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Exploring Model Complexity in Machine Learned Potentials for Simulated Properties
Andrew Rohskopf, James Goff, Dionysios Sema +5
Machine learning (ML) enables the development of interatomic potentials that promise the accuracy of first principles methods while retaining the low cost and parallel efficiency o…
Predicting electronic structures at any length scale with machine learning
Lenz Fiedler, Normand A. Modine, Steve Schmerler +5
The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects…
Permutation-adapted complete and independent basis for atomic cluster expansion descriptors
James M. Goff, Charles Sievers, Mitchell A. Wood +1
Atomic cluster expansion (ACE) methods provide a systematic way to describe particle local environments of arbitrary body order. For practical applications it is often required tha…
Machine Learning Interatomic Potential for Simulations of Carbon at Extreme Conditions
Jonathan T. Willman, Kien Nguyen-Cong, Ashley S. Williams +5
A Spectral Neighbor Analysis (SNAP) machine learning interatomic potential (MLIP) has been developed for simulations of carbon at extreme pressures (up to 5 TPa) and temperatures (…
Quantum-accurate magneto-elastic predictions with classical spin-lattice dynamics
Svetoslav Nikolov, Mitchell A. Wood, Attila Cangi +5
A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice dynamics simulations. The magneto-e…
Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks
J. Austin Ellis, Lenz Fiedler, Gabriel A. Popoola +5
We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite elec…
Multiscale Modeling of Shock Wave Localization in Porous Energetic Material
Mitchell A. Wood, David E. Kittell, Cole D. Yarrington +1
Shock wave interactions with defects, such as pores, are known to play a key role in the chemical initiation of energetic materials. The shock response of hexanitrostilbene is stud…
Extending the Accuracy of the SNAP Interatomic Potential Form
Mitchell A. Wood, Aidan P. Thompson
The Spectral Neighbor Analysis Potential (SNAP) is a classical interatomic potential that expresses the energy of each atom as a linear function of selected bispectrum components o…
A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
Aidan P. Thompson, Laura P. Swiler, Christian R. Trott +2
We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learni…