2 citations · 3 across the 5 of their papers we have counts for
3 papers · 1 filter
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…