Mechanics-related tasks often present unique challenges in achieving accurate geometric
and physical representations, particularly for non-uniform structures. Graph neural networks …

Graph neural networks (GNNs) process the graph-structured data using neural networks
and have proven successful in various graph processing tasks. Currently, graph pooling …

Architected materials possessing physico-chemical properties adaptable to disparate
environmental conditions embody a disruptive new domain of materials science. Fueled by …

Single-cell analysis is currently one of the most high-resolution techniques to study biology.
The large complex datasets that have been generated have spurred numerous …

Materials informatics (MI) is aimed to accelerate the materials discovery using computational
intelligence and data science. Progress of MI depends on the strength of database and …

This publication introduces a novel open-access 31P Nuclear Magnetic Resonance (NMR)
shift database. With 14,250 entries encompassing 13,730 distinct molecules from 3,648 …

Property prediction accuracy has long been a key parameter of machine learning in
materials informatics. Accordingly, advanced models showing state-of-the-art performance …

Machine learning interatomic potential (MLIP) overcomes the challenges of high
computational costs in density-functional theory and the relatively low accuracy in classical …

The methanol economy envisioned by Nobel laureate George Olah is growing by leaps and
bounds. This growth is spurred by its burgeoning use not only as a major feedstock for a vast …