PolyDiscover: an integrated trust-aware platform for multi-objective polymer discovery and simulation validation
Abstract
Data-driven polymer discovery remains fragmented across databases, models, screening, literature, and simulations. We present PolyDiscover, a platform unifying multi-source property lookup, graph-neural-network prediction, trust-aware multi-objective screening, literature extraction, generative candidate exploration, and automated handoff to molecular-dynamics validation. Building on published ADEPT-PolyGraphMT, it harmonizes experimental, molecular-dynamics, density-functional-theory, and group-contribution data spanning 28 properties across about 13,000 real and one million virtual polymers. Its engine combines feasibility filtering and exact Pareto ranking with a trust score integrating similarity to known polymers, physical consistency, predictive uncertainty, and synthesizability, followed by diversity-aware shortlisting. Two case studies on lightweight, thermally stable structural materials and thermally stable electrical insulators, using monomer HOMO-LUMO gap as an insulation proxy, reduced a million-polymer search space to 30 chemically diverse candidates per task. Higher-fidelity molecular-dynamics and density-functional-theory calculations, interpreted after correcting systematic density underestimation by the molecular-dynamics protocol, showed retention of targeted property combinations. Ablation showed that trust-aware ranking shifted about one fifth of each shortlist toward more synthesizable chemistry closer to known polymers. A constrained natural-language interface translated a free-text request into a validated specification matching expert screening exactly, while staged literature evidence corroborated high glass-transition temperatures in polyimide-dominated shortlists against published experiments. PolyDiscover offers a traceable route from design intent to simulation-validated candidates, easing exploration of polymer chemical space.
Related
- Integrating artificial intelligence and molecular simulation for peptide discovery: limitations, validation and decision-centered workflows
- Finding the Right TEMPO: Multi-Objective Virtual Screening and Experimental Validation of TEMPO Derivatives for Aqueous Redox Flow Battery Catholytes
- Integrated Approach for the Discovery of Antifungal and Antibiofilm Agents From Cerrado Plants
- Scalable Thermoset Informatics Integrated with Autonomous Experimentation for On-Demand Discovery
- Integrated High-Throughput Mass Spectrometry Framework for Multiplexed Substrate Screening in Enzyme Discovery and Development
BibTeX
@misc{alosious2026polydiscover,
title = {{PolyDiscover: an integrated trust-aware platform for multi-objective polymer discovery and simulation validation}},
author = {Sobin Alosious and Guoyue Xu and Yuhan Liu and Renzheng Zhang and Meng Jiang and Tengfei Luo},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.38843},
eprint = {2610.38843},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.38843},
}
RIS
TY - UNPB
TI - PolyDiscover: an integrated trust-aware platform for multi-objective polymer discovery and simulation validation
AU - Sobin Alosious
AU - Guoyue Xu
AU - Yuhan Liu
AU - Renzheng Zhang
AU - Meng Jiang
AU - Tengfei Luo
PY - 2026
DA - 2026/10/28
PB - arXiv
DO - 10.48550/arxiv.2610.38843
UR - https://arxiv.org/abs/2610.38843
AB - Data-driven polymer discovery remains fragmented across databases, models, screening, literature, and simulations. We present PolyDiscover, a platform unifying multi-source property lookup, graph-neural-network prediction, trust-aware multi-objective screening, literature extraction, generative candidate exploration, and automated handoff to molecular-dynamics validation. Building on published ADEPT-PolyGraphMT, it harmonizes experimental, molecular-dynamics, density-functional-theory, and group-contribution data spanning 28 properties across about 13,000 real and one million virtual polymers. Its engine combines feasibility filtering and exact Pareto ranking with a trust score integrating similarity to known polymers, physical consistency, predictive uncertainty, and synthesizability, followed by diversity-aware shortlisting. Two case studies on lightweight, thermally stable structural materials and thermally stable electrical insulators, using monomer HOMO-LUMO gap as an insulation proxy, reduced a million-polymer search space to 30 chemically diverse candidates per task. Higher-fidelity molecular-dynamics and density-functional-theory calculations, interpreted after correcting systematic density underestimation by the molecular-dynamics protocol, showed retention of targeted property combinations. Ablation showed that trust-aware ranking shifted about one fifth of each shortlist toward more synthesizable chemistry closer to known polymers. A constrained natural-language interface translated a free-text request into a validated specification matching expert screening exactly, while staged literature evidence corroborated high glass-transition temperatures in polyimide-dominated shortlists against published experiments. PolyDiscover offers a traceable route from design intent to simulation-validated candidates, easing exploration of polymer chemical space.
ER -