[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126636-en":3,"doc-seo-126636-105":29,"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},126636,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Rapid Inverse Design of Metamaterials based on Prescribed Mechanical Behavior through Machine Learning","Peer review comments and the authors’ detailed rebuttal address revisions to a manuscript on using a machine-learning pipeline for inverse design of metamaterials. The reviewer feedback focuses on overstated generality in the title, the scope of predictions (1D uniaxial compression and 3D-printed polymers), the novelty of a forward surrogate NN plus inverse NN approach, and concerns about the small training set. The responses outline how the model could extend to other loading cases, include multiobjective extensions, and justify training and implementation choices.","Peer Review File  \nRapid Inverse Design of Metamaterials based on Prescribed Mechanical Behavior through Machine Learning  \nOpen Access This file is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. In the cases where the authors are anonymous, such as is the case for the reports of anonymous peer reviewers, author attribution should be to 'Anonymous Referee' followed by a clear attribution to the source work. The images or other third party material in this file are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material isnot included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/) .  \nEditorial Note: This manuscript has been previously reviewed at another journal that is not operating a transparent peer review scheme. This document only contains reviewer comments and rebuttal letters for versions considered at Nature Communications.  \nREVIEWER COMMENTS  \nReviewer \\#1 (Remarks to the Author):  \nThe authors have thoroughly revised their manuscript and added a significant body of new and helpful information. Especially the provided details on the curve parameterization and on the (newly revised) ML framework are appreciated. The quality of the manuscript has hence improved considerably, yet the following points should still be considered:  \n1. The manuscript, especially in the title, still overstates the generality of the findings; e.g., the model only predicts a 1D uniaxial compressive response (not the general \"mechanical behavior\"), it only applies to 3D-printed polymers (as also pointed out by reviewer 2), etc.  \n2. The newly revised ML approach, consisting of a forward surrogate NN and an inverse NN, is not novel nor original but was in fact introduced in reference [19] of the manuscript (Kumar et al. ) . [19] focused on elastic stiffness but otherwise used the same inverse design setup of two sequentially trained NNs for forward and inverse maps to overcome exactly the non-uniqueness addressed here. It would be warranted to properly credit in the manuscript and SI where this approach is adopted from.  \n3. The training set seems tiny compared to comparable inverse design approaches in the literature. Could the authors provide a reasonable explanation for why such a small data set is sufficient for training?  \nDetailed response to Reviewer 1  \nWe thank the referee for the time in conducting the review. We have addressed the reviewer’s comments one-by-one below. Within the manuscript and Supplementary Information itself, the text where key revisions have been made is marked in red.  \nReviewer \\#1 (Remarks to the Author):  \n1. The manuscript, especially in the title, still overstates the generality of the findings; e.g., the model only predicts a 1D uniaxial compressive response (not the general \"mechanical behavior\"), it only applies to 3D-printed polymers (as also pointed out by reviewer 2), etc.  \nResponse: We agree with the reviewer and, here, we comment on how the current model can be extended to other loading cases. We envision that the presented ML pipeline can be readily extended to inverse-design other mechanical behaviors, since the stress-strain curves used herein as input could represent other types of loading such as tension, shear, torsion, and bending, or different strain rate conditions.  \nIn detail, the ML pipeline can be applied to inverse-design of metamaterials that replicates ot","cbCaiseAvhqNXm4R","https://ap.wps.com/l/cbCaiseAvhqNXm4R","pdf",1007271,1,"English","en",105,"# Reviewer Comments\n## Reviewer #1 (Remarks to the Author)\n# Detailed Response to Reviewer 1\n## Response to Comment 1","[{\"question\":\"Why did the reviewer question the generality claimed in the manuscript title?\",\"answer\":\"The reviewer noted the model targets only a 1D uniaxial compressive response and the method is demonstrated for 3D-printed polymers rather than broad “mechanical behavior.”\"},{\"question\":\"What machine-learning strategy did the reviewer say was not novel?\",\"answer\":\"The reviewer argued that the forward surrogate NN plus inverse NN sequential setup existed in earlier work (reference [19]) and should be properly credited.\"},{\"question\":\"How do the authors propose extending the pipeline beyond uniaxial compression?\",\"answer\":\"They propose extending the pipeline by using stress-strain curves for other loading types (tension, shear, torsion, bending) and selecting corresponding training datasets from a databank, with a new input variable classifying the loading type.\"}]","Rapid Inverse Design of Metamaterials based on Prescribed Mechanical Behavior through Machine Learning | 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