Combining materials science, artificial intelligence (AI), physical chemistry, and other
disciplines, materials informatics is continuously accelerating the vigorous development of …

Material innovation plays a very important role in technological progress and industrial
development. Traditional experimental exploration and numerical simulation often require …

In real-world materials research, machine learning (ML) models are usually expected to
predict and discover novel exceptional materials that deviate from the known materials. It is …

Mechanical and thermal properties of materials are extremely important for various
engineering and scientific fields such as energy conversion and energy storage. However …

This paper evaluates the capabilities and limitations of the Generative Pre-trained
Transformer 4 (GPT-4) in chemical research. Although GPT-4 exhibits remarkable …

Fast and accurate crystal structure prediction (CSP) algorithms and web servers are highly
desirable for the exploration and discovery of new materials out of the infinite chemical …

Materials informatics and cheminformatics struggle with data scarcity, hindering the
extraction of significant relationships between structures and properties. The “Ugly Duckling” …

Deep learning (DL) has indeed emerged as a powerful tool for rapidly and accurately
predicting materials properties from big data, such as the design of current commercial Li …

Predicting crystal structure from the chemical composition is one of the most challenging and
long-standing problems in condensed matter physics. This problem resides at the interface …

Many mechanical engineering applications call for multiscale computational modeling and
simulation. However, solving for complex multiscale systems remains computationally …