[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-ai-cuts-search-time-for-growing-catalytic-2d-dendrites":10,"sections":46},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":35,"tags":36,"sources":41,"feedback":45,"feedback_at":22,"cost_usd":45,"total_tokens":45},5590,"ai-cuts-search-time-for-growing-catalytic-2d-dendrites","AI Cuts Search Time for Growing Catalytic 2D Dendrites","Researchers used active learning to find an optimal recipe for branched ReSe2 dendrites in a fraction of the possible experiments, then modeled why it works.","Scientists have used a machine-learning framework to drastically cut the number of lab experiments needed to grow a nanomaterial with catalytic promise.\n\nThe material is ReSe2, grown as two-dimensional dendrites via chemical vapor deposition - a branching, snowflake-like structure useful in catalysis. Researchers built a framework that folds active learning into the experimental process, and it converged on an optimal recipe for highly-branched, electrocatalytically active dendrites in 60 experiments across four iterations, covering less than 1.3% of the possible parameter combinations. From there, a prediction-accuracy-guided data augmentation method paired with a tree-based ML algorithm mapped a non-linear relationship between five process variables and the dendrites' fractal dimension, using just nine more experiments. That let the team dial in a specific, user-defined fractal dimension on demand. A final model combining cross-scale characterization, interpretable ML, and thermodynamics and kinetics knowledge explains how those parameters interact to shape the final structure.\n\nThe real story is the bottleneck this attacks. Materials synthesis is usually parameter-heavy and data-poor: too many variables to brute-force test, and too few successful runs to train a normal model on. This framework tries to solve optimization, customization, and mechanistic explanation in one pipeline instead of treating them as separate research projects.\n\nThe approach was validated on one material system in one lab. Whether it holds up on messier, less cooperative reactions elsewhere is the real test.","[\"materials-science\",\"machine-learning\",\"catalysis\",\"nanomaterials\"]","2026-08-18T04:00:00.000Z","2026-08-19T02:43:26.881Z","2026-08-19T02:43:38.726Z","published",null,[24,30],{"id":25,"reviewer":26,"round":27,"reason":28,"status":29},"publisher-r1","publisher",1,"The body says active learning found the optimal recipe 'in just 60 experiments across four iterations' and separately claims this is 'under 1.3% of the possible parameter combinations,' but 60 out of a search space consistent with 1.3% would require roughly 4,600 total combinations, which is inconsistent with the headline's implied precision and is not substantiated or reconciled anywhere in the text.","resolved",{"id":31,"reviewer":32,"round":33,"reason":34,"status":29},"editor-r2","editor",2,"Remove the closing paragraph that flags the 60-experiments\u002F1.3% figure as an unverified claim to check against the full text — that's an internal fact-checking note addressed to staff, not reporting for readers, so either verify the arithmetic against the paper and state it plainly or drop the number instead of hedging it into the article.","science",[37,38,39,40],"materials-science","machine-learning","catalysis","nanomaterials",[42],{"name":43,"url":44},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2603.16959",0,{"sections":47},[48,53,57,62,67,72,77,81,86,90,95,100,105,110],{"name":49,"slug":50,"count":51,"latest_published_at":52},"AI","ai",3293,"2026-08-20T04:00:00.000Z",{"name":54,"slug":55,"count":56,"latest_published_at":52},"Security","security",435,{"name":58,"slug":59,"count":60,"latest_published_at":61},"Policy","policy",210,"2026-08-19T09:32:27.000Z",{"name":63,"slug":64,"count":65,"latest_published_at":66},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":68,"slug":69,"count":70,"latest_published_at":71},"Hardware","hardware",140,"2026-08-19T18:25:42.000Z",{"name":73,"slug":74,"count":75,"latest_published_at":76},"Consumer Tech","consumer-tech",95,"2026-08-18T16:05:00.000Z",{"name":78,"slug":35,"count":79,"latest_published_at":80},"Science",90,"2026-08-19T18:41:02.000Z",{"name":82,"slug":83,"count":84,"latest_published_at":85},"Software","software",73,"2026-08-18T07:51:50.000Z",{"name":87,"slug":88,"count":89,"latest_published_at":18},"Dev Tools","dev-tools",69,{"name":91,"slug":92,"count":93,"latest_published_at":94},"Startups","startups",47,"2026-08-19T19:13:46.000Z",{"name":96,"slug":97,"count":98,"latest_published_at":99},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":101,"slug":102,"count":103,"latest_published_at":104},"General","general",33,"2026-08-18T22:18:13.000Z",{"name":106,"slug":107,"count":108,"latest_published_at":109},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":111,"slug":112,"count":113,"latest_published_at":114},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]