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Machine learning interatomic potentials for the dynamics of transition metal catalysis

CORDIS · observation · Publication date unknown

Machine learning interatomic potentials for the dynamics of transition metal catalysis Current global challenges, such as the energy and climate crises, elicit a constant demand for new materials with specific properties required for driving the development of novel technologies. Transition metal complexes (TMCs) in particular, are interesting since they can be utilized for a wide range of diverse applications, for example, as catalysts for facilitating the hydrogenation reaction of carbon dioxide to green methanol. Despite recent advances, traditional approaches such as density functional theory and ab initio molecular dynamics to study their properties often suffer from either prohibitively high computational costs or insufficient accuracy. In recent years, machine learning interatomic potentials (MLIPs) have emerged as a promising alternative for studying complex and dynamical chemica

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award

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SIGNED

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EU

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260347.92

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EUR