159 citations · 267 across the 14 of their papers we have counts for
6 papers · 1 filter
MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models
Srivathsan Badrinarayanan, Rishikesh Magar, Akshay Antony +2
The discovery of Metal-Organic Frameworks (MOFs) with application-specific properties remains a central challenge in materials chemistry, owing to the immense size and complexity o…
GPCR-BERT: Interpreting Sequential Design of G Protein Coupled Receptors Using Protein Language Models
Seongwon Kim, Parisa Mollaei, Akshay Antony +2
With the rise of Transformers and Large Language Models (LLMs) in Chemistry and Biology, new avenues for the design and understanding of therapeutics have opened up to the scientif…
Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction
Hongshuo Huang, Rishikesh Magar, Changwen Xu +1
Recently, the remarkable capabilities of large language models (LLMs) have been illustrated across a variety of research domains such as natural language processing, computer visio…
MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property Prediction
Zhonglin Cao, Rishikesh Magar, Yuyang Wang +1
Metal-Organic Frameworks (MOFs) are materials with a high degree of porosity that can be used for applications in energy storage, water desalination, gas storage, and gas separatio…
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction
Rishikesh Magar, Yuyang Wang, Amir Barati Farimani
Machine learning (ML) models have been widely successful in the prediction of material properties. However, large labeled datasets required for training accurate ML models are elus…
Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast
Yuyang Wang, Rishikesh Magar, Chen Liang +1
Deep learning has been a prevalence in computational chemistry and widely implemented in molecule property predictions. Recently, self-supervised learning (SSL), especially contras…