A physics-informed neural network-based numerical inverse method for optimization of diffusion coefficients in NiCoFeCr multi principal element alloy

Kumar, H and Dash, A and Paul, A and Bhattacharyya, S (2022) A physics-informed neural network-based numerical inverse method for optimization of diffusion coefficients in NiCoFeCr multi principal element alloy. In: Scripta Materialia, 214 .

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Abstract

The composition-dependent pseudo-binary (PB) interdiffusion coefficients and the main intrinsic diffusion coefficients of all the components at the near equiatomic composition of NiCoFeCr system are estimated following the PB diffusion couple method. These are otherwise impossible to estimate directly following the conventional method. Subsequently, a physics-informed machine learning based numerical inverse method is used to optimize the diffusion parameters in two steps. Initially, optimization is done by developing a good match with the diffusion profiles and estimated interdiffusion coefficients over whole composition range of the diffusion couples. However, a mismatch was found in the extracted intrinsic diffusion coefficients. Therefore, the second level of optimization is done with estimated intrinsic diffusion coefficients at the Kirkendall plane as constraints demonstrating the need for these diffusion parameters for generating a reliable mobility database. The direct estimation and optimization of diffusion coefficients without using thermodynamic details is an added advantage, especially in multicomponent alloy systems. © 2022 Acta Materialia Inc.

Item Type:	Journal Article
Publication:	Scripta Materialia
Publisher:	Acta Materialia Inc
Additional Information:	The copyright for this article belongs to the Acta Materialia Inc.
Keywords:	Chromium alloys; Cobalt alloys; Constrained optimization; Inverse problems; Iron alloys; Numerical methods, Diffusion couple; Diffusion parameters; Interdiffusion coefficients; Intrinsic diffusion; Inverse methods; Multi-component alloy; Neural-networks; Optimisations; PDE-constrained optimization; Pseudo-binaries, Diffusion
Department/Centre:	Division of Mechanical Sciences > Materials Engineering (formerly Metallurgy)
Date Deposited:	22 Jun 2022 05:48
Last Modified:	22 Jun 2022 05:48
URI:	https://eprints.iisc.ac.in/id/eprint/73616

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