11 citations · 35 across the 15 of their papers we have counts for
15 papers
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 exhibit non-Ohmic current--voltage I-V characteristics with a sharp conduction threshold, making them promising for sensing and transistor-like devices.…
Catalyst-Agent: Autonomous heterogeneous catalyst screening with an LLM Agent
Achuth Chandrasekhar, Janghoon Ock, Amir Barati Farimani
The discovery of catalysts for electrochemical applications such as the oxygen reduction reaction (ORR), nitrogen reduction reaction (NRR), and CO2 reduction reaction (CO2RR) remai…
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…
UniMat: Unifying Materials Embeddings through Multi-modal Learning
Janghoon Ock, Joseph Montoya, Daniel Schweigert +3
Materials science datasets are inherently heterogeneous and are available in different modalities such as characterization spectra, atomic structures, microscopic images, and text-…
Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agent
Janghoon Ock, Radheesh Sharma Meda, Tirtha Vinchurkar +2
Adsorption energy is a key reactivity descriptor in catalysis. Determining adsorption energy requires evaluating numerous adsorbate-catalyst configurations, making it computational…
Explainable Data-driven Modeling of Adsorption Energy in Heterogeneous Catalysis
Tirtha Vinchurkar, Janghoon Ock, Amir Barati Farimani
The increasing popularity of machine learning (ML) in catalysis has spurred interest in leveraging these techniques to enhance catalyst design. Our study aims to bridge the gap bet…
GradNav: Accelerated Exploration of Potential Energy Surfaces with Gradient-Based Navigation
Janghoon Ock, Parisa Mollaei, Amir Barati Farimani
The exploration of molecular systems' potential energy surface is important for comprehending their complex behaviors, particularly through identifying various metastable states. H…
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…
Catalyst Property Prediction with CatBERTa: Unveiling Feature Exploration Strategies through Large Language Models
Janghoon Ock, Chakradhar Guntuboina, Amir Barati Farimani
Efficient catalyst screening necessitates predictive models for adsorption energy, a key property of reactivity. However, prevailing methods, notably graph neural networks (GNNs),…
Beyond Independent Error Assumptions in Large GNN Atomistic Models
Janghoon Ock, Tian Tian, John Kitchin +1
The practical applications of determining the relative difference in adsorption energies are extensive, such as identifying optimal catalysts, calculating reaction energies, and de…