待翻译：DISTAL: Distillation and Self-Supervised Pretraining for Structure-Agnostic Materials Property Prediction

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--> [Submitted on 30 Aug 2026] Title:DISTAL: Distillation and Self-Supervised Pretraining for Structure-Agnostic Materials Property Prediction View a PDF of the paper titled DISTAL: Distillation and Self-Supervised Pretraining for Structure-Agnostic Materials Property Prediction, by Weiran Wang and 9 other authors View PDF Abstract:Materials property prediction remains difficult in low-data settings, where many target properties are supported by only a limited number of labeled samples. Models with the strongest predictive accuracy often depend on crystal structures, which restricts their use in early-stage screening when structural information is limited or unavailable. To address this challenge, we propose DISTAL, a dual-prior framework for structure-agnostic materials property prediction that combines self-supervised compositional pretraining with structure-aware knowledge distillation. DISTAL first learns transferable compositional representations from a large virtual composition space using 145 composition-derived descriptors. It then distills structural knowledge from a pretrained ALIGNN teacher into a composition-conditioned student. This setting allows structural priors to be used during training without requiring structural inputs at inference. By integrating explicit compositional descriptors, pretrained latent features, and distilled structural features within a unified prediction pipeline, DISTAL captures complementary signals that are difficult to recover from any single representation alone. Across 39 benchmark tasks, the best-performing multimodal configuration combines all three signals, and improves over the reference benchmark on 37 tasks. DISTAL achieves the strongest overall performance among all evaluated feature combinations. These results indicate that compositional pretraining and structural distillation provide complementary priors and offer a practical route to robust composition-only prediction in small-data materials informatics. The source code and the pre-trained models are anonymously available at: this https URL and will be released at the official link after acceptance. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET) Cite as: arXiv:2609.00059 [cs.LG] (or arXiv:2609.00059v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.00059 arXiv-issued DOI via DataCite Submission history From: Fengfeng Zhou [view email] [v1] Sun, 30 Aug 2026 14:00:55 UTC (2,220 KB) Full-text links: Access Paper: View a PDF of the paper titled DISTAL: Distillation and Self-Supervised Pretraining for Structure-Agnostic Materials Property Prediction, by Weiran Wang and 9 other authors View PDF view license Current browse context: cs.LG new | recent | 2026-09 Change to browse by: cs cs.AI cs.ET References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)