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04/20/2023
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size
This work brings the leading accuracy, sample efficiency, and robustness...
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11/17/2022
Fast Uncertainty Estimates in Deep Learning Interatomic Potentials
Deep learning has emerged as a promising paradigm to give access to high...
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05/13/2022
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
The rapid progress of machine learning interatomic potentials over the p...
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04/11/2022
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
A simultaneously accurate and computationally efficient parametrization ...
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01/08/2021
SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
This work presents Neural Equivariant Interatomic Potentials (NequIP), a...
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12/07/2020
Multitask machine learning of collective variables for enhanced sampling of rare events
Computing accurate reaction rates is a central challenge in computationa...
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08/26/2020
Fast Bayesian Force Fields from Active Learning: Study of Inter-Dimensional Transformation of Stanene
We present a way to dramatically accelerate Gaussian process models for ...
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03/24/2020
AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance
The ever-growing availability of computing power and the sustained devel...
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05/07/2019
