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Topic: Exploring Materials Informatics: Emerging Technologies Inspired

by the 2024 Nobel Prize

Guest Editors

Prof. Tong-Yi Zhang
Materials Genome Institute, Shanghai University, Shanghai, China.

Prof. Wenhui Duan
Department of Physics, Tsinghua University, Beijing, China.

Prof. William Yi Wang
School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi, China.

Dr. Sheng Sun
Materials Genome Institute, Shanghai University, Shanghai, China.

Special Topic Introduction

The 2024 Nobel Prizes in Physics and Chemistry highlight the revolutionary advances in artificial
intelligence (AI) technologies, which are revolutionizing many scientific disciplines, including materials
science. Innovations in generative models, deep learning, and reinforcement learning are ushering in a new
era in materials discovery, design, and optimization.

These AI-driven approaches are accelerating the development of next-generation materials and enabling
the efficient, sustainable discovery of novel materials with tailored properties. In particular, multi-scale
modeling and data-driven methodologies are providing deeper insights of material behavior across various
scales, from atomic structures to large-scale applications.

This Special Issue celebrates and builds upon the transformative insights inspired by the 2024 Nobel Prizes,
exploring the intersection of AI and materials science, and fostering innovations poised to redefine the future
of materials research. We invite contributions from researchers and innovators in materials informatics and
AI, with a focus on the following topics:
● AI for Materials, AI for Science, and AI for smart manufacturing;
● AI-enabled materials data acquisition and database construction;
● Machine learning models for material property prediction and optimization;
● Self-driving laboratories and experimental automation;
● Multi-scale modeling and simulations powered by AI;
● Data-driven approaches to uncovering fundamental material behaviors;
● Large language models (LLM) and AI agents, along with their applications in material design and
manufacturing.

Journal of Materials Informatics I