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Machine Learning and Monte Carlo Methods for Surface-Assisted Molecular Self-Assembly

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Cell-Inspired Materials and Engineering

Part of the book series: Fundamental Biomedical Technologies ((FBMT))

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Abstract

While molecular self-assembly processes appear widely throughout materials science and cell biology, our ability to simulate them using computational methods remains poor. In this chapter, we summarize our efforts to predict on-surface molecular self-assembly processes using recent machine learning and Monte Carlo methods. Our summary includes introductions to kernelized machine learning methods, Bayesian optimization, and equivalence class Monte Carlo sampling, and should serve as a gateway into the technical literature of the field. We discuss the concepts and shortcomings of each method, and show how they can make predictions which are not possible with conventional computational physics at present.

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Acknowledgments

The work reported here has been supported by the following grants: Japan Science and Technology Agency PRESTO “Collaborative Mathematics for Real-World Issues” Grant No. 100167050008, JSPS Kakenhi Shingakujyutsu “Exploration of Nanostrucure-Property Relationships for Materials Innovation” Grant No. 836167050004, JSPS Kakenhi Wakate Kenkyu Grant No. 18K14126, and the World Premier Research Institute Initiative promoted by the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT) for the Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto University, and the Advanced Institute for Materials Research (AIMR), Tohoku University. Collaboration with Patrick Han (Tohoku University) and Taro Hitosugi (Tokyo Institute of Technology) is kindly acknowledged.

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  1. Kyoto University iCeMS, Kyoto, Japan

    Daniel Packwood

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  1. Daniel Packwood
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Correspondence to Daniel Packwood .

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  1. Kyoto University iCeMS, Sakyo-ku, Kyoto, Japan

    Dan Ohtan Wang

  2. Kyoto University iCeMS, Sakyo-ku, Kyoto, Japan

    Daniel Packwood

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Packwood, D. (2021). Machine Learning and Monte Carlo Methods for Surface-Assisted Molecular Self-Assembly. In: Wang, D.O., Packwood, D. (eds) Cell-Inspired Materials and Engineering. Fundamental Biomedical Technologies. Springer, Cham. https://doi.org/10.1007/978-3-030-55924-3_3

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