Discovery of heterogeneous material catalysts using deep learning

Machine Learning


  • Stamenkovic, VR, Strmcnik, D., Lopes, PP & Markovic, NM Energy and fuels from electrochemical interfaces. nut. meter. 1657–69 (2017).

    Article CAS Google Scholar

  • Lagadec, MF & Grimaud, A. Water electrolysis devices with closed and open electrochemical systems. nut. meter. 191140–1150 (2020).

    Article CAS PubMed Google Scholar

  • RM Block et al. Use nature’s blueprint to scale catalysis with Earth’s abundant metals. science 369eabc3183 (2020).

    Article CAS PubMed PubMed Central Google Scholar

  • García de Arquel, FP and other COs2 Electrolyze to produce multi-carbon products with activity greater than 1 A cm−2. science 367661–666 (2020).

    Article PubMed Google Scholar

  • Ding, CT et al. Multisite electrocatalyst for hydrogen evolution in neutral media by destabilization of water molecules. nut. energy 4107–114 (2019).

    Article CAS Google Scholar

  • Jing, W. et al. Surface and interfacial coordination chemistry learned from models of heterogeneous metal nanocatalysts, from atomically dispersed catalysts to atomically precise clusters. Chemistry. pastor one two three5948–6002 (2022).

    Article PubMed Google Scholar

  • Deng, D. et al. Catalysis by two-dimensional materials and their heterostructures. nut. nanotechnology. 11218–230 (2016).

    Article CAS PubMed Google Scholar

  • Dionigi, F. et al. In situ structure and catalytic mechanism of NiFe and CoFe layered double hydroxides during oxygen evolution. nut. common. 112522 (2020).

    Article CAS PubMed PubMed Central Google Scholar

  • Bergman, A. et al. A unifying structural motif of the catalytic active state of co(oxyhydr)oxides during electrochemical oxygen evolution reactions. nut. catarrh. 1711–719 (2018).

    Article CAS Google Scholar

  • Suen, NT et al. Electrocatalysts for oxygen evolution reactions: recent developments and future prospects. Chemistry. social pastor 46337–365 (2017).

    Article CAS PubMed Google Scholar

  • Moon, J. et al. Active learning leads to the discovery of 4-metal perovskite oxides as potent oxygen-evolving electrocatalysts. nut. meter. twenty three108–115 (2024).

    Article CAS PubMed Google Scholar

  • Zhang, B. et al. Uniformly dispersed polymetallic oxygen generating catalyst. science 352333–337 (2016).

    Article CAS PubMed Google Scholar

  • Han, L. et al. Single atom library for guided monometal and concentration complex multimetal designs. nut. meter. twenty one681–688 (2022).

    Article CAS PubMed Google Scholar

  • Yang J., Li, W., Wang, D. & Li, Y. Electrometal-support interaction of single-atom catalysts and applications in electrocatalysis. Advanced meter. 322003300 (2020).

    Article CAS Google Scholar

  • Ji, S. et al. Chemical synthesis of single atom site catalysts. Chemistry. pastor 12011900–11955 (2020).

    Article CAS PubMed Google Scholar

  • Lazaridou, A. et al. Recognize the best catalyst for your reaction. nut. Rev. Chem. 7287–295 (2023).

    Article CAS PubMed Google Scholar

  • Martín, AJ, Mitchell, S., Mondelli, C., Jaydev, S., Pérez-Ramírez, J. A unified view of catalyst deactivation. nut. catarrh. 5854–866 (2022).

    Article Google Scholar

  • Yao Y. et al. High-entropy nanoparticles: Synthesis, structure, property relationships and data-driven discoveries. science 376ebn3103 (2022).

    Article CAS PubMed Google Scholar

  • Zhong, M. et al. Accelerating the discovery of CO2 Electrocatalysis using active machine learning. nature 581178–183 (2020).

    Article CAS PubMed Google Scholar

  • Saart, AF et al. Prediction of highly selective catalysts using computer-driven workflows and machine learning. science 363eau5631 (2019).

    Article CAS PubMed PubMed Central Google Scholar

  • Wall, C.B. et al. Accelerate the design and synthesis of multi-element heterostructures using machine learning. Science. advanced 7eabj5505 (2021).

    Article CAS PubMed PubMed Central Google Scholar

  • Sutton, C. et al. Identifying areas of applicability of machine learning models for materials science. nut. common. 114428 (2020).

    Article CAS PubMed PubMed Central Google Scholar

  • Moosavi, SM, Jablonka, KM, Smit, B. The role of machine learning in materials understanding and design. J.Am. Chemistry. society 14220273–20287 (2020).

    Article CAS PubMed PubMed Central Google Scholar

  • Batra, R. Accurate machine learning in materials science is facilitated by using diverse data sources. nature 589524–525 (2021).

    Article CAS PubMed Google Scholar

  • Gupta, V. et al. A cross-property deep transfer learning framework to enhance predictive analysis of small materials data. nut. common. 126595 (2021).

    Article CAS PubMed PubMed Central Google Scholar

  • New Jersey Szymanski et al. An autonomous laboratory to accelerate the synthesis of new materials. nature 62486–91 (2023).

    Article CAS PubMed PubMed Central Google Scholar

  • Moore, M. et al. A basic model for generalist medical artificial intelligence. nature 616259–265 (2023).

    Article CAS PubMed Google Scholar

  • Boiko, D.A., MacKnight, R., Kline, B. & Gomes, G. Autonomous chemical research using large-scale language models. nature 624570–578 (2023).

    Article CAS PubMed PubMed Central Google Scholar

  • Gregoire, JM, Zhou, L., Haber, JA Combinatorial synthesis for AI-driven materials discovery. nut. Synth. 2493–504 (2023).

    Article CAS Google Scholar

  • Batra, R., Song, L. & Ramprasad, R. A new material intelligence ecosystem powered by machine learning. nut. Pastor Mater. 6655–678 (2021).

    Article Google Scholar

  • Hippalgaonkar, K. et al. Knowledge-integrated machine learning for materials: Lessons from gameplay and robotics. nut. Pastor Mater. 8241–260 (2023).

    Article Google Scholar

  • Louie, SG, Chan, YH, da Jornada, FH, Li, Z. & Qiu, DY Computational discovery and understanding of materials. nut. meter. 20728–735 (2021).

    Article CAS PubMed Google Scholar

  • Esterhuizen, JA, Goldsmith, BR & Linic, S. Interpretable machine learning for knowledge generation in heterogeneous catalysis. nut. catarrh. 5175–184 (2022).

    Article Google Scholar

  • Fedick, N. et al. Extend machine learning beyond interatomic potentials to predict molecular properties. nut. Rev. Chem. 6653–672 (2022).

    Article CAS PubMed Google Scholar

  • Zhao, M. & Xia, Y. Crystal phase and surface structure engineering of ruthenium nanocrystals. nut. Pastor Mater. 5440–459 (2020).

    Article CAS Google Scholar

  • Bai, L., Hsu, CS, Alexander, DTL, Chen, HM & Hu, X. Diatomic catalysts as molecular platforms for heterogeneous oxygen evolution electrocatalysis. nut. energy 61054–1066 (2021).

    Article CAS Google Scholar

  • Chen, PC et al. Design of interfaces and heterostructures in multi-element nanoparticles. science 363959–964 (2019).

    Article CAS PubMed Google Scholar

  • Yoo, S. et al. Low-temperature atomic metal deposition for efficient dual-site integrated photocatalysts. Advanced meter. 37e06402 (2025).

    Article CAS PubMed Google Scholar

  • Lever, J., Krzywinski, M., Altman, N. Model selection and overfitting. nut. method 13703–704 (2016).

    Article CAS Google Scholar

  • Xu, H., Cheng, D., Cao, D. & Zeng, XC Revisiting universal principles for rational design of single-atom electrocatalysts. nut. catarrh. 7207–218 (2024).

    Article CAS Google Scholar

  • Pan, Y. et al. Direct evidence for enhanced oxygen evolution on perovskites due to enhanced lattice oxygen participation. nut. common. 112002 (2020).

    Article CAS PubMed PubMed Central Google Scholar

  • Papa, V., Cao, Y., Spannenberg, A., Junge, K. & Beller, M. Development of practical nonmetallic catalysts for the hydrogenation of N-heteroarenes. nut. catarrh. 3135–142 (2020).

    Article CAS Google Scholar

  • Gallegos, M., Vasilev-Galindo, V., Poltavsky, I., Martín-Pendas, Á. & Tkatchenko, A. Explainable chemical artificial intelligence with accurate machine learning of real-space chemical descriptors. nut. common. 154345 (2024).

    Article CAS PubMed PubMed Central Google Scholar

  • Selvaraj, RR et al. Grad-CAM: Visual explanation from deep networks via gradient-based localization. internal. J. Compute. Vis. 128336–359 (2020).

    Article Google Scholar

  • Zhao, JW, Li, Y., Luan, D. & Lou, XW Structural evolution and catalytic mechanism of perovskite oxides in electrocatalysts. Science. advanced 10eadq4696 (2024).

    Article CAS PubMed PubMed Central Google Scholar

  • Lin, X et al. Machine learning-assisted dual atom site design with interpretable descriptors to unify electrocatalytic reactions. nut. common. 158169 (2024).

    Article CAS PubMed PubMed Central Google Scholar

  • Tong, Y. et al. Spin state control of perovskite cobaltite to improve oxygen evolution activity. chemistry 3812–821 (2017).

    Article CAS Google Scholar

  • Lu, M. et al. By controlling the oxygen defect content of perovskite, we artificially control the oxygen evolution reaction mechanism by electrocatalyst. Science. advanced 8eabq3563 (2022).

    Article CAS PubMed PubMed Central Google Scholar

  • McCrory, CCL, Jung, S., Peters, JC & Jaramillo, TF Benchmarking heterogeneous electrocatalysts for oxygen evolution reactions. J.Am. Chemistry. society 13516977–16987 (2013).

    Article CAS PubMed Google Scholar

  • Moon, J. SAC2025. Zenodo https://doi.org/10.5281/zenodo.19133272 (2026).



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