[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124387-en":3,"doc-seo-124387-105":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},124387,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Optical materials discovery and design with federated databases and machine learning","Federated materials databases are increasingly available for computational discovery of optical compounds, with OPTIMADE providing a standardized way to represent crystal structures and their properties across many providers. This work presents a non-exhaustive screening framework for next-generation optical materials using MODNet, a neural-network model for property prediction, integrated with active learning and high-throughput density-functional computations. The approach isolates promising structures and chemistries for high-refractive-index candidates, while releasing the automated, periodically re-assessable workflows as new data and OPTIMADE-backed databases expand the search space.","arXiv :2405 . 11393v1 [ cond-mat .mtrl-sci ] 18 May 2024  \nOptical materials discovery and design with federated databases and machine learning  \nVictor Trinquet,∗a Matthew L. Evans,∗ab Cameron J. Hargreaves,a PierrePaul De Breuck,‡a and Gian-Marco Rignanese a  \nCombinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested.  \na UCLouvain, Institut de la Matiere Condensée et des Nanosciences (IMCN), Chemin des Étoiles 8, Louvain-laNeuve 1348, Belgium  \nb Matgenix SRL, 185 Rue Armand Bury, 6534 Gozée, Belgium  \n† Electronic Supplementary Information (ESI) available: The structure and properties of the materials selected by the active learning loops for consideration with density-functional perturbation theory have been deposited to the Materials Cloud Archive (10.24435/materialscloud:5p-vq) .  \n∗ These authors contributed equally to this work.‡ Present address: Ruhr-Universität Bochum, Universitätsstr. 150, 44801 Bochum, Germany  \n1–32 | 1  \n1 Introduction  \nThe advent of robust quantum mechanical calculations of material properties has expanded the opportunities for computational materials design. It is now relatively commonplace fora single materials design study to consider vast swathes of the space of known inorganic compounds (105 entries), as curated in experimental 1,2 and computational 3,4 databases. In recent years a large number of hypothetical materials have been suggested to be stable (for some definition) by data-driven and machine-learning approaches 5–8. These hypothetical materials can eventually make their way into curated databases, but often are released as static datasets that are hard to discover programmatically. This new space is too large to study exhaustively, and the feasibility of said hypothetical materials requires significant attention 9. Data-driven screening methods must therefore be adapted to be able to rationale the most efficient allocation of experimental resources within this dynamic, growing, decentralised design space, especially when targeting specific material properties.  \nIn this work, we devise a framework to search for materials with strong linear optical response, i.e., high-refractive-index (high-n) materials. These are sought after for their application in waveguides, interference filters, mirrors, sensors, and anti-reflective coatings for solar cells 10–16. Nonlinear optical materials are typically used in signal processing, wavelength conversion, and quantum optics, but are more difficult to find 17–19. As the linear optical response has been shown to be an indicator of non-linear (higher-order) optical response 20,21 , high-n semiconductors may be used as the starting point in the s","cbCaitevZdjt4FJP","https://ap.wps.com/l/cbCaitevZdjt4FJP","pdf",6573798,1,32,"English","en",105,"# Introduction\n## Linear and nonlinear optical materials\n## Target properties and optimization trade-offs\n## Prior high-throughput and machine-learning approaches\n## Proposed federated, active-learning screening framework","[{\"question\":\"What role does OPTIMADE play in this optical materials discovery approach?\",\"answer\":\"OPTIMADE standardizes how crystal structures and properties are represented and queried across federated data providers, enabling access to a large and dynamically growing set of materials.\"},{\"question\":\"How does the framework identify high-refractive-index optical materials?\",\"answer\":\"It applies MODNet for machine-learning property prediction within an active learning loop combined with high-throughput density-functional computations to select the most promising structures and chemistries.\"},{\"question\":\"Why is multi-objective optimization necessary in this search?\",\"answer\":\"The refractive index and electronic band gap impose competing physical requirements, leading to a Pareto front that must be optimized to balance transparency, operating range, and refractive performance across the spectral region.\"}]","Optical materials discovery and design with federated databases and machine learning | 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role does OPTIMADE play in this optical materials discovery approach?","Question",{"text":75,"@type":76},"OPTIMADE standardizes how crystal structures and properties are represented and queried across federated data providers, enabling access to a large and dynamically growing set of materials.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the framework identify high-refractive-index optical materials?",{"text":80,"@type":76},"It applies MODNet for machine-learning property prediction within an active learning loop combined with high-throughput density-functional computations to select the most promising structures and chemistries.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is multi-objective optimization necessary in this search?",{"text":84,"@type":76},"The refractive index and electronic band gap impose competing physical requirements, leading to a Pareto front that must be optimized to balance transparency, operating range, and refractive performance across 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