Atomic Energies from a Convolutional Neural Network-Reference-Cited by-同舟云学术

Atomic Energies from a Convolutional Neural Network

Published:2018-05-29 Issue:7 Volume:14 Page:3933-3942
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Chen Xin¹²,Jørgensen Mathias S.²^ORCID,Li Jun¹^ORCID,Hammer Bjørk²^ORCID

Affiliation:

1. Department of Chemistry and Laboratory of Organic Optoelectronics & Molecular Engineering of the Ministry of Education, Tsinghua University, Beijing 100084, China

2. Interdisciplinary Nanoscience Center (iNANO) and Department of Physics and Astronomy, Aarhus University, Aarhus DK-8000, Denmark

Funder

Natur og Univers, Det Frie Forskningsr?d

Villum Fonden

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.8b00149

Reference68 articles.

1. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

2. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

3. The many-body expansion combined with neural networks

4. Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network

5. Quantum-chemical insights from deep tensor neural networks

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