2 citations · 2 across the 4 of their papers we have counts for
5 papers
Simulation-Supervised Foundation Models for Retention Time Prediction in High-Performance Liquid Chromatography beyond Experimental Data Coverage
Stephen Wu, Yufeng Han, Yasuhiro Mito +5
Accurate prediction of high-performance liquid chromatography (HPLC) retention times (RTs) across diverse molecules and chromatographic methods remains challenging because experime…
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…
Machine Learning for Polymer Chemical Resistance to Organic Solvents
Shogo Kunieda, Mitsuru Yambe, Hiromori Murashima +6
Predicting the chemical resistance of polymers to organic solvents is a longstanding challenge in materials science, with significant implications for sustainable materials design…
SPACIER: On-Demand Polymer Design with Fully Automated All-Atom Classical Molecular Dynamics Integrated into Machine Learning Pipelines
Shun Nanjo, Arifin, Hayato Maeda +5
Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems. First-principles calculations and other computer expe…
Scaling Law of Sim2Real Transfer Learning in Expanding Computational Materials Databases for Real-World Predictions
Shunya Minami, Yoshihiro Hayashi, Stephen Wu +6
To address the challenge of limited experimental materials data, extensive physical property databases are being developed based on high-throughput computational experiments, such…