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12 papers
Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations
Gabriel de Miranda Nascimento, Marc L. Descoteaux, Laura Zichi +9
First-principles atomistic simulations are essential for understanding complex material phenomena but are fundamentally limited by their computational cost. While Machine Learning…
Polymer Composites Informatics for Flammability, Thermal, Mechanical and Electrical Property Predictions
Huan Tran, Chiho Kim, Rishi Gurnani +8
Polymer composite performance depends significantly on the polymer matrix, additives, processing conditions, and measurement setups. Traditional physics-based optimization methods…
Orbital Mixer: Using Atomic Orbital Features for Basis Dependent Prediction of Molecular Wavefunctions
Kirill Shmilovich, Devin Willmott, Ivan Batalov +3
Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm o…
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
Albert Musaelian, Simon Batzner, Anders Johansson +4
A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural sciences.…
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon Batzner, Albert Musaelian, Lixin Sun +6
This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations f…
A community-powered search of machine learning strategy space to find NMR property prediction models
Lars A. Bratholm, Will Gerrard, Brandon Anderson +26
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML st…
Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture
Cheol Woo Park, Mordechai Kornbluth, Jonathan Vandermause +3
Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural n…
Spectral denoising for unsupervised analysis of correlated ionic transport
Nicola Molinari, Yu Xie, Ian Leifer +3
Computation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive as one cannot rely on the affordable mean square displacement…
Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems
Jonathan P. Mailoa, Mordechai Kornbluth, Simon L. Batzner +5
Neural network force field (NNFF) is a method for performing regression on atomic structure-force relationships, bypassing expensive quantum mechanics calculation which prevents th…
A group theoretical approach to computing phonons and their interactions
Lyuwen Fu, Mordechai Kornbluth, Zhengqian Cheng +1
Here we present four independent advances which facilitate the computation of phonons and their interactions from first-principles. First, we implement a group-theoretical approach…
Origin of Spinel Nanocheckerboards via First Principles
Mordechai Kornbluth, Chris Marianetti
Self-organizing nanocheckerboards have been experimentally fabricated in Mn-based spinels, but have not yet been explained with first principles. Using density-functional-theory, w…
New Supercoherent States
M. Kornbluth, F. R. Zypman
This study generalizes the supersymmetric coherent states introduced by Aragone and Zypman in 1986. The Hamiltonian of the supersymmetric quantum harmonic oscillator leads to the d…