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Faraday Discussions|May 11, 2026
Rethinking catalysis: interpretable AI and description of real-world conditions via materials genesLucas Foppa, Matthias SchefflerDigital Discovery|July 17, 2025
Coherent collections of rules describing exceptional materials identified with a multi-objective optimization of subgroupsLucas Foppa, Matthias SchefflerScientific Data|August 29, 2025
Materials Database from All-electron Hybrid Functional DFT CalculationsAkhil S Nair, Lucas Foppa, Matthias SchefflerFaraday Discussions|May 1, 2026
Interpretable Bayesian optimization for catalyst discoveryAkhil S Nair, Lucas Foppa, Matthias SchefflerJournal of the American Chemical Society|February 20, 2024
Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: An Artificial Intelligence Approach Integrating Theoretical and Experimental DataRay Miyazaki, Kendra S Belthle, Harun Tüysüz, et al.Physical Review Letters|August 12, 2022
Hierarchical Symbolic Regression for Identifying Key Physical Parameters Correlated with Bulk Properties of PerovskitesLucas Foppa, Thomas A R Purcell, Sergey V Levchenko, et al.ACS Catalysis|February 28, 2022
Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial IntelligenceLucas Foppa, Christopher Sutton, Luca M Ghiringhelli, et al.Faraday Discussions|May 14, 2026
Role of monodentate formate in product selectivity for CO2 hydrogenation on Pd-based alloy catalystsIgor Kowalec, Herzain I Rivera-Arrieta, Zhongwei Lu, et al.ACS Catalysis|August 7, 2025
Modeling Time-On-Stream Catalyst Reactivity in the Selective Hydrogenation of Concentrated Acetylene Streams under Industrial Conditions via Experiments and AIJonathan M Mauß, Klara S Kley, Rohini Khobragade, et al.Chemical Society Reviews|January 16, 2015
Benzene partial hydrogenation: advances and perspectivesLucas Foppa, Jairton DupontPageof 20