{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T22:19:46Z","timestamp":1777673986542,"version":"3.51.4"},"reference-count":117,"publisher":"Tech Science Press","issue":"2","license":[{"start":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T00:00:00Z","timestamp":1740268800000},"content-version":"vor","delay-in-days":53,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.060109","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T03:16:24Z","timestamp":1736824584000},"page":"1463-1492","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":16,"title":["Machine Learning-Based Methods for Materials Inverse Design: A Review"],"prefix":"10.32604","volume":"82","author":[{"given":"Yingli","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yuting","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Haihe","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Sheng","family":"Lei","sequence":"additional","affiliation":[]},{"given":"Haibin","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Jiancheng","family":"Yin","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","first-page":"15","article-title":"Phase mapper: accelerating materials discovery with AI","volume":"39","author":"Bai","year":"2018","journal-title":"AI Mag"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41524-019-0173-4","article-title":"New frontiers for the materials genome initiative","volume":"5","author":"De Pablo","year":"2019","journal-title":"npj Comput. Mater"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"2302530","DOI":"10.1002\/adma.202302530","article-title":"Deep learning in mechanical metamaterials: from prediction and generation to inverse design","volume":"35","author":"Zheng","year":"2023","journal-title":"Adv Mater"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"eade0828","DOI":"10.1126\/sciadv.ade0828","article-title":"Classification of properties and their relation to chemical bonding: essential steps toward the inverse design of functional materials","volume":"8","author":"Sch\u00f6n","year":"2022","journal-title":"Sci Adv"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"3490","DOI":"10.1021\/acs.chemmater.4c00643","article-title":"Artificial intelligence driving materials discovery? Perspective on the article: scaling deep learning for materials discovery","volume":"36","author":"Cheetham","year":"2024","journal-title":"Chem Mater"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"e12425","DOI":"10.1002\/inf2.12425","article-title":"Methods, progresses, and opportunities of materials informatics","volume":"5","author":"Li","year":"2023","journal-title":"InfoMat"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1002\/inf2.12028","article-title":"Machine learning in materials science","volume":"1","author":"Wei","year":"2019","journal-title":"InfoMat"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1557\/mrs.2019.158","article-title":"Artificial intelligence for materials discovery","volume":"44","author":"Gomes","year":"2019","journal-title":"MRS Bull"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"e12194","DOI":"10.1002\/eom2.12194","article-title":"Application of machine learning for advanced material prediction and design","volume":"4","author":"Chan","year":"2022","journal-title":"EcoMat"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.jmst.2020.12.010","article-title":"Accelerating materials discovery using machine learning","volume":"79","author":"Juan","year":"2021","journal-title":"J Mater Sci Technol"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"e1171","DOI":"10.1002\/smm2.1171","article-title":"Perspective on machine learning in energy material discovery","volume":"4","author":"Li","year":"2023","journal-title":"SmartMat"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"3115","DOI":"10.1039\/D0NA00388C","article-title":"Machine learning-driven new material discovery","volume":"2","author":"Cai","year":"2020","journal-title":"Nanoscale Adv"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.jmat.2020.02.011","article-title":"Metaheuristic-based inverse design of materials\u2014a survey","volume":"6","author":"Liao","year":"2020","journal-title":"J. Materiomics"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1038\/533022a","article-title":"Can artificial intelligence create the next wonder material?","volume":"533","author":"Nosengo","year":"2016","journal-title":"Nature"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1557\/mrs.2016.164","article-title":"Role of materials data science and informatics in accelerated materials innovation","volume":"41","author":"Kalidindi","year":"2016","journal-title":"MRS Bull"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1021\/acs.jpclett.2c00576","article-title":"Materials data toward machine learning: advances and challenges","volume":"13","author":"Zhu","year":"2022","journal-title":"J Phys Chem Lett"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1007\/s40843-023-2777-2","article-title":"Dealing with the big data challenges in AI for thermoelectric materials","volume":"67","author":"Jia","year":"2024","journal-title":"Sci China Mater"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/s41586-018-0337-2","article-title":"Machine learning for molecular and materials science","volume":"559","author":"Butler","year":"2018","journal-title":"Nature"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"036001","DOI":"10.1088\/2515-7639\/ab13bb","article-title":"The NOMAD laboratory: from data sharing to artificial intelligence","volume":"2","author":"Draxl","year":"2019","journal-title":"J Phys Mater"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.3390\/nano12172957","article-title":"A review of performance prediction based on machine learning in materials science","volume":"12","author":"Fu","year":"2022","journal-title":"Nanomater"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3616864","article-title":"Explainable reinforcement learning: a survey and comparative review","volume":"56","author":"Milani","year":"2024","journal-title":"ACM Comput Surv"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"7908","DOI":"10.1039\/D3SC05281H","article-title":"Materials discovery with extreme properties via reinforcement learning-guided combinatorial chemistry","volume":"15","author":"Kim","year":"2024","journal-title":"Chem Sci"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"109386","DOI":"10.1016\/j.asoc.2022.109386","article-title":"Deep reinforcement learning with a critic-value-based branch tree for the inverse design of two-dimensional optical devices","volume":"127","author":"Hwang","year":"2022","journal-title":"Appl Soft Comput"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"7392","DOI":"10.1021\/acs.jcim.3c01220","article-title":"VGAE-MCTS: a new molecular generative model combining the variational graph auto-encoder and monte carlo tree search","volume":"63","author":"Iwata","year":"2023","journal-title":"J Chem Inf Model"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TCIAIG.2012.2186810","article-title":"A survey of monte carlo tree search methods","volume":"4","author":"Browne","year":"2012","journal-title":"IEEE Trans Comput Intell AI Games"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"4924","DOI":"10.1021\/acs.jpclett.2c00624","article-title":"Deep reinforcement learning for molecular inverse problem of nuclear magnetic resonance spectra to molecular structure","volume":"13","author":"Sridharan","year":"2022","journal-title":"J Phys Chem Lett"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1613\/jair.5507","article-title":"On monte carlo tree search and reinforcement learning","volume":"60","author":"Vodopivec","year":"2017","journal-title":"J Artif Intell Res"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"3594","DOI":"10.1021\/acs.jpclett.3c00242","volume":"14","author":"Song","year":"2023","journal-title":"J Phys Chem Lett"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1038\/s41524-023-01128-y","article-title":"A continuous action space tree search for INverse desiGn (CASTING) framework for materials discovery","volume":"9","author":"Banik","year":"2023","journal-title":"NPJ Comput Mater"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"23653","DOI":"10.1039\/D0NR06091G","article-title":"Accelerating copolymer inverse design using monte carlo tree search","volume":"12","author":"Patra","year":"2020","journal-title":"Nanoscale"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s00500-016-2474-6","article-title":"Particle swarm optimization algorithm: an overview","volume":"22","author":"Wang","year":"2018","journal-title":"Soft Comput"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1021\/ci800374h","article-title":"Modified particle swarm optimization algorithm for adaptively configuring globally optimal classification and regression trees","volume":"49","author":"Zhou","year":"2009","journal-title":"J Chem Inf Model"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1021\/ci3000503","article-title":"Searching for coordinated activity cliffs using particle swarm optimization","volume":"52","author":"Namasivayam","year":"2012","journal-title":"J Chem Inf Model"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"110881","DOI":"10.1016\/j.commatsci.2021.110881","article-title":"A reverse design model for high-performance and low-cost magnesium alloys by machine learning","volume":"201","author":"Mi","year":"2022","journal-title":"Comput Mater Sci"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"6702","DOI":"10.1021\/acs.macromol.7b01204","article-title":"Inverse design of bulk morphologies in multiblock polymers using particle swarm optimization","volume":"50","author":"Khadilkar","year":"2017","journal-title":"Macromolecules"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1038\/nature17439","article-title":"Machine-learning-assisted materials discovery using failed experiments","volume":"533","author":"Raccuglia","year":"2016","journal-title":"Nature"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"2006245","DOI":"10.1002\/adfm.202006245","article-title":"Surrogate model via artificial intelligence method for accelerating screening materials and performance prediction","volume":"31","author":"Wang","year":"2021","journal-title":"Adv Funct Mater"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1038\/s41524-019-0227-7","article-title":"A property-oriented design strategy for high performance copper alloys via machine learning","volume":"5","author":"Wang","year":"2019","journal-title":"npj Comput Mater"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.jmst.2021.05.011","volume":"98","author":"Jiang","year":"2022","journal-title":"J Mater Sci Technol"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1021\/acsphotonics.7b01377","article-title":"Training deep neural networks for the inverse design of nanophotonic structures","volume":"5","author":"Liu","year":"2018","journal-title":"ACS Photonics"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"108527","DOI":"10.1016\/j.engappai.2024.108527","article-title":"Using a generative adversarial network for the inverse design of soft morphing composite beams","volume":"133","author":"Brzin","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"109544","DOI":"10.1016\/j.matdes.2021.109544","article-title":"Exploration of optimal microstructure and mechanical properties in continuous microstructure space using a variational autoencoder","volume":"202","author":"Kim","year":"2021","journal-title":"Mater Des"},{"key":"ref44","first-page":"3","article-title":"Generative deep learning as a tool for inverse design of high-entropy refractory alloys","volume":"1","author":"Debnath","year":"2021","journal-title":"J Mater Inform"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"2101207","DOI":"10.1002\/advs.202101207","article-title":"Machine-learning microstructure for inverse material design","volume":"8","author":"Pei","year":"2021","journal-title":"Adv Sci"},{"key":"ref46","doi-asserted-by":"crossref","first-page":"107214","DOI":"10.1016\/j.nanoen.2022.107214","article-title":"Machine learning assisted synthesis of lithium-ion batteries cathode materials","volume":"93","author":"Liow","year":"2022","journal-title":"Nano Energy"},{"key":"ref47","doi-asserted-by":"crossref","first-page":"110166","DOI":"10.1016\/j.commatsci.2020.110166","article-title":"Inverse design of composite metal oxide optical materials based on deep transfer learning","volume":"188","author":"Dong","year":"2021","journal-title":"Comput Mater Sci"},{"key":"ref48","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1016\/j.jma.2024.01.005","article-title":"Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization","volume":"12","author":"Mi","year":"2024","journal-title":"J Magnes Alloys"},{"key":"ref49","doi-asserted-by":"crossref","unstructured":"Ra\u00dfloff A, Seibert P, Kalina KA, K\u00e4stner M. Inverse design of spinodoid structures using Bayesian optimization. arXiv:2402.13054. 2024.","DOI":"10.1007\/s00466-024-02587-w"},{"key":"ref50","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.mattod.2021.08.012","article-title":"Accelerating materials discovery with Bayesian optimization and graph deep learning","volume":"51","author":"Zuo","year":"2021","journal-title":"Mater Today"},{"key":"ref51","doi-asserted-by":"crossref","first-page":"134902","DOI":"10.1063\/5.0012392","article-title":"Inverse design of acoustic metamaterials based on machine learning using a Gauss-Bayesian model","volume":"128","author":"Zheng","year":"2020","journal-title":"J Appl Phys"},{"key":"ref52","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1038\/s41524-019-0263-3","article-title":"Attribute driven inverse materials design using deep learning Bayesian framework","volume":"5","author":"Tagade","year":"2019","journal-title":"npj Comput Mater"},{"key":"ref53","doi-asserted-by":"crossref","first-page":"B247","DOI":"10.1364\/PRJ.416294","article-title":"Genetic-algorithm-based deep neural networks for highly efficient photonic device design","volume":"9","author":"Ren","year":"2021","journal-title":"Photonics Res"},{"key":"ref54","unstructured":"Lee Y, Choi G, Yoon M, Kim C. Genetic algorithm for constrained molecular inverse design. arXiv:2112.03518. 2021."},{"key":"ref55","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1002\/lpor.201000014","article-title":"Topology optimization for nano-photonics","volume":"5","author":"Jensen","year":"2011","journal-title":"Laser Photonics Rev"},{"key":"ref56","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1364\/JOSAB.406048","article-title":"Inverse design in photonics by topology optimization: tutorial","volume":"38","author":"Christiansen","year":"2021","journal-title":"J Opt Soc Am B"},{"key":"ref57","doi-asserted-by":"crossref","first-page":"2000642","DOI":"10.1002\/adfm.202000642","article-title":"Topology optimization-based inverse design of plasmonic nanodimer with maximum near-field enhancement","volume":"30","author":"Chen","year":"2020","journal-title":"Adv Funct Mater"},{"key":"ref58","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1109\/TC.1984.1676394","article-title":"Computer performance evaluation methodology","volume":"33","author":"Heidelberger","year":"1984","journal-title":"IEEE Trans Comput"},{"key":"ref59","doi-asserted-by":"crossref","first-page":"4954","DOI":"10.1021\/acs.chemmater.0c01907","article-title":"Machine learning for materials scientists: an introductory guide toward best practices","volume":"32","author":"Wang","year":"2020","journal-title":"Chem Mater"},{"key":"ref60","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1016\/j.jmst.2016.04.003","article-title":"First-principles calculations of strengthening compounds in magnesium alloy: a general review","volume":"32","author":"Liu","year":"2016","journal-title":"J Mater Sci Technol"},{"key":"ref61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jmst.2023.04.072","article-title":"Simultaneous enhancement in mechanical and corrosion properties of Al-Mg-Si alloys using machine learning","volume":"167","author":"Feng","year":"2023","journal-title":"J Mater Sci Technol"},{"key":"ref62","doi-asserted-by":"crossref","first-page":"173144","DOI":"10.1016\/j.jallcom.2023.173144","article-title":"Inverse design of high entropy alloys using a deep interpretable scheme for materials attribution analysis","volume":"976","author":"Lee","year":"2024","journal-title":"J Alloys Compd"},{"key":"ref63","doi-asserted-by":"crossref","first-page":"11012","DOI":"10.1038\/s41598-021-90237-z","article-title":"A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys","volume":"11","author":"Lee","year":"2021","journal-title":"Sci Rep"},{"key":"ref64","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/j.msea.2018.12.049","article-title":"Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach","volume":"744","author":"Wang","year":"2019","journal-title":"Mater Sci Eng A"},{"key":"ref65","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.jmst.2024.01.089","article-title":"High-strength extruded magnesium alloys: a critical review","volume":"199","author":"Wang","year":"2024","journal-title":"J Mater Sci Technol"},{"key":"ref66","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.jmst.2024.01.086","article-title":"Recent advances in machine learning-assisted fatigue life prediction of additive manufactured metallic materials: a review","volume":"198","author":"Wang","year":"2024","journal-title":"J Mater Sci Technol"},{"key":"ref67","doi-asserted-by":"crossref","first-page":"215888","DOI":"10.1016\/j.ccr.2024.215888","article-title":"Combining machine learning and metal-organic frameworks research: novel modeling, performance prediction, and materials discovery","volume":"514","author":"Li","year":"2024","journal-title":"Coord Chem Rev"},{"key":"ref68","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s12613-022-2458-8","article-title":"Recent progress in the machine learning-assisted rational design of alloys","volume":"29","author":"Fu","year":"2022","journal-title":"Int J Miner Metall Mater"},{"key":"ref69","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.jmst.2023.04.074","article-title":"Materials genome strategy for metallic glasses","volume":"166","author":"Lu","year":"2023","journal-title":"J Mater Sci Technol"},{"key":"ref70","doi-asserted-by":"crossref","first-page":"100746","DOI":"10.1016\/j.mser.2023.100746","article-title":"Recent applications of machine learning in alloy design: a review","volume":"155","author":"Hu","year":"2023","journal-title":"Mater Sci Eng R Rep"},{"key":"ref71","doi-asserted-by":"crossref","first-page":"100722","DOI":"10.1016\/j.pmatsci.2020.100722","article-title":"Sustainability through alloy design: challenges and opportunities","volume":"117","author":"Cann","year":"2021","journal-title":"Prog Mater Sci"},{"key":"ref72","doi-asserted-by":"crossref","first-page":"046401","DOI":"10.1103\/PhysRevLett.97.046401","article-title":"Searching for alloy configurations with target physical properties: impurity design via a genetic algorithm inverse band structure approach","volume":"97","author":"Dudiy","year":"2006","journal-title":"Phys Rev Lett"},{"key":"ref73","doi-asserted-by":"crossref","first-page":"109723","DOI":"10.1016\/j.isci.2024.109723","article-title":"Experimentally validated inverse design of multi-property Fe-Co-Ni alloys","volume":"27","author":"Padhy","year":"2024","journal-title":"iScience"},{"key":"ref74","doi-asserted-by":"crossref","first-page":"1448","DOI":"10.1007\/s10853-023-09317-2","article-title":"Inverse design of aluminium alloys using multi-targeted regression","volume":"59","author":"Bhat","year":"2024","journal-title":"J Mater Sci"},{"key":"ref75","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/s41377-021-00499-5","article-title":"Demonstration of an integrated nanophotonic chip-scale alkali vapor magnetometer using inverse design","volume":"10","author":"Sebbag","year":"2021","journal-title":"Light Sci Appl"},{"key":"ref76","doi-asserted-by":"crossref","first-page":"2002923","DOI":"10.1002\/advs.202002923","article-title":"Tackling photonic inverse design with machine learning","volume":"8","author":"Liu","year":"2021","journal-title":"Adv Sci"},{"key":"ref77","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/s42005-018-0058-8","article-title":"Machine learning inverse problem for topological photonics","volume":"1","author":"Pilozzi","year":"2018","journal-title":"Commun Phys"},{"key":"ref78","doi-asserted-by":"crossref","first-page":"2300500","DOI":"10.1002\/lpor.202300500","article-title":"Inverse design of photonic systems","volume":"18","author":"MacLellan","year":"2024","journal-title":"Laser Photonics Rev"},{"key":"ref79","doi-asserted-by":"crossref","first-page":"24264","DOI":"10.1021\/acsami.9b05857","article-title":"Simultaneous inverse design of materials and structures via deep learning: demonstration of dipole resonance engineering using core-shell nanoparticles","volume":"11","author":"So","year":"2019","journal-title":"ACS Appl Mater Interfaces"},{"key":"ref80","doi-asserted-by":"crossref","first-page":"2100548","DOI":"10.1002\/adom.202100548","article-title":"Global inverse design across multiple photonic structure classes using generative deep learning","volume":"9","author":"Yeung","year":"2021","journal-title":"Adv Opt Mater"},{"key":"ref81","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1038\/s41377-023-01253-9","article-title":"Inverse-designed silicon carbide quantum and nonlinear photonics","volume":"12","author":"Yang","year":"2023","journal-title":"Light Sci Appl"},{"key":"ref82","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1515\/nanoph-2023-0750","article-title":"Inverse design in photonic crystals","volume":"13","author":"Deng","year":"2024","journal-title":"Nanophotonics"},{"key":"ref83","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1002\/aenm.201200538","article-title":"Inverse design of high absorption thin-film photovoltaic materials","volume":"3","author":"Yu","year":"2013","journal-title":"Adv Energy Mater"},{"key":"ref84","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1038\/s41566-018-0246-9","article-title":"Outlook for inverse design in nanophotonics","volume":"12","author":"Molesky","year":"2018","journal-title":"Nat Photonics"},{"key":"ref85","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1021\/acsphotonics.1c01636","article-title":"Enhancing adjoint optimization-based photonic inverse design with explainable machine learning","volume":"9","author":"Yeung","year":"2022","journal-title":"ACS Photonics"},{"key":"ref86","doi-asserted-by":"crossref","first-page":"4206","DOI":"10.1126\/sciadv.aar4206","article-title":"Nanophotonic particle simulation and inverse design using artificial neural networks","volume":"4","author":"Peurifoy","year":"2018","journal-title":"Sci Adv"},{"key":"ref87","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1002\/adma.201901111","article-title":"Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy","volume":"31","author":"Ma","year":"2019 Aug 28","journal-title":"Adv Mater"},{"key":"ref88","doi-asserted-by":"crossref","first-page":"2409","DOI":"10.1038\/s41467-022-29973-3","article-title":"Inverse design enables large-scale high-performance meta-optics reshaping virtual reality","volume":"13","author":"Li","year":"2022 May 3","journal-title":"Nat Commun"},{"key":"ref89","doi-asserted-by":"crossref","first-page":"068701","DOI":"10.1103\/PhysRevLett.108.068701","article-title":"Identification of potential photovoltaic absorbers based on first-principles spectroscopic screening of materials","volume":"108","author":"Yu","year":"2012","journal-title":"Phys Rev Lett"},{"key":"ref90","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1364\/OL.512100","article-title":"Exploring the fundamental limits of integrated beam splitters with arbitrary phase via topology optimization","volume":"49","author":"Nanda","year":"2024","journal-title":"Opt Lett"},{"key":"ref91","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1021\/acsphotonics.1c00260","article-title":"Inverse design of plasmonic structures with FDTD","volume":"8","author":"Zeng","year":"2021","journal-title":"ACS Photonics"},{"key":"ref92","doi-asserted-by":"crossref","first-page":"4467","DOI":"10.1364\/OE.442074","article-title":"High-performance hybrid time\/frequency-domain topology optimization for large-scale photonics inverse design","volume":"30","author":"Hammond","year":"2022","journal-title":"Opt Express"},{"key":"ref93","doi-asserted-by":"crossref","first-page":"101141","DOI":"10.1016\/j.apmt.2021.101141","article-title":"Recent progress in acoustic materials and noise control strategies\u2014a review","volume":"24","author":"Tao","year":"2021","journal-title":"Appl Mater Today"},{"key":"ref94","doi-asserted-by":"crossref","first-page":"15322","DOI":"10.1038\/s41598-019-51662-3","article-title":"Predicting the dispersion relations of one-dimensional phononic crystals by neural networks","volume":"9","author":"Liu","year":"2019","journal-title":"Sci Rep"},{"key":"ref95","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.cad.2016.05.011","article-title":"Efficient wave-based acoustic material design optimization","volume":"78","author":"Morales","year":"2016","journal-title":"Comput-Aided Des"},{"key":"ref96","doi-asserted-by":"crossref","first-page":"108153","DOI":"10.1016\/j.apacoust.2021.108153","article-title":"Inverse design and experimental verification of an acoustic sink based on machine learning","volume":"180","author":"Gao","year":"2021","journal-title":"Appl Acoust"},{"key":"ref97","doi-asserted-by":"crossref","first-page":"113263","DOI":"10.1016\/j.cma.2020.113263","article-title":"Inverse band gap design of elastic metamaterials for P and SV wave control","volume":"370","author":"Goh","year":"2020","journal-title":"Comput Methods Appl Mech Eng"},{"key":"ref98","doi-asserted-by":"crossref","first-page":"108190","DOI":"10.1016\/j.ymssp.2021.108190","article-title":"A physics-constrained deep learning based approach for acoustic inverse scattering problems","volume":"164","author":"Wu","year":"2022","journal-title":"Mech Syst Signal Process"},{"key":"ref99","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1515\/nanoph-2021-0639","article-title":"Intelligent on-demand design of phononic metamaterials","volume":"11","author":"Jin","year":"2022","journal-title":"Nanophotonics"},{"key":"ref100","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.jsv.2008.03.042","article-title":"Acoustic design by topology optimization","volume":"317","author":"D\u00fchring","year":"2008","journal-title":"J Sound Vib"},{"key":"ref101","doi-asserted-by":"crossref","first-page":"108522","DOI":"10.1016\/j.apacoust.2021.108522","article-title":"Machine learning inversion design and application verification of a broadband acoustic filtering structure","volume":"187","author":"Cheng","year":"2022","journal-title":"Appl Acoust"},{"key":"ref102","doi-asserted-by":"crossref","first-page":"112737","DOI":"10.1016\/j.cma.2019.112737","article-title":"Designing phononic crystal with anticipated band gap through a deep learning based data-driven method","volume":"361","author":"Li","year":"2020","journal-title":"Comput Methods Appl Mech Eng"},{"key":"ref103","doi-asserted-by":"crossref","unstructured":"Sun X, Jia H, Yang Y, Zhao H, Bi Y, Sun Z, et al. Acoustic structure inverse design and optimization using deep learning. 2021 [cited 2024 May 20]. Available from: https:\/\/www.researchsquare.com\/article\/rs-255615\/v1.","DOI":"10.21203\/rs.3.rs-255615\/v1"},{"key":"ref104","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1021\/acs.accounts.0c00785","article-title":"Data-driven strategies for accelerated materials design","volume":"54","author":"Pollice","year":"2021","journal-title":"Acc Chem Res"},{"key":"ref105","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1109\/OJUFFC.2023.3314396","article-title":"Neural network-based inverse design of nonlinear phononic crystals","volume":"3","author":"Huang","year":"2023","journal-title":"IEEE Open J Ultrason Ferroelectr Freq Control"},{"key":"ref106","doi-asserted-by":"crossref","first-page":"4838","DOI":"10.1038\/s41467-023-40459-8","article-title":"Applied machine learning as a driver for polymeric biomaterials design","volume":"14","author":"McDonald","year":"2023","journal-title":"Nat Commun"},{"key":"ref107","doi-asserted-by":"crossref","first-page":"2200243","DOI":"10.1002\/aisy.202200243","article-title":"The rise of machine learning in polymer discovery","volume":"5","author":"Yan","year":"2023","journal-title":"Adv Intell Syst"},{"key":"ref108","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1016\/j.polymdegradstab.2013.06.024","article-title":"Review of polymer oxidation and its relationship with materials performance and lifetime prediction","volume":"98","author":"Celina","year":"2013","journal-title":"Polym Degrad Stab"},{"key":"ref109","doi-asserted-by":"crossref","first-page":"7607","DOI":"10.1039\/D1SM00725D","article-title":"Data-driven algorithms for inverse design of polymers","volume":"17","author":"Sattari","year":"2021","journal-title":"Soft Matter"},{"key":"ref110","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1038\/s41524-019-0209-9","article-title":"Machine learning enables polymer cloud-point engineering via inverse design","volume":"5","author":"Kumar","year":"2019","journal-title":"npj Comput Mater"},{"key":"ref111","doi-asserted-by":"crossref","first-page":"0121","DOI":"10.1038\/s41570-018-0121","article-title":"Inverse design in search of materials with target functionalities","volume":"2","author":"Zunger","year":"2018","journal-title":"Nat Rev Chem"},{"key":"ref112","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/s41524-018-0128-1","article-title":"Deep-learning-based inverse design model for intelligent discovery of organic molecules","volume":"4","author":"Kim","year":"2018","journal-title":"npj Comput Mater"},{"key":"ref113","unstructured":"Geng Y, van Anders G, Glotzer SC. Predicting colloidal crystals from shapes via inverse design and machine learning. arXiv:1801.06219. 2018."},{"key":"ref114","doi-asserted-by":"crossref","first-page":"6177","DOI":"10.1038\/s41467-023-41951-x","article-title":"Inverse design of chiral functional films by a robotic AI-guided system","volume":"14","author":"Xie","year":"2023","journal-title":"Nat Commun"},{"key":"ref115","doi-asserted-by":"crossref","first-page":"5359","DOI":"10.1038\/s41467-021-25490-x","article-title":"Inverse design of glass structure with deep graph neural networks","volume":"12","author":"Wang","year":"2021","journal-title":"Nat Commun"},{"key":"ref116","doi-asserted-by":"crossref","first-page":"2636","DOI":"10.1038\/s41467-021-22897-4","article-title":"Inverse-design magnonic devices","volume":"12","author":"Wang","year":"2021","journal-title":"Nat Commun"},{"key":"ref117","doi-asserted-by":"crossref","first-page":"129948","DOI":"10.1016\/j.fuel.2023.129948","article-title":"Active learning based reverse design of hydrogen production from biomass fuel","volume":"357","author":"Zheng","year":"2024","journal-title":"Fuel"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-82-2\/TSP_CMC_60109\/TSP_CMC_60109.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T06:13:36Z","timestamp":1763100816000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v82n2\/59511"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":117,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.060109","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2024-10-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-26","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-17","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}