Research Topic
potential energy
7 publications exploring this topic
2020
Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens
(2020)
Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles.
TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials
(2020)
This paper presents TorchANI, a PyTorch based software for training/inferenceof ANI (ANAKIN-ME) deep learning models to obtain potential energy surfaces andother physical properties of molecular systems.
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
Scientific Data, 7 (2020)
Abstract Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.
The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
(2020)
Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.
The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
(2020)
Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.
2019
The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
(2019)
Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.
2018
Less is more: Sampling chemical space with active learning
The Journal of Chemical Physics, 148 (2018)
The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task.