Foundation language model for generative polymer design
POLYT5
An encoder-decoder chemical language model that predicts polymer properties and generates new polymer structures conditioned on target performance.
Read moreMaterials informatics / polymer ML / computational chemistry
Harikrishna Sahu
I build computational and machine-learning workflows that accelerate materials discovery, with recent work spanning polymer foundation models, physics-informed Gaussian Process Regression, autonomous structure generation, and data-driven design.
Focus
Research and engineering themes
Physics-informed Gaussian Process Regression for materials informatics
matgpr
A public Python package for reproducible materials-informatics workflows with Gaussian Process Regression, uncertainty-aware prediction, materials fingerprints, and physics-informed mean functions.
Read moreAutonomous atomic-scale polymer model generation
Polymer Structure Predictor
A Python toolkit that builds a hierarchy of polymer models from repeat-unit SMILES, including oligomers, infinite chains, crystals, and amorphous structures.
Read moreSelected work
Publications
Load-dependent Hardness Prediction for Materials using Machine Learning
arXiv, 2026, 2604.20636
POLYT5: an encoder-decoder foundation chemical language model for generative polymer design
npj Artificial Intelligence, 2026, 2, 30
From Corpus to Innovation: Advancing Organic Solar Cell Design with Large Language Models
npj Computational Materials, 2026, 12, 27
polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design
Findings of ACL: EMNLP 2025, 2025, 12104-12119
Elucidating Photochemical Conversion Mechanism of PDMS to Silica under Deep UV Light and Ozone
Journal of Physical Chemistry Letters, 2025, 16, 747-753
Latest
Writing
POLYT5: Turning Polymer Design Into a Text-to-Text Problem
A first note on POLYT5, a polymer-native encoder-decoder language model for property prediction, conditional generation, and high-temperature dielectric polymer design.