1. the complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result; Freeform generative design of complex functional structures 2. the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s); Note only David will provide his phone number. Gerald G. Pereira CSIRO Data61, Private Bag 10, Clayton South, VIC, 3169, Australia gerald.pereira@csiro.au David Howard CSIRO Data61, 1 Technology Court Pullenvale QLD 4069 Australia david.howard@csiro.au +61 (07)3277 4714 Paulus Lahur CSIRO IMT, Private Bag 10, Clayton South, VIC, 3169, Australia paulus.lahur@csiro.au Michael Breedon CSIRO Manufacturing, Private Bag 10, Clayton South, VIC, 3169, Australia michael.breedon@csiro.au Phil Kilby CSIRO Data61, Private Bag 10, Clayton South, VIC, 3169, Australia phil.kilby@csiro.au Christian H. Hornung CSIRO Manufacturing, Private Bag 10, Clayton South, VIC, 3169, Australia christian.hornung@csiro.au 3. the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition); David Howard 4. the abstract of the paper(s); Generative machine learning is poised to revolutionise a range of domains where rational design has long been the de facto approach: where design is practically a time consuming and frustrating process guided by heuristics and intuition. In this article we focus on the domain of flow chemistry, which is an ideal candidate for generative design approaches. We demonstrate a generative machine learning framework that optimises diverse, bespoke reactor elements for flow chemistry applications, combining evolutionary algorithms and a scalable fluid dynamics solver for in silico performance assessment. Experimental verification confirms the discovery of never-before-seen bespoke mixers whose performance exceeds the state of the art by 45%. These findings highlight the power of autonomous generative design to improve the operational performance of complex functional structures, with potential wide-ranging industrial applications. 5. a list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies; A, B, G 6. a statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission); (A) The result has been patented, with active patents in the following: 1) Machine-learning based method and system for design of mixing devices, 202657PRV (Static mixer design algorithm, App. # 2022903490 ) 2) Static mixer element, 538129PRV (design registration for tree and ribbons, App # 2022903488) 3) Adsorbent structures and method and system for designing adsorbent structures, 538159PRV (App. # 2023900789). Several of the flow reactors designed through evolution are shown to be equal to or better than the human-designed state of the art for the reaction class considered. (B) The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal. The result in question is the flow reactor we beat: Patent: Electrochemical flow reactor. Michael David Horne, Bita Bayatsarmadi, Theo Rodopoulos, John Tsanaktsidis, Dayalan Romesh Gunasegaram, Christian Hornung, Darren Fraser, Dylan Marley, Andrew Joseph Urban Publication date: 2021/8/19 Patent office: US Application number: 17266192 Genet, C., Nguyen, X., Bayatsarmadi, B. et al. Reductive aminations using a 3D printed supported metal(0) catalyst system. J Flow Chem 8, 81–88 (2018). https://doi.org/10.1007/s41981-018-0013-6 (G) Designing bespoke flow reactors is incredibly difficult for humans and AI systems alike. The ability to tailor reactor geometry to the reaction requirements is estimated to be a multi-million dollar industry with impacts across Flow Chemistry and related domains. 7. a full citation of the paper (that is, author names; title, publication date; name of journal, conference, or book in which article appeared; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable); Pereira, G.G., Howard, D., Lahur, P. Breedon, M., Kilby, P., Hornung, C. Freeform generative design of complex functional structures. Sci Rep 14, 11918 (2024). https://doi.org/10.1038/s41598-024-62830-5 8. a statement either that "any prize money, if any, is to be divided equally among the co-authors" OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors; "any prize money, if any, is to be divided equally among the co-authors" 9. a statement stating why the authors expect that their entry would be the "best," and The evolved Flow Reactor beats all known human-designed baselines by 45% for the class of reactions considered. This result is experimentally verified with a 3D printed flow reactor designed by evolution, which is directly compared against the current state of the art used industrially for the reaction class. The reactor is clearly 'evolved', displaying features and geometric arrangements that a human would find near-impossible to design themselves. 10. An indication of the general type of genetic or evolutionary computation used, such as GA (genetic algorithms), GP (genetic programming), ES (evolution strategies), EP (evolutionary programming), LCS (learning classifier systems), GI (genetic improvement), GE (grammatical evolution), GEP (gene expression programming), DE (differential evolution), etc. GA - NSGAII 11. The date of publication of each paper. If the date of publication is not on or before the deadline for submission, but instead, the paper has been unconditionally accepted for publication and is “in press” by the deadline for this competition, the entry must include a copy of the documentation establishing that the paper meets the "in press" requirement. Published 24 May 2024.