2 citations · 5 across the 7 of their papers we have counts for
7 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…
Advancing Extrapolative Predictions of Material Properties through Learning to Learn
Kohei Noda, Araki Wakiuchi, Yoshihiro Hayashi +1
Recent advancements in machine learning have showcased its potential to significantly accelerate the discovery of new materials. Central to this progress is the development of rapi…
Transfer learning with affine model transformation
Shunya Minami, Kenji Fukumizu, Yoshihiro Hayashi +1
Supervised transfer learning has received considerable attention due to its potential to boost the predictive power of machine learning in scenarios where data are scarce. Generall…