Integrated Computational Engineering of High-performance Materials
Title: Integrated Computational Engineering of High-performance Materials
SNIC Project: SNIC 2022/5-105
Project Type: SNIC Medium Compute
Principal Investigator: Pavel Korzhavyi <>
Affiliation: Kungliga Tekniska högskolan
Duration: 2022-03-01 – 2023-03-01
Classification: 20506 10402 10304


The purpose of this proposal to allocate supercomputer resources for research studies in Computational Materials Science. The calculations during year 2022 will be performed according to the research plans of several projects supported by the Swedish Governmental Agency for Innovation Systems (Vinnova), Industry and KTH: • “Hero-m 2 Innovation” (Vinnova Competence Center 2016-00668), 2017-2022. Funding source: Vinnova, Swedish Industry, and KTH; • “Use of Machine Learning for seamless ab initio input in Calphad magnetic models”, internal grant by the Scientific Advisory Board of Hero-m 2i Competence Center, KTH; • Structure and Mobility of Defects in Copper (continuation) 2022. Funding source: Swedish Nuclear Fuel and Waste Management Company SKB. Within the Hero-m 2i project, the Applicant is responsible for Generic Ab-initio project, which involves industrial partners from Thermo-Calc Software and Sandvik. This year, additional studies of phase stability in ternary Fe-Cr-Ni alloys (using machine learning) will be conducted at Hero-m 2i Competence Center. Defects in copper will be studied according to the project with SKB. The planned studies deal with computational modeling of lattice defects (point defects, surfaces, interfaces, and dislocations) and other types of disorder (vibrational, electronic, or magnetic) in multicomponent alloys of 3d metals (Fe, Co, Ni, and Cu) such as steels, superalloys, and metal--ceramic composites based on cubic or hexagonal MeC. Intrinsic defects, as well as impurities or minor alloying additions may have strong impact on the mechanical properties and performance of these alloys. Uncovering the atomic mechanisms of such influence, using advanced theoretical models, is the primary scientific goal of the present project. This goal will be acheived by conducting atomistic simulations for the system of interest. In these simulations, thermally activated degrees of freedom (electronic, magnetic, vibrational, and configurational) will be taken into account to compute the Helmholtz free energy as a function of temperature and volume, from which the thermal properties of the considered materials will be derived. The project is planned to proceed along the following two main research directions: Direction 1: High-performance alloy optimization using free energy modeling Task 1.1 Magnetic model development for ternary Fe-Cr-Ni alloys using machine learning Task 1.2 Ab initio modeling of sigma-phase stability in ternary Fe-Ni-Cr system Direction 2: Structure and dynamics of lattice defects in materials Task 2.1. Ab-initio/classical MD studies of defects and diffusion in metal--metalloid compounds Task 2.2 Dynamics at extended lattice defects in alloys and at interfaces in composite materials These computational studies are expected to accelerate computer-aided optimization of the compositions the heat treatment procedures for new grades of steel, superalloys, and refractory ceramics, in order adapt these materials to novel applications in which unusual combinations of properties are required. Re-optimization of materials is necessary in connection with the global challenges of today's world (climate issues, nature pollution, criticality of raw materials, etc.) that are constantly setting new restrictions on materials' composition and their manufacturing processes.