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Related Experiment Video

Updated: Jun 8, 2025

Surrogate Model Development for Digital Experiments in Welding

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Interpretable Surrogate Learning for Electronic Material Generation.

Zhilong Wang^1,2,3,4, Sixian Liu^1,2, Kehao Tao^1,2

¹National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, China.

|November 1, 2024

Summary

This summary is machine-generated.

We developed EMGen, an interpretable AI framework for designing electronic materials with specific properties. EMGen rapidly generates materials like Gallium Oxide with wide band gaps for advanced optoelectronic and power electronics applications.

Keywords:

active learning band gap electronic materials ensemble learning first-principles calculations machine learning

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Area of Science:

Materials Science
Artificial Intelligence
Condensed Matter Physics

Background:

Developing novel electronic materials with tailored properties remains a significant challenge.
Existing AI models often lack interpretability, hindering effective materials design.
There is a need for AI tools that can actively generate materials meeting specific performance requirements.

Purpose of the Study:

To introduce EMGen, an interpretable surrogate learning framework for the active design and generation of electronic materials.
To demonstrate EMGen's capability in designing materials with target electronic properties, specifically band gaps.
To showcase the application of EMGen in discovering and optimizing materials for optoelectronic and power electronics.

Main Methods:

Development of an interpretable surrogate learning framework named EMGen.
Utilizing EMGen to screen elements and fractions for desired material properties.
Case study focused on designing electronic materials with specific band gaps.
Creation of a large hybrid functional band gap database.

Main Results:

EMGen achieved benchmarking predictive accuracy and designed a material with a target band gap in just 1.7 minutes.
A comprehensive hybrid functional band gap database was established using EMGen.
EMGen successfully designed Gallium Oxide (Ga2O3) with a wide band gap (>5.0 eV).
The designed Ga2O3 demonstrated enhanced performance for deep ultraviolet (DUV) optoelectronics and power electronics.

Conclusions:

EMGen is an effective and interpretable AI tool for on-demand electronic materials generation.
The framework enables the design of materials with improved properties, such as wide band gaps for DUV applications.
EMGen facilitates breakthroughs in materials discovery for optoelectronics and power electronics, extending the applicability of materials like Ga2O3.