59 citations · 83 across the 7 of their papers we have counts for
5 papers · 1 filter
Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
Attila Cangi, Lenz Fiedler, Bartosz Brzoza +11
We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for…
Probing Iron in Earth's Core With Molecular-Spin Dynamics
Svetoslav Nikolov, Kushal Ramakrishna, Andrew Rohskopf +5
Dynamic compression of iron to Earth-core conditions is one of the few ways to gather important elastic and transport properties needed to uncover key mechanisms surrounding the ge…
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…
Proper Orthogonal Descriptors for Efficient and Accurate Interatomic Potentials
Ngoc Cuong Nguyen, Andrew Rohskopf
We present the proper orthogonal descriptors for efficient and accuracy representation of the potential energy surface. The potential energy surface is represented as a many-body e…