976 citations · 1.8k across the 20 of their papers we have counts for
21 papers
Into the Unknown: Benchmarking AI agents on open scientific research problems
Seungone Kim, Jiayi Geng, Young-Jun Lee +25
Can AI agents carry out research on open scientific problems? We introduce Into the Unknown, a benchmark that provides an agent with a science paper along with a research direction…
Materials Behavior as Mechanism Ensembles: A Probabilistic Framework for Emergent Behaviors
Brad L. Boyce, Mitchell A. Wood, Krishna Garikipati +16
Materials behavior is often treated as a deterministic mapping from structure to properties, yet many important phenomena emerge from the conditional activation of multiple mechani…
Dynamic Formation of Preferentially Lattice Oriented, Self Trapped Hydrogen Clusters
M. A. Cusentino, E. L. Sikorski, M. J. McCarthy +2
A series of MD and DFT simulations were performed to investigate hydrogen self-clustering and retention in tungsten. Using a newly develop machine learned interatomic potential, sp…
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…
JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
Kamal Choudhary, Daniel Wines, Kangming Li +35
Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experiment…
Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter
Sandeep Kumar, Hossein Tahmasbi, Kushal Ramakrishna +5
We present a study on the transport and materials properties of aluminum spanning from ambient to warm dense matter conditions using a machine-learned interatomic potential (ML-IAP…
Atomic Representations of Local and Global Chemistry in Complex Alloys
Megan J. McCarthy, Jacob Startt, Rémi Dingreville +2
The exceptional properties observed in complex concentrated alloys (CCAs) arise from the interplay between crystalline order and chemical disorder at the atomic scale, complicating…
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 (…
Training Data Selection for Accuracy and Transferability of Interatomic Potentials
David Montes de Oca Zapiain, Mitchell A. Wood, Nicholas Lubbers +3
Advances in machine learning (ML) techniques have enabled the development of interatomic potentials that promise both the accuracy of first principles methods and the low-cost, lin…
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…
Efficacy of the Radial Pair Potential Approximation for Molecular Dynamics Simulations of Dense Plasmas
Lucas J. Stanek, Raymond C. Clay, M. W. C. Dharma-wardana +3
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…
Thermodynamically consistent physics-informed neural networks for hyperbolic systems
Ravi G. Patel, Indu Manickam, Nathaniel A. Trask +4
Physics-informed neural network architectures have emerged as a powerful tool for developing flexible PDE solvers which easily assimilate data, but face challenges related to the P…
A physics-informed operator regression framework for extracting data-driven continuum models
Ravi G. Patel, Nathaniel A. Trask, Mitchell A. Wood +1
The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate a…
Explicit Multi-element Extension of the Spectral Neighbor Analysis Potential for Chemically Complex Systems
Mary Alice Cusentino, Mitchell A. Wood, Aidan P. Thompson
A natural extension of the descriptors used in the Spectral Neighbor Analysis Potential (SNAP) method is derived to treat atomic interactions in chemically complex systems. Atomic…
A Performance and Cost Assessment of Machine Learning Interatomic Potentials
Yunxing Zuo, Chi Chen, Xiangguo Li +8
Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the d…
Data-driven Material Models for Atomistic Simulation
Mitchell A. Wood, Mary Alice Cusentino, Brian D. Wirth +1
The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and inge…
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…
Quantum-Accurate Molecular Dynamics Potential for Tungsten
Mitchell A. Wood, Aidan P. Thompson
The purpose of this short contribution is to report on the development of a Spectral Neighbor Analysis Potential (SNAP) for tungsten. We have focused on the characterization of ela…
Nonlinear Electromagnetic Interactions in Energetic Materials
M. A. Wood, D. A. R. Dalvit, D. S. Moore
We study the scattering of electromagnetic waves in anisotropic energetic materials. Nonlinear light-matter interactions in molecular crystals result in frequency-conversion and po…