Machine learning interatomic potentials (MLIPs) enable molecular simulations at longer time
scales without compromising accuracy and at lower computational costs compared to …

To enable fast, resource efficient development and broad scale deployment of of high
accuracy Machine-Learned Interatomic Potentials (MLIPs) with minimum expert …

Heterogeneous catalysis plays a significant role in the modern chemical industry.
Computational investigation has been an indispensable approach to reveal catalyst …

Progress towards the energy breakthroughs needed to combat climate change can be
significantly accelerated through the efficient simulation of atomistic systems. However …

Reactive chemistry atomistic simulation has a broad range of applications from drug design
to energy to materials discovery. Machine learning interatomic potentials (MLIP) have …

Efficiently creating a concise but comprehensive data set for training machine-learned
interatomic potentials (MLIPs) is an under-explored problem. Active learning, which uses …

Atomistic simulation with machine learning-based potentials (MLPs) is an emerging tool for
understanding materials' properties and behaviors and predicting novel materials. Neural …

Abstract Machine learning surrogate models for quantum mechanical simulations have
enabled the field to efficiently and accurately study material and molecular systems …

Abstract Machine learning provides a path toward fast, accurate, and large-scale materials
simulation, promising to combine the accuracy of ab initio methods with the computational …

Reactive chemistry atomistic simulation has a broad range of applications from drug design
to energy to materials discovery. Machine learning interatomic potentials (MLIPs) have …