# Multimodal machine learning with large language embedding model for polymer property prediction

arXiv: https://arxiv.org/abs/2503.22962 · HTML: https://mlsanity.com/paper/2503.22962/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 50 references that are in the map.
- First posted on arXiv 29 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics](https://mlsanity.com/paper/2209.14803/) (2022)
- [TransPolymer: a Transformer-based language model for polymer property predictions](https://mlsanity.com/paper/2209.01307/) (2022)
- [LLMatDesign: Autonomous Materials Discovery with Large Language Models](https://mlsanity.com/paper/2406.13163/) (2024)
- [BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-inspired Materials](https://mlsanity.com/paper/2309.08788/) (2023)
- [Polymer Informatics with Multi-Task Learning](https://mlsanity.com/paper/2010.15166/) (2020)
- [MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property Prediction](https://mlsanity.com/paper/2406.04727/) (2024)
- [Graph Convolutional Neural Networks for Polymers Property Prediction](https://mlsanity.com/paper/1811.06231/) (2018)
- [Transferring a molecular foundation model for polymer property predictions](https://mlsanity.com/paper/2310.16958/) (2023)

## Built on by (most cited first, 5 of 5)

- [MSP-LLM: A Unified Large Language Model Framework for Complete Material Synthesis Planning](https://mlsanity.com/paper/2602.07543/) (2026)
- [Multimodal Protein Language Models for Enzyme Kinetic Parameters: From Substrate Recognition to Conformational Adaptation](https://mlsanity.com/paper/2603.12845/) (2026)
- [Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction](https://mlsanity.com/paper/2511.05577/) (2025)
- [Teaching and Evaluating LLMs to Reason About Polymer Design Related Tasks](https://mlsanity.com/paper/2601.16312/) (2026)
- [Molecular representation shapes the balance between target fidelity and exploration in flow based polymer generation](https://mlsanity.com/paper/2609.16028/) (2026)

## Abstract (arXiv)

Contemporary large language models (LLMs), such as GPT-4 and Llama, have harnessed extensive computational power and diverse text corpora to achieve remarkable proficiency in interpreting and generating domain-specific content, including materials science. To leverage the domain knowledge embedded within these models, we propose a simple yet effective multimodal architecture, PolyLLMem, which integrates text embeddings generated by Llama 3 with molecular structure embeddings derived from Uni-Mol, for polymer properties prediction tasks. In our model, Low-rank adaptation (LoRA) layers were also incorporated during the property prediction tasks to refine the embeddings based on our limited polymer dataset, thereby enhancing their chemical relevance for polymer SMILES representation. This balanced fusion of fine-tuned textual and structural information enables PolyLLMem to accurately predict a variety of polymer properties despite the scarcity of training data. Its performance is comparable to, and in some cases exceeds, that of graph-based models, as well as transformer-based models that typically require pretraining on millions of polymer samples. These findings demonstrate that LLM, such as Llama, can effectively capture chemical information encoded in polymer PSMILES, and underscore the efficacy of multimodal fusion of LLM embeddings and molecular structure embeddings in overcoming data scarcity and accelerating the discovery of advanced polymeric materials.