Deep Learning Accelerated Multi-Criteria Screening of Chromium-Based A2+B2 Superalloys Across 11 Elements

This repository contains datasets and scripts to reproduce selected datasets and results of the article 'Accelerated discovery of Cr-based A2+B2 superalloys across 11 elements with a deep-learning CALPHAD surrogate'.

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Prerequisites

A requirements.txt file is included for installing the required Python packages (excluding tc-python, which must be installed after Thermo-Calc).

1. Install Thermo-Calc (tested with version 2025b).
2. Install Python packages from requirements.txt (Python 3.9.17).
3. Install tc_python (tc-python==2025.2.30) using the Python wheel provided with Thermo-Calc 2025b.

Note: As Thermo-Calc license do not permit the open release of large calculated datasets, only a 100-composition 'dummy' version of the full set of feasible compositions (roughly 15,000 compositions). Therefore, to reproduce some of the figures in the manuscript, a license to the commercial TCHEA7 database is needed.

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Description of folders

• generate_surrogate_model: contains the scripts to generate Thermo-Calc data and the CALPHAD surrogate model.
• analyze_feasible_compositions: contains the scripts to analyze the feasible compositions (alloy families, VEC-strength scatter plots).
• analyze_experimental_compositions: contains the scripts to visualize DFT-derived properties (strength, D-parameters).
• helper_functions: functions to calculate edge-slip disclocation strength, VEC, and misfit volumes.

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License

This project is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later). See the LICENSE file.

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Citation

• TBA