Joint Embedding Predictive Architecture for self-supervised pretraining on polymer molecular graphs

• URL: http://arxiv.org/abs/2506.18194v1
• Date: Sun, 22 Jun 2025 22:51:53 GMT
• Title: Joint Embedding Predictive Architecture for self-supervised pretraining on polymer molecular graphs
• Authors: Francesco Picolli, Gabriel Vogel, Jana M. Weber,
• Abstract summary: We study the use of the very recent 'Joint Embedding Predictive Architecture' (JEPA) on polymer molecular graphs.<n>Our results indicate that JEPA-based self-supervised pretraining on polymer graphs enhances downstream performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in machine learning (ML) have shown promise in accelerating the discovery of polymers with desired properties by aiding in tasks such as virtual screening via property prediction. However, progress in polymer ML is hampered by the scarcity of high-quality labeled datasets, which are necessary for training supervised ML models. In this work, we study the use of the very recent 'Joint Embedding Predictive Architecture' (JEPA), a type of architecture for self-supervised learning (SSL), on polymer molecular graphs to understand whether pretraining with the proposed SSL strategy improves downstream performance when labeled data is scarce. Our results indicate that JEPA-based self-supervised pretraining on polymer graphs enhances downstream performance, particularly when labeled data is very scarce, achieving improvements across all tested datasets.

Related papers

• Supervised Pretraining for Material Property Prediction [0.36868085124383626]
  Self-supervised learning (SSL) offers a promising alternative by pretraining on large, unlabeled datasets to develop foundation models.<n>In this work, we propose supervised pretraining, where available class information serves as surrogate labels to guide learning.<n>To further enhance representation learning, we propose a graph-based augmentation technique that injects noise to improve robustness without structurally deforming material graphs.
  arXiv  Detail & Related papers  (2025-04-27T19:00:41Z)
• Towards Data-Efficient Pretraining for Atomic Property Prediction [51.660835328611626]
  We show that pretraining on a task-relevant dataset can match or surpass large-scale pretraining.<n>We introduce the Chemical Similarity Index (CSI), a novel metric inspired by computer vision's Fr'echet Inception Distance.
  arXiv  Detail & Related papers  (2025-02-16T11:46:23Z)
• Extrapolative ML Models for Copolymers [1.901715290314837]
  Machine learning models have been progressively used for predicting materials properties. These models are inherently interpolative, and their efficacy for searching candidates outside a material's known range of property is unresolved. Here, we determine the relationship between the extrapolation ability of an ML model, the size and range of its training dataset, and its learning approach.
  arXiv  Detail & Related papers  (2024-09-15T11:02:01Z)
• Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning [0.0]
  We introduce a Multi-Modal Fusion (MMF) framework that harnesses the analytical prowess of Graph Neural Networks (GNNs) and the linguistic generative and predictive abilities of Large Language Models (LLMs) Our framework combines the effectiveness of GNNs in modeling graph-structured data with the zero-shot and few-shot learning capabilities of LLMs, enabling improved predictions while reducing the risk of overfitting.
  arXiv  Detail & Related papers  (2024-08-27T11:10:39Z)
• MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization [86.61052121715689]
  MatPlotAgent is a model-agnostic framework designed to automate scientific data visualization tasks. MatPlotBench is a high-quality benchmark consisting of 100 human-verified test cases.
  arXiv  Detail & Related papers  (2024-02-18T04:28:28Z)
• Simulation-Enhanced Data Augmentation for Machine Learning Pathloss Prediction [9.664420734674088]
  This paper introduces a novel simulation-enhanced data augmentation method for machine learning pathloss prediction. Our method integrates synthetic data generated from a cellular coverage simulator and independently collected real-world datasets. The integration of synthetic data significantly improves the generalizability of the model in different environments.
  arXiv  Detail & Related papers  (2024-02-03T00:38:08Z)
• Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes [57.62036621319563]
  We introduce CLLM, which leverages the prior knowledge of Large Language Models (LLMs) for data augmentation in the low-data regime. We demonstrate the superior performance of CLLM in the low-data regime compared to conventional generators.
  arXiv  Detail & Related papers  (2023-12-19T12:34:46Z)
• MLLM-DataEngine: An Iterative Refinement Approach for MLLM [62.30753425449056]
  We propose a novel closed-loop system that bridges data generation, model training, and evaluation. Within each loop, the MLLM-DataEngine first analyze the weakness of the model based on the evaluation results. For targeting, we propose an Adaptive Bad-case Sampling module, which adjusts the ratio of different types of data. For quality, we resort to GPT-4 to generate high-quality data with each given data type.
  arXiv  Detail & Related papers  (2023-08-25T01:41:04Z)
• From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning [52.257422715393574]
  We introduce a self-guided methodology for Large Language Models (LLMs) to autonomously discern and select cherry samples from open-source datasets. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal metric to identify discrepancies between a model's expected responses and its intrinsic generation capability.
  arXiv  Detail & Related papers  (2023-08-23T09:45:29Z)
• To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis [50.31589712761807]
  Large language models (LLMs) are notoriously token-hungry during pre-training, and high-quality text data on the web is approaching its scaling limit for LLMs. We investigate the consequences of repeating pre-training data, revealing that the model is susceptible to overfitting. Second, we examine the key factors contributing to multi-epoch degradation, finding that significant factors include dataset size, model parameters, and training objectives.
  arXiv  Detail & Related papers  (2023-05-22T17:02:15Z)
• Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction [8.048439531116367]
  We introduce Crystal Twins (CT): an SSL method for crystalline materials property prediction. We pre-train a Graph Neural Network (GNN) by applying the redundancy reduction principle to the graph latent embeddings of augmented instances. By sharing the pre-trained weights when fine-tuning the GNN for regression tasks, we significantly improve the performance for 7 challenging material property prediction benchmarks.
  arXiv  Detail & Related papers  (2022-05-04T05:08:46Z)
• Automated Machine Learning Techniques for Data Streams [91.3755431537592]
  This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time. The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
  arXiv  Detail & Related papers  (2021-06-14T11:42:46Z)

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.