首页|Max-Planck-Institute for Coal Research Reports Findings in Artificial Intelligence (Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: An Artificial Intelligence Approach Integrating Theoretical and Experimental Data)
Max-Planck-Institute for Coal Research Reports Findings in Artificial Intelligence (Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: An Artificial Intelligence Approach Integrating Theoretical and Experimental Data)
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New research on Artificial Intelligence is the subject of a report. According to news reporting out of Mulheim an der Ruhr, Germany, by NewsRx editors, research stated, "Designing materials for catalysis is challenging because the performance is governed by an intricate interplay of various multiscale phenomena, such as the chemical reactions on surfaces and the materials' restructuring during the catalytic process. In the case of supported catalysts, the role of the support material can be also crucial." Our news journalists obtained a quote from the research from Max-Planck-Institute for Coal Research, "Here, we address this intricacy challenge by a symbolic-regression artificial intelligence (AI) approach. We identify the key physicochemical parameters correlated with the measured performance, out of many offered candidate parameters characterizing the materials, reaction environment, and possibly relevant underlying phenomena. Importantly, these parameters are obtained by both experiments and ab initio simulations. The identified key parameters might be called 'materials genes', in analogy to genes in biology: they correlate with the property or function of interest, but the explicit physical relationship is not (necessarily) known. To demonstrate the approach, we investigate the CO hydrogenation catalyzed by cobalt nanoparticles supported on silica. Crucially, the silica support is modified with the additive metals magnesium, calcium, titanium, aluminum, or zirconium, which results in six materials with significantly different performances. These systems mimic hydrothermal vents, which might have produced the first organic molecules on Earth. The key parameters correlated with the CHOH selectivity reflect the reducibility of cobalt species, the adsorption strength of reaction intermediates, and the chemical nature of the additive metal. By using an AI model trained on basic elemental properties of the additive metals (e.g., ionization potential) as physicochemical parameters, new additives are suggested."
Mulheim an der RuhrGermanyEuropeArtificial IntelligenceCobaltEmerging TechnologiesGeneticsHeavy MetalsMachine LearningTransition Elements
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