2 papers
physics.chem-ph2025
Accelerating Materials Discovery: Learning a Universal Representation of Chemical Processes for Cross-Domain Property Prediction
Mikhail Tsitsvero, Atsuyuki Nakao, Hisaki Ikebata
Experimental validation of chemical processes is slow and costly, limiting exploration in materials discovery. Machine learning can prioritize promising candidates, but existing da…
physics.chem-ph2025
Omics-scale polymer computational database transferable to real-world artificial intelligence applications
Ryo Yoshida, Yoshihiro Hayashi, Hidemine Furuya +103
Developing large-scale foundational datasets is a critical milestone in advancing artificial intelligence (AI)-driven scientific innovation. However, unlike AI-mature fields such a…