SEARCH · Search NASA

Results for “INTERATOMIC FORCES”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

223 records · Page 13

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Top Loading Helium Cryostat Integrated with High-Pressure Cell with Fast Remote Pressure Control (Final Report for SBIR Phase 1)

A large number of physical phenomena, such as superconductivity and quantum critical phenomena, often appear only at very low temperatures below 5 K. There is an immense interest to investigate these phenomena at high pressure as a means of tuning interatomic distances, and thus the interaction parameters controlling these phenomena, in a continuous and controlled fashion. The current P-T condition for neutron scattering experiments are limited to either relatively low pressures of about 2 GPa at temperatures below 5 K, or to relatively high temperatures at pressures or tens of GPa. The purpose of this research is to develop neutron sample environment instrumentation for reaching 10-20 GPa at 1-2K with rapid and reliable online pressure and temperature control (i.e. without having to interrupt the experiment). The ultimate goal of the project is to design an integrated fast-cooling low-temperature sample environment cryogenic system compatible with state of the art neutron diamond anvil cells suitable for single-crystal neutron scattering experiments for temperatures down to 2K and pressures of several tens of GPa. The integrated system will consist of top-loading Helium flow cryostat with in-situ sample alignment mechanisms, large-volume diamond anvil cells (DAC) made from novel superalloy Pascalloy and optimized for fast cooling and heating, and a compact remote pressure control mechanism for the DAC based on a novel concept of inflatable bellows integrated with a lever-arm based force amplifier. In Phase I, we have designed, manufactured, and tested prototypes of the novel compact force-amplified pneumatic pressure control mechanism for Neutron Diamond Anvil Cell (nDAC), which allows to use pneumatic bellows system for smooth remote pressure control in the nDAC inside a top-loading cryostat with bore size of 70 mm or larger. This allows significant minimum temperature decrease in remotely controlled nDAC from 5-10 K down to 2 K. We also studied mechanical properties of a novel non-magnetic superalloy Pascalloy with different heat-treatment conditions and preliminary results indicate that up to date this is probably the strongest and most suitable material for making more compact and lightweight cryogenic nDACs for neuron scattering experiments at extreme conditions, allowing much faster cooling and heating. Potential applications: The new developments will allow to create a wide range of compact Diamond Anvil Cells for neutron diffraction which can reach several tens of GPA pressure at 2-4 K while preserving accurate remote pressure control capabilities. The new concepts can be used for developing sample environment instrumentation outside the neutron scattering field. The new development have very strong potential for expanding experimental capabilities in materials sciences and have high commercialization potential. The proposed new cryogenic high-pressure system or any of its individual components will be in high demand not only in neutron scattering facilities, but also at synchrotron beamlines and other high-pressure research facilities around the world.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Interatomic interactions and thermally induced shifts and broadenings of energy levels of atoms in circular Rydberg states

Analytical expressions are obtained for the constants determining the energy of the long-range (at large distances exceeding the LeRoy radius R{sub LR} ≈ 4n{sup 2}) polarisation interaction of the multipole electric moments of two atoms in identical Rydberg states with large principal (n) and maximum orbital (l) and magnetic (m) quantum numbers (|m| = l = n − 1). Asymptotic expansions in powers of n are found for the components of the van der Waals interaction tensor, which determine the dependence of the dispersion energy on the orientation of the interatomic axis. The spontaneous and thermally induced broadenings of the energy levels of circular states are represented analytically as functions of the principal quantum number and temperature. It is found that analytical formulae for the sum of spontaneous and thermally induced line widths in the region of high temperatures and large principal quantum numbers (assuming n{sup 3}k{sub B}T >> 1) do not contain a temperature-independent contribution of the spontaneous part of the broadening. Closed analytical expressions are also obtained for the Farley − Wing function and for the n- and T-dependent corrections to the asymptotic behaviour of the thermally induced shift. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Efficacy of the radial pair potential approximation for molecular dynamics simulations of dense plasmas

Macroscopic simulations of dense plasmas rely on detailed microscopic information that can be computationally expensive and is difficult to verify experimentally. In this work, we delineate the accuracy boundary between microscale simulation methods by comparing Kohn–Sham density functional theory molecular dynamics (KS-MD) and radial pair potential molecular dynamics (RPP-MD) for a range of elements, temperature, and density. By extracting the optimal RPP from KS-MD data using force matching, we constrain its functional form and dismiss classes of potentials that assume a constant power law for small interparticle distances. Our results show excellent agreement between RPP-MD and KS-MD for multiple metrics of accuracy at temperatures of only a few electron volts. The use of RPPs offers orders of magnitude decrease in computational cost and indicates that three-body potentials are not required beyond temperatures of a few eV. Due to its efficiency, the validated RPP-MD provides an avenue for reducing errors due to finite-size effects that can be on the order of ∼20%.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Microscopic force driving the photoinduced ultrafast phase transition: Time-dependent density functional theory simulations of IrTe 2

Photoinduced phase transitions can have complex and intriguing behaviors more than material ground-state dynamics. Understanding the underlying mechanism can help us to design new ways to manipulate the materials. A variety of mechanisms has been proposed to explain the photoinduced phase transitions of IrTe 2 , but a consensus has yet to be reached. Here, we study the photo-induced phase transitions of IrTe 2 by performing the real-time time-dependent density functional theory (rt-TDDFT) simulations in combination with the occupation constrained DFT method. We reveal that the microscopic driving force for the photo-induced phase transitions arises from the tendency to lower the energy levels occupied by the excited carriers, through the increase or decrease of the associated atomic pair distances, depending on whether the newly occupied states are antibonding or bonding states, respectively. The geometric constraints between different bonds represented by the Poisson ratio can bring together different tendencies from different atomic pairs, thus forming a complex intriguing dynamic picture depending on the intensity of the excitation. Furthermore, we also find that phonons don't play a primary role, but can assist the phase transition. These findings imply that one can control the structural phase transitions by selectively exciting photocarriers into designated atomic states using appropriate photon sources.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Bridging the gap between simulated and experimental ionic conductivities in lithium superionic conductors

Lithium superionic conductors (LSCs) are of major importance as solid electrolytes for next-generation all-solid-state lithium-ion batteries. While ab initio molecular dynamics have been extensively applied to study these materials, there are often large discrepancies between predicted and experimentally measured ionic conductivities and activation energies due to the high temperatures and short time scales of such simulations. Here, we present a strategy to bridge this gap using moment tensor potentials (MTPs). We show that MTPs trained on energies and forces computed using the van der Waals optB88 functional yield much more accurate lattice parameters, which in turn leads to accurate prediction of ionic conductivities and activation energies for the Li 0·33 La 0·56 TiO 3 , Li 3 YCl 6 and Li 7 P 3 S 11 LSCs. NPT MD simulations using the optB88 MTPs also reveal that all three LSCs undergo a transition between two quasi-linear Arrhenius regimes at relatively low temperatures. This transition can be traced to an increase in the number and diversity of diffusion pathways, in some cases with a change in the dimensionality of diffusion. Furthermore, this work presents not only an approach to develop high accuracy MTPs, but also outlines the diffusion characteristics for LSCs which is otherwise inaccessible through ab initio computation.

25 ENERGY STORAGE↗

DOE OSTI2024

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, in this study, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

36 MATERIALS SCIENCE↗

DOE OSTI2026

matsim-agents v1.0

matsim-agents is a multi-agent AI framework for atomistic materials simulation and discovery. It orchestrates large language models (LLMs), machine-learned interatomic potentials (MLIPs), and DFT codes into a single agentic loop running on laptops and DOE leadership-class supercomputers. MULTI-AGENT ORCHESTRATION A LangGraph state machine with three nodes: a Planner that converts a natural-language research objective into structured tasks; an Executor that dispatches atomistic tools and loops until the queue is empty; and an Analyst that summarizes results into a human-readable report. State is checkpointed after every step and human-in-the-loop gates can be inserted at any edge. HYPOTHESIS-DRIVEN DISCOVERY CHAT An interactive REPL (matsim-agents chat) that couples LLM dialogue with atomistic simulation. Chemical formulas are automatically detected in conversation turns and trigger a full crystal-phase exploration: structure generation → relaxation → stability scoring → result injection back into the conversation, creating a closed hypothesis-refinement loop. CRYSTAL PHASE ENUMERATION Given a composition, the phase explorer enumerates prototypes by stoichiometry: elemental (fcc/bcc/hcp/sc/diamond), binary 1:1 (rocksalt/CsCl/zincblende/ wurtzite/fluorite/rutile), ternary 1:1:3 (cubic perovskite), ternary 1:2:4 (perovskite + spinel), quaternary 1:1:2:6 (Fm-3m double perovskite). 2-D prototypes (graphene, h-BN, MoS2 2H/1T) and multilayer stacking are also supported via --include-2d and --num-layers. SUPERCELL GENERATION AND SITE DECORATION Auto-tiling to a minimum atom count (--min-atoms), explicit NxNxN tiling (--supercell), symmetry-distinct site decorations (--n-orderings), and isotropic lattice-scale sweeps (--lattice-scales) for volume bracketing. MLFF RELAXATION AND STABILITY SCORING HydraGNN (multi-headed GNN) drives structure relaxation via ASE with FIRE, BFGS, or BFGSLineSearch. Stability output: delta-E/atom ranking across phases and a max-residual-force dynamical-stability proxy. Other MLIPs (MACE, NequIP, Orb) can be plugged in through the same interface. DFT BACKENDS Quantum ESPRESSO pw.x and VASP 6.6 are first-class labellers. Both have validated GPU builds and SLURM/PBS launchers for three DOE platforms: Frontier (AMD MI250X, ROCm), Aurora (Intel PVC, oneAPI), Perlmutter (NVIDIA A100, CUDA). QE produces ~100 binaries (pw.x, ph.x, epw.x, ...). VASP supports scf, relax, vc-relax, and vc-relax-shape run types. ACTIVE-LEARNING LOOP matsim-agents al run CONFIG.yaml drives an iterative HydraGNN-DFT loop: MD generates candidates → ensemble/MC-dropout uncertainty selects the most informative → DFT labels them in parallel inside one allocation → dataset grows → HydraGNN retrains → repeat. DFT backend is a single YAML toggle (dft.backend: vasp | qe). LLM-generated seed structures are supported (no curated POSCAR library needed). Config uses ${VAR}, ${VAR:-default}, ${VAR:?msg} shell-style substitution for cross-user/cross-site portability. LLM BACKENDS Ollama (local, default), vLLM (HPC multi-GPU serving), OpenAI, Anthropic, HuggingFace Transformers+Accelerate. Selected at runtime via flag or env var with no code changes. HPC PORTABILITY Same Python entry points run on Frontier (ROCm 7.2), Aurora (oneAPI), and Perlmutter (CUDA 12). DFT and ML stacks are never co-loaded in the same shell; they couple through the scheduler and filesystem. Advanced multi-node launchers (serve, discovery-chat, single-relaxation, active-learning, QE warm-start) are provided for all three platforms. CODABENCH COMPETITION BUNDLE A self-contained benchmark: 159 atomistic test structures across 11 material classes, 5 tasks (formation energy, forces, ML relaxation, AI-DFT relaxation, phase stability ranking), public/private leaderboard split (30/70), and four ready-to-run baselines: MACE-MP-0, HydraGNN, UMA, AllScAIP.

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗