Energy landscapes for machine learning-Reference-Cited by-同舟云学术

Energy landscapes for machine learning

Published:2017 Issue:20 Volume:19 Page:12585-12603
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Ballard Andrew J.¹²³,Das Ritankar¹²³,Martiniani Stefano¹²³^ORCID,Mehta Dhagash⁴⁵⁶,Sagun Levent⁷⁸⁹⁶^ORCID,Stevenson Jacob D.¹⁰¹¹³,Wales David J.¹²³^ORCID

Affiliation:

1. University Chemical Laboratories

2. Cambridge CB2 1EW

3. UK

4. Department of Applied and Computational Mathematics and Statistics

5. University of Notre Dame

6. USA

7. Mathematics Department

8. Courant Institute

9. New York University

10. Microsoft Research Ltd

11. Cambridge

Abstract

The energy landscapes framework developed in molecular science provides new insight in the field of machine learning.

Funder

Engineering and Physical Sciences Research Council

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2017/CP/C7CP01108C

Reference127 articles.

1. Benchmarks for Characterization of Minima, Transition States, and Pathways in Atomic, Molecular, and Condensed Matter Systems

2. D. J. Wales , Energy Landscapes, Cambridge University Press, Cambridge, 2003

3. Energy landscapes: some new horizons

4. Archetypal energy landscapes

5. The energy landscape as a unifying theme in molecular science

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