PolyFusionAgent: A Multimodal Foundation Model and Autonomous AI Assistant for Polymer Property Prediction and Inverse Design

• URL: http://arxiv.org/abs/2605.26543v1
• Date: Tue, 26 May 2026 04:45:11 GMT
• Title: PolyFusionAgent: A Multimodal Foundation Model and Autonomous AI Assistant for Polymer Property Prediction and Inverse Design
• Abstract summary: We introduce PolyFusionAgent, an interactive framework coupling a multimodal polymer foundation model (PolyFusion) with a tool-augmented, literature-grounded design agent (PolyAgent)<n>PolyFusion aligns complementary polymer views including sequence, topology, 3D geometry, and fingerprints across millions of polymers to learn a shared latent space transferable across chemistries and data regimes.<n>PolyAgent closes the design loop by linking prediction and inverse design with evidence retrieval from the polymer literature, proposing, evaluating, and contextualizing hypotheses with explicit precedent in one workflow.
• Score: 6.729205331174991
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Polymer discovery is central to fields ranging from energy storage to biomedicine, but it is hindered by an astronomically large chemical design space and fragmented representations of structure, properties, and prior knowledge. This fragmentation leaves many AI models disconnected from physical and experimental reality, restricting their ability to support directly actionable design decisions. Here we introduce PolyFusionAgent, an interactive framework coupling a multimodal polymer foundation model (PolyFusion) with a tool-augmented, literature-grounded design agent (PolyAgent). PolyFusion aligns complementary polymer views including sequence, topology, 3D geometry, and fingerprints across millions of polymers to learn a shared latent space transferable across chemistries and data regimes, improving thermophysical property prediction and enabling property-conditioned generation of chemically valid, structurally novel polymers beyond the reference design space. PolyAgent closes the design loop by linking prediction and inverse design with evidence retrieval from the polymer literature, proposing, evaluating, and contextualizing hypotheses with explicit precedent in one workflow. Together, PolyFusionAgent enables interactive, evidence-linked polymer discovery combining large-scale representation learning, multimodal chemical knowledge, and verifiable scientific reasoning.

Related papers

• HiPoly: a hierarchical polymer-native AI framework for property prediction and generative design [2.5787479220592155]
  HiPoly is a polymer-native AI framework that processes complete polymer descriptions through a three-level hierarchical graph architecture.<n>It encodes inter-monomer connectivity, composition, and molecular weight directly within its architecture.<n>We demonstrate state-of-the-art prediction accuracy for thermophysical properties of multi-component polymer systems, with ablation studies confirming that each hierarchical design choice contributes independently to model performance.
  arXiv  Detail & Related papers  (2026-09-02T15:48:11Z)
• A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era [57.5440098776759]
  Molecular property prediction integrates quantum chemistry, cheminformatics, and deep learning to connect molecular structure with biological behavior.<n>This survey traces four complementary paradigms, including Quantum, Descriptor Machine Learning, Geometric Deep Learning, and Foundation Models.<n> Benchmark analyses integrate evidence from both widely used datasets and datasets reflecting industry perspectives.
  arXiv  Detail & Related papers  (2026-04-17T15:16:33Z)
• PolyJarvis: LLM Agent for Autonomous Polymer MD Simulations [49.024137619510526]
  We present PolyJarvis, an agent that couples a large language model (LLM) with the RadonPy simulation platform.<n>Given a polymer name or SMILES string, PolyJarvis autonomously executes monomer construction, charge assignment, polymerization, force field parameterization, and property calculation.<n>Results show density predictions within 0.1--4.8% and bulk moduli within 17-24% of reference values for aPS and PMMA.
  arXiv  Detail & Related papers  (2026-04-02T21:43:35Z)
• Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis [51.83339196548892]
  ChemCraft is a novel framework that decouples chemical reasoning from knowledge storage.<n>ChemCraft achieves superior performance with minimal inference costs.<n>This work establishes a cost-effective and privacy-preserving paradigm for AI-aided chemistry.
  arXiv  Detail & Related papers  (2026-01-25T04:23:34Z)
• PolyAgent: Large Language Model Agent for Polymer Design [10.596902977676807]
  We present a closed-loop polymer structure-property predictor integrated in a terminal for early-stage polymer discovery.<n>The framework is powered by LLM reasoning to provide users with property prediction, property-guided polymer structure generation, and structure modification capabilities.
  arXiv  Detail & Related papers  (2026-01-23T00:17:52Z)
• Foundation Models for Discovery and Exploration in Chemical Space [57.97784111110166]
  MIST is a family of molecular foundation models trained on large unlabeled datasets.<n>We demonstrate the ability of these models to solve real-world problems across chemical space.
  arXiv  Detail & Related papers  (2025-10-20T17:56:01Z)
• Unifying Polymer Modeling and Design via a Conformation-Centric Generative Foundation Model [29.414571977709098]
  PolyConFM is a polymer foundation model that unifies polymer modeling and design through conformation-centric pretraining.<n>We construct the first high-quality polymer conformation dataset via molecular dynamics simulations to mitigate data sparsity.<n>Experiments demonstrate that PolyConFM consistently outperforms representative task-specific methods on diverse downstream tasks.
  arXiv  Detail & Related papers  (2025-10-15T17:11:44Z)
• ToPolyAgent: AI Agents for Coarse-Grained Topological Polymer Simulations [0.0]
  ToPolyAgent is a multi-agent AI framework for performing molecular dynamics simulations of topological polymers.<n>It supports both interactive and autonomous simulation across diverse polymer architectures.<n>It lays the foundation for autonomous and multi-agent scientific research ecosystems.
  arXiv  Detail & Related papers  (2025-10-14T02:54:19Z)
• Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction [62.05532524197309]
  equivariant deep models have shown promise in accurately predicting atomic potentials and force fields in molecular dynamics simulations. In this work, we analyze the equivariant functions for equivariant architecture, and introduce a novel equivariant network, named PACE. As experimented in commonly used benchmarks, PACE demonstrates state-of-the-art performance in predicting atomic energy and force fields.
  arXiv  Detail & Related papers  (2024-11-06T19:34:40Z)
• TransPolymer: a Transformer-based language model for polymer property predictions [9.04563945965023]
  TransPolymer is a Transformer-based language model for polymer property prediction. Our proposed polymer tokenizer with chemical awareness enables learning representations from polymer sequences.
  arXiv  Detail & Related papers  (2022-09-03T01:29:59Z)
• Geometric Transformer for End-to-End Molecule Properties Prediction [92.28929858529679]
  We introduce a Transformer-based architecture for molecule property prediction, which is able to capture the geometry of the molecule. We modify the classical positional encoder by an initial encoding of the molecule geometry, as well as a learned gated self-attention mechanism.
  arXiv  Detail & Related papers  (2021-10-26T14:14:40Z)
• Polymer Informatics: Current Status and Critical Next Steps [1.3238373064156097]
  Surrogate models are trained on available polymer data for instant property prediction. Data-driven strategies to tackle unique challenges resulting from the extraordinary chemical and physical diversity of polymers at small and large scales are being explored. Methods to solve inverse problems, wherein polymer recommendations are made using advanced AI algorithms that meet application targets, are being investigated.
  arXiv  Detail & Related papers  (2020-11-01T14:17:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.