4 papers
Pretraining Objective Matters in Extreme Low-Data FGVC: A Backbone-Controlled Study
Alexander Hackett, Srikanth Thudumu, Ginny Fisher +1
Extreme low-data fine-grained classification is common in expert domains where labeling is expensive, yet practitioners still need principled guidance for selecting pretrained enco…
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…
Autonomous Multi-Agent AI for High-Throughput Polymer Informatics: From Property Prediction to Generative Design Across Synthetic and Bio-Polymers
Mahule Roy, Adib Bazgir, Arthur da Silva Sousa Santos +1
We present an integrated multiagent AI ecosystem for polymer discovery that unifies high-throughput materials workflows, artificial intelligence, and computational modeling within…
Quantum-Aware Generative AI for Materials Discovery: A Framework for Robust Exploration Beyond DFT Biases
Mahule Roy
Conventional generative models for materials discovery are predominantly trained and validated using data from Density Functional Theory (DFT) with approximate exchange-correlation…