Session

DescriptionMachine learning (ML) methods have been widely adopted in material science in recent years. While these approaches have been used for years in engineering and science in general, the widespread application in computational materials science is relatively new [1]. For modeling computationally heavy quantum-chemistry calculations, two major approaches can be discriminated. In the first, one tries to replace certain parts of already established frameworks with ML models, e.g., the parameterization of molecular forcefields [2] or the functionals in density-functional theory [3]. The second approach tries to create a surrogate model for prediction of materials properties given only the fingerprints as an input. Recent efforts also focus on the prospects of creating "new" materials from generative models or directly feeding the structural graph to a neural-network approximator [4]. This minisymposium aims to give an overview on some of the most relevant developments concerning ML methods in material science.
References:
[1] Rogers, D.; Hahn, M.; J. Chem. Inf. Model. 2010, 50, 742−754.
[2] Behler, J.; Parrinello, M.; Phys. Rev. Lett. 2007, 98, No. 146401.
[3] Rupp, M.; et al..; Phys. Rev. Lett. 2012, 108, No. 058301.
[4] Xie, T.; Grossman, J. C.; Phys. Rev. Lett. 2018, 120, No. 145301.