Two-step machine learning enables optimized nanoparticle synthesis

作     者：Flore Mekki-Berrada Zekun Ren Tan Huang Wai Kuan Wong Fang Zheng Jiaxun Xie Isaac Parker Siyu Tian Senthilnath Jayavelu Zackaria Mahfoud Daniil Bash Kedar Hippalgaonkar Saif Khan Tonio Buonassisi Qianxiao Li Xiaonan Wang 

作者机构：Department of Chemical and Biomolecular EngineeringNational University of SingaporeSingaporeSingapore Singapore-MIT Alliance for Research and Technology SMARTSingaporeSingapore Institute for Infocomm ResearchAgency for ScienceTechnology and Research(A*STAR)SingaporeSingapore Institute of Materials Research&EngineeringSingaporeSingapore Department of Materials Science and EngineeringNanyang Technological UniversitySingaporeSingapore Massachusetts Institute of TechnologyCambridgeMAUSA Department of MathematicsNational University of SingaporeSingaporeSingapore Institute of High Performance ComputingSingaporeSingapore 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2021年第7卷第1期

页      面：498-507页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070205[理学-凝聚态物理] 081104[工学-模式识别与智能系统] 08[工学] 080501[工学-材料物理与化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：We would like to thank Swee Liang Wong,Lim Yee-Fun,Xu Yang,Jatin Kumar,Liu Xiali and Li Jiali for equipment support and helpful discussions.Support was provided by the Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science,Technology and Research under Grant no.A1898b0043,(F.M.B.,Z.R.,T.H.,W.K.W.,F.Z.,J.X.,S.J.,Z.M.,D.B.,K.H.,S.A.K.,Q.L.,and X.W.) Singapore’s National Research Foundation through the Singapore MIT Alliance for Research and Technology’s Low energy electronic systems(LEES)IRG(Z.R.,I.P.S.T.,and T.B.) 

主　　题：spectrum synthesis. synthesis 

摘      要：In materials science,the discovery of recipes that yield nanomaterials with defined optical properties is costly and *** this study,we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance *** a Gaussian process-based Bayesian optimization(BO)with a deep neural network(DNN),the algorithmic framework is able to converge towards the target spectrum after sampling 120 *** the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum,the DNN is used to predict the colour palette accessible with the reaction *** remaining interpretable by humans,the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties,such as the role of each reactant on the shape and amplitude of the absorbance spectrum.