Publikation: Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics : Application to Materials and Biological Systems
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Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.
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GKEKA, Paraskevi, Gabriel STOLTZ, Amir BARATI FARIMANI, Zineb BELKACEMI, Michele CERIOTTI, John D. CHODERA, Aaron R. DINNER, Andrew L. FERGUSON, Jean-Bernard MAILLET, Christine PETER, 2020. Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics : Application to Materials and Biological Systems. In: Journal of Chemical Theory and Computation (JCTC). American Chemical Society (ACS). 2020, 16(8), pp. 4757-4775. ISSN 1549-9618. eISSN 1549-9626. Available under: doi: 10.1021/acs.jctc.0c00355BibTex
@article{Gkeka2020-08-11Machi-50937, year={2020}, doi={10.1021/acs.jctc.0c00355}, title={Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics : Application to Materials and Biological Systems}, number={8}, volume={16}, issn={1549-9618}, journal={Journal of Chemical Theory and Computation (JCTC)}, pages={4757--4775}, author={Gkeka, Paraskevi and Stoltz, Gabriel and Barati Farimani, Amir and Belkacemi, Zineb and Ceriotti, Michele and Chodera, John D. and Dinner, Aaron R. and Ferguson, Andrew L. and Maillet, Jean-Bernard and Peter, Christine} }
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