Perspective: Advances, challenges, and insight for predictive coarse-grained models
WG Noid - The Journal of Physical Chemistry B, 2023 - ACS Publications
By averaging over atomic details, coarse-grained (CG) models provide profound
computational and conceptual advantages for studying soft materials. In particular, bottom …
computational and conceptual advantages for studying soft materials. In particular, bottom …
Machine learned coarse-grained protein force-fields: Are we there yet?
The successful recent application of machine learning methods to scientific problems
includes the learning of flexible and accurate atomic-level force-fields for materials and …
includes the learning of flexible and accurate atomic-level force-fields for materials and …
CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling
Large-scale simulations with complex electron interactions remain one of the greatest
challenges for atomistic modelling. Although classical force fields often fail to describe the …
challenges for atomistic modelling. Although classical force fields often fail to describe the …
Accurate global machine learning force fields for molecules with hundreds of atoms
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 …
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …
A Euclidean transformer for fast and stable machine learned force fields
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 …
(MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the …
[HTML][HTML] Evaluation of the MACE force field architecture: From medicinal chemistry to materials science
The MACE architecture represents the state of the art in the field of machine learning force
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Machine learning coarse-grained potentials of protein thermodynamics
A generalized understanding of protein dynamics is an unsolved scientific problem, the
solution of which is critical to the interpretation of the structure-function relationships that …
solution of which is critical to the interpretation of the structure-function relationships that …
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size
This work brings the leading accuracy, sample efficiency, and robustness of deep
equivariant neural networks to the extreme computational scale. This is achieved through a …
equivariant neural networks to the extreme computational scale. This is achieved through a …
How to validate machine-learned interatomic potentials
Machine learning (ML) approaches enable large-scale atomistic simulations with near-
quantum-mechanical accuracy. With the growing availability of these methods, there arises …
quantum-mechanical accuracy. With the growing availability of these methods, there arises …