Machine learning the thermodynamics of complex materials with ab initio accuracy

• Applicants: Professor Dr. Blazej Grabowski; Dr. Fritz Körmann
• Subject Area: Computer-Aided Design of Materials and Simulation of Materials Behaviour from Atomic to Microscopic Scale
• Term: from 2020 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 429582718

The general aim of the proposed project Mach-Initio is the ab initio investigation of the fundamental physical finite-temperature excitation mechanisms and their mutual coupling effects that determine the basic thermodynamic properties in complex materials. For that purpose, we leverage ideas and knowledge of the PIs from the field of machine learning and the field of ab initio materials design into a single and unique framework. The PIs have successfully collaborated in the recent past providing a solid basis for Mach-Initio. The combination of their expertise into a single joint project guarantees a successful fulfillment of the ambitious goals.Ab initio methods have been successfully applied for many years to calculate the zero Kelvin ground state energy of materials, however, direct ab initio calculations of excitations at finite temperatures are in most cases prohibitively expensive. Recent joint work of the PIs has shown that effective Hamiltonians based on machine-learning potentials, in particular moment tensor potentials (MTPs) and low-rank potentials (LRPs), can be utilized to reduce the computational effort drastically, thus enabling a highly efficient study of the vibrational and configurational excitations with ab initio accuracy. The aim of the present project is to further advance these finite-temperature ab initio approaches based on MTPs and LRPs. In particular, we will develop algorithms for the computation of a highly accurate free energy surface including all relevant excitation mechanisms related to vibrations, configurational entropy, magnetism, and their mutual coupling effects. To this end, a novel type of MTPs will be developed that accounts for the magnetic degrees of freedom (mMTPs). The conceptual and methodological framework of the mMTPs will be developed by the Russian side. The integration of the mMTPs into an ab initio-based thermodynamic methodology including the application to and validation for technologically relevant material systems will be pursued by the German side.

• DFG Programme: Research Grants
• International Connection: Russia
• Partner Organisation: Russian Foundation for Basic Research, until 3/2022
• Cooperation Partner: Professor Dr. Alexander Shapeev, until 3/2022