首页|Italy National Research Council Institute of Chemistry of Organometallic Compoun ds Reports Findings in Machine Learning (Machine-Learning-Accelerated DFT Confor mal Sampling of Catalytic Processes)
Italy National Research Council Institute of Chemistry of Organometallic Compoun ds Reports Findings in Machine Learning (Machine-Learning-Accelerated DFT Confor mal Sampling of Catalytic Processes)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Pisa, Italy, by NewsRx correspondents, research stated, “Computational modeling of catalytic processes at gas/solid interfaces plays an increasingly important role in chemi stry, enabling accelerated materials and process optimization and rational desig n. However, efficiency, accuracy, thoroughness, and throughput must be enhanced to maximize its practical impact.” Our news editors obtained a quote from the research from the Italy National Rese arch Council Institute of Chemistry of Organometallic Compounds, “By combining i nterpolation of DFT energetics via highly accurate Machine-Learning Potentials w ith conformal techniques for building the training database, we present here an original approach (that we name Conformal Sampling of Catalytic Processes, CSCP) , to accelerate and achieve an accurate and thorough sampling of novel systems b y exporting existing information on a worked-out case. We use methanol decomposi tion (of interest in the field of hydrogen production and storage) as a test cat alytic reaction. Starting from worked-out Pt-based systems, we show that after o nly two iterations of active-learning CSCP is able to provide reaction energy di agrams for a set of 7 diverse systems (Pd, Ni, Au, Ag, Cu, Co, Fe) leading to DF T-accuracy-level predictions. Cases exhibiting a change in adsorption sites and mechanisms are also successfully reproduced as tests of catalytic path modificat ion.”
PisaItalyEuropeCyborgsEmerging T echnologiesMachine Learning
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