CORDIS Project
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META-LEARN aims to enhance the construction of machine-learning interatomic potentials for simulating materials at a quantum level. By utilizing a meta-learning framework, it seeks to optimize the accuracy and efficiency of simulations, enabling broader access to advanced materials research, particularly in sustainabil…
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 pro…
UNIVERSITY OF STUTTGART
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