[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82138-en":3,"doc-seo-82138-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82138,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Model Agnostic Graph Prompt Learning for Crystal Property Prediction","Graph Neural Networks improve crystal property prediction, yet their graph encoders often embed domain-specific chemical and structural knowledge directly, expanding parameter counts and increasing reliance on expert-designed features. Explicitly feeding all relevant chemical/structural attributes into the encoder is difficult. This work proposes a lightweight soft prompt learning framework that learns latent node-level chemical semantics and graph-level global structural symmetry, seamlessly integrating with existing GNN encoders. Experiments show 3%–15% gains on benchmarks and enable cross-property transfer for data-limited settings.","Model Agnostic Graph Prompt Learning for Crystal Property Prediction  \nShrimon Mukherjee 1,† Kishalay Das2,† Partha Basuchowdhuri 1 Pawan Goyal2 Niloy Ganguly2  \n1 School of Mathematical & Computational Sciences, Indian Association for the Cultivation of Science, Kolkata, India  \n2Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, India  \n†Equal Contribution  \narXiv :2607 .08996v 1 [ cs .LG] 9 Jul 2026  \nAbstract  \nGraph Neural Networks have emerged as a powerful tool for the fast and accurate prediction of various crystal properties. These models often encode domain-specific knowledge into their graph encoding modules, which increases their parameter size and makes their performance heavily dependent on domain expertise. Added to this, explicitly incorporating all chemical and structural features, that might influence a specific crystal property into the GNN encoder, is a challenging task. In this work, we propose a soft prompt learning framework that captures latent features essential for property prediction, which are not explicitly provided to the GNN. We introduce a novel multilevel graph prompt learning framework comprising both nodelevel and graph-level soft prompts. At the node level, we capture the local chemical semantics of different atom types, while at the graph level, we encode the global structural symmetry of the crystal graph. Our proposed prompt learning framework is lightweight and seamlessly integrates with any existing GNN encoder. Extensive experiments on popular benchmark datasets show that incorporating prompt learning significantly improves (3% - 15%) the performance of state-of-the-art GNN models in crystal property prediction tasks.  \nFurthermore, the learned soft prompts enable crossproperty knowledge transfer, enhancing prediction performance for properties with limited training data. Code is available at [https://github](https://github) . com/shrimonmuke0202/Prompt .git  \n1 INTRODUCTION  \nPredicting crystal properties remains a significant challenge in materials science. Unlike molecules, which are often represented as regular graphs, crystal materials are modeled as  \na 3D point cloud of atoms arranged in a well-defined shape with a repeating, orderly structure. Accurate and efficient prediction of crystal properties facilitates the identification of new materials with desired characteristics, reducing the need for extensive physical synthesis or testing and significantly accelerating the discovery process. A natural approach to modeling crystals involves representing them as graphs. As a result, numerous studies have focused on developing Graph Neural Network (GNN) models for crystal property prediction. Earlier approaches Xie and Grossman [2018], Chen et al. [2019], Louis et al. [2020], Park and Wolverton [2020], Schmidt et al. [2021] construct a multiedge graph from the 3D crystal structure and apply GNN model to encode the neighborhood structural information around an atom.  \nSubsequently, numerous studies have proposed various GNN architecture variants that integrate specific domain knowledge into the encoder to enhance crystal representation learning. ALIGNN Choudhary and DeCost [2021] incorporates bond angular information among edges to encode many-body interactions, Matformer Yan et al. [2022] is designed to be invariant to periodicity to capture repeating patterns explicitly, and PotNet Lin et al. [2023] directly models inter-atomic potentials based on physics principles. A unifying principle across these models is the assumption that crystal properties, such as formation energy, band gap, total energy, etc. are inherently linked to the chemical semantics of the constituent atoms and the 3D periodic structure. Consequently, predicting these properties necessitates capturing both the local chemical semantics at the node-level and the global 3D structural symmetry at the graph level of the crystal material. Thus, over the years, state-of-the-art GNN m","cbCaitahRNlI9sIo","https://ap.wps.com/l/cbCaitahRNlI9sIo","pdf",3516943,1,23,"English","en",105,"# Abstract\n# Introduction\n## Motivation and challenge in crystal property prediction\n## Prior GNN approaches and limitations\n## Proposed multilevel graph prompt learning framework","[{\"question\":\"What problem does soft prompt learning address in crystal property prediction with GNNs?\",\"answer\":\"It alleviates the difficulty of explicitly providing all chemical and structural features that influence a property, by learning latent representations instead of relying on exhaustive domain-engineered inputs.\"},{\"question\":\"How does the proposed framework use prompts at different levels?\",\"answer\":\"It introduces node-level soft prompts to capture local chemical semantics and graph-level soft prompts to encode global structural symmetry of the crystal graph.\"},{\"question\":\"What benefits do the learned soft prompts provide beyond accuracy?\",\"answer\":\"They support cross-property knowledge transfer, improving prediction performance when training data is limited for certain properties.\"}]",1784178398,58,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"model-agnostic-graph-prompt-learning-for-crystal-property-prediction","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/model-agnostic-graph-prompt-learning-for-crystal-property-prediction/82138/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does soft prompt learning address in crystal property prediction with GNNs?","Question",{"text":74,"@type":75},"It alleviates the difficulty of explicitly providing all chemical and structural features that influence a property, by learning latent representations instead of relying on exhaustive domain-engineered inputs.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed framework use prompts at different levels?",{"text":79,"@type":75},"It introduces node-level soft prompts to capture local chemical semantics and graph-level soft prompts to encode global structural symmetry of the crystal graph.",{"name":81,"@type":72,"acceptedAnswer":82},"What benefits do the learned soft prompts provide beyond accuracy?",{"text":83,"@type":75},"They support cross-property knowledge transfer, improving prediction performance when training data is limited for certain 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