2.2k citations · 2.2k across the 5 of their papers we have counts for
5 papers
MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network
Akihiro Kishimoto, Hiroshi Kajino, Masataka Hirose +6
Property prediction plays an important role in material discovery. As an initial step to eventually develop a foundation model for material science, we introduce a new autoencoder…
Molecule Generation Experience: An Open Platform of Material Design for Public Users
Seiji Takeda, Toshiyuki Hama, Hsiang-Han Hsu +10
Artificial Intelligence (AI)-driven material design has been attracting great attentions as a groundbreaking technology across a wide spectrum of industries. Molecular design is pa…
Molecular Inverse-Design Platform for Material Industries
Seiji Takeda, Toshiyuki Hama, Hsiang-Han Hsu +17
The discovery of new materials has been the essential force which brings a discontinuous improvement to industrial products' performance. However, the extra-vast combinatorial desi…
AI-driven Inverse Design System for Organic Molecules
Seiji Takeda, Toshiyuki Hama, Hsiang-Han Hsu +7
Designing novel materials that possess desired properties is a central need across many manufacturing industries. Driven by that industrial need, a variety of algorithms and tools…
Recent Advances in Physical Reservoir Computing: A Review
Gouhei Tanaka, Toshiyuki Yamane, Jean Benoit Héroux +6
Reservoir computing is a computational framework suited for temporal/sequential data processing. It is derived from several recurrent neural network models, including echo state ne…