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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Custom-trained Machine-learning Interatomic Potentials: ZnCl2 Aqueous Solution

This dataset was generated using an iterative active-learning strategy implemented in the ArcaNN software package (https://github.com/arcann-chem/arcann_training) to train machine-learning interatomic potentials for aqueous ZnCl2 solutions. Each active-learning cycle consisted of three stages: training, exploration, and labeling. The initial training set combined configurations generated in this work from enhanced-sampling ab initio molecular dynamics simulations with configurations from a previously reported neural-network-potential study of aqueous ZnCl2. The enhanced-sampling ab initio molecular dynamics simulations involved Zn–Cl separation and the chloride coordination number around Zn²? as collective variables. These configurations served as the seed dataset. Subsequent active-learning cycles expanded the training set by identifying and labeling configurations that were poorly represented by the current models, thereby improving coverage of ion-association states and changes in local coordination and charge-state environments relevant to the solution free-energy landscape. For all selected configurations, single-point calculations of the total energies and atomic forces were performed within density functional theory using the CP2K Quickstep module. Reference calculations employed the revPBE-D3 and r2SCAN exchange-correlation functionals. Motivated by recent work on aqueous Zn²?, the main revPBE calculations omitted D3 dispersion contributions involving Zn²?, while retaining the D3 correction for water and chloride. For comparison, fully dispersion-corrected revPBE-D3 reference calculations were also performed, with D3 applied to all species, including Zn²?. Valence electrons were treated explicitly, while core electrons were represented using norm-conserving Goedecker–Teter–Hutter pseudopotentials. The wave functions were expanded using the mixed Gaussian-and-plane-wave scheme with TZV2P-MOLOPT basis sets for all elements and a 600 Ry auxiliary plane-wave cutoff for the electron density. Self-consistent-field convergence was accelerated using the orbital-transformation and Direct Inversion in the Iterative Subspace algorithms, with a convergence threshold of 10?6. All single-point calculations were performed in periodic orthorhombic cells. The CELL_REF keyword in CP2K was used to define a fixed reference cell with a box length of 25 Å. This treatment ensured a consistent reference for configurations extracted from NpT trajectories with fluctuating cell dimensions. The resulting DFT energies and atomic forces constitute the ground-truth labels used to train the MLIPs. The resulting MLIP was trained for aqueous ZnCl2 solutions spanning concentrations from 0 to 30 molal and a broad pH range, from strongly acidic to strongly basic conditions. Representative examples of configurations included in the MLIP training dataset are provided below. These include 1) Representative configurations from the dataset labeled at the revPBE-D3 level, with D3 dispersion interactions involving Zn2+ excluded (revPBE-wo-D3). 2) Representative configurations from the dataset labeled at the fully dispersion-corrected revPBE-D3 level, with D3 interactions applied to all species, including Zn2+ (revPBE-D3). 3) Representative configurations from the dataset labeled at the r2SCAN level of theory (r2SCAN).

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗
DOE OSTI2025

Efficient machine learning interatomic potentials robust for liquid and multiple solid polymorphs of NaF and KF

Achieving atomic-level understanding of crystallization of molten salts is of importance to a wide range of technological applications. Recent work [Fan et al., Proc. Natl. Acad. Sci. USA 122, e2425702122 (2025)] revealed that crystal nucleation in molten LiF salt is a multistage process according to the molecular-dynamics (MD) simulations based on an atomic cluster expansion (ACE) machine-learning interatomic potential (MLIP). In order to understand the influence of increasing cation size on nucleation pathways and nucleation rates of molten fluoride salts, here we develop two new ACE MLIPs for NaF and KF. The two ACE MLIPs feature DFT-SCAN-level accuracy for liquid and multiple solid polymorphs over a wide temperature (0–2000 K) and pressure (0–100 GPa) range, and also reproduce well a number of experimental data for solid and liquid equilibrium properties. The efficiency of the two ACE MLIPs enable million-atom-scale or microsecond-scale MD simulations. The two general-purpose ACE MLIPs are expected to be useful for atomistic simulations for different purposes, in addition to studying crystallization of molten NaF and KF salts.

Crystal melting↗
DOE OSTI2024

Data Generation for Machine Learning Interatomic Potentials and Beyond

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space.

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

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials

Accurate molecular property predictions require 3D geometries, which are typically obtained using expensive methods such as density functional theory (DFT). Here, we attempt to obtain molecular geometries by relying solely on machine learning interatomic potential (MLIP) models. To this end, we first curate a large-scale molecular relaxation dataset comprising 3.5 million molecules and 300 million snapshots. Then MLIP pre-trained models are trained with supervised learning to predict energy and forces given 3D molecular structures. Once trained, we show that the pre-trained models can be used in different ways to obtain geometries either explicitly or implicitly. First, it can be used to obtain approximate low-energy 3D geometries via geometry optimization. While these geometries do not consistently reach DFT-level chemical accuracy or convergence, they can still improve downstream performance compared to non-relaxed structures. To mitigate potential biases and enhance downstream predictions, we introduce geometry fine-tuning based on the relaxed 3D geometries. Second, the pre-trained models can be directly fine-tuned for property prediction when ground truth 3D geometries are available. Our results demonstrate that MLIP pre-trained models trained on relaxation data can learn transferable molecular representations to improve downstream molecular property prediction and can provide practically valuable but approximate molecular geometries that benefit property predictions. Our code is publicly available at: https://github.com/divelab/AIRS/.

Fu, Cong [Texas A & M Univ., College Station, TX (↗
DOE OSTI2025

Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials

There is a growing need in the simulation community for software that provides a transparent, reproducible, usable, and extensible (TRUE) Monte Carlo (MC) simulation framework employing energies from ab initio methods and machine-learning interatomic potentials (MLIPs). We introduce a Python library (ASE-MC) that adds Monte Carlo functionality to the Atomic Simulation Environment (ASE) package. Now, we can combine the powerful tools used to build systems and perform ab initio and MLIP in ASE with MC simulation algorithms to sample the configurational space with a concise Python script. After presenting the design philosophy, we demonstrate the flexibility of our approach using selected examples. These example simulations include liquid water described with a message-passing MLIP in the canonical and isothermal–isobaric ensembles, sampling the characteristic dihedral angle of biphenyl and comparing an MLIP to first-principles calculations, and a grand canonical Monte Carlo simulation of ammonia adsorption on Pt(111). These examples showcase the main features of the software, which include flexibility in the choice of ab initio or MLIP engine, ab initio or MLIP grand canonical MC with cavity bias insertions and deletions, the ability to add custom MC moves to the move set, and how users can condense complex MC workflows into a single Python script. Finally, this library serves as a framework for reproducible Monte Carlo simulations, facilitating easy reproduction of the work and application to new systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

MP-ALOE: an r2SCAN dataset for universal machine learning interatomic potentials

We present MP-ALOE, a dataset of nearly 1 million DFT calculations using the accurate r2SCAN meta-generalized gradient approximation. Covering 89 elements, MP-ALOE was created using active learning and primarily consists of off-equilibrium structures. We benchmark a machine learning interatomic potential trained on MP-ALOE, and evaluate its performance on a series of benchmarks, including predicting the thermochemical properties of equilibrium structures; predicting forces of far-from-equilibrium structures; maintaining physical soundness under static extreme deformations; and molecular dynamic stability under extreme temperatures and pressures. MP-ALOE shows strong performance on all of these benchmarks and is made public for the broader community to utilize.

Kuner, Matthew C↗
DOE OSTI2026

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials

Silicon carbide (SiC) divacancies are attractive candidates for spin-defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. Here, the trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multidefect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies.

Computer simulations↗
DOE OSTI2026

Stoichiometry dependent properties of cerium hydride: An active learning developed interatomic potential study

Cerium hydride has a variety of interesting properties, including a known lattice contraction and densification with increasing hydrogen content. However, precise stoichiometric control is not experimentally straightforward and ab initio approaches are not computationally feasible for many properties such as melting and low temperature diffusion. Therefore, we develop a machine-learned interatomic potential for cerium hydride that is valid for H to Ce ratios from 2.0 to 3.0. A query-by-committee active learning approach is used to develop the training set. Leveraging classical molecular dynamics simulations, we assess a range of properties and provide fundamental mechanisms for the trends with stoichiometry. Finally, a majority of the properties follow the trend of lattice contraction, being governed by the stronger lattice binding induced by adding octahedral atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Effects of Composition and Oxidation States on the Structures of Chromium-Containing Sodium Silicate Glasses: Molecular Dynamics Simulations using Machine Learning Interatomic Potentials

Chromium represents a significant challenge for the vitrification of high-level nuclear waste into silicate and borosilicate glasses due to its low solubility and variable oxidation states, which can limit the waste loading due to promotion of crystallization or phase separation during processing. In this study, we modeled chromium containing silicate glasses using molecular dynamics simulations with three machine learning interatomic potentials (MLIPs), MACE, CHGNet, and PFP were employed, to gain insights on glass composition and oxidation states on the structures of these glasses. One of the goals is to evaluate their ability of these MLIPs to accurately represent the general structure of silicate glasses and chromium local environments as a function of chromium oxidation states. Density Functional Theory (DFT) based calculations and experimental data such as neutron structure factors were used to validate the structural models. It was found that the foundation models of all three MLIPs are able to reproduce general structural features of the sodium silicate glass structure consistent with experimental and DFT data, but only CHGNet and PFP can accurately capture the oxidation states and local environment of chromium: tetrahedral for Cr6+ and octahedral for Cr3+. Furthermore, we studied the effect of varying Cr3+/ Cr6+ (Cr3+/Crtotal) ratio and total chromium content using PFP. Our results show that Cr6+ enhances network polymerization by reducing non-bridging oxygens through Na? charge compensation required due to the formation of chromate (CrO42-) species, while Cr³? acts as a network modifier that disrupts connectivity. System size effects on the structural characteristics and chromium environments were also tested using the PFP potential. This work highlights the importance of careful validation on the precision, transferability, and potential of MLIPs for modeling glasses containing transition metal elements that can exist in multiple oxidation states. It is also encouraging to see the foundational models are all three MLFFs are able to reproduce the basic sodium silicate glass structures, while suggesting additional training or refining is needed to improve the description of more complex systems containing transition metals.

Puga, Christina L.↗
DOE OSTI2025

Developing Machine Learning Interatomic Potential for Fe-Cr-Ni Alloys

Accurate prediction of creep and fatigue behavior of stainless steel at elevated temperatures in hydrogen environment requires fundamental understanding of alloy-hydrogen interaction at cross-scale including bulk lattice and key defects such as vacancies, grain boundaries, surfaces, stacking faults, dislocations, and precipitates. This project aims to predict creep behavior of 347H stainless steel with H using machine learning interatomic potentials based on first-principles density functional theory simulations. The Moment Tensor Potentials platform is adopted for this work since it demonstrates a fine balance between model accuracy and computational efficiency. The potential is well trained based on large amount of high-fidelity density functional theory calculations. The validation is carried out by comparing various important properties including short range order, coefficient of thermal expansion, elastic properties, stacking fault energy, grain boundary energy, and surface energy. This work lays the foundation for reliable atomistic simulation of high temperature hydrogen attack of stainless steel.

density functional theory (DFT)↗
DOE OSTI2025

Modeling the Behavior of Complex Aqueous Electrolytes Using Machine Learning Interatomic Potentials: The Case of Sodium Sulfate

Understanding the structure and thermodynamics of solvated ions is essential for advancing applications in electrochemistry, water treatment, and energy storage. While ab initio molecular dynamics methods are highly accurate, they are limited by short accessible time and length scales whereas classical force fields struggle with accuracy. Herein, we explore the structure and thermodynamics of complex monovalent-divalent ion pairs using Na 2 SO 4 (aq) as a case study by applying a machine learning interatomic potential (MLIP) trained on density functional theory (DFT) data. Our MLIP-based approach reproduces key bulk properties such as density and radial distribution functions of water. We provide the hydration structure of the sodium and sulfate ions in the 0.1–2 M concentration range and the one-dimensional and two-dimensional potentials of mean force for the sodium–sulfate ion pairing at the low concentration limit (0.1 M), which are inaccessible to DFT. At low concentrations, the sulfate ion is strongly solvated, leading to the stabilization of solvent-separated ion pairs over contact ion pairs. Minimum energy pathway analysis revealed that coordinating two sodium ions with a sulfate ion is a multistep process whereby the sodium ions coordinate to the sulfate ion sequentially. Finally, we demonstrate that MLIPs allow the study of solvated ions beyond simple monovalent pairs with DFT-level accuracy in their low concentration limit (0.1 M) via statistically converged properties from ns-long simulations.

anions↗
DOE OSTI2025

Mechanistic insights into superionic thioarsenate argyrodite solid electrolytes via machine learning interatomic potentials

The lithium argyrodite sulfide solid electrolyte Li 6 PS 5 Cl has attracted considerable interest for all-solid-state batteries owing to its high ionic conductivity, which can be further enhanced through ionic substitution. Although a variety of substitutions have been investigated, thioarsenate argyrodites remain comparatively underexplored. Here, we systematically investigate the phase stability and Li-ion conduction mechanisms in superionic Br-incorporated thioarsenate argyrodites using first-principles calculations and molecular dynamics simulations based on machine learning interatomic potentials (MLIPs). Systematic variation of S/Br site inversion reveals that an optimal degree of anion disorder significantly enhances inter-cage connectivity and facilitates long-range Li-ion diffusion. Configurational entropy serves as an effective quantitative descriptor of anion disorder, exhibiting a strong correlation with ionic conductivity. While greater anion disorder induced by site inversion and higher Br content enhances ionic conductivity up to 50 mS cm −1 , it simultaneously reduces structural stability. This trade-off results in an optimal window in which a moderate level of disorder yields conductivities exceeding 20 mS cm −1 while maintaining synthetic feasibility. In conclusion, this work highlights the reliability and efficiency of MLIPs for elucidating ion-transport mechanisms and accelerating the design of novel superionic argyrodites.

Jang, Myeongcho [Korea Institute of Science and Te↗
DOE OSTI2026

Machine-learning interatomic potentials for interfaces in all-solid-state batteries: Perspectives on training data, model selection, and validation

Interfaces play a pivotal role in dictating the performance and reliability of all-solid-state batteries (ASSBs), where complex electro-chemo-mechanical phenomena at grain boundaries (GBs) and interfaces can lead to degradation and failure. Traditional atomistic simulation methods, such as first-principles calculations and classical molecular dynamics, face limitations in modeling these interfaces due to either high computational cost or insufficient transferability to the diverse atomic environments evolving at interfaces. Machine-learning interatomic potentials (MLIPs) have emerged as a transformative approach, enabling large-scale, high-accuracy simulations of disordered and chemically complex systems by leveraging the predictability of machine learning models trained on first-principles data. Recent applications of MLIPs have demonstrated their ability to capture intricate behaviors at ASSB interfaces, including ion transport, interfacial evolution, and degradation mechanisms, with accuracy and efficiency unattainable by conventional methods. This prospective paper presents comprehensive analysis and practical guidance for MLIP development for GBs and interfaces in ASSBs, with a focus on three key pillars: data generation, model selection, and validation. Here, we review the current state of MLIP applications for GBs and interfaces in both general and ASSB-specific materials, highlighting best practices and challenges in constructing diverse and representative datasets, choosing appropriate machine learning architectures, and rigorously validating model performance. We also discuss emerging strategies and opportunities for improved reliability and efficiency of MLIPs to simulate realistic interfaces in ASSBs.

Energy - Storage↗
DOE OSTI2026

Ab Initio Modeling of Aqueous Methanol Mixtures at DFT-SCAN Level Using Machine Learning Interatomic Potentials

Abstract Methanol–water mixtures find use in many applications, particularly catalytic energy conversion processes. Their importance has motivated numerous computational studies, most of which employed molecular dynamics based on classical force fields. These enable simulations of large systems on long time scales but do not reliably describe reactive dynamics involving bond breaking and bond formation. In contrast, ab initio molecular dynamics (AIMD) based on density functional theory (DFT) is generally more reliable for such applications but has a high computational cost, which discourages systematic studies of alcohol-water mixtures. To remedy this, we trained a machine learning interatomic potential capable of probing the properties of aqueous methanol mixtures at the DFT level using the SCAN functional. Our results show that SCAN qualitatively reproduces multiple key experimental features arising from the amphiphilic nature of methanol, including density, diffusion coefficients, X-ray structure factors, and Kirkwood–Buff integrals. We also find that structural correlations between water molecules are somewhat overestimated, leading to a stronger preferential association than that predicted by experiments. However, increasing the temperature by 30 K mitigates this effect and also recovers the correct mobilities of both methanol and water. These results indicate that SCAN provides an accurate description of methanol–water mixtures, making it a reliable choice for investigating the reactive dynamics in such systems.

Park, Sanghyun J. [Princeton University , , , ,]↗
DOE OSTI2026

Machine Learning Interatomic Potentials for Modeling Framework Flexibility and Water Uptake in NbOFFIVE-1-Ni Metal–Organic Framework

Metal–organic frameworks (MOFs), with their distinctive porous structures and tunable chemical properties, have shown immense promise in the separation and storage of gases. Currently, the accurate simulation of their adsorptive properties remains challenging, especially for systems where the molecules fit very tightly into the pores. Traditional simulation methods often approximate the frameworks as rigid and do not account for the framework flexibility seen in materials such as NbOFFIVE-1-Ni. First-principles molecular dynamics (FPMD) simulations offer the desired accuracy in modeling this flexibility but are limited by their extensive computational demands, rendering them impractical for long simulations. Conversely, classical force field-based simulations offer computational efficiency but lack the necessary accuracy. Here, to break this accuracy-efficiency trade-off, we have developed machine learning interatomic potentials trained on energies and forces from FPMD to model the framework flexibility of NbOFFIVE-1-Ni in the presence of water over nanosecond time scales. Furthermore, by integrating MLIP-driven molecular dynamics (MLIP-MD) with grand canonical Monte Carlo (GCMC) simulations, we further incorporated framework flexibility into adsorption predictions, yielding water adsorption isotherms that better align with experimental data compared to those of conventional GCMC simulations. These advances offer new opportunities for the design and optimization of MOFs in gas storage and separation applications.

adsorption↗
DOE OSTI2025

High temperature melting of dense molecular hydrogen from machine-learning interatomic potentials trained on quantum Monte Carlo

We present results and discuss methods for computing the melting temperature of dense molecular hydrogen using a machine learned model trained on quantum Monte Carlo data. In this newly trained model, we emphasize the importance of accurate total energies in the training. We integrate a two phase method for estimating the melting temperature with estimates from the Clausius–Clapeyron relation to provide a more accurate melting curve from the model. We make detailed predictions of the melting temperature, solid and liquid volumes, latent heat, and internal energy from 50 to 180 GPa for both classical hydrogen and quantum hydrogen. At pressures of roughly 173 GPa and 1635 K, we observe molecular dissociation in the liquid phase. Here, we compare with previous simulations and experimental measurements.

08 HYDROGEN↗
DOE OSTI2026

A new interatomic potential for mixed Mg-Al-Ga-In spinels

While density functional theory (DFT) has become the de facto approach for accurate simulation of materials at the atomic scale, there are many aspects of materials that are simply out of reach of DFT methods. In particular, finite temperature properties such as diffusivities, the structure and properties of grain boundaries and interfaces, and the study of defect properties in complex alloys are computationally challenging for DFT methods. Recently, a new class of spinels in which three cations order over two sublattices was discovered. In order to predict the properties of these types of structures, classical potentials are a must. Here, in this work, we derive a new classical potential for Mg-bearing spinels in which the B cations are Al, Ga, and/or In. The potential does well in describing the DFT energetics of various spinel structures as a function of chemistry and inversion. In particular, it reproduces the thermodynamically favorable MgAlGaO 4 structure while correctly predicting that neither MgAlInO 4 nor MgGaInO 4 are stable. Further, it reproduces physical trends in elastic properties as compared against experiment.

36 MATERIALS SCIENCE↗