From the 1 of 12 linked papers with an AI index.
12 papers
Catalyst-Agent: Autonomous heterogeneous catalyst screening with an LLM Agent
Achuth Chandrasekhar, Janghoon Ock, Amir Barati Farimani
The paper presents Catalyst-Agent, an LLM-powered autonomous system that integrates database search, slab construction, MLIP-based adsorption energy calculations, and feedback-driv…
Graph-Based Kirchhoff Modeling of Non-Ohmic Electron Transport in Self-Assembled Nanonecklace Networks
Obed Issakah, Srivathsan Badrinarayanan, Ravi F. Saraf +1
Gold nanonecklace networks are promising platforms for single-electron switching, chemical sensing, and biogating devices because of their nonlinear current--voltage (--) cha…
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…
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…
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…
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…