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Updated: Sep 4, 2025

Surrogate Model Development for Digital Experiments in Welding

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Published on: March 28, 2025

Moving closer to experimental level materials property prediction using AI.

Dipendra Jha¹, Vishu Gupta¹, Wei-Keng Liao¹

¹Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA.

Scientific Reports

|July 13, 2022

Summary

Artificial intelligence (AI) combined with density functional theory (DFT) computations can now predict material properties more accurately than DFT alone. This AI-driven approach significantly reduces errors in calculating the formation energy of crystalline materials.

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Area of Science:

Materials Science
Computational Materials Science
Artificial Intelligence in Materials Discovery

Background:

Traditional methods for understanding crystalline materials, such as experiments and density functional theory (DFT) computations, face limitations including high costs, time consumption, and significant discrepancies.
Existing predictive models based on DFT inherit these inaccuracies, hindering rapid screening of new materials.
Accurate prediction of material properties, like formation energy, is crucial for efficient materials discovery.

Purpose of the Study:

To demonstrate how artificial intelligence (AI) can enhance the accuracy of computing material properties when integrated with DFT.
To address the critical materials science task of predicting the formation energy of a material based on its structure and composition.
To outperform DFT computations in predicting formation energy using an AI-driven approach.

Main Methods:

Development and application of an AI model trained using DFT data.
Evaluation of the AI model's performance on an experimental hold-out test set.
Comparison of AI predictions against traditional DFT computations for formation energy.

Main Results:

The AI model achieved a mean absolute error (MAE) of 0.064 eV/atom in predicting formation energy on an experimental test set.
AI predictions demonstrated significantly lower discrepancies compared to DFT computations for the same task.
This marks the first instance where AI has outperformed DFT in predicting material formation energy.

Conclusions:

AI, when leveraged with DFT, offers a more accurate method for computing crystalline material properties than DFT alone.
The developed AI approach provides a powerful tool for accurate and efficient materials screening, overcoming limitations of traditional methods.
This study establishes a new benchmark for predictive accuracy in computational materials science.