29 citations · 47 across the 9 of their papers we have counts for
13 papers
CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling
Yusei Ito, Tatsunori Taniai, Ryo Igarashi +2
Crystal structure modeling with graph neural networks is essential for various applications in materials informatics, and capturing SE(3)-invariant geometric features is a fundamen…
Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science
Yuta Suzuki, Tatsunori Taniai, Ryo Igarashi +4
Understanding structure-property relationships is an essential yet challenging aspect of materials discovery and development. To facilitate this process, recent studies in material…
Deep Probabilistic Traversability with Test-time Adaptation for Uncertainty-aware Planetary Rover Navigation
Masafumi Endo, Tatsunori Taniai, Genya Ishigami
Traversability assessment of deformable terrain is vital for safe rover navigation on planetary surfaces. Machine learning (ML) is a powerful tool for traversability prediction but…
Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding
Tatsunori Taniai, Ryo Igarashi, Yuta Suzuki +4
Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science. In peripheral areas such as the prediction of molecular pro…
A Transformer Model for Symbolic Regression towards Scientific Discovery
Florian Lalande, Yoshitomo Matsubara, Naoya Chiba +3
Symbolic Regression (SR) searches for mathematical expressions which best describe numerical datasets. This allows to circumvent interpretation issues inherent to artificial neural…
Risk-aware Path Planning via Probabilistic Fusion of Traversability Prediction for Planetary Rovers on Heterogeneous Terrains
Masafumi Endo, Tatsunori Taniai, Ryo Yonetani +1
Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover operations on deformable terrains but suffers from inevitable prediction errors. Especia…