[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121697-en":3,"doc-seo-121697-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},121697,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Accelerating Materials Discovery for Optical Applications using Machine Learning, Natural Language Processing and Density Functional Theory","The thesis proposes a new materials-discovery strategy that unifies materials informatics with theoretical calculations to accelerate identification of substances with desirable optical properties. It covers foundational optical-property physics and prior work in materials informatics and machine learning, then details NLP-driven information extraction, machine-learning models, and π-conjugation quantification. Results include a 109,880-record database for refractive-index and dielectric-constant extraction, predictive reconstruction via Sellmeier relations, a π-conjugation metric for nonlinear-optical screening, and neural language models (OpticalBERT, OpticalTableSQA) for improved data retrieval.","Accelerating Materials Discovery for Optical Applications using Machine Learning, Natural Language Processing  \nand Density Functional Theory  \nJiuyang Zhao  \nDepartment of Physics  \nUniversity of Cambridge  \nThis dissertation is submitted for the degree of Doctor of Philosophy  \nChrist’s College September 2022  \nDeclaration  \nI hereby declare that this thesis is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. I further state that no substantial part of my thesis has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. It does not exceed the prescribed word limit of 60,000 words set by the Department of Physics.  \nJiuyang Zhao September 2022  \nAccelerating Materials Discovery for Optical Applications using Machine Learning, Natural Language Processing and  \nDensity Functional Theory  \nThis thesis presents a novel approach that combines materials informatics and theoretical calculations to accelerate the discovery of materials with desirable optical properties. Unlike traditional experimental research programmes, this work emphasises the significant contributions in terms of knowledge and technological outcomes resulting from the study.  \nThe thesis begins by reviewing the fundamental physics of optical properties of materials and the existing literature on materials informatics and the applications of machine learning in materials discovery (Chapter 1) . Subsequently, the methodologies employed throughout the research are outlined, including the utilisation of natural language processing (NLP) tools for information extraction, various machine learning models, and techniques for quantifying π-conjugation in organic molecules (Chapter 2) .  \nThe research presents compelling results, starting with the development of a complete workflow that extracts refractive indices and dielectric constants from scientific publications, resulting in a substantial database comprising 109,880 records of experimental data on optical materials (Chapter 3) . Building upon this, second-order Sellmeier equations are used toreconstruct chromatic-dispersion relations of various compounds, while machine learning techniques are employed to model refractive indices of inorganic compounds, showcasing the potential of auto-generated databases for property prediction and visualisation (Chapter 4) .  \nMoreover, a new algorithm or metric is introduced to characterise π-conjugation in organic molecules, which plays a crucial role in determining their nonlinear optical properties (Chapter 5) . This algorithm enables a high-throughput computational study on more than 20,000 molecules, leading to the identification of four commercially available organic molecules that hold sufficient potential to be used as nonlinear optical materials (Chapter 6) . This metric enables the accelerated discovery of organic compounds with exceptional molecular hyperpolarisability coefficients, a domain where literature data is notably scarce.  \nTo enhance data extraction capabilities in the field of optical materials, two novel neural network-based language models, OpticalBERT and OpticalTableSQA, are presented (Chapter 7) . OpticalBERT is a BERT-based model that is pre-trained on an extensive corpus of optical materials and exhibits remarkable advancements in various NLP tasks compared to traditional rule-based approaches. OpticalTableSQA is a table-based question-answering model that is  \nspecifically designed for question-answering tasks on optical materials tables, further enhancing data extraction capabilities.  \nThis thesis concludes by summarising the contributions made and outlining potential avenues for future research (Chapter 8) . By leveraging materials informatics, mac","cbCaideoX2cmvnnr","https://ap.wps.com/l/cbCaideoX2cmvnnr","pdf",23178501,1,308,"English","en",105,"# Introduction\n## Research Overview and Motivation\n# Methods\n## NLP-Based Information Extraction\n## Machine Learning Models and π-Conjugation Quantification\n# Results and Contributions\n## Workflow for Extracting Optical Constants\n## Property Reconstruction and Auto-Generated Databases\n## π-Conjugation Metric and High-Throughput Screening\n## Neural Language Models for Data Extraction\n# Conclusion\n## Summary of Contributions and Future Work","[{\"question\":\"What main goal does the thesis target for optical materials discovery?\",\"answer\":\"To accelerate the discovery of materials with desirable optical properties by combining materials informatics with theoretical calculations.\"},{\"question\":\"How does the thesis build data resources from literature?\",\"answer\":\"It develops a workflow using NLP to extract refractive indices and dielectric constants from scientific publications, producing a database of 109,880 experimental records.\"},{\"question\":\"Which models and metrics are introduced to improve prediction and screening?\",\"answer\":\"It proposes a π-conjugation algorithm/metric for nonlinear-optical property relevance and presents OpticalBERT and OpticalTableSQA to enhance table and text question answering for optical materials.\"}]","Accelerating Materials Discovery for Optical Applications using Machine Learning, Natural Language Processing and Density Functional Theory | PDF",1785806319,776,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"accelerating-materials-discovery-for-optical-applications-using-machine-learning-natural-language-processing-and-density-functional-theory","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/accelerating-materials-discovery-for-optical-applications-using-machine-learning-natural-language-processing-and-density-functional-theory/121697/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What main goal does the thesis target for optical materials discovery?","Question",{"text":76,"@type":77},"To accelerate the discovery of materials with desirable optical properties by combining materials informatics with theoretical calculations.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the thesis build data resources from literature?",{"text":81,"@type":77},"It develops a workflow using NLP to extract refractive indices and dielectric constants from scientific publications, producing a database of 109,880 experimental records.",{"name":83,"@type":74,"acceptedAnswer":84},"Which models and metrics are introduced to improve prediction and screening?",{"text":85,"@type":77},"It proposes a π-conjugation algorithm/metric for nonlinear-optical property relevance and presents OpticalBERT and OpticalTableSQA to enhance table and text question answering for optical materials.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]