Work

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

Year: 2026

Type: article

Abstract: ) exhibit large systematic errors, thereby limiting the overall predictive accuracy based on first-principles thermodynamics alone. To overcome this limitation, we integrated the physically meaningful computed descriptors into our recently developed symbolic regression framework, SyMANTIC, which learns a corrective model for solubility prediction in nonaqueous electrolytes. Despite the modest data set size, the resulting hybrid model shows good predictive performance under cross-validation and on held-out test compounds, illustrating that this framework can recover compact, chemically meaningful structure-property relationships and offers a new path toward rational design of low-solubility OEMs with reduced reliance on trial-and-error experimentation.

Source: The Journal of Physical Chemistry B

DOI: 10.1021/acs.jpcb.6c01822

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

Institutions: The Ohio State University, University of Wisconsin–Madison

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Ohio State University, Division of Materials Research, Division of Chemical, Bioengineering, Environmental, and Transport Systems

Awards: G3062877667, G5351689335