11 citations · 35 across the 15 of their papers we have counts for
6 papers · 1 filter
Meta-Learning for Cross-Task Generalization in Protein Mutation Property Prediction
Srivathsan Badrinarayanan, Yue Su, Janghoon Ock +3
Protein mutations can have profound effects on biological function, making accurate prediction of property changes critical for drug discovery, protein engineering, and precision m…
Large Language Model Agent for Modular Task Execution in Drug Discovery
Janghoon Ock, Radheesh Sharma Meda, Srivathsan Badrinarayanan +3
We present a modular framework powered by large language models (LLMs) that automates and streamlines key tasks across the early-stage computational drug discovery pipeline. By com…
LLM-guided Chemical Process Optimization with a Multi-Agent Approach
Tong Zeng, Srivathsan Badrinarayanan, Janghoon Ock +2
Chemical process optimization maximizes production efficiency and economic performance, but optimization algorithms, including gradient-based solvers, numerical methods, and parame…
Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Shagun Maheshwari, Zhengxian Tang, Janghoon Ock +3
Machine-learning force fields (MLFFs) have emerged as a promising solution for speeding up ab initio molecular dynamics (MD) simulations, where accurate force predictions are criti…
NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Research
Achuth Chandrasekhar, Omid Barati Farimani, Olabode T. Ajenifujah +2
This paper presents the development and application of a Large Language Model Retrieval-Augmented Generation (LLM-RAG) system tailored for nanotechnology research. The system lever…
Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap Prediction
Ying-Ting Yeh, Janghoon Ock, Achuth Chandrasekhar +2
We investigate transformer-based language models, including RoBERTa, T5, Llama-3, and MatSciBERT, for predicting the band gaps of semiconductor materials directly from textual desc…