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RESEARCH PAPER · Physical Chemistry Chemical Physics · 2025

Harnessing the leading edge: machine learning ventures in chemistry and materials science

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Harnessing the leading edge: machine learning ventures in chemistry and materials scienceOriginalInvestigating Tree‐Based Models Across Positive–Unlabeled Learning Frameworks for Crystal Synthesizability Prediction2Chemistry-Informed Machine Learning Framework for Predicting Structural Properties in Osmabenzene Complexes0Integrating High-Throughput DFT and Machine Learning for Predictive Design of Hydrogen-Donor Solvents in Coal Liquefaction0

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  1. Investigating Tree‐Based Models Across Positive–Unlabeled Learning Frameworks for Crystal Synthesizability Prediction

    Advanced Theory and Simulations · 2026-04-01

    Ayhan Aydın, Ümit Kaya Eryılmaz, Onur Bahattin Alkan, Pınar Kocagöz, Koray Açıcı, Fatih Ekinci, Mehmet Serdar Güzel

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  2. Chemistry-Informed Machine Learning Framework for Predicting Structural Properties in Osmabenzene Complexes

    The Journal of Physical Chemistry Letters · 2026-06-11

    Linchao Zhu, Xin Qin, Jun Chen, Tianmin Wu, Lu Zhang, Zhe‐Ning Chen

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  3. Integrating High-Throughput DFT and Machine Learning for Predictive Design of Hydrogen-Donor Solvents in Coal Liquefaction

    ACS Omega · 2026-06-24

    Runze Zhao, QiZhao Liu, Congfu Lin, Jing Xie, Dao Li, Rongheng Gou, Lei Gong, Shansong Gao

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