ArticleACS omega2026
Integrating High-Throughput DFT and Machine Learning for Predictive Design of Hydrogen-Donor Solvents in Coal Liquefaction.
Article in ACS omega, 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
8 authors.
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Abstract
The depletion of conventional oil and gas resources, together with increasing environmental concerns, has renewed interest in direct coal liquefaction, where accurate prediction of reaction energy barriers is critical for mechanistic understanding, process optimization, and the screening of hydrogen-donor solvents. However, the substantial computational cost of density functional theory (DFT) calculations restricts its application in high-throughput exploration of radical-solvent combinations. Here, we develop a reaction-pathway-informed machine learning framework for rapid and accurate energy barrier prediction. By integrating physicochemical descriptors of coal-derived radicals and hydrogen-donor molecules, the baseline model achieves a coefficient of determination (
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