3 citations · 6 across the 3 of their papers we have counts for
3 papers
SELF-BART : A Transformer-based Molecular Representation Model using SELFIES
Indra Priyadarsini, Seiji Takeda, Lisa Hamada +3
Large-scale molecular representation methods have revolutionized applications in material science, such as drug discovery, chemical modeling, and material design. With the rise of…
Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model
Indra Priyadarsini, Vidushi Sharma, Seiji Takeda +3
Development of efficient and high-performing electrolytes is crucial for advancing energy storage technologies, particularly in batteries. Predicting the performance of battery ele…
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…