Work

Artificial intelligence-guided design of high-entropy materials from bulk to nanoscale

Year: 2026

Type: review

Abstract: ), and valence electron concentration (VEC), AI models enable researchers to perform composition and property optimization more easily than ever before. The AI-guided frameworks, despite their limitations like the lack of data, can enable autonomous and high-throughput design of HEMs. This viewpoint reflects recent developments, examples of energy and catalytic processes, and the future outlook and the potential transformative nature of AI to provide next-generation high performance entropy-stabilized materials.

Source: Nanoscale Advances

DOI: 10.1039/d6na00479b

Authors: Bhavana Gupta, Shashank Sundriyal, Sanjeev Kumar, Prashant Kumar

Institutions: Heidelberg University, University of Petroleum and Energy Studies, Central Power Research Institute

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,962

OpenAlex ID: W7221344740