ICAMS / Interdisciplinary Centre for Advanced Materials Simulation


Application of machine learning methods to support the development of the 3d generation CALPHAD databases

I. Roslyakova, A. Obaied, S. Zomorodpoosh.

17th Discussion Meeting on Thermodynamics of Alloys (TOFA), Bad Staffelstein, Germany, (2020)

Recently, one of the most intriguing topic in the CALPHAD community is the development of the third generation CALPHAD databases and their application to re-assessment of binary and ternary systems from 0K [1-5]. During the development of the third generation of CALPHAD databases, not only newly available DFT [4] and experimental data [5] should be considered to build a new pure elements database from 0 K, but the existing physical laws [6] and newly discovered relationships [7-9] between relevant thermodynamic properties should be established and integrated. Taking into account that the classical re-assessment procedure of high order thermodynamic systems is very time consuming, a combination of machine-learning (ML) methods with the well-established CALPHAD-type assessment will be presented as one of possible solution [10, 11]. In this work, we will show several successful partial applications of machine learning to accelerate and support the development the 3d generation CALPHAD database from 0K. References: 1. I. Roslyakova, et al., CALPHAD 55, 165-180, (2016). 2. Y. Jiang, et al., CALPHAD 62, 109-118, (2018) 3. Y. Jiang, et al., Journal of Materials Research, 110, 797-807, (2019) 4. S. Bigdeli, et al., CALPHAD 65, 79–85, (2019) 5. A. Khvan, et al. CALPHAD 60, 144-155, (2018) 6. G. Grimvall, Thermodynamic properties of materials (1986). 7. C. A. Becker, Phys. Status Solidi B 251, 1, 33–52 (2014) 8. A. Obaied, et al., Calphad 69, 101762, (2020) 9. D. Sergeev, et al., J. Chem. Thermodynamics, 134, 187-194, (2019) 10. B. Bocklund, et al., MRS Communications: Artificial Intelligence Research Letter, 9, 618-627, (2019) 11. N. H. Paulson, et al., CALPHAD 68, 1-9, (2020)

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