37 citations · 42 across the 5 of their papers we have counts for
5 papers
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…
Improving Molecular Properties Prediction Through Latent Space Fusion
Eduardo Soares, Akihiro Kishimoto, Emilio Vital Brazil +3
Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enh…
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…
AI powered, automated discovery of polymer membranes for carbon capture
Ronaldo Giro, Hsianghan Hsu, Akihiro Kishimoto +6
The generation of molecules with Artificial Intelligence (AI) is poised to revolutionize materials discovery. Potential applications range from development of potent drugs to effic…
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
Matteo Manica, Jannis Born, Joris Cadow +21
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful represe…