3 citations · 6 across the 4 of their papers we have counts for
4 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…
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…