Journal
EUROPEAN PHYSICAL JOURNAL B
Volume 94, Issue 10, Pages -Publisher
SPRINGER
DOI: 10.1140/epjb/s10051-021-00220-w
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Funding
- Center for Computational Study of Excited State Phenomena in Energy Materials (C2SEPEM) at the Lawrence Berkeley National Laboratory - U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division [DE-AC02-05CH11231]
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Collective variable-based enhanced sampling methods have been widely used to study the thermodynamic properties of complex systems. The development of machine learning techniques has improved the quality of collective variables and the accuracy of free energy surfaces, but there are still challenges and unresolved questions in this field.
Collective variable-based enhanced sampling methods have been widely used to study thermodynamic properties of complex systems. Efficiency and accuracy of these enhanced sampling methods are affected by two factors: constructing appropriate collective variables for enhanced sampling and generating accurate free energy surfaces. Recently, many machine learning techniques have been developed to improve the quality of collective variables and the accuracy of free energy surfaces. Although machine learning has achieved great successes in improving enhanced sampling methods, there are still many challenges and open questions. In this perspective, we shall review recent developments on integrating machine learning techniques and collective variable-based enhanced sampling approaches. We also discuss challenges and future research directions including generating kinetic information, exploring high-dimensional free energy surfaces, and efficiently sampling all-atom configurations. Graphic abstract
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