High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …

Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …

Recent years have seen vast progress in the development of machine learned force fields
(MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the …

In the field of molecular simulation for drug design, traditional molecular mechanic force
fields and quantum chemical theories have been instrumental but limited in terms of …

Abstract Machine learning has enabled the prediction of quantum chemical properties with
high accuracy and efficiency, allowing to bypass computationally costly ab initio …

The atomic cluster expansion (ACE) was proposed recently as a new class of data-driven
interatomic potentials with a formally complete basis set. Since the development of any …

Abstract Machine learned interatomic interaction potentials have enabled efficient and
accurate molecular simulations of closed systems. However, external fields, which can …

As the sophistication of machine learning force fields (MLFF) increases to match the
complexity of extended molecules and materials, so does the need for tools to properly …

Approximate density functional theory has become indispensable owing to its balanced cost–
accuracy trade-off, including in large-scale screening. To date, however, no density …

Excited-state molecular dynamics simulations are crucial for understanding processes like
photosynthesis, vision, and radiation damage. However, the computational complexity of …