37 citations · 45 across the 6 of their papers we have counts for
6 papers
LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery
Onur Boyar, Indra Priyadarsini, Seiji Takeda +1
Discovering materials with desirable properties in an efficient way remains a significant problem in materials science. Many studies have tackled this problem by using different se…
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…
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…