activity
20222025
most citedCrystalformer: Infinitely Connected Attention for Periodic Structure Encoding

5 citations · 10 across the 4 of their papers we have counts for

collaborators

5 papers

cs.RO2025

SCU-Hand with Integrated Single-Sheet Valve: A Funnel-Shaped Robotic Hand for Milligram-Scale Powder Handling

Tomoya Takahashi, Yusaku Nakajima, Cristian Camilo Beltran-Hernandez +5

Laboratory Automation (LA) has the potential to accelerate solid-state materials discovery by enabling continuous robotic operation without human intervention. While robotic system…

cs.LG2025

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…

cs.LG2025★ 2 cited

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…

cs.LG2024★ 5 cited

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…

cond-mat.mtrl-sci2022★ 3 cited

Neural Structure Fields with Application to Crystal Structure Autoencoders

Naoya Chiba, Yuta Suzuki, Tatsunori Taniai +4

Representing crystal structures of materials to facilitate determining them via neural networks is crucial for enabling machine-learning applications involving crystal structure es…