Thermodynamics-Informed Machine Learning of Organic Electrode Material Solubility in Nonaqueous Electrolytes.

Abigail M Houser, Madhav R Muthyala, Farshud Sorourifar, Justin Xu, Joel A Paulson, Shiyu Zhang

Abstract read

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Article in The journal of physical chemistry. B, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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2 · The registry

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5 · Who and what money

Authors and funding

6 authors.

Abigail M HouserDepartment of Chemistry & Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, Ohio 43210, United States.ORCID 0009-0003-8090-7082

Madhav R MuthyalaDepartment of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, Wisconsin 53706, United States.ORCID 0000-0003-3781-6743

Farshud SorourifarDepartment of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, Wisconsin 53706, United States.ORCID 0000-0002-1611-8658

Justin XuDepartment of Chemistry & Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, Ohio 43210, United States.

Joel A PaulsonDepartment of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, Wisconsin 53706, United States.ORCID 0000-0002-1518-7985

Shiyu ZhangDepartment of Chemistry & Biochemistry, The Ohio State University, 100 West 18th Avenue, Columbus, Ohio 43210, United States.ORCID 0000-0002-2536-4324

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Organic electrode materials (OEMs) based on abundant elements, such as C, N, O, and S, represent a promising class of sustainable alternatives to traditional transition-metal-based cathodes. However, discovery of new OEMs remains slow and resource-intensive because the design process is dominated by empirical trial-and-error. Although recent machine-learning approaches have accelerated the identification of redox-active compounds, the prediction of OEM solubility─a critical property governing cycle life─remains a major bottleneck. Here, we developed a physics-informed model for predicting OEM solubility using solvation energies (Δ

Identifiers

PMID42143775

DOI10.1021/acs.jpcb.6c01822

PMCPMC13224181

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