DOI: https://doi.org/10.29363/nanoge.dynamic.2023.029
Publication date: 15th February 2023
The past few years have seen a rapid increase in the use of machine learning (ML) approaches to the fields of chemistry and materials science, in particular in the prediction of physical and chemical properties of existing and novel compounds. Databases of experimental structures — in particular, crystalline structures — continue to grow at a steady pace and are complemented with larger and larger databases of physical and chemical properties. We present here several examples of a multi-scale computational methodology to this problem, by combining the existing tools of theoretical chemistry (i.e., quantum chemical calculations and classical molecular simulations) with statistical learning approaches. [1] We show how these have been integrated together in our group and allow not only the prediction of properties, but also a deeper understanding of the structure/property relationships that can provide chemical insight. [2] We focus in particular on how these tools can accelerate the discovery of materials with stimuli-responsive properties (whether thermal or mechanical). [3]
1. “Best practices in machine learning for chemistry”, N. Artrith, K. T. Butler, F.-X. Coudert, S. Han, O. Isayev, A. Jain and A. Walsh, Nature Chem., 2021, 13 (6), 505–508.
2. “Machine learning approaches for the prediction of materials properties”, S. Chibani and F.-X. Coudert, APL Mater., 2020, 8 (8), 080701.
3. “Speeding Up Discovery of Auxetic Zeolite Frameworks by Machine Learning”, R. Gaillac, S. Chibani and F.-X. Coudert, Chem. Mater., 2020, 32 (6), 2653–2663.
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