ERC Advanced Grant · 2024
Materials engineers have dreamed for decades of optimizing materials starting from the quantum mechanical laws of nature. Mastering the inherent chemical and microstructural complexity promises access to outstanding properties of structural and functional materials. Machine-learning interatomic potentials (MLIPs) ignited hope by offering quantum-mechanical accuracy for systems with many atoms. However, the full capacity of MLIPs remains untapped due to the complexity of the MLIP construction process and the required simulations. META-LEARN will cut the MLIP Gordian knot and raise MLIP construction to the next level. Our vision is a meta-learning framework that unleashes the full strength of…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.