{"topic":"ai-for-science","sort":"top","limit":25,"offset":0,"count":25,"papers":[{"id":"doi:10.1038/s41524-019-0221-0","kind":"article","title":"Recent advances and applications of machine learning in solid-state materials science","authors":["Jonathan Schmidt","Mário R. G. Marques","Silvana Botti","Miguel A. L. Marques"],"author_count":4,"year":2019,"published":"2019-08-08","venue":"npj Computational Materials","cited_by":2636,"doi":"10.1038/s41524-019-0221-0","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41524-019-0221-0","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41524-019-0221-0","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2968923792","pdf_url":"https://www.nature.com/articles/s41524-019-0221-0.pdf","abstract":"Abstract One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Recent advances and applications of machine learning in solid-state materials science\" by Jonathan Schmidt, Mário R. G. Marques, Silvana Botti et al., https://doi.org/10.1038/s41524-019-0221-0, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1039/d0cs01079k","kind":"review","title":"Water electrolysis: from textbook knowledge to the latest scientific strategies and industrial developments","authors":["Marian Chatenet","Bruno Georges Pollet","Dario R. Dekel","Fabio Dionigi","Jonathan Deseure","Pierre Millet","Richard D. Braatz","Martin Z. Bazant","Michael Hermann Eikerling","Iain Staffell","Paul Balcombe","Yang Shao‐Horn","Helmut Schäfer"],"author_count":13,"year":2022,"published":"2022-01-01","venue":"Chemical Society Reviews","cited_by":2022,"doi":"10.1039/d0cs01079k","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1039/d0cs01079k","archive_url":"https://space-kitty.com/papers/doi:10.1039/d0cs01079k","updated":null,"pmid":"35575644","pmcid":null,"openalex_id":"W4280617757","pdf_url":"https://pubs.rsc.org/en/content/articlepdf/2022/cs/d0cs01079k","abstract":"footprint. The viability of water electrolysis still hinges on the availability of durable earth-abundant electrocatalyst materials and the overall process efficiency. This review spans from the fundamentals of electrocatalytically initiated water splitting to the very latest scientific findings from university and institutional research, also covering specifications and special features of the current industrial processes and those processes currently being tested in large-scale applications. Recently developed strategies are described for the optimisation and discovery of active and durable materials for electrodes that ever-increasingly harness first-principles calculations and machine learning. In addition, a technoeconomic analysis of water electrolysis is included that allows an assessment of the extent to which a large-scale implementation of water splitting can help to combat climate change. This review article is intended to cross-pollinate and strengthen efforts from fundamental understanding to technical implementation and to improve the 'junctions' between the field's physical chemists, materials scientists and engineers, as well as stimulate much-needed exchange among these groups on challenges encountered in the different domains.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Electrocatalysts for Energy Conversion"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Water electrolysis: from textbook knowledge to the latest scientific strategies and industrial developments\" by Marian Chatenet, Bruno Georges Pollet, Dario R. Dekel et al., https://doi.org/10.1039/d0cs01079k, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1007/s11837-013-0755-4","kind":"article","title":"Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)","authors":["James E. Saal","Scott J. Kirklin","Muratahan Aykol","Bryce Meredig","Chris M. Wolverton"],"author_count":5,"year":2013,"published":"2013-09-27","venue":"JOM","cited_by":2540,"doi":"10.1007/s11837-013-0755-4","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1007/s11837-013-0755-4","archive_url":"https://space-kitty.com/papers/doi:10.1007/s11837-013-0755-4","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W1976492731","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1126/science.aat2663","kind":"review","title":"Inverse molecular design using machine learning: Generative models for matter engineering","authors":["Benjamín Sánchez-Lengeling","Alán Aspuru‐Guzik"],"author_count":2,"year":2018,"published":"2018-07-26","venue":"Science","cited_by":2071,"doi":"10.1126/science.aat2663","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1126/science.aat2663","archive_url":"https://space-kitty.com/papers/doi:10.1126/science.aat2663","updated":null,"pmid":"30049875","pmcid":null,"openalex_id":"W2883583109","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1038/s41586-023-06734-w","kind":"article","title":"An autonomous laboratory for the accelerated synthesis of inorganic materials","authors":["Nathan J. Szymanski","Bernardus Rendy","Yuxing Fei","Rishi E. Kumar","Tanjin He","David Milsted","Matthew J. McDermott","Max C. Gallant","Ekin D. Cubuk","Amil Merchant","Haegyeom Kim","Anubhav Jain","Christopher J. Bartel","Kristin Aslaug Persson","Yan Zeng","Gerbrand Ceder"],"author_count":16,"year":2023,"published":"2023-11-29","venue":"Nature","cited_by":1037,"doi":"10.1038/s41586-023-06734-w","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-023-06734-w","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-023-06734-w","updated":null,"pmid":"38030721","pmcid":null,"openalex_id":"W4389132715","pdf_url":"https://www.nature.com/articles/s41586-023-06734-w.pdf","abstract":", we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Abstract of \"An autonomous laboratory for the accelerated synthesis of inorganic materials\" by Nathan J. Szymanski, Bernardus Rendy, Yuxing Fei et al., https://doi.org/10.1038/s41586-023-06734-w, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/nature17439","kind":"article","title":"Machine-learning-assisted materials discovery using failed experiments","authors":["Paul Raccuglia","Katherine C. Elbert","Philip Adler","Casey Falk","Malia B. Wenny","Aurelio Mollo","Matthias Zeller","Sorelle A. Friedler","Joshua Schrier","Alexander J. Norquist"],"author_count":10,"year":2016,"published":"2016-05-03","venue":"Nature","cited_by":1724,"doi":"10.1038/nature17439","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/nature17439","archive_url":"https://space-kitty.com/papers/doi:10.1038/nature17439","updated":null,"pmid":"27147027","pmcid":null,"openalex_id":"W2347129741","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"arxiv:2202.02450","kind":"article","title":"A universal graph deep learning interatomic potential for the periodic table","authors":["Chi Chen","Shyue Ping Ong"],"author_count":2,"year":2022,"published":"2022-11-28","venue":"Nature Computational Science","cited_by":997,"doi":"10.1038/s43588-022-00349-3","arxiv_id":"2202.02450","nct_id":null,"source_url":"https://doi.org/10.1038/s43588-022-00349-3","archive_url":"https://space-kitty.com/papers/arxiv:2202.02450","updated":"2022-08-14","pmid":"38177366","pmcid":null,"openalex_id":"W4310135808","pdf_url":"https://www.researchsquare.com/article/rs-1348213/latest.pdf","abstract":"Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow chemistries or too inaccurate for general applications. Here, we report a universal IAP for materials based on graph neural networks with three-body interactions (M3GNet). The M3GNet IAP was trained on the massive database of structural relaxations performed by the Materials Project over the past 10 years and has broad applications in structural relaxation, dynamic simulations and property prediction of materials across diverse chemical spaces. About 1.8 million materials were identified from a screening of 31 million hypothetical crystal structures to be potentially stable against existing Materials Project crystals based on M3GNet energies. Of the top 2000 materials with the lowest energies above hull, 1578 were verified to be stable using DFT calculations. These results demonstrate a machine learning-accelerated pathway to the discovery of synthesizable materials with exceptional properties.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1016/j.jmat.2017.08.002","kind":"article","title":"Materials discovery and design using machine learning","authors":["Yue Liu","Tianlu Zhao","Wangwei Ju","Siqi Shi"],"author_count":4,"year":2017,"published":"2017-08-08","venue":"Journal of Materiomics","cited_by":1276,"doi":"10.1016/j.jmat.2017.08.002","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.jmat.2017.08.002","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.jmat.2017.08.002","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2742835787","pdf_url":"https://www.sciencedirect.com/science/article/pii/S2352847817300515/pdf","abstract":null,"abstract_license":null,"license":"cc-by-nc-nd","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1038/s41586-019-1335-8","kind":"article","title":"Unsupervised word embeddings capture latent knowledge from materials science literature","authors":["Vahe Tshitoyan","John M. Dagdelen","Leigh Weston","Alexander Dunn","Ziqin Rong","Olga Kononova","Kristin Aslaug Persson","Gerbrand Ceder","Anubhav Jain"],"author_count":9,"year":2019,"published":"2019-07-03","venue":"Nature","cited_by":1123,"doi":"10.1038/s41586-019-1335-8","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-019-1335-8","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-019-1335-8","updated":null,"pmid":"31270483","pmcid":null,"openalex_id":"W2953641512","pdf_url":"https://escholarship.org/content/qt082091b4/qt082091b4.pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1126/science.abo4940","kind":"article","title":"Machine learning–enabled high-entropy alloy discovery","authors":["Ziyuan Rao","Po‐Yen Tung","Ruiwen Xie","Ye Wei","Hongbin Zhang","Alberto Ferrari","T.P.C. Klaver","Fritz Körmann","Prithiv Thoudden Sukumar","Alisson Kwiatkowski da Silva","Yao Chen","Zhiming Li","Dirk Ponge","Jörg Neugebauer","Oliver Gutfleisch","Stefan Bauer","Dierk Raabe"],"author_count":17,"year":2022,"published":"2022-10-06","venue":"Science","cited_by":755,"doi":"10.1126/science.abo4940","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1126/science.abo4940","archive_url":"https://space-kitty.com/papers/doi:10.1126/science.abo4940","updated":null,"pmid":"36201584","pmcid":null,"openalex_id":"W4302425308","pdf_url":null,"abstract":null,"abstract_license":null,"license":"other-oa","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1038/srep02810","kind":"article","title":"Accelerating materials property predictions using machine learning","authors":["Ghanshyam Pilania","Chenchen Wang","Xun Jiang","Sanguthevar Rajasekaran","Ramamurthy Ramprasad"],"author_count":5,"year":2013,"published":"2013-09-30","venue":"Scientific Reports","cited_by":856,"doi":"10.1038/srep02810","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/srep02810","archive_url":"https://space-kitty.com/papers/doi:10.1038/srep02810","updated":null,"pmid":"24077117","pmcid":null,"openalex_id":"W2074616700","pdf_url":"https://www.nature.com/articles/srep02810.pdf","abstract":"The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, property predictions. Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces, and can significantly accelerate the discovery of new application-specific materials.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Accelerating materials property predictions using machine learning\" by Ghanshyam Pilania, Chenchen Wang, Xun Jiang et al., https://doi.org/10.1038/srep02810, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41578-018-0005-z","kind":"review","title":"Accelerating the discovery of materials for clean energy in the era of smart automation","authors":["Daniel P. Tabor","Loı̈c M. Roch","Semion K. Saikin","Christoph Kreisbeck","Dennis Sheberla","Joseph H. Montoya","Shyam Dwaraknath","Muratahan Aykol","C. Ortiz","Hermann Tribukait","Carlos Amador‐Bedolla","Christoph Josef Brabec","Benji Maruyama","Kristin Aslaug Persson","Alán Aspuru‐Guzik"],"author_count":15,"year":2018,"published":"2018-04-26","venue":"Nature Reviews Materials","cited_by":778,"doi":"10.1038/s41578-018-0005-z","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41578-018-0005-z","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41578-018-0005-z","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2799915630","pdf_url":"https://www.nature.com/articles/s41578-018-0005-z.pdf","abstract":"The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace. The discovery and development of advanced materials are imperative for the clean energy sector. We envision that a closed-loop approach, which combines high-throughput computation, artificial intelligence and advanced robotics, will sizeably reduce the time to deployment and the costs associated with materials development.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Accelerating the discovery of materials for clean energy in the era of smart automation\" by Daniel P. Tabor, Loı̈c M. Roch, Semion K. Saikin et al., https://doi.org/10.1038/s41578-018-0005-z, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s44160-022-00231-0","kind":"article","title":"The rise of self-driving labs in chemical and materials sciences","authors":["Milad Abolhasani","Eugenia Kumacheva"],"author_count":2,"year":2023,"published":"2023-01-30","venue":"Nature Synthesis","cited_by":584,"doi":"10.1038/s44160-022-00231-0","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s44160-022-00231-0","archive_url":"https://space-kitty.com/papers/doi:10.1038/s44160-022-00231-0","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W4318486595","pdf_url":"https://www.nature.com/articles/s44160-022-00231-0.pdf","abstract":null,"abstract_license":null,"license":"other-oa","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"arxiv:2106.14583","kind":"article","title":"Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements","authors":["So Takamoto","Chikashi Shinagawa","Daisuke Motoki","Kosuke Nakago","Wenwen Li","Iori Kurata","Taku Watanabe","Yoshihiro Yayama","Hiroki Iriguchi","Yusuke Asano","Tasuku Onodera","Takafumi Ishii","Takao Kudo","Hideki Ono","Ryohto Sawada","Ryuichiro Ishitani","Marc Ong","Taiki Yamaguchi","Toshiki Kataoka","Akihide Hayashi","Nontawat Charoenphakdee","Takeshi Ibuka"],"author_count":22,"year":2021,"published":"2021-06-28","venue":"Nature Communications","cited_by":444,"doi":"10.1038/s41467-022-30687-9","arxiv_id":"2106.14583","nct_id":null,"source_url":"https://arxiv.org/abs/2106.14583","archive_url":"https://space-kitty.com/papers/arxiv:2106.14583","updated":"2022-04-01","pmid":null,"pmcid":null,"openalex_id":"W4282041110","pdf_url":"https://arxiv.org/pdf/2106.14583","abstract":"Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such purposes. However, existing NNPs are generally designed for narrow target materials, making them unsuitable for broader applications in material discovery. To overcome this issue, we have developed a universal NNP called PreFerred Potential (PFP), which is able to handle any combination of 45 elements. Particular emphasis is placed on the datasets, which include a diverse set of virtual structures used to attain the universality. We demonstrated the applicability of PFP in selected domains: lithium diffusion in LiFeSO${}_4$F, molecular adsorption in metal-organic frameworks, an order-disorder transition of Cu-Au alloys, and material discovery for a Fischer-Tropsch catalyst. They showcase the power of PFP, and this technology provides a highly useful tool for material discovery.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["cond-mat.mtrl-sci","physics.comp-ph"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1038/s41467-018-05761-w","kind":"article","title":"Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning","authors":["Shuaihua Lu","Qionghua Zhou","Yixin Ouyang","Yilv Guo","Qiang Li","Jinlan Wang"],"author_count":6,"year":2018,"published":"2018-08-20","venue":"Nature Communications","cited_by":725,"doi":"10.1038/s41467-018-05761-w","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41467-018-05761-w","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41467-018-05761-w","updated":null,"pmid":"30143621","pmcid":null,"openalex_id":"W2888395196","pdf_url":"https://www.nature.com/articles/s41467-018-05761-w.pdf","abstract":"Rapidly discovering functional materials remains an open challenge because the traditional trial-and-error methods are usually inefficient especially when thousands of candidates are treated. Here, we develop a target-driven method to predict undiscovered hybrid organic-inorganic perovskites (HOIPs) for photovoltaics. This strategy, combining machine learning techniques and density functional theory calculations, aims to quickly screen the HOIPs based on bandgap and solve the problems of toxicity and poor environmental stability in HOIPs. Successfully, six orthorhombic lead-free HOIPs with proper bandgap for solar cells and room temperature thermal stability are screened out from 5158 unexplored HOIPs and two of them stand out with direct bandgaps in the visible region and excellent environmental stability. Essentially, a close structure-property relationship mapping the HOIPs bandgap is established. Our method can achieve high accuracy in a flash and be applicable to a broad class of functional material design.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning\" by Shuaihua Lu, Qionghua Zhou, Yixin Ouyang et al., https://doi.org/10.1038/s41467-018-05761-w, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1103/physrevb.89.094104","kind":"article","title":"Combinatorial screening for new materials in unconstrained composition space with machine learning","authors":["Bryce Meredig","Ankit Agrawal","Scott J. Kirklin","James E. Saal","Jeff W. Doak","Alexander E. Thompson","K. Zhang","Alok Choudhary","Chris M. Wolverton"],"author_count":9,"year":2014,"published":"2014-03-14","venue":"Physical Review B","cited_by":735,"doi":"10.1103/physrevb.89.094104","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1103/physrevb.89.094104","archive_url":"https://space-kitty.com/papers/doi:10.1103/physrevb.89.094104","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2313966941","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.5445/ir/1000128111","kind":"article","title":"Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation","authors":["Mario Krenn","Florian Häse","AkshatKumar Nigam","Pascal Friederich","Alán Aspuru‐Guzik"],"author_count":5,"year":2020,"published":"2020-01-01","venue":"Repository KITopen (Karlsruhe Institute of Technology)","cited_by":616,"doi":"10.5445/ir/1000128111","arxiv_id":null,"nct_id":null,"source_url":"https://publikationen.bibliothek.kit.edu/1000128111","archive_url":"https://space-kitty.com/papers/doi:10.5445/ir/1000128111","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3045928028","pdf_url":null,"abstract":"The discovery of novel materials and functional molecules can help to solve some of society's most urgent challenges, ranging from efficient energy harvesting and storage to uncovering novel pharmaceutical drug candidates. Traditionally matter engineering -- generally denoted as inverse design -- was based massively on human intuition and high-throughput virtual screening. The last few years have seen the emergence of significant interest in computer-inspired designs based on evolutionary or deep learning methods. The major challenge here is that the standard strings molecular representation SMILES shows substantial weaknesses in that task because large fractions of strings do not correspond to valid molecules. Here, we solve this problem at a fundamental level and introduce SELFIES (SELF-referencIng Embedded Strings), a string-based representation of molecules which is 100\\% robust. Every SELFIES string corresponds to a valid molecule, and SELFIES can represent every molecule. SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid. In our experiments, the model's internal memory stores two orders of magnitude more diverse molecules than a similar test with SMILES. Furthermore, as all molecules are valid, it allows for explanation and interpretation of the internal working of the generative models.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Computational Drug Discovery Methods"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation\" by Mario Krenn, Florian Häse, AkshatKumar Nigam et al., https://doi.org/10.5445/ir/1000128111, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1021/acscatal.9b04186","kind":"article","title":"Machine Learning for Catalysis Informatics: Recent Applications and Prospects","authors":["Takashi Toyao","Zen Maeno","Satoru Takakusagi","Takashi Kamachi","Ichigaku Takigawa","Ken‐ichi Shimizu"],"author_count":6,"year":2019,"published":"2019-12-16","venue":"ACS Catalysis","cited_by":617,"doi":"10.1021/acscatal.9b04186","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1021/acscatal.9b04186","archive_url":"https://space-kitty.com/papers/doi:10.1021/acscatal.9b04186","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2996448308","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1002/aenm.201903242","kind":"article","title":"A Critical Review of Machine Learning of Energy Materials","authors":["Chi Chen","Yunxing Zuo","Weike Ye","Xiangguo Li","Zhi Deng","Shyue Ping Ong"],"author_count":6,"year":2020,"published":"2020-01-29","venue":"Advanced Energy Materials","cited_by":609,"doi":"10.1002/aenm.201903242","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1002/aenm.201903242","archive_url":"https://space-kitty.com/papers/doi:10.1002/aenm.201903242","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3003769243","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1021/acs.chemrev.2c00061","kind":"review","title":"Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery","authors":["Haoxin Mai","Tu C. Le","Dehong Chen","David Alan Winkler","Rachel A. Caruso"],"author_count":5,"year":2022,"published":"2022-07-21","venue":"Chemical Reviews","cited_by":565,"doi":"10.1021/acs.chemrev.2c00061","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1021/acs.chemrev.2c00061","archive_url":"https://space-kitty.com/papers/doi:10.1021/acs.chemrev.2c00061","updated":null,"pmid":"35862246","pmcid":null,"openalex_id":"W4286489048","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"arxiv:2007.01831","kind":"article","title":"The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design","authors":["Kamal Choudhary","Kevin F. Garrity","Andrew C. E. Reid","Brian DeCost","Adam J. Biacchi","Angela R. Hight Walker","Zachary Trautt","Jason Hattrick-Simpers","A. Gilad Kusne","Andrea Centrone","Albert Davydov","Jie Jiang","Ruth Pachter","Gowoon Cheon","Evan J. Reed","Ankit Agrawal","Xiaofeng Qian","Vinit Sharma","Houlong Zhuang","Sergei V. Kalinin","Bobby G. Sumpter","Ghanshyam Pilania","Pinar Acar","Subhasish Mandal","Kristjan Haule"],"author_count":28,"year":2020,"published":"2020-11-12","venue":"npj Computational Materials","cited_by":575,"doi":"10.1038/s41524-020-00440-1","arxiv_id":"2007.01831","nct_id":null,"source_url":"https://doi.org/10.1038/s41524-020-00440-1","archive_url":"https://space-kitty.com/papers/arxiv:2007.01831","updated":"2021-07-12","pmid":null,"pmcid":null,"openalex_id":"W3039596418","pdf_url":"https://www.nature.com/articles/s41524-020-00440-1.pdf","abstract":"The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-Tools. To date, JARVIS consists of 40,000 materials and 1 million calculated properties in JARVIS-DFT, 1,500 materials and 110 force-fields in JARVIS-FF, and 25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-Tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov .","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1038/s41578-022-00490-5","kind":"review","title":"Machine learning for a sustainable energy future","authors":["Zhenpeng Yao","Yanwei Lum","Andrew K. Johnston","L.M. Mejía-Mendoza","Xin Zhou","Yonggang Wen","Alán Aspuru‐Guzik","Edward Hartley Sargent","Zhi Wei Seh"],"author_count":9,"year":2022,"published":"2022-10-18","venue":"Nature Reviews Materials","cited_by":526,"doi":"10.1038/s41578-022-00490-5","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41578-022-00490-5","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41578-022-00490-5","updated":null,"pmid":"36277083","pmcid":null,"openalex_id":"W4306698380","pdf_url":"https://www.nature.com/articles/s41578-022-00490-5.pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"arxiv:2006.06141","kind":"article","title":"On-the-fly closed-loop materials discovery via Bayesian active learning","authors":["A. Gilad Kusne","Heshan Yu","Changming Wu","Huairuo Zhang","Jason Hattrick-Simpers","Brian DeCost","Suchismita Sarker","Corey Oses","Cormac Toher","Stefano Curtarolo A","Albert V. Davydov","Ritesh Agarwal","Leonid A. Bendersky","Mo Li","Apurva Mehta","Ichiro Takeuchi"],"author_count":16,"year":2020,"published":"2020-11-24","venue":"Nature Communications","cited_by":552,"doi":"10.1038/s41467-020-19597-w","arxiv_id":"2006.06141","nct_id":null,"source_url":"https://doi.org/10.1038/s41467-020-19597-w","archive_url":"https://space-kitty.com/papers/arxiv:2006.06141","updated":"2020-11-10","pmid":"33235197","pmcid":null,"openalex_id":"W3034954837","pdf_url":"https://www.nature.com/articles/s41467-020-19597-w.pdf","abstract":"Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics [1]. In this work we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate autonomous research methodology (i.e. autonomous hypothesis definition and evaluation) that can place complex, advanced materials in reach, allowing scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. Additionally, this robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. We used the real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) at the synchrotron beamline to accelerate the fundamentally interconnected tasks of rapid phase mapping and property optimization, with each cycle taking seconds to minutes, resulting in the discovery of a novel epitaxial nanocomposite phase-change memory material.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1016/j.matt.2021.02.015","kind":"article","title":"Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery","authors":["Andrew Rosen","Shaelyn M. Iyer","Debmalya Ray","Zhenpeng Yao","Alán Aspuru‐Guzik","Laura Gagliardi","Justin M. Notestein","Randall Q. Snurr"],"author_count":8,"year":2021,"published":"2021-04-05","venue":"Matter","cited_by":543,"doi":"10.1016/j.matt.2021.02.015","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.matt.2021.02.015","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.matt.2021.02.015","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3143460494","pdf_url":"http://www.cell.com/article/S2590238521000709/pdf","abstract":"Metal–organic frameworks (MOFs) are a widely investigated class of crystalline solids with tunable structures that make it possible to impart specific chemical functionality tailored for a given application. However, the enormous number of possible MOFs that can be synthesized makes it difficult to determine which materials would be the most promising candidates, especially for applications governed by electronic structure properties that are often computationally demanding to simulate and time-consuming to probe experimentally. Here, we have developed the first publicly available quantum-chemical database for MOFs (the “QMOF database”), which consists of properties derived from density functional theory (DFT) for over 14,000 experimentally synthesized MOFs. Throughout this study, we demonstrate how this new database can be used to identify MOFs with targeted electronic structure properties. As a proof-of-concept, we use the QMOF database to evaluate the performance of several machine learning models for the prediction of DFT-computed band gaps and find that crystal graph convolutional neural networks are capable of achieving superior predictive performance, making it possible to circumvent computationally expensive quantum-chemical calculations. We also show how unsupervised learning methods can aid the discovery of otherwise subtle structure–property relationships using the computational findings in this work. We conclude by highlighting several MOFs with low band gaps, a challenging task given the electronically insulating nature of most MOF structures. The data and predictive models generated in this work, as well as the database of MOF structures, should be highly useful to other researchers interested in the predictive design and discovery of MOFs for the many applications dictated by quantum-chemical phenomena.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery\" by Andrew Rosen, Shaelyn M. Iyer, Debmalya Ray et al., https://doi.org/10.1016/j.matt.2021.02.015, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1021/cm100795d","kind":"article","title":"Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory","authors":["Geoffroy Hautier","Christopher C. Fischer","Anubhav Jain","Tim Mueller","Gerbrand Ceder"],"author_count":5,"year":2010,"published":"2010-05-27","venue":"Chemistry of Materials","cited_by":580,"doi":"10.1021/cm100795d","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1021/cm100795d","archive_url":"https://space-kitty.com/papers/doi:10.1021/cm100795d","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W1982598895","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."}],"next":"https://space-kitty.com/api/v1/topics/ai-for-science/papers?sort=top&limit=25&offset=25"}