nb using a generalized embedded atom interatomic potential

The advancement in experimental techniques, like the atom probe tomography and high resolution electron microscopy, is fueling interest in studying structural transformations of grain boundaries in metal and alloys to uncover correlations between mechanical properties and solute or impurity segregation to grain boundaries. Atomistic modeling is an important tool that can pinpoint the intricate dynamics of grain boundary phase transitions, but the lack of accurate interatomic potentials needed to simulate the complex dynamics of grain boundary structural transitions and identify different metastable phases has been the bottleneck. To this end, we use niobium as a model body centered cubic (BCC) metal and develop an interatomic potential to study grain boundary phase transitions. The potential for Nb is based on a generalization of the embedded atomic method potential and has sufficient flexibility to learn complex energy landscapes using a small set of training structures. We systematically test and validate the using data from ab initio density functional theory calculations and experiments. Using this potential, we calculate energies of multiple symmetric-tilt grain boundaries spanning a wide range of misorientation angles. Additionally, we explore different metastable structures of the Σ 27(552) [$1\overline{1}0$] grain boundary and use molecular dynamic simulations to study the coexistence of metastable phases and grain boundary transitions at finite temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predicting the proximity to macroscopic failure using local strain populations from dynamic in situ X-ray tomography triaxial compression experiments on rocks

Predicting the proximity of large-scale dynamic failure is a critical concern in the engineering and geophysical sciences. Here we use evolving contractive, dilatational, and shear strain deformation preceding failure in dynamic X-ray tomography experiments to examine which strain components best predict the proximity to failure. We develop machine learning models to predict the proximity to failure using time series of three-dimensional local incremental strain tensor fields acquired in rock deformation experiments under stress conditions of the upper crust. Three-dimensional scans acquired in situ throughout triaxial compression experiments provide a distribution of density contrasts from which we estimate the three-dimensional incremental strain that accumulates between each scan acquisition. Training machine learning models on multiple experiments of six rock types provides suites of feature importance that indicate the predictive power of each feature. Comparing the average importance of groups of features that include information about each strain component quantifies the ability of the contractive, dilatational and shear strain to predict the proximity of macroscopic failure. A total of 24 models of four machine learning algorithms with six rock types indicate that 1) the dilatational strain provides the best predictive power of the strain components, and 2) the intermediate values (25th-75th percentile) of the strain population provide the best predictive power of the statistics of the strain populations. In addition, the success of the predictions of models trained on one rock type and tested on other rock types quantifies the similarities and differences of the precursory strain accumulation process in the six rock types. These similarities suggest the potential existence of a unified theory of brittle rock deformation for a range of rock types.

58 GEOSCIENCES↗
DOE OSTI2026

A generative machine learning model for designing metal hydrides applied to hydrogen storage

Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases, such as the Materials Project, contain a limited number of well-characterized hydrides, which constrains the discovery of optimal candidates. This work presents a framework that integrates causal discovery with a lightweight generative machine learning model to generate novel metal hydride candidates that may not exist in current databases. Using a dataset of 450 samples (270 training, 90 validation, and 90 testing), the model generates 1000 candidates. After ranking and filtering, six previously unreported chemical formulas and crystal structures are identified, four of which are validated by density functional theory simulations and show strong potential for future experimental investigation. Overall, the proposed framework provides a scalable and time-efficient approach for expanding hydrogen storage datasets and accelerating materials discovery.

generative model↗
DOE OSTI2020

Insights on keto-hydroperoxide formation from O 2 addition to the beta-tetrahydrofuran radical

Cyclic ethers provide an interesting case study of low-temperature oxidation chemistry, especially in relevance to biofuels. A recent experimental study revealed new questions regarding the low-temperature oxidation mechanism of tetrahydrofuran (THF) concerning the formation of keto-hydroperoxides. In particular, keto-hydroperoxides originating from the THF-β radical were not captured accurately by current literature models, motivating this work to calculate the energetics of the first and second O 2 -addition pathways for THF radicals. Electronic structure calculations at the CCSD(T)/cc-pV∞Z//M06-2X/cc-pVTZ level of theory were used to generate potential energy surfaces for the α-C and β-C THF radicals and subsequent pathways to the formation of the keto-hydroperoxide isomers. These are the first theoretical calculations of the second O 2 -addition radical pathways for the THF-β radical. Results from the theorical work provided further insight into the low-temperature oxidation of THF. This included identifying the pathways most likely to form the keto-hydroperoxide isomers observed in prior experimental work; and detecting that the shortcomings in prior models are likely due to uncertainties in R + THF abstraction reaction rates. Here, these conclusions will motivate future work for accurate THF kinetic model development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials

Here we employed Density Functional Theory (DFT) to compute oxidation potentials of 1,400 homobenzylic ether molecules to search for the ideal sustainable redoxmer design. The generated data were used to construct an active learning model based on Bayesian optimization (BO) that targets candidates with desired oxidation potentials utilizing only a minimal number of DFT calculations. The active learning model demonstrated not only significant efficiency improvement over the random selection approach but also robust capability in identifying desired candidates in an untested set of 112,000 homobenzylic ether molecules. Our findings highlight the efficacy of quantum chemistry-informed active learning to accelerate the discovery of materials with desired properties from a vast chemical space.

25 ENERGY STORAGE↗
DOE OSTI2026

Ion Distribution and Cation Exchange at Mica–Electrolyte Interfaces Probed with Deep Potential Molecular Dynamics

Here, we investigate the Stern layer structure and cation exchange mechanism at muscovite mica-electrolyte interfaces using nanosecond timescale molecular dynamics simulations based on deep neural network interatomic potentials trained on Density Functional Theory (DFT) data. Focusing on mica with exposed surface K + interfaced with aqueous NaCl and mica with surface Na + interfaced with KCl solution, we find that K + remains predominantly in inner-sphere configurations, while Na + exhibits notable populations in outer-sphere states. Most importantly, our simulations show that contact with an electrolyte solution results in the co-adsorption of multiple cation species, making the mica surface locally overcharged and thus reshaping the cation speciation in a manner that enhances the tendency of neighboring surface cations to desorb. These findings are consistent with recent experimental observations that co-adsorption of different cation species induces changes in cation speciation and slow kinetics of cation exchange at the muscovite-water interface, providing a basis for their detailed understanding.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Plutonium Hybrid Materials: A Platform to Explore Assembly and Metal–Ligand Bonding

In this work, we report the synthesis of five new hybrid materials containing the [PuCl 6 ] 2- anion and charge balancing, non-covalent interaction donating 4-X-pyridinium (X = H, Cl, Br, I) cations. Single crystals of the title compounds were grown and harvested from acidic, chloride-rich, aqueous media and their structures were determined via X-ray diffraction. Compounds 1-4, (4XPyH) 2 [PuCl 6 ] and 5, (4IPyH) 4 [PuCl 6 ] · 2Cl, exhibit two distinct sheet-like structure types. Structurally relevant non-covalent interactions were tabulated from crystallographic data and verified computationally using electrostatic surface potential maps and the quantum theory of atoms in molecules (QTAIM) approach. The strength of the hydrogen and halogen bonds was quantified using Kohn-Sham density functional theory and a hierarchy of acceptor-donor pairings established. In turn, the PuIV-Cl bonds were studied using the QTAIM and natural localized molecular orbital (NLMO) approaches to delineate the underlying bond mechanism and hybrid atomic orbital contributions therein. Energy decomposition (ED) and natural ED analyses were also explored to probe the bond mechanism and, more broadly, explore the efficacy of these techniques in studying these anionic systems. The results of the PuIV-Cl bond analyses were compared across composition via analogous treatments of previously reported [PuO 2 Cl 4 ] 2- and [PuCl 3 (H 2 O) 5 ] molecular units. In summary, our study indicates that the Pu-Cl bonds are predominately ionic, yet exhibit small varying degrees of covalent character that increase from [PuCl 3 (H 2 O) 5 ], [PuO 2 Cl 4 ] 2- , to [PuCl 6 ] 2- , while the participation of the Pu based s/d and f orbitals concurrently decrease and increase, respectively.

transuranic↗
DOE OSTI2020

Gas-Phase Complexes of Americium and Lanthanides with a Bis-triazinyl Pyridine: Reactivity and Bonding of Archetypes for F-Element Separations

Bis-triazinyl pyridines (BTPs) exhibit solution selectivity for trivalent americium over lanthanides (Ln), the origins of which remain uncertain. Here in this paper, electrospray ionization was used to generate gas-phase complexes [ML 3 ] 3+ , where M = La, Lu, or Am and L is EtBTP 2,6-bis(5,6-diethyl-1,2,4-triazin-3-yl)-pyridine. Collision-induced dissociation (CID) of [ML 3 ] 3+ in the presence of H 2 O yielded a protonated ligand [L(H)] + and hydroxide [ML 2 (OH)] 2+ or hydrate [ML(L–H)(H 2 O)] 2+ , where (L–H) - is a deprotonated ligand. Although solution affinities indicate stronger binding of BTPs toward Am 3+ versus Ln 3+ , the observed CID process is contrastingly more facile for M = Am versus Ln. To understand the disparity, density functional theory was employed to compute potential energy surfaces for two possible CID processes, for M = La and Am. In accordance with the CID results, both the rate determining transition state barrier and the net energy are lower for [AmL 3 ] 3+ versus [LaL 3 ] 3+ and for both product isomers, [ML 2 (OH)] 2+ and [ML(L–H)(H 2 O)] 2+ . More facile removal of a ligand from [AmL 3 ] 3+ by CID does not necessarily contradict stronger Am 3+ –L binding, as inferred from solution behavior. In particular, the formation of new bonds in the products can distort kinetics and thermodynamics expected for simple bond cleavage reactions. In addition to correctly predicting the seemingly anomalous CID behavior, the computational results indicate greater participation of Am 5f versus La 4f orbitals in metal–ligand bonding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

ALD with Alternative Coreactants: Which Work, Which Do Not, and Why

In this paper, we present a combined experimental and theoretical study that systematically evaluates a series of alcohols (i.e., primary, secondary, and tertiary) as coreactants for trimethylaluminum (TMA)-based atomic layer deposition (ALD) of Al 2 O 3 . We employed in situ quartz crystal microbalance techniques and ex situ X-ray photoelectron spectroscopy to probe growth across a range of temperatures from T = 120 to 285 °C. Dispersion-inclusive hybrid density functional theory was employed to identify potential reaction pathways and compute corresponding Gibbs free-energy barriers and rate coefficients. Experimentally on an Al 2 O 3 surface, sustained thin-film growth, where the rate is constant over many cycles, was only observed with tertiary alcohols (tert-butanol and 2-methyl-2-butanol) at elevated temperatures (e.g., T = 285 °C); for tert-butanol specifically, sustained growth also occurred at T = 240 °C, with no sustained growth at or below T = 210 °C. Growth using primary and secondary alcohols decayed to a negligible value after only 2–3 cycles on an Al 2 O 3 surface at all tested temperatures. Intentional trace additions of H 2 O to anhydrous alcohols restored sustained growth but at reduced deposition rates relative to pure H 2 O. Theoretical analysis supports a bimolecular alkoxy β-H elimination mechanism as the primary reaction pathway: initiated by the formation of a bound alkoxy species, the subsequent β-H elimination step is rate-determining, with a barrier that systematically decreases as the degree of the alcohol coreactant is increased from primary to secondary to tertiary. Computed rate coefficients (k) also indicate a strong temperature dependence (with k increasing by ∼4–5 orders of magnitude from T = 120 to 285 °C) and are consistent with the experimental observation that only tertiary alcohols lead to steady thin-film growth at elevated temperatures. This integrated approach establishes why only certain alcohols can sustain Al 2 O 3 ALD and delivers a predictive framework for identifying effective alcohol coreactants.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Valence Electron Distributions from Sub-Angstrom Convergent Beam Electron Diffraction

Modern aberration-corrected scanning transmission electron microscopes can acquire four-dimensional data sets (“4D STEM”) by recording convergent beam electron diffraction (CBED) patterns, using precisely positioned, sub-angstrom probes. Here, we demonstrate that these patterns can probe the site symmetry, atomic displacements, and valence electron distributions at individual atomic columns. To this end, 4D STEM CBED patterns were acquired from SrTiO 3 single crystals and compared with patterns calculated using scattering potentials derived from density functional theory. Here, we show that an aspherical valence electron charge build-up at the oxygen sites causes intensity asymmetries in the low-angle scattering portion of the patterns. Using strained SrTiO 3 films containing subtle polar displacements within nanometer-sized domains, it is shown that the high-angle scattering portion in each pattern is sensitive to atomic displacements.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Actinide Molten Salts: A Machine-Learning Potential Molecular Dynamics Study

We know that actinide molten salts represent a class of important materials in nuclear energy. Understanding them at a molecular level is critical to proper and optimal design of relevant technological applications. Yet, owing to the complexity of electronic structure due to the 5f orbitals, computational studies of heavy elements in condensed phases using ab initio potentials to study the structure and dynamics of these elements embedded in molten salts are difficult. This lack of efficient computational protocols makes it difficult to obtain information on properties that require extensive statistical sampling like transport. To tackle this problem, we adopted a machine-learning approach to study ThCl 4 -NaCl and UCl 3 -NaCl binary systems. The machine-learning potential, with the density functional theory accuracy, allows us to obtain long molecular dynamics trajectories (ns) for large systems (10 3 atoms) at a considerably low computing cost, thereby efficiently gaining information about their bonding structures, thermodynamics, and dynamics at a range of temperature. We observed a considerable change in the coordination environments of actinide elements and their characteristic coordination-sphere lifetime. Our study also suggests that actinides in molten salts may not follow well known entropy-scaling laws.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Automatic Time Step Control to Resolve Hydromechanically Driven Fault Reactivation, Spontaneous Nucleation, and Seismic Arrest

Abstract A physical understanding of the progression from flow‐driven (quasi‐static) poromechanical deformation to dynamic fault rupture is critical to the resilient operations of several engineering systems. These processes are bridged by a progression from fault reactivation to the spontaneous nucleation of unstable sliding. Toward addressing this challenge, novel automatic time step size control methods are developed to enable accurate and efficient simulation of these dynamics and transitions from the first principles. The controllers combine local models for discretization error and Coulomb failure conditions to automatically adjust the time step size across several orders of magnitude. The methods do not require additional empirical or theoretical input and can resolve the pre‐rupture, interseismic, and seismic periods to the allowed accuracy. The computational results reveal that the proposed methods automatically capture the onset of reactivation and nucleation for homogeneous and heterogeneous fields. Hydrodynamic and structural heterogeneity lead to disparate critical nucleation sizes compared to those predicted by theory. The results highlight its potential in predicting induced seismicity in realistic subsurface engineering systems and at practical scales.

Environmental Sciences & Ecology↗