activity
20202026
most citedOrbital Graph Convolutional Neural Network for Material Property Prediction

159 citations · 267 across the 14 of their papers we have counts for

collaborators

14 papers

cond-mat.mtrl-sci2026

From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

Aritra Roy, Kevin Shen, Andrew MacBride +350

Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…

cs.LG2025★ 2 cited

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…

cs.CE2024★ 2 cited

Multimodal Language and Graph Learning of Adsorption Configuration in Catalysis

Janghoon Ock, Srivathsan Badrinarayanan, Rishikesh Magar +2

Adsorption energy is a reactivity descriptor that must be accurately predicted for effective machine learning (ML) application in catalyst screening. This process involves determin…

cs.LG2023★ 1 cited

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…

physics.chem-ph2023★ 13 cited

GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction

Suryanarayanan Balaji, Rishikesh Magar, Yayati Jadhav +1

With the emergence of Transformer architectures and their powerful understanding of textual data, a new horizon has opened up to predict the molecular properties based on text desc…

cs.LG2023★ 4 cited

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…