Work

A Machine Learning Inversion Scheme for Determining Interaction from Scattering

Year: 2021

Type: preprint

Abstract: We outline a machine learning strategy for determining the effective interaction in the condensed phases of matter using scattering. Via a case study of colloidal suspensions, we showed that the effective potential can be probabilistically inferred from the scattering spectra without any restriction imposed by model assumptions. Comparisons to existing parametric approaches demonstrate the superior performance of this method in accuracy, efficiency, and applicability. This method can effectively enable quantification of interaction in highly correlated systems using scattering and diffraction experiments.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2103.14883

Authors: Tung, Chi-Huan, Chang, Shou-Yi, Carrillo, Jan-Michael, Sumpter, Bobby G., Do, Changwoo, Chen, Wei-Ren

Institutions: National Tsing Hua University, Oak Ridge National Laboratory, Center for Nanophase Materials Sciences

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, State University of New York, Office of Science, University at Albany, Basic Energy Sciences, Oak Ridge National Laboratory

Awards: G1019100964, G121915436, G1645119126, G203625172, G2988778103, G4423657506, G4573907496, G6089394659, G675320460, G8053029320, G807938293, G8269158468, G8906985441

OpenAlex ID: W4297791376

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