2.1k citations · 3.9k across the 15 of their papers we have counts for
5 papers · 1 filter
Learning Together: Towards foundational models for machine learning interatomic potentials with meta-learning
Alice E. A. Allen, Nicholas Lubbers, Sakib Matin +4
The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditio…
Lightweight and Effective Tensor Sensitivity for Atomistic Neural Networks
Michael Chigaev, Justin S. Smith, Steven Anaya +4
Atomistic machine learning focuses on the creation of models which obey fundamental symmetries of atomistic configurations, such as permutation, translation, and rotation invarianc…
Transferable Molecular Charge Assignment Using Deep Neural Networks
Ben Nebgen, Nick Lubbers, Justin S. Smith +6
We use HIP-NN, a neural network architecture that excels at predicting molecular energies, to predict atomic charges. The charge predictions are accurate over a wide range of molec…
ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules
Justin S. Smith, Olexandr Isayev, Adrian E. Roitberg
One of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy. Machine learning…
ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
Justin S. Smith, Olexandr Isayev, Adrian E. Roitberg
Deep learning is revolutionizing many areas of science and technology, especially image, text and speech recognition. In this paper, we demonstrate how a deep neural network (NN) t…