53 citations · 127 across the 10 of their papers we have counts for
10 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…
TabPFN Extensions for Interpretable Geotechnical Modelling
Taiga Saito, Yu Otake, Daijiro Mizutani +1
Geotechnical site characterisation relies on sparse, heterogeneous borehole data, where uncertainty quantification and interpretability matter as much as predictive accuracy. We ev…
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…
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…
Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks
Qi Zhang, Chang Liu, Stephen Wu +1
In the last few years, de novo molecular design using machine learning has made great technical progress but its practical deployment has not been as successful. This is mostly owi…
Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach
Pol Torres, Stephen Wu, Shenghong Ju +4
Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to find new materials interesting for science and engineering. The h…