Health AI Central
Share:
Peer-Reviewed Publication
ACS Omega2026;11(33):49205-49213.August 25, 2026Journal Article

Accelerating Electrolyte Discovery Using Generative Models and Molecular Dynamics Simulations.

Varisara Deerattrakul1,2, Salatan Duangdangchote3, Adisak Boonchun4, Pawin Iamprasertkun1,2
1School of Bio-Chemical Engineering and Technology, Sirindhron International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand.
2Research Unit in Sustainable Electrochemical Intelligent, Thammasat University, Pathum Thani 12120, Thailand.
3Maple Synapse Inc, North York, Ontario M2K 0C4, Canada.
4Department of Physics, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand.
PubMed PMC DOI PDF

Abstract

Operating lithium-ion batteries (LIBs) at high voltages and low temperatures demands electrolyte solvents with highly tailored electronic properties. Because traditional discovery is limited by historical solvent libraries and human intuition, expanding the accessible chemical space requires advanced computational methods. Here, we deploy the generative machine learning model G-SchNet, trained on…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.