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Accurate Surface Chemistry Enabled by Neural NeTworks

CORDIS · observation · Publication date unknown

ims to overcome this challenge by developing a computational framework that can accurately simulate electronic excitations at surfaces and their subsequent dynamics at the atomic scale by integrating machine learning, multiscale modelling, density functional theory, and multiconfigurational wavefunction theory. These advances will unlock new understanding across photochemistry, electrochemistry, spectroscopy, and materials science. The fellowship will merge the researcher’s expertise in multiconfigurational methods with the host’s leadership in data-driven approaches, creating a unique interdisciplinary profile. ASCENNT will deliver both conceptual breakthroughs and practical tools while preparing the researcher to launch an independent career as an innovative leader in computational material chemistry. Multiconfigurational Wavefunction Theory, Density Functional Embedding Theory, Multilevel Embedded Simulations, Machine Learning Accelerted Excited State Dynamics

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recordType

award

status

SIGNED

region

EU

value

260347.92

unit

EUR