Research Paradigm of Materials Science Tetrahedra with Artificial Intelligence

TL;DR

This paper reviews the classical material tetrahedron paradigm in materials science and proposes two new AI-driven paradigms to enhance data-driven and AI-augmented research, aiming to refine scientific thinking and technological progress.

Contribution

It introduces two novel AI-based research paradigms for materials science, expanding beyond the classical tetrahedron to integrate AI techniques effectively.

Findings

Proposes Matter-Data-Model-Potential-Agent tetrahedron for AI in materials science

Introduces Data-Architecture-Encoding-Optimization-Inference framework

Discusses the connection and potential of these paradigms for scientific advancement

Abstract

The classical material tetrahedron that represents the Structure-Property-Processing-Performance-Characterization relationship is the most important research paradigm in materials science so far. It has served as a protocol to guide experiments, modeling, and theory to uncover hidden relationships between various aspects of a certain material. This substantially facilitates knowledge accumulation and material discovery with desired functionalities to realize versatile applications. In recent years, with the advent of artificial intelligence (AI) techniques, the attention of AI towards scientific research is soaring. The trials of implementing AI in various disciplines are endless, with great potential to revolutionize the research diagram. Despite the success in natural language processing and computer vision, how to effectively integrate AI with natural science is still a grand challenge, bearing in mind their fundamental differences. Inspired by these observations and limitations, we delve into the current research paradigm dictated by the classical material tetrahedron and propose two new paradigms to stimulate data-driven and AI-augmented research. One tetrahedron focuses on AI for materials science by considering the Matter-Data-Model-Potential-Agent diagram. The other demonstrates AI research by discussing Data-Architecture-Encoding-Optimization-Inference relationships. The crucial ingredients of these frameworks and their connections are discussed, which will likely motivate both scientific thinking refinement and technology advancement. Despite the widespread enthusiasm for chasing AI for science, we must analyze issues rationally to come up with well-defined, resolvable scientific problems in order to better master the power of AI.