7 citations · 17 across the 6 of their papers we have counts for
6 papers
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…
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…
WeaveNet for Approximating Two-sided Matching Problems
Shusaku Sone, Jiaxin Ma, Atsushi Hashimoto +2
Matching, a task to optimally assign limited resources under constraints, is a fundamental technology for society. The task potentially has various objectives, conditions, and cons…
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…
Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
Yoshitomo Matsubara, Naoya Chiba, Ryo Igarashi +1
This paper revisits datasets and evaluation criteria for Symbolic Regression (SR), specifically focused on its potential for scientific discovery. Focused on a set of formulas used…