Energy landscapes for machine learning
Literature Information
Publication Date
2017-04-03
DOI
10.1039/C7CP01108C
Impact Factor
3.676
Authors
Andrew J. Ballard, Ritankar Das, Stefano Martiniani, Dhagash Mehta, Levent Sagun, Jacob D. Stevenson, David J. Wales
View Original
Abstract
Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as local minima can be analysed in terms of the corresponding machine learning landscape. Methods to explore and visualise molecular potential energy landscapes can be applied to these machine learning landscapes to gain new insight into the solution space involved in training and the nature of the corresponding predictions. In particular, we can define quantities analogous to molecular structure, thermodynamics, and kinetics, and relate these emergent properties to the structure of the underlying landscape. This Perspective aims to describe these analogies with examples from recent applications, and suggest avenues for new interdisciplinary research.
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Source Journal
Physical Chemistry Chemical Physics
CiteScore:
5.5
Self-citation Rate:
10.3%
Articles per Year:
3036
Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
Recommended Compounds
![(3R,4aR,7aS,9aR,10S,11R,13aR,13bS,15aS,15bR)-3,11-Dihydroxy-10-(hydroxymethyl)-4,4,7a,10,13a,15b-hexamethyl-1,2,3,4,4a,7,7a,8,9,9a,10,11,12,13,13a,13b,14,15,15a,15b-icosahydro-5H-naphtho[2',1':4,5]cyc
lohepta[1,2-a]naphthalen-5-one structure](https://static.chemtradehub.com/structs/538/53800-21-8-9f18.webp "(3R,4aR,7aS,9aR,10S,11R,13aR,13bS,15aS,15bR)-3,11-Dihydroxy-10-(hydroxymethyl)-4,4,7a,10,13a,15b-hexamethyl-1,2,3,4,4a,7,7a,8,9,9a,10,11,12,13,13a,13b,14,15,15a,15b-icosahydro-5H-naphtho[2',1':4,5]cyc
lohepta[1,2-a]naphthalen-5-one structure")
![[1-(5-Methyl-2-pyridinyl)-1H-pyrazol-4-yl]methanol structure](https://static.chemtradehub.com/structs/143/1439822-99-7-6cc9.webp "[1-(5-Methyl-2-pyridinyl)-1H-pyrazol-4-yl]methanol structure")
[1-(5-Methyl-2-pyridinyl)-1H-pyrazol-4-yl]methanol
CAS Number:
1439822-99-7
Molecular Formula:
C10H11N3O
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