Journal
PHYSICAL REVIEW LETTERS
Volume 119, Issue 15, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.119.150601
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Funding
- National Science Foundation partially through the Materials Research Science and Engineering Center (MRSEC) program [DMR-1420073]
- National Science Foundation [CHE-1565980]
- Division Of Chemistry
- Direct For Mathematical & Physical Scien [1565980] Funding Source: National Science Foundation
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The generation of free energy landscapes corresponding to conformational equilibria in complex molecular systems remains a significant computational challenge. Adding to this challenge is the need to represent, store, and manipulate the often high-dimensional surfaces that result from rare-event sampling approaches employed to compute them. In this Letter, we propose the use of artificial neural networks as a solution to these issues. Using specific examples, we discuss network training using enhanced-sampling methods and the use of the networks in the calculation of ensemble averages.
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