[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119685-en":3,"doc-seo-119685-105":30,"detail-sidebar-cat-0-en-105":91},{"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":27,"seo_description":14,"update_tm":28,"read_time":29},119685,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Interpretable Machine Learning for Materials Design","Machine learning has become central to data-driven materials design, especially alongside high-throughput screening that accelerates in-silico prediction of properties. Training models to predict material behavior often forces a trade-off between interpretability and predictive performance. This study examines that tension by comparing four leading techniques—XGBoost, SISSO, Roost, and TPOT—on structural and electronic property prediction for perovskites and 2D materials. It evaluates future integration challenges, dataset scale growth, and model complexity, then proposes practical ways to preserve interpretability while strengthening the impact of ML in materials discovery.","arXiv :2112 .00239v1 [ cond-mat .mtrl-sci ] 1 Dec 2021  \nInterpretable Machine Learning for Materials Design  \nJames Dean 1 , Matthias Sche􀀏er2,3 , Thomas A. R. Purcell3 , Sergey V. Barabash4 , Rahul  \nBhowmik5 , and Timur Bazhirov 1,*  \n1 Exabyte Inc, San Francisco, CA, United States  \n2 University of California Santa Barbara, Isla Vista, CA, United States  \n3 The NOMAD Laboratory at the Fritz Haber Institute, Berlin, Germany  \n4 Intermolecular Inc, San Jose, CA, United States  \n5 Polaron Analytics, Beavercreek, OH, United States  \n* Corresponding Author Email: [timur@exabyte. io](timur@exabyte. io)  \nDecember 2, 2021  \nAbstract  \nFueled by the widespread adoption of Machine Learning and the high-throughput screening of materials, the data-centric approach to materials design has asserted itself as a robust and powerful tool for the in-silico prediction of materials properties. When training models to predict material properties, researchers often face a di􀀎cult choice between a model's interpretability or its performance.  \nWe study this trade-o􀀋 by leveraging four di􀀋erent state-of-the-art Machine Learning techniques: XGBoost, SISSO, Roost, and TPOT for the prediction of structural and electronic properties of perovskites and 2D materials. We then assess the future outlook of the continued integration of Machine Learning into materials discovery, and identify key problems that will continue to challenge researchers as the size of the literature's datasets and complexity of models increases. Finally, we o􀀋er several possible solutions to these challenges with a focus on retaining interpretability, and share our thoughts on magnifying the impact of Machine Learning on materials design.  \nKeywords: machine learning, materials science, chemistry, interpretability, rational design.  \n1 Introduction  \nToday, big data and arti􀀌cial intelligence revolutionize many areas of our daily life, and materials science is no exception [1{3] . More scienti􀀌c data is available now than ever before and the size of the literature is growing at an exponential rate [4{7] . This has led to multiple e􀀋orts in building the digital ecosystem for material discovery, most notably the Materials Genome Initiative (MGI) [8,9] . The MGI is a multinational e􀀋ort focused on improving the tools and techniques surrounding materials research, which recently has included suggestions to adopt the set of Findable, Accessible, Interoperable, and Reusable (FAIR) principles when reporting data [10] . In the years since the creation of the MGI, a number of large materials and chemical datasets have emerged, including the 2D Materials Encyclopedia (2DMatPedia) [11], Automatic Flow (AFLOW) database [12,13], Computational 2D Materials Database (C2DB) [14,15], Computational Materials Repository (CMR) [16], Joint Automated Repository for Various Integrated Simulations (JARVIS) [17], Materials Project [18], Novel Materials Discovery (NOMAD) repository [19], and the Open Quantum Materials Database (OQMD) [20] . We note that all of these are primarily computational in nature, and that there is still a scarcity of large databases containing comprehensively-characterized experimental data. Despite this, at least in computational materials discovery, the current availability of data has been a boon for exploration of the materials space, as it allows for highly 􀀍exible, data-hungry [21] models to be trained.  \nOne such approach that has seen widespread popularity in recent years is gradient boosting. Gradient boosting [22] is an ensemble technique in which a collection of weak learners (typically decision trees) are incrementally trained with respect to the gradient of the loss function [23] . A well-known implementation  \n(with over 5,500 citations as of November 2021) is eXtreme Gradient Boosting (XGBoost) [24], which reformulates the algorithm to provide stronger regularization and improved protection against over-􀀌tting. In chemistry, its applications have been diverse: XGBo","cbCaiuV0pSDNI8Qr","https://ap.wps.com/l/cbCaiuV0pSDNI8Qr","pdf",4300123,1,38,"English","en",105,"# Introduction\n## Data-driven materials discovery and the FAIR ecosystem\n## Model choice: interpretability vs performance\n## Machine learning techniques used in the study\n# Methods\n## Gradient boosting and XGBoost\n## Representation learning approaches (Roost, CGCNN)\n## Automated machine learning and TPOT","[{\"question\":\"Why is interpretability a challenge in machine learning for materials design?\",\"answer\":\"Training predictive models for materials properties often involves a difficult choice between achieving high performance and maintaining interpretability.\"},{\"question\":\"Which machine learning techniques are compared in the study?\",\"answer\":\"The study leverages four state-of-the-art techniques: XGBoost, SISSO, Roost, and TPOT for predicting structural and electronic properties.\"},{\"question\":\"What future challenges are highlighted as ML is integrated into materials discovery?\",\"answer\":\"The work identifies issues tied to increasing dataset sizes and growing model complexity, which can continue to challenge researchers while scaling the field.\"}]","Interpretable Machine Learning for Materials Design | 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is interpretability a challenge in machine learning for materials design?","Question",{"text":75,"@type":76},"Training predictive models for materials properties often involves a difficult choice between achieving high performance and maintaining interpretability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning techniques are compared in the study?",{"text":80,"@type":76},"The study leverages four state-of-the-art techniques: XGBoost, SISSO, Roost, and TPOT for predicting structural and electronic properties.",{"name":82,"@type":73,"acceptedAnswer":83},"What future challenges are highlighted as ML is integrated into materials discovery?",{"text":84,"@type":76},"The work identifies issues tied to increasing dataset sizes and growing model complexity, which can continue to challenge researchers while scaling the 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