Force-matched empirical potential for martensitic transitions and plastic deformation in Ti-Nb alloys
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Ehemann, Robert C.↗
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A comparative study of the effectiveness of empirical potentials for molecular dynamics simulations of borosilicate glasses
Despite their practical importance, atomistic modeling of B 2 O 3 containing glasses have been challenging due to the lack of reliable empirical potentials. Fortunately, a few recent developments have shown promises to simulate these glasses where the boron coordination has complex and non-linear dependence on glass composition. This work aims to provide an evaluation of the effectiveness of three recently developed potentials by a systematic study of a series (~20) of sodium borosilicate glasses with constant K (ratio of [SiO 2 ]/[B 2 O 3 ]) =2 and varying R (ratio of [Na 2 O]/[B 2 O 3 ] ranging from 0.1 to 4) values and several sodium boroaluminosilicate glasses. A comparative assessment was established on the basis of the short- and medium-range structure features, such as boron N 4 values, total correlation functions, bond angle distribution, oxygen speciation, and mechanical properties using experimental or well-established models as criteria. Furthermore, this work provides insights on the choices of empirical potentials for MD simulations of borosilicate glasses and gives directions of future potential development and refining.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Development of a semi-empirical potential for simulation of Ni solute segregation into grain boundaries in Ag
An Ag–Ni semi-empirical potential was developed to simulate the segregation of Ni solutes at Ag grain boundaries (GBs). The potential combines a new Ag potential fitted to correctly reproduce the stable and unstable stacking fault energies in this metal and the existing Ni potential from Mendelev et al (2012 Phil. Mag. 92 4454–69). The Ag–Ni cross potential functions were fitted to ab initio data on the liquid structure of the Ag 80 Ni 20 alloy to properly incorporate the Ag–Ni interaction at small atomic separations, and to the Ni segregation energies at different sites within a high-energy Σ9 (221) symmetric tilt GB. By deploying this potential with hybrid Monte Carlo/molecular dynamics simulations, it was found that heterogeneous segregation and clustering of Ni atoms at GBs and twin boundary defects occur at low Ni concentrations, 1 and 2 at%. This behavior is profoundly different from the homogeneous interfacial dispersion generally observed for the Cu segregation in Ag. A GB transformation to amorphous intergranular films was found to prevail at higher Ni concentrations (10 at%). In conclusion, the developed potential opens new opportunities for studying the selective segregation behavior of Ni solutes in interface-hardened Ag metals and its effect on plasticity.
36 MATERIALS SCIENCE↗
Implementation of metal-friendly EAM/FS-type semi-empirical potentials in HOOMD-blue: A GPU-accelerated molecular dynamics software
We present an implementation of EAM and FS interatomic potentials, which are widely used in simulating metallic systems, in HOOMD-blue, a software designed to perform classical molecular dynamics simulations using GPU accelerations. We first discuss the details of our implementation and then report extensive benchmark tests. We demonstrate that single-precision floating point operations efficiently implemented on GPUs can produce sufficient accuracy when compared against double-precision codes, as demonstrated in test simulations of calculations of the glass-transition temperature of Cu 64.5 Zr 35.5 , and pair correlation function of liquid Ni 3 Al. Our code scales well with the size of the simulating system on NVIDIA Tesla M40 and P100 GPUs. Compared with another popular software LAMMPS running on 32 cores of AMD Opteron 6220 processors, the GPU/CPU performance ratio can reach as high as 4.6. In conclusion, the source code can be accessed through the HOOMD-blue web page for free by any interested user.
36 MATERIALS SCIENCE↗
Small uranium and oxygen interstitial clusters in UO 2 : An empirical potential study
The aggregation of defect clusters in UO 2 under irradiations creates dislocation loops and cavities, which in turn contribute to swelling of the fuels, leading to detrimental pellet-clad mechanical interactions. The kinetics of the defect clusters, however, have received less attention and are not well understood. As such, using two interatomic potentials, the Morelon potential and a many-body model by Cooper, Rushton, and Grimes (CRG), the energetics and kinetics of small uranium interstitial clusters, with and without bound oxygen interstitials, are investigated with molecular dynamics and accelerated molecular dynamics simulations. For the single bound anti-Schottky interstitial cluster (U i 2O i ), both potentials find the interstitialcy migration mechanism to be a low barrier migration mechanism. However, for the CRG potential, an oxygen Frenkel pair assisted interstitial cluster migration mechanism with a lower migration barrier is identified during accelerated molecular dynamics simulations. For the Morelon potential, the interstitial clusters have geometrically compact structures, and small interstitial clusters (two and three anti-Schottky clusters) are more mobile than the single bound anti-Schottky interstitial cluster; for the CRG potential, the interstitial clusters have geometrically non-compact structures, yielding a more complex energy landscape. The rapid migration of interstitials and interstitial clusters are important for the irradiation response of UO 2 and correlates well with the very low barriers reported in a few historic experimental studies.
38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
Molecular Dynamics of High Pressure Tin Phases I: Strength and deformation evaluations of empirical potentials [Slides]
Multi-phase problems have so many more unknowns, we’d like to have a tool to constrain some open questions related to microstructure and twin & dislocation behavior. We want an atomistic scale perspective on aspects of strength. Some multi-scale questions accessible to atomistic study: What lattice-specific behavior influences dislocation production/mobility and/or twinning? Do the phase transformations wipe-out, modify or preserve grain size and orientation? Does plastic strain reset at phase transition? If so under what conditions? Tin is the material chosen for the effort because it is non-hazardous and has multiple accessible solid phases at relatively low pressures.
36 MATERIALS SCIENCE↗
Characterizing silicon nitride crystallization with empirical potentials in LAMMPS
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Janicki, Tesia Danielle↗
Accelerated computation of free energy profile at ab initio quantum mechanical/molecular mechanical accuracy via a semi-empirical reference potential. II. Recalibrating semi-empirical parameters with force matching
An efficient and accurate reference potential simulation protocol is proposed for producing ab initio quantum mechanical/molecular mechanical (AI-QM/MM) quality free energy profiles for chemical reactions in a solvent or macromolecular environment.
Pan, Xiaoliang↗
Assessment of empirical interatomic potential to predict thermal conductivity in ThO 2 and UO 2
Computing vibrational properties of crystals in the presence of complex defects often necessitates the use of (semi-)empirical potentials, which are typically not well characterized for perfect crystals. In this study we explore the efficacy of a commonly used embedded-atomempirical interatomic potential for the U x Th 1- x O 2 system, to compute phonon dispersion, lifetime, and branch specific thermal conductivity. Our approach for ThO 2 involves using lattice dynamics and the linearized Boltzmann transport equation to calculate phonon transport properties based on second and third order force constants derived from the empirical potential and from first-principles calculations. For UO 2 , to circumvent the accuracy issues associated with first-principles treatments of strong electronic correlations, we compare results derived from the empirical interatomic potential to previous experimental results. It is found that the empirical potential can reasonably capture the dispersion of acoustic branches, but exhibits significant discrepancies for the optical branches, leading to overestimation of phonon lifetime and thermal conductivity. The branch specific conductivity also differs significantly with either first-principles based results (ThO 2 ) or experimental measurements (UO 2 ). These findings suggest that the empirical potential needs to be further optimized for robust prediction of thermal conductivity both in perfect crystals and in the presence of complex defects.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Parameterizing empirical interatomic potentials for predicting thermophysical properties via an irreducible derivative approach: the case of ThO 2 and UO 2
The accuracy of classical physical property predictions using molecular dynamics simulations is determined by the quality of the interatomic potentials. Here we introduce a training approach for empirical interatomic potentials (EIPs) which is well suited for capturing phonons and phonon-related properties. Our approach is based on direct comparisons of the second- and third-order irreducible derivatives (IDs) between an EIP and the Born–Oppenheimer potential within density functional theory (DFT) calculations. IDs fully exploit space group symmetry and allow for training without redundant information. We demonstrate the fidelity of our approach in the context of ThO 2 and UO 2 , where we optimize parameters of an embedded-atom method potential in addition to core–shell interactions. Our EIPs provide thermophysical properties in good agreement with DFT and outperform widely utilized EIPs for phonon dispersion and thermal conductivity predictions. Reasonable estimates of thermal expansion and formation energies of Frenkel pairs are also obtained.
empirical interatomic potential↗
Deep potential molecular dynamics simulations of low-temperature plasma-surface interactions
Machine learning approaches to potential generation for molecular dynamics (MD) simulations of low-temperature plasma-surface interactions could greatly extend the range of chemical systems that can be modeled. Empirical potentials are difficult to generalize to complex combinations of multiple elements with interactions that might include covalent, ionic, and metallic bonds. This work demonstrates that a specific machine learning approach, Deep Potential Molecular Dynamics (DeepMD), can generate potentials that provide a good model of plasma etching in the Si-Cl-Ar system. Comparisons are made between MD results using DeepMD models and empirical potentials, as well as experimental measurements. Pure Si properties predicted by the DeepMD model are in reasonable agreement with experimental results. Simulations of Si bombardment by Ar + ions demonstrate the ability of the DeepMD method to predict sputtering yields as well as the depth of the amorphous-crystalline interface. Etch yields as a function of flux ratio and ion energy for simultaneous Cl 2 and Ar + impacts are in good agreement with previous simulation results and experiment. Predictions of etch yields and etch products during plasma-assisted atomic layer etching of Si-Cl 2 -Ar are shown to be in good agreement with MD predictions using empirical potentials and with experiment. Finally, good agreement was also seen with measurements for the spontaneous etching of Si by Cl atoms at 300 K. Further, the demonstration that DeepMD can reproduce results from MD simulations using empirical potentials is a necessary condition to future efforts to extend the method to a much wider range of systems for which empirical potentials may be difficult or impossible to obtain.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Enhancing high-fidelity neural network potentials through low-fidelity sampling
The efficacy of neural network potentials (NNPs) critically depends on the quality of the configurational datasets used for training. Prior research using empirical potentials has shown that well-selected liquid–solid transitional configurations of a metallic system can be translated to other metallic systems. This study demonstrates that such validated configurations can be relabeled using density functional theory (DFT) calculations, thereby enhancing the development of high-fidelity NNPs. Training strategies and sampling approaches are efficiently assessed using empirical potentials and subsequently relabeled via DFT in a highly parallelized fashion for high-fidelity NNP training. Our results reveal that relying solely on energy and force for NNP training is inadequate to prevent overfitting, highlighting the necessity of incorporating stress terms into the loss functions. To optimize training involving force and stress terms, we propose employing transfer learning to fine-tune the weights, ensuring that the potential surface is smooth for these quantities composed of energy derivatives. This approach markedly improves the accuracy of elastic constants derived from simulations in both empirical potential-based NNPs and relabeled DFT-based NNPs. Overall, this study offers significant insights into leveraging empirical potentials to expedite the development of reliable and robust NNPs at the DFT level.
97 MATHEMATICS AND COMPUTING↗
Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to $α$-Ag 2 Se
First-principles molecular dynamics (FPMD) simulations are highly accurate, but due to their high calculation cost, the computational scale is often limited to hundreds of atoms and few picoseconds under specific temperature and pressure conditions. As such, we present here the guidelines for creating artificial neural network empirical interatomic potential (ANN potential) trained with such a limited FPMD data, which can perform long time scale MD simulations at least under the same conditions. The FPMD data for training are prepared on the basis of the convergence of radial distribution function [$g(r)$]. While training the ANN using total energy and atomic forces of the FPMD data, the error of pressure is also monitored and minimized. To create further robust potential, we add a small amount of FPMD data to reproduce the interaction between two atoms that are close to each other. ANN potentials for $α$-Ag 2 Se were created as an application example, and it has been confirmed that not only $g(r)$ and mean square displacements but also the specific heat requiring a long time scale simulation matched the FPMD and the experimental values. In addition, the MD simulation using the ANN potential achieved over 10 4 acceleration over the FPMD one. The guidelines proposed here mitigate the creation difficulty of the ANN potential, and a lot of FPMD data sleeping on the hard disk after the research may be put on the front stage again.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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↗
Determination of the structure of lithium niobosilicate glasses by molecular dynamics simulation with a new Nb-O potential
A partial charge empirical potential for the Nb-O pair has been developed based on existing parameters of other pairs in a Teter potential to enable molecular dynamics simulations of both lithium niobate crystal and lithium niobosilicate glasses. The developed potential is capable of describing structural features of lithium niobosilicate glasses in a wide composition range, including niobium coordination number and bond length, density, bond angle distribution, polyhedral distribution and fraction of bridging oxygen. Furthermore, the results obtained using this new potential show good agreement with experimental data of density and structure from Raman spectroscopy of lithium niobosilicate glasses and X-ray absorption spectroscopy near Nb K-edge.
36 MATERIALS SCIENCE↗
The behavior of methane–water mixtures under elevated pressures from simulations using many-body potentials
Non-polarizable empirical potentials have been proven to be incapable of capturing the mixing of methane–water mixtures at elevated pressures. Although density functional theory-based ab initio simulations may circumvent this discrepancy, they are limited in terms of the relevant time and length scales associated with mixing phenomena. Here, we show that the many-body MB-nrg potential, designed to reproduce methane–water interactions with coupled cluster accuracy, successfully captures this phenomenon up to 3 GPa and 500 K with varying methane concentrations. Two-phase simulations and long time scales that are required to fully capture the mixing, affordable due to the speed and accuracy of the MBX software, are assessed. Constructing the methane–water equation of state across the phase diagram shows that the stable mixtures are denser than the sum of their parts at a given pressure and temperature. As a result, we find that many-body polarization plays a central role, enhancing the induced dipole moments of methane by 0.20 D during mixing under pressure. Overall, the mixed system adopts a denser state, which involves a significant enthalpic driving force as elucidated by a systematic many-body energy decomposition analysis.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Accuracy, transferability, and computational efficiency of interatomic potentials for simulations of carbon under extreme conditions
Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fidelity of these simulations depends heavily on the accuracy of classical interatomic potentials governing the dynamics of many-atom systems. Here, this study critically assesses several popular empirical potentials for carbon, as well as machine learning interatomic potentials (MLIPs), in their ability to simulate a range of physical properties at high pressures and temperatures, including the diamond equation of state, its melting line, shock Hugoniot, uniaxial compressions, and the structure of liquid carbon. Empirical potentials fail to accurately predict the behavior of carbon under high pressure–temperature conditions. In contrast, MLIPs demonstrate quantum accuracy, with Spectral Neighbor Analysis Potential (SNAP) and atomic cluster expansion (ACE) being the most accurate in reproducing the density functional theory results. ACE displays remarkable transferability despite not being specifically trained for extreme conditions. Furthermore, ACE and SNAP exhibit superior computational performance on graphics processing unit-based systems in billion atom MD simulations, with SNAP emerging as the fastest. In addition to offering practical guidance in selecting an interatomic potential with a fine balance of accuracy, transferability, and computational efficiency, this work also highlights transformative opportunities for groundbreaking scientific discoveries facilitated by quantum-accurate MD simulations with MLIPs on emerging exascale supercomputers.
36 MATERIALS SCIENCE↗
Local-environment dependence of stacking fault energies in concentrated solid-solution alloys
Concentrated solid-solution alloys (CSAs) based on 3d transition metals have demonstrated extraordinary mechanical properties and radiation resistance associated with their low stacking fault energies (SFEs). Owing to the intrinsic disorder, SFEs in CSAs exhibit distributions depending on local atomic configurations. In this work, the distribution of SFEs in equiatomic CSAs of NiCo, NiFe, and NiCoCr are investigated based on empirical potential and first-principles calculations. We show that the calculated distribution of SFEs in chemically disordered CSAs depends on the stacking fault area using empirical potential calculations. Based on electronic structure calculations, we find that local variations of SFEs in CSAs correlate with the charge density redistribution in the stacking fault region. We further propose a bond breaking and forming model to understand and predict the SFEs in CSAs based on the local structure alone. It is shown that the perturbation induced by a stacking fault is localized in the first-nearest planes for NiCo, but extends up to the third nearest planes for NiFe and NiCoCr because of partially filled d electrons in Fe and Cr.
36 MATERIALS SCIENCE↗