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Results for “Density functional theory simulations”

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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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, the theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. Additionally, the pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Visualizing the SEI formation between lithium metal and solid-state electrolyte

The solid electrolyte interphase (SEI) is regarded as the most important factor affecting the durability of lithium-metal anode in all-solid-state batteries (ASSBs). Despite its significance, the nucleation and growth mechanism of SEI is not yet well understood. Here, we elucidate the thermodynamics and kinetics governing SEI formation at the Li|β-Li 3 PS 4 interface at the atomic scale via thermodynamic phase equilibrium analysis and machine-learning-potential-assisted molecular dynamics (MD) simulations. An accurate moment tensor potential using the machine-learning method is developed for a reactive model of Li|β-Li 3 PS 4 . This potential enabled us to perform large-scale MD simulations with the model size expanded to the experimental dimensions (~40 nm) while maintaining the same level of accuracy as density functional theory calculations. The results reveal a four-stage evolution process at the Li|β-Li 3 PS 4 interface, namely (i) fast ion diffusion, (ii) nucleation, (iii) Li 2 S growth, and (iv) stabilization. Notably, we demonstrate that the SEI can be categorized into crystalline and amorphous regions. The simulated SEI thickness, structure, and composition closely match experimental findings, validating the accuracy of the MD simulations. We further disclose the significant impact of ion diffusion kinetic limitations on the phase formation and crystallization of interfacial products. Furthermore, we shed light on the detailed potential energy (PE) distribution of lithium along the direction perpendicular to the interface. Finally, this information is crucial for better understanding interfacial ion mobility.

25 ENERGY STORAGE↗
DOE OSTI2024

A reactive molecular dynamics model for uranium/hydrogen containing systems

Uranium-based materials are valuable assets in the energy, medical, and military industries. However, understanding their sensitivity to hydrogen embrittlement is particularly challenging due to the toxicity of uranium and the computationally expensive nature of quantum-based methods generally required to study such processes. In this regard, we have developed a Chebyshev Interaction Model for Efficient Simulation (ChIMES) that can be employed to compute energies and forces of U and UH3 bulk structures with vacancies and hydrogen interstitials with accuracy similar to that of Density Functional Theory (DFT) while yielding linear scaling and orders of magnitude improvement in computational efficiency. Here, we show that the bulk structural parameters, uranium and hydrogen vacancy formation energies, and diffusion barriers predicted by the ChIMES potential are in strong agreement with the reference DFT data. We then use ChIMES to conduct molecular dynamics simulations of the temperature-dependent diffusion of a hydrogen interstitial and determine the corresponding diffusion activation energy. Our model has particular significance in studies of actinides and other high-Z materials, where there is a strong need for computationally efficient methods to bridge length and time scales between experiments and quantum theory.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations

Machine learning (ML) approximations to density functional theory (DFT) potential energy surfaces (PESs) are showing great promise for reducing the computational cost of accurate molecular simulations, but at present, they are not applicable to varying electronic states, and in particular, they are not well suited for molecular systems in which the local electronic structure is sensitive to the medium to long-range electronic environment. With this issue as the focal point, we present a new machine learning approach called "BpopNN" for obtaining efficient approximations to DFT PESs. Conceptually, the methodology is based on approaching the true DFT energy as a function of electron populations on atoms; in practice, this is realized with available density functionals and constrained DFT (CDFT). Additionally, the new approach creates approximations to this function with neural networks. These approximations thereby incorporate electronic information naturally into a ML approach, and optimizing the model energy with respect to populations allows the electronic terms to self-consistently adapt to the environment, as in DFT. We confirm the effectiveness of this approach with a variety of calculations on LinHn clusters.

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

Prospects for detecting UF6 hydrolysis intermediates by mass spectrometry, resonance Raman, and NQR spectroscopy

Simulations are performed to consider several spectrometric and spectroscopic candidates for elucidating the mechanism of the hydrolysis of uranium hexafluoride (UF6). This study is among the first to benchmark the def-mTZVP basis sets for actinide-containing molecules, and it is shown to be a suitable basis set for surveying geometrical structures and vibrational spectra when used in conjunction with density functional theory. An experiment is proposed coupling mass spectrometric ion selection with vibrational spectroscopy, and supporting infrared and Raman spectral simulations demonstrate that ionization blue-shifts bands and can change their qualitative features. Ultraviolet resonance Raman is shown to have good prospects for discriminating U–O–U bridged intermediates, evidence for which was observed recently [L. E. McNamara et al., J. Phys. Chem. A 130, 775–786 (2026)]. As a more speculative approach, we also consider the prospect of addressing 233U or 235U nuclei by quadrupole resonance (NQR) spectroscopy for assigning early stage intermediate complexes. In doing so, we provide a first order-of-magnitude estimate for the collision-induced NQR signal for the UF6 dimer, which is of interest for the interpretation of a fast decoherence time observed in liquid-phase nuclear magnetic resonance. Having provided critical insights into the formation and stability of UF6 hydrolysis intermediates in previous studies, this computational spectroscopy survey is expected to help guide and expedite future laboratory campaigns.

Lutz, Jesse J. [Center for Computing Research, San↗
DOE OSTI2026

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Heterogeneous Catalyst Discovery

Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We demonstrate that hierarchical agentic large language model reasoning can efficiently drive simulation and scientific exploration. Across two chemical applications, CO adsorption on Cu surface transition metal adatoms and on M–N–C catalysts, reasoning-guided exploration reduces required atomistic simulations by up to 90% relative to heuristic or random selection. Comparisons across single-agent, multi-agent, and stochastic baselines show that hierarchical strategies yield more coherent and information-efficient search trajectories. Reasoning traces reveal chemically grounded decisions that cannot be explained by semantic bias or stochastic sampling. We realize these agentic reasoning strategies in Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), a multimodal system that translates natural language into density functional theory workflows. Altogether, multi-agent collaboration accelerates heterogeneous catalyst discovery and marks a step toward more autonomous, reasoning-guided scientific exploration.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Spin-informed universal graph neural networks for simulating magnetic ordering

The screening and discovery of magnetic materials are hindered by the computational cost of first-principles density-functional theory (DFT) calculations required to find the ground state magnetic ordering. Although universal machine-learning interatomic potentials (uMLIPs), also known as atomistic foundation models, offer high-fidelity models of many atomistic systems with significant speedup, they currently lack the inputs required for predicting magnetic ordering. In this work, we present a data-efficient, spin-informed graph neural network framework that incorporates spin degrees of freedom as inputs and preserves physical symmetries, extending the functionality of uMLIPs to simulate magnetic orderings. This framework speeds up DFT calculations through better initial guesses for magnetic moments, determines the ground-state ordering of bulk materials and even generalizes to magnetic ordering in surfaces. Furthermore, we implement a closed-loop anomaly detection approach that effectively addresses the classic "chicken-and-egg" problem of creating a high-quality dataset while developing a uMLIP, unearthing anomalies in large benchmark datasets and boosting model accuracy.

Xu, Wenbin↗
DOE OSTI2020

Effects of cluster expansion on the locations of phase transition boundary as a first step to quantify uncertainty in first principles statistical mechanics framework

Predicting phase diagrams from first principle calculations eliminates the need of tedious experimental trials and errors. Fully automating first principle phase diagram calculations without any sort of human intervention has been a long daunting task and troubling scientific communities for decades. This grand problem remains not fully resolved, largely due to the vastly high-dimensional parameter space associated with density functional theory, cluster expansion, lattice Monte Carlo, and the substantial uncertainty propagating through a set of complex simulations. As a first step to tackle this grand problem, we reported a first demonstration of how sensitive phase boundary locations can be to various cluster expansion fittings and input DFT training data. To the best knowledge of the authors, this study reported the first ever attempt to quantify uncertainty in first principles statistical mechanics framework. In addition, a semi-automated phase transition detection algorithm has been devised in this paper to deal with the associated statistical errors and uncertainties from Monte Carlo method and its predecessor DFT calculations and cluster expansions. This algorithm has been applied in a classical cluster expansion Mg-Cd binary alloy system to detect phase transitions at various locations of phase diagram using different cluster expansions to demonstrate its predictive power and quantify uncertainties. The results suggested that using Chebyshev basis function shifted the transition locations toward the dilute solid solution phase and expanded the phase stability of concentrated ordered phases to wider composition range. The addition of perturbed defect configurations into training data set lowered the order-disorder transition temperature to be away from true transition temperature, suggesting the transition nature is indeed configurational disorder dominated rather than defect assisted. Finally, we found that the weighting has negligible effect on the transition locations, except for the case of using Chebyshev basis function to fit all non-weighted configurations that can be susceptible to Monte Carlo sampling hysteresis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Quantum simulation of exact electron dynamics can be more efficient than classical mean-field methods

Quantum algorithms for simulating electronic ground states are slower than popular classical mean-field algorithms such as Hartree–Fock and density functional theory but offer higher accuracy. Accordingly, quantum computers have been predominantly regarded as competitors to only the most accurate and costly classical methods for treating electron correlation. However, here we tighten bounds showing that certain first-quantized quantum algorithms enable exact time evolution of electronic systems with exponentially less space and polynomially fewer operations in basis set size than conventional real-time time-dependent Hartree–Fock and density functional theory. Although the need to sample observables in the quantum algorithm reduces the speedup, we show that one can estimate all elements of the k-particle reduced density matrix with a number of samples scaling only polylogarithmically in basis set size. We also introduce a more efficient quantum algorithm for first-quantized mean-field state preparation that is likely cheaper than the cost of time evolution. We conclude that quantum speedup is most pronounced for finite-temperature simulations and suggest several practically important electron dynamics problems with potential quantum advantage.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Active sampling for neural network potentials: Accelerated simulations of shear-induced deformation in Cu–Ni multilayers

Neural network potentials (NNPs) can greatly accelerate atomistic simulations relative to ab initio methods, allowing one to sample a broader range of structural outcomes and transformation pathways. In this work, we demonstrate an active sampling algorithm that trains an NNP that is able to produce microstructural evolutions with accuracy comparable to those obtained by density functional theory, exemplified during structure optimizations for a model Cu–Ni multilayer system. We then use the NNP, in conjunction with a perturbation scheme, to stochastically sample structural and energetic changes caused by shear-induced deformation, demonstrating the range of possible intermixing and vacancy migration pathways that can be obtained as a result of the speedups provided by the NNP. The code to implement our active learning strategy and NNP-driven stochastic shear simulations is openly available at https://github.com/pnnl/Active-Sampling-for-Atomistic-Potentials .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Strong Correlation DMRG and DFT

This project developed new ways to improve computer simulations of materials where electrons interact strongly with each other, a challenge for today’s most widely used method, density functional theory (DFT). We used an exact numerical method, the density matrix renormalization group (DMRG), to create highly accurate reference results for simple model systems, and used these to test DFT, prove when it will converge, and even train machine-learned functionals. We also invented new kinds of localized basis functions (“gausslets” and “multi-sliced gausslets”) and a “sliced-basis” approach that make high-accuracy simulations faster and more practical. These methods were applied to extended hydrogen systems, enabling the direct derivation of accurate low-energy models from first-principles calculations. We also introduced a new formalism, Conditional-Probability DFT, which could bypass traditional approximations. The tools and results from this work, including open-source software releases, will help scientists design and understand complex quantum materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Communication—First-Principles Simulations of LiPF 6 Decomposition in Ethylene Carbonate-Based Electrolytes

We revisit a theoretical result by Okamoto (2013 Journal of The Electrochemical Society , 160 , A404) who calculated the energy barrier for the decomposition of lithium hexafluorophosphate (LiPF 6 ) into LiF + PF 5 when solvated in Ethylene carbonate (EC)-based electrolyte. Using different numerical techniques to discretize the Density Functional Theory (DFT) equations, and different continuum solvation models with the same dielectric constant, our results largely confirm the original calculation. However, simulations with a higher dielectric permittivity value, closer to that of EC, show a lower energy barrier. More importantly, First-Principles simulations with an explicit solvent show a substantially lower energy barrier.

25 ENERGY STORAGE↗
DOE OSTI2024

WFOT: A Wave Function Overlap Tool between Single- and Multi-Reference Electronic Structure Methods for Spectroscopy Simulation

We report the development of a novel diagnostic tool, named wave function overlap tool (WFOT), designed to evaluate the overlap between wave functions computed at single-reference [i.e., time-dependent density functional theory or configuration interaction singles (CIS)] and multireference (i.e., CASSCF/CASPT2) electronic structure levels of theory. It relies on truncating the single- and multireference WFs to CIS-like expansions spanning the same configurational space and maximizing the molecular orbital overlap by means of a unitary transformation. To demonstrate the functionality of the tool, we calculate the transient spectrum of acetylacetone by evaluating excited state absorption signals with multireference quality on top of single-reference on-the-fly dynamics simulations. Semiautomatic spectra generation is facilitated by interfacing the tool with the COBRAMM package, which also allows one to use WFOT with several quantum chemistry codes such as Gaussian, NWChem, and OpenMolcas. Finally, other exciting possibilities for the utilization of the code beyond the simulation of transient absorption spectroscopy are eventually discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Machine Learning Approaches for Rare-Earth Silicate Environmental Barrier Coating Thermochemical and Thermomechanical Property Predictions

Environmental barrier coatings (EBCs) are a necessary enabling technology for the transition from superalloys to silicon carbide (SiC) ceramic matrix composites (CMCs) in gas turbine engines for increased efficiency and decreased fuel costs. SiC-based CMCs are prone to oxidation-based degradation in the engine hot section, and rare-earth (RE) silicates are promising candidates for EBCs due to their close thermal expansion match to the composite substrate and oxidation resistance. However, the design of EBCs is hindered by the large chemical space of candidate materials and the difficulty in obtaining material properties for engineering optimization. This is especially difficult as research continues into mixed-cation or “high-entropy” RE silicates. First-principles computational methods such as density functional theory (DFT) are highly effective at calculating material properties to guide coating design but are limited by their computational cost. Atomistic simulations have the potential to both accelerate property calculations and expand the properties able to be calculated due to their lower computational compared to DFT. However, they require interatomic potentials (IAPs) specific to the material system of interest, and, to our knowledge, there are no suitable IAPs for RE silicates. Machine learning (ML) is a promising technique to accelerate material property predictions indirectly by generating IAPs for atomistic simulations or via direct prediction. In this work, we present two ML approaches to accelerate the calculation of RE silicate properties relevant to EBC design: 1) a ML-derived interatomic potential (IAP) for atomistic simulations of yttrium disilicate (Y2Si2O7) from DFT training data, and 2) a neural network (NN) model to directly predict thermochemical properties of RE silicates and oxides directly from easily obtainable unit cell parameters. Classical MD simulations using the IAP yield lattice properties and bond lengths in good agreement with both DFT and experimental results from x-ray diffraction. Thermodynamic properties calculated using the finite-displacement phonon method and quasi-harmonic approximation were orders of magnitude faster than DFT with good agreement to the DFT results. The IAP was also used to calculate properties such as coefficient of thermal expansion (CTE) that require large simulation supercells and are therefore difficult with DFT. The IAP correctly predicted the anisotropic nature of the CTE in three different phases of Y2Si2O7. The NN model predicts constant pressure heat capacity, Cp, orders of magnitude faster than DFT calculations, which can enable its use as a surrogate model for multiscale simulations. The two methods presented in this work demonstrate the utility of ML for accelerating the prediction of RE silicate properties, which can in turn accelerate EBC design and optimization.

machine learning↗
DOE OSTI2024

Cellular Phosphate Sensing and Anion Binding by an Azacrown‐Calixpyrrole Hybrid

A hybrid receptor-sensor for anions originating from the merging of positively charged ammonium moieties for electrostatic attraction/stronger binding of azacrowns with directionality of calixpyrrole hydrogen bond donors for selectivity is investigated. As demonstrated this hybrid receptor-sensor shows a remarkable selectivity for orthophosphate even in the presence of other phosphates and anions found in cellular materials (K assoc H 2 PO 4 − >H 2 P 2 O 7 2− >AMP − ≫ADP 2− or ATP 3− over halides, nitrate, or hydrogen sulfate; all Na + salts in water) but also cellular polyphosphate or phospholipids. This selectivity is harnessed in a real-time monitoring of cell lysis by lysozyme, which releases orthophosphate and other phosphates and anions from the cells. This sensitive (LOD 0.4 μM) fluorescence-based microscale method compares favorably with the state-of-the-art techniques but can easily be practiced in a high-throughput screening (HTS) manner. The anion binding and selectivity in aqueous solutions were investigated by NMR and put in context with phosphate binding of the parent calix[4]pyrrole. The microscopic understanding of anion binding by the hybrid receptor was then obtained from a combination of density functional theory (DFT), classical molecular dynamics (MD) with explicit water solvation, and ab initio MD (AIMD) simulations. Correlating the NMR and fluorescence binding data with studies of solvation of the receptor, phosphate anion, and the resulting complex confirms the binding is largely driven by entropic component (TΔS) associated with receptor and anion desolvation.

Anions↗
DOE OSTI2021

Barriers to carriers: faults and recombination in non-stoichiometric perovskite scintillators

Abstract Tuning the efficiency and speed of charge carrier recombination in inorganic scintillators can potentially improve their performance in diverse applications. Recent work suggests that this maybe be achieved via a two-phase scintillator AB that naturally phase separates into A -rich and B -rich domains. In addition, a favorable electronic structure and band-edge alignment such that the charge carriers are confined or are thermodynamically driven to preferentially accumulate in one of the two domains, might lead to an improved radiative recombination rate. Here, we use density functional theory computations and ab initio molecular dynamics (AIMD), including non-adiabatic molecular dynamics (NAMD) simulations, to examine an alternative phase structure and its potential impact on recombination. Using a model perovskite SrTiO $$_3$$ 3 system with one-, two- and three-dimensional Ruddlesden–Popper (RP) phases, we demonstrate that RP faults induce band structure changes in the material that can act as barriers to carrier transport. Our AIMD/NAMD simulations indicate competing effects of a lower mean free path (potentially enhancing the desired radiative recombination and overall scintillating efficiency) and faster non-radiative recombination (undesired) due to enhanced electron–phonon coupling in the faulted system. Full exploitation of such a rational design approach would require tuning of the effective scintillation efficiency by varying the perovskite chemistry using appropriate arrangements of RP faults in the bulk material. Finally, other effects, such as the tendency of point defects to segregate at the interface, that might affect the overall performance, are briefly discussed. We expect the basic results found here to apply to other nanostructured scintillators. Graphical Abstract

36 MATERIALS SCIENCE↗
DOE OSTI2020

Uncertainty Quantification in Atomistic Modeling of Metals and Its Effect on Mesoscale and Continuum Modeling: A Review

The design of next-generation alloys through the integrated computational materials engineering (ICME) approach relies on multiscale computer simulations to provide thermodynamic properties when experiments are difficult to conduct. Atomistic methods such as density functional theory (DFT) and molecular dynamics (MD) have been successful in predicting properties of never before studied compounds or phases. However, uncertainty quantification (UQ) of DFT and MD results is rarely reported due to computational and UQ methodology challenges. Over the past decade, studies that mitigate this gap have emerged. These advances are reviewed in the context of thermodynamic modeling and information exchange with mesoscale methods such as the phase-field method (PFM) and calculation of phase diagrams (CALPHAD). The importance of UQ is illustrated using properties of metals, with aluminum as an example, and highlighting deterministic, frequentist, and Bayesian methodologies. Finally, challenges facing routine uncertainty quantification and an outlook on addressing them are also presented.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Dependence of predicted bulk properties of hexagonal hydroxyapatite on exchange-correlation functional

Hydroxyapatite (HA) is the main component of human bones and teeth. HA has also been widely applied in various technological fields due to its unique properties. Reliable computational simulations for this critical material often require input parameters obtained from the first-principles density functional theory (DFT) calculations with appropriate exchange–correlation functionals. Previous DFT calculations are insufficient to verify the reliabilities of various functionals, particularly as they fail to assess predictions for multiple properties of the material. In this paper, we first select 18 different functionals to calculate geometric, elastic, electronic, and thermodynamic properties of hexagonal HA bulk crystal. We find that the results from optB86b-vdW and optB88-vdW functionals with dispersion corrections have the overall best agreement with available experimental data. Then, we choose optB88-vdW functional, as well as PBE functional without dispersion corrections as a comparison, to perform extensive first-principles DFT phonon calculations under the quasiharmonic approximation. Various thermodynamic properties (including phonon contributions to internal energy, entropy, and Helmholtz free energy) and thermal parameters (including thermal expansions of volume, thermal expansion coefficients, heat capacities, isothermal bulk moduli, etc.) versus temperature are consequently obtained. By comparing these quantities, we report that the results from optB88-vdW functional have significantly better agreement with available experimental data than those from PBE functional although the latter has been widely used in previous DFT calculations for HA-based material systems.

36 MATERIALS SCIENCE↗