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Results for “Inter-Atomic Potential”

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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12 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Performance Modeling of Vectorized SNAP Inter-Atomic Potentials on CPU Architectures.

SNAP potentials are inter-atomic potentials for molecular dynamics that enable simulations at accuracy levels comparable to density functional theory(DFT) at a fraction of the cost. As such, SNAP scales to on the order of 10 4 — 10 6 atoms. In this work, we explore CPU optimization of potentials computation using SIMD. We note that efficient use of SIMD is non-obvious as the application features an irregular iteration space for various potential terms, necessitating use of SIMD across atoms in a cross matrix, batched fashion. We present a preliminary analytic model to determine the correct batch size for several CPU architectures across several vendors, and show end-to-end speedups between 1.66x and 3.22x compared to the original.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Multi-reward reinforcement learning based development of inter-atomic potential models for silica

Abstract Silica is an abundant and technologically attractive material. Due to the structural complexities of silica polymorphs coupled with subtle differences in Si–O bonding characteristics, the development of accurate models to predict the structure, energetics and properties of silica polymorphs remain challenging. Current models for silica range from computationally efficient Buckingham formalisms (BKS, CHIK, Soules) to reactive (ReaxFF) and more recent machine-learned potentials that are flexible but computationally costly. Here, we introduce an improved formalism and parameterization of BKS model via a multireward reinforcement learning (RL) using an experimental training dataset. Our model concurrently captures the structure, energetics, density, equation of state, and elastic constants of quartz (equilibrium) as well as 20 other metastable silica polymorphs. We also assess its ability in capturing amorphous properties and highlight the limitations of the BKS-type functional forms in simultaneously capturing crystal and amorphous properties. We demonstrate ways to improve model flexibility and introduce a flexible formalism, machine-learned ML-BKS, that outperforms existing empirical models and is on-par with the recently developed 50 to 100 times more expensive Gaussian approximation potential (GAP) in capturing the experimental structure and properties of silica polymorphs and amorphous silica.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Atomistic measurement and modeling of intrinsic fracture toughness of two-dimensional materials

Quantifying the intrinsic mechanical properties of two-dimensional (2D) materials is essential to predict the long-term reliability of materials and systems in emerging applications ranging from energy to health to next-generation sensors and electronics. Currently, measurements of fracture toughness and identification of associated atomistic mechanisms remain challenging. Herein, we report an integrated experimental–computational framework in which in-situ high-resolution transmission electron microscopy (HRTEM) measurements of the intrinsic fracture energy of monolayer MoS 2 and MoSe 2 are in good agreement with atomistic model predictions based on an accurately parameterized interatomic potential. Changes in crystalline structures at the crack tip and crack edges, as observed in in-situ HRTEM crack extension tests, are properly predicted. Such a good agreement is the result of including large deformation pathways and phase transitions in the parameterization of the inter-atomic potential. The established framework emerges as a robust approach to determine the predictive capabilities of molecular dynamics models employed in the screening of 2D materials, in the spirit of the materials genome initiative. Moreover, it enables device-level predictions with superior accuracy (e.g., fatigue lifetime predictions of electro- and opto-electronic nanodevices).

Zhang, Xu↗
DOE OSTI2023

ORNL’s Continuing Efforts in Evaluating and Validating TSLs [Slides]

This presentation finds that INS measurements are not as variable based on sample composition, because phonon spectra is a properly of the bulk material. Additionally, INS and transmission measurements are and should be the most significant part of TSL validation, as they are the fundamental quantities used by Monte Carlo neutronics codes. This presentation reasons that relaying solely on theoretical atomistic calculations for TSL evaluation is not enough, they are highly dependent on inter-atomic potentials (which are not the most accurate depending on the quantity trying to be reproduced) and evaluators knowledge and understanding of the material being studied. Finally on Critical benchmarks, while extremely useful for validation of nuclear data at all energies, critical benchmarks are not the best tool to provide a definitive answer on conflicting TSLs. The presentation concludes that INS and transmission measurements need to be the basis of validation of TSLs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Advancements in Validation of TSLs through Inelastic Neutron Scattering and Transmission Measurements [Slides]

This presentation finds that INS measurements are not as variable based on sample composition, because phonon spectra is a properly of the bulk material. Additionally, INS and transmission measurements are and should be the most significant part of TSL validation, as they are the fundamental quantities used by Monte Carlo neutronics codes. Relaying solely on theoretical atomistic calculations for TSL evaluation is not enough, they are highly dependent on inter-atomic potentials (which are not the most accurate depending on the quantity trying to be reproduced) and evaluators knowledge and understanding of the material being studied. When dealing with Crit. benchmarks - While extremely useful for validation of nuclear data at all energies, critical benchmarks are not the best tool to provide a definitive answer on conflicting TSLs. Finally, INS and transmission measurements need to be the basis of validation of TSLs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Modeling graphene sheet growth and dynamical matrix calculations using molecular dynamics

Molecular dynamics (MD) has been an incredibly useful tool to model physical processes that were synthesized experimentally but not fully understood. MD, through the use of semi-empirical inter-atomic potentials, has allowed understanding of different physical processes in materials science. Yet as well as providing useful insights into materials science, molecular dynamics has a wider range of usability. In this report, I will be detailing how MD can be used to study graphene formation from a carbon liquid which requires high temperatures and pressures. Beyond this, I will describe the usefulness of MD for understanding the physics for phonon transport quantum sensors. To do this, MD was employed to determine the dynamical matrix by treating atoms as coupled oscillators. An accurate understanding of the dynamical matrix of a system is required to calculate the non-equilibrium Green’s function used to describe the phonon transport within phonon wave-guides. I found that, across multiple pressures and temperatures, randomly placed carbon atoms will show evidence of pent-first formation with semi-empirical models. Density functional theory (DFT), on the other hand, was too computationally expensive to use for full scale MD simulations, but we have the possibility of training a machine learned interatomic potential to approximate DFT for carbon in the environments being studied for pent-first graphene sheet formation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Final Report (October 2024): University of Tennessee, Knoxville (UTK) contribution to: FusMatML: Machine Learning Atomistic Modeling for Fusion Materials Collaborative Project led by Dr. Aidan Thompson, Sandia National Laboratory

The rapid growth of the field of Machine Learning Inter-Atomic Potentials (MLIAP) has lead to a profusion of methods, all of which have some similarity to each other, but each also restricted to particular design choices, often arrived at in a rather ad hoc fashion. Beyond anecdotal evidence, and some benchmarking studies on specific problems, little progress has been made in developing design principles for MLIAPs. The goal of this project is to use machine learning, data science, and uncertainty quantification methods to optimize the design choices for MLIAP.

Density functional theory, Helium and Hydrogen↗
DOE OSTI2017

Modeling material interfaces with hybrid adhesion method

A molecular dynamics simulation approach is presented to approximate layered material structures using discrete interatomic potentials through classical mechanics and the underlying principles of quantum mechanics. This method isolates the energetic contributions of the system into two pure material layers and an interfacial region used to simulate the adhesive properties of the diffused interface. The strength relationship of the adhesion contribution is calculated through small-scale separation calculations and applied to the molecular surfaces through an inter-layer bond criterion. By segregating the contributions into three regions and accounting for the interfacial excess energies through the adhesive surface bonds, it is possible to model each material with an independent potential while maintaining an acceptable level of accuracy in the calculation of mechanical properties. This method is intended for the atomistic study of the delamination mechanics, typically observed in thin-film applications. Therefore, the work presented in this paper focuses on mechanical tensile behaviors, with observations in the elastic modulus and the delamination failure mode. To introduce the hybrid adhesion method, we apply the approach to an ideal bulk copper sample, where an interface is created by disassociating the force potential in the middle of the structure. Various mechanical behaviors are compared to a standard EAM control model to demonstrate the adequacy of this approach in a simple setting. In addition, we demonstrate the robustness of this approach by applying it on (1) a Cu-Cu 2 O interface with interactions between two atom types, and (2) an Al-Cu interface with two dissimilar FCC lattices. These additional examples are verified against EAM and COMB control models to demonstrate the accurate simulation of failure through delamination, and the formation and propagation of dislocations under loads. Finally, the results conclude that by modeling the energy contributions of an interface using hybrid adhesion bonds, we can provide an accurate approximation method for studies of large-scale mechanical properties, as well as the representation of various delamination phenomena at the atomic scale.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Bose–Einstein condensation under the cubic–quintic Gross–Pitaevskii equation in radial domains

We study stationary and quasi-stationary solutions for the cubic–quintic Gross–Pitaevskii equation modeling Bose–Einstein condensates (BECs) in one, two, and three spatial dimensions under the assumption of radial symmetry with the BEC dynamics influenced by a confining potential. We consider both repulsive and attractive cubic interactions – corresponding respectively to repulsive and attractive two-body interactions – under similar frameworks in order to deduce the effects of the potentials in each case. We also carefully consider the role played by the quintic nonlinearity (modeling the strength of inter-atomic coupling) in modifying the solutions arising due to a purely cubic interaction term. In one spatial dimension, we obtain a variety of exact solutions in the zero-potential limit (including new periodic solutions which generalize known soliton solutions) as well as perturbation solutions for small amplitude confining potentials. For more general forms of the confining potential, we rely on numerical simulations, but these agree with the analytical results when the latter are valid. We also consider the limit where the quintic term dominates the cubic term (with such a limit relevant in the study of a Tonks–Girardeau gas). Under the assumption of radial symmetry, we also consider cylindrical (or, cigar-shaped) and spherical BECs. We consider the nonperturbative regime where either the potential or the amplitude of the solutions is large, obtaining various qualitative analytical results. When the kinetic energy term is small (relative to the nonlinearity and the confining potential), we recover the expected Thomas–Fermi approximation for the stationary solutions. Numerical simulations, under a variety of external confining potentials, are then used to understand the role these potentials play on the BEC solution structure for both the attractive and repulsive regimes. This assortment of analytical and numerical results allows us to better understand the structure of BECs modeled via cubic–quintic Gross–Pitaevskii equation under confining potentials with radial symmetry. Some of the results are natural extensions of those for the cubic Gross–Pitaevskii equation, while others appear new.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Probing periodic potential of crystals via strong-field re-scattering

Strong-field ionization and re-scattering phenomena have been used to image angstrom-scale structures of isolated molecules in the gas phase. These methods typically make use of the anisotropic response of the participating molecular orbital. Recently, an anisotropic strong-field response has also been observed in high-order harmonic generation (HHG) from bulk crystals. In a (100) cut magnesium oxide crystal, extreme ultraviolet high-harmonics are found to depend strongly on the crystal structure and inter-atomic bonding. Here, we extend these measurements to other two important crystal orientations: (111) and (110). We find that HHG from these orientations is also strongly anisotropic. The underlying dynamics is understood using a real-space picture, where high-harmonics are produced via coherent collision of strong-field driven electrons from the atomic sites, including from the nearest neighbor atoms. We find that harmonic efficiency is enhanced when semi-classical electron trajectories connect to the concentrated valence charge distribution regions around the atomic cores. Similarly, the efficiency is suppressed when the trajectories miss the atomic cores. Furthermore, these results further support the real-space picture of HHG with implications for retrieving the periodic potential of the crystal, if not the wavefunctions in three-dimensions.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

A platform for quantum information and large-scale entanglement with Rydberg atoms in programmable optical potentials

Large-scale quantum systems with controllable interactions are important for understanding complex phenomena in nature, and are the basis for advanced quantum technologies. Realizing a controllable platform for controlling, understanding, and ultimately harnessing the entanglement is an outstanding challenge in quantum science. This project demonstrated reconfigurable arrays of individually-trapped ultracold atoms, thus realizing a platform that could demonstrate large-scale quantum entanglement with the addition of strong inter-atomic interactions. Arrays of more than 50 trap sites were formed via digital holography and a high-numerical aperture imaging system that featured in-situ trap diagnostics and single-atom imaging resolution. We further discovered a new implementation of a controlled-phase gate that utilized coherent excitation to Rydberg states. This method will enable robust entanglement protocols in many-atom systems such as the one developed here.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Protein Conformational States—A First Principles Bayesian Method

Automated identification of protein conformational states from simulation of an ensemble of structures is a hard problem because it requires teaching a computer to recognize shapes. We adapt the naïve Bayes classifier from the machine learning community for use on atom-to-atom pairwise contacts. The result is an unsupervised learning algorithm that samples a ‘distribution’ over potential classification schemes. We apply the classifier to a series of test structures and one real protein, showing that it identifies the conformational transition with >95% accuracy in most cases. A nontrivial feature of our adaptation is a new connection to information entropy that allows us to vary the level of structural detail without spoiling the categorization. This is confirmed by comparing results as the number of atoms and time-samples are varied over 1.5 orders of magnitude. Further, the method’s derivation from Bayesian analysis on the set of inter-atomic contacts makes it easy to understand and extend to more complex cases.

97 MATHEMATICS AND COMPUTING↗