Predictive modeling of Néel temperature in austenitic alloys using CALPHAD and data analytics

Rangasayee Kannan; Peeyush Nandwana

doi:10.1016/j.scriptamat.2025.116773

Predictive modeling of Néel temperature in austenitic alloys using CALPHAD and data analytics

Research output: Contribution to journal › Article › peer-review

Abstract

The Néel temperature is a crucial yet often overlooked parameter in calculating the stacking fault energy (SFE) of austenitic alloys. Several empirical equations have been proposed to estimate the Néel temperature of austenitic alloys, which are then used to calculate the SFE and explain deformation mechanisms. However, these empirical equations, typically derived using linear regression algorithms, are often simplistic and may fail to capture the complex interactions among multiple alloying elements that influence the Néel temperature. Moreover, their applicability is usually limited to specific compositional ranges. In this study, we propose a CALPHAD based approach and develop a surrogate decision tree based regression model capable of capturing the interactions among multiple alloying elements to predict the Néel temperature. Predictions from both the CALPHAD approach and the regression model show close agreement with experimental measurements reported in the literature. The implications of accurate Néel temperature predictions on the calculated SFE and deformation mechanisms are also discussed.

Original language	English
Article number	116773
Journal	Scripta Materialia
Volume	266
DOIs	https://doi.org/10.1016/j.scriptamat.2025.116773
State	Published - Sep 1 2025

Funding

This research is sponsored by US Department of Energy Advanced Materials and Manufacturing Technologies Office. Research was performed at the U.S. Department of Energy's Manufacturing Demonstration Facility, located at Oak Ridge National Laboratory. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. Notice of Copyright: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

Keywords

Austenitic alloys
CALPHAD
Cryogenic
Data analytics
Néel temperature

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10.1016/j.scriptamat.2025.116773

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title = "Predictive modeling of N{\'e}el temperature in austenitic alloys using CALPHAD and data analytics",

abstract = "The N{\'e}el temperature is a crucial yet often overlooked parameter in calculating the stacking fault energy (SFE) of austenitic alloys. Several empirical equations have been proposed to estimate the N{\'e}el temperature of austenitic alloys, which are then used to calculate the SFE and explain deformation mechanisms. However, these empirical equations, typically derived using linear regression algorithms, are often simplistic and may fail to capture the complex interactions among multiple alloying elements that influence the N{\'e}el temperature. Moreover, their applicability is usually limited to specific compositional ranges. In this study, we propose a CALPHAD based approach and develop a surrogate decision tree based regression model capable of capturing the interactions among multiple alloying elements to predict the N{\'e}el temperature. Predictions from both the CALPHAD approach and the regression model show close agreement with experimental measurements reported in the literature. The implications of accurate N{\'e}el temperature predictions on the calculated SFE and deformation mechanisms are also discussed.",

keywords = "Austenitic alloys, CALPHAD, Cryogenic, Data analytics, N{\'e}el temperature",

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AU - Kannan, Rangasayee

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Y1 - 2025/9/1

N2 - The Néel temperature is a crucial yet often overlooked parameter in calculating the stacking fault energy (SFE) of austenitic alloys. Several empirical equations have been proposed to estimate the Néel temperature of austenitic alloys, which are then used to calculate the SFE and explain deformation mechanisms. However, these empirical equations, typically derived using linear regression algorithms, are often simplistic and may fail to capture the complex interactions among multiple alloying elements that influence the Néel temperature. Moreover, their applicability is usually limited to specific compositional ranges. In this study, we propose a CALPHAD based approach and develop a surrogate decision tree based regression model capable of capturing the interactions among multiple alloying elements to predict the Néel temperature. Predictions from both the CALPHAD approach and the regression model show close agreement with experimental measurements reported in the literature. The implications of accurate Néel temperature predictions on the calculated SFE and deformation mechanisms are also discussed.

AB - The Néel temperature is a crucial yet often overlooked parameter in calculating the stacking fault energy (SFE) of austenitic alloys. Several empirical equations have been proposed to estimate the Néel temperature of austenitic alloys, which are then used to calculate the SFE and explain deformation mechanisms. However, these empirical equations, typically derived using linear regression algorithms, are often simplistic and may fail to capture the complex interactions among multiple alloying elements that influence the Néel temperature. Moreover, their applicability is usually limited to specific compositional ranges. In this study, we propose a CALPHAD based approach and develop a surrogate decision tree based regression model capable of capturing the interactions among multiple alloying elements to predict the Néel temperature. Predictions from both the CALPHAD approach and the regression model show close agreement with experimental measurements reported in the literature. The implications of accurate Néel temperature predictions on the calculated SFE and deformation mechanisms are also discussed.

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Predictive modeling of Néel temperature in austenitic alloys using CALPHAD and data analytics

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Keywords

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