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Chemistrees: Data-Driven Identification of Reaction Pathways via Machine Learning
- Source :
- Journal of Chemical Theory and Computation
- Publication Year :
- 2021
- Publisher :
- American Chemical Society (ACS), 2021.
-
Abstract
- We propose to analyze molecular dynamics (MD) output via a supervised machine learning (ML) algorithm, the decision tree. The approach aims to identify the predominant geometric features which correlate with trajectories that transition between two arbitrarily defined states. The data-driven algorithm aims to identify these features without the bias of human “chemical intuition”. We demonstrate the method by analyzing the proton exchange reactions in formic acid solvated in small water clusters. The simulations were performed with ab initio MD combined with a method to efficiently sample the rare event, path sampling. Our ML analysis identified relevant geometric variables involved in the proton transfer reaction and how they may change as the number of solvating water molecules changes.
- Subjects :
- FORMIC-ACID
DECOMPOSITION
LIQUID WATER
INITIO MOLECULAR-DYNAMICS
Computer science
Grotthuss
116 Chemical sciences
Decision tree
Ab initio
FOS: Physical sciences
DEPROTONATION
010402 general chemistry
Machine learning
computer.software_genre
01 natural sciences
Article
Data-driven
Molecular dynamics
Physics - Chemical Physics
0103 physical sciences
Physical and Theoretical Chemistry
Event (probability theory)
Chemical Physics (physics.chem-ph)
010304 chemical physics
business.industry
Metadynamics
Sampling (statistics)
METADYNAMICS
DISSOCIATION
Computational Physics (physics.comp-ph)
SIMULATIONS
0104 chemical sciences
Computer Science Applications
INSIGHTS
Path (graph theory)
Artificial intelligence
business
Physics - Computational Physics
computer
Subjects
Details
- ISSN :
- 15499626 and 15499618
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- Journal of Chemical Theory and Computation
- Accession number :
- edsair.doi.dedup.....032bfdc8159332a9e4ad5f979fc918b5