SOURCE-LINKED INTELLIGENCE

Meta-Learned Machine-Learning Interatomic Potentials for Ab initio Engineering of Chemical and Microstructural Complexity

CORDIS · observation · Publication date unknown

l complexity: Multicomponent H-storage and coating materials. META-LEARN will encode the knowledge of the MLIP construction and make it openly available via the MLIP-COPILOT, a knowledge-graph-based artificial intelligence tool. The MLIP-COPILOT will provide the optimal combinations of MLIP algorithms, hyperparameters, training datasets, and training sequences for different materials and simulation tasks. The MLIP-COPILOT will remain flexibly extensible for the community beyond the project’s scope, allowing for the addition of new types of simulations, materials, and MLIPs. machine learning, interatomic potentials, ab initio simulations, large-scale atomistic simulations, thermodynamics, high entropy alloys, molecular dynamics

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recordType

award

status

SIGNED

region

EU

value

2500000

unit

EUR