The fourth paradigm of science has achieved great success in material discovery and it highlights the sharing and interoperability of data. However, most material data are scattered among various research institutions, and a big data transmission wil...
Immense effort has been exerted in the materials informatics community towards enhancing the accuracy of machine learning (ML) models; however, the uncertainty quantification (UQ) of state-of-the-art algorithms also demands further development. Most ...
Accurate ab initio calculations are of fundamental importance in physics, chemistry, biology, and materials science, which have witnessed rapid development in the last couple of years with the help of machine learning computational techniques such as...
Journal of chemical information and modeling
36216342
In recent years, there has been a rapid growth in the use of machine learning in material science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a functi...
The journal of physical chemistry letters
36579474
Currently, computational materials science involves human-computer interaction through coding in software or neural networks. There is still no direct way for human intelligence endorsement. The digitalization of human intelligence should be the ulti...
Journal of chemical information and modeling
38084508
The artificial intelligence (AI) tools based on large-language models may serve as a demonstration that we are reaching a groundbreaking new paradigm in which machines themselves will generate knowledge autonomously. This statement is based on the as...
Journal of chemical information and modeling
38237025
The pursuit of designing smart and functional materials is of paramount importance across various domains, such as material science, engineering, chemical technology, electronics, biomedicine, energy, and numerous others. Consequently, researchers ar...
Journal of chemical information and modeling
39569801
In this study, we introduced Matini-Net, which is a versatile framework for feature engineering and automated architecture design for materials informatics research using deep neural networks. Matini-Net provides the flexibility to design feature-bas...
Journal of chemical information and modeling
39488852
Traditional methods of materials discovery, often relying on intuition and trial-and-error experimentation, are time-consuming and limited in their ability to explore the vast design space effectively. The emergence of machine learning (ML) as a powe...