@article{10.1109/SSCI52147.2023.10372035,
	abstract = {{A fundamental problem in chemical product design is how to suitably identify chemical compounds that optimise multiple properties for a given application whilst satisfying relevant constraints. Current product synthesis generally uses trial-and-error experimentation, requiring lengthy and expensive research and development efforts. This paper introduces a novel computational chemistry approach for product design combining geometric deep learning for inference of property values and evolutionary multi-objective optimisation for identification of products of interest. Preliminary empirical results indicate that the proposed approach can be used to optimise product design considering multiple objectives and constraints given incomplete molecular attribute information.}},
	author = {Aslan, Bilal and Silva, Flavio S Correa da and Nitschke, Geoff},
	date-modified = {2024-06-21 18:42:00 -0600},
	journal = {2023 IEEE Symposium Series on Computational Intelligence (SSCI)},
	month = {12},
	pages = {152--157},
	title = {{Multi-Objective Evolution for Automated Chemistry}},
	volume = {00},
	year = {2023},
	url = {https://url.org/10.1109/SSCI52147.2023.10372035}}

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