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11/21/2022
Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective
Learning from data has led to substantial advances in a multitude of dis...
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05/17/2022
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Molecular dynamics (MD) simulations allow atomistic insights into chemic...
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06/08/2021
BIGDML: Towards Exact Machine Learning Force Fields for Materials
Machine-learning force fields (MLFF) should be accurate, computationally...
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10/14/2020
Machine Learning Force Fields
In recent years, the use of Machine Learning (ML) in computational chemi...
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11/07/2019
Machine learning for molecular simulation
Machine learning (ML) is transforming all areas of science. The complex ...
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12/11/2018
Learning representations of molecules and materials with atomistic neural networks
Deep Learning has been shown to learn efficient representations for stru...
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06/27/2018
Quantum-chemical insights from interpretable atomistic neural networks
With the rise of deep neural networks for quantum chemistry applications...
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06/26/2017
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Deep learning has the potential to revolutionize quantum chemistry as it...
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09/12/2011
