29 citations · 50 across the 6 of their papers we have counts for
6 papers
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
Peter Eastman, Pavan Kumar Behara, David L. Dotson +9
Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe…
NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic
Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr +5
Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational…
Classical Quantum Optimization with Neural Network Quantum States
Joseph Gomes, Keri A. McKiernan, Peter Eastman +1
The classical simulation of quantum systems typically requires exponential resources. Recently, the introduction of a machine learning-based wavefunction ansatz has led to the abil…
Predicting Gene Expression Between Species with Neural Networks
Peter Eastman, Vijay S. Pande
We train a neural network to predict human gene expression levels based on experimental data for rat cells. The network is trained with paired human/rat samples from the Open TG-GA…
Predicting Toxicity from Gene Expression with Neural Networks
Peter Eastman, Vijay S. Pande
We train a neural network to predict chemical toxicity based on gene expression data. The input to the network is a full expression profile collected either in vitro from cultured…
Weakly-Supervised Deep Learning of Heat Transport via Physics Informed Loss
Rishi Sharma, Amir Barati Farimani, Joe Gomes +2
In typical machine learning tasks and applications, it is necessary to obtain or create large labeled datasets in order to to achieve high performance. Unfortunately, large labeled…