Developing flow photo-thiol–ene functionalizations of cinchona alkaloids with an autonomous self-optimizing flow reactor

Kouakou Eric Konan, Abollé Abollé, Elvina Barré, Ehu Camille Aka, Vincent Coeffard and François-Xavier Felpin

React. Chem. Eng., 2022, 7, 1346
DOI: 10.1039/D1RE00509J

A micro-flow rapid dual activation approach for urethane-protected α-amino acid N-carboxyanhydride synthesis

Ren Okabe, Naoto Sugisawa and Shinichiro Fuse

Org. Biomol. Chem., 2022, 20, 3303
DOI: 10.1039/D2OB00167E

Automated optimization under dynamic flow conditions

Jonathan P. McMullen and Brian M. Wyvratt

React. Chem. Eng., 2023, 8, 137
DOI: 10.1039/D2RE00256F

Artificial intelligence and automation in computer aided synthesis planning

Amol Thakkar, Simon Johansson, Kjell Jorner, David Buttar, Jean-Louis Reymond and Ola Engkvist

React. Chem. Eng., 2021, 6, 27
DOI: 10.1039/D0RE00340A

Autonomous closed-loop photochemical reaction optimization for the synthesis of various angiotensin II receptor blocker molecules

Dnyaneshwar Aand, Abhilash Rana, Amirreza Mottafegh, Dong Pyo Kim and Ajay K. Singh

React. Chem. Eng., 2024, 9, 2427
DOI: 10.1039/D4RE00138A

Continuous stirred-tank reactor cascade platform for self-optimization of reactions involving solids

Kakasaheb Y. Nandiwale, Travis Hart, Andrew F. Zahrt, Anirudh M. K. Nambiar, Prajwal T. Mahesh, Yiming Mo, María José Nieves-Remacha, Martin D. Johnson, Pablo García-Losada, Carlos Mateos, Juan A. Rincón and Klavs F. Jensen

React. Chem. Eng., 2022, 7, 1315
DOI: 10.1039/D2RE00054G

Toward autonomous design and synthesis of novel inorganic materials

Nathan J. Szymanski, Yan Zeng, Haoyan Huo, Christopher J. Bartel, Haegyeom Kim and Gerbrand Ceder

Mater. Horiz., 2021, 8, 2169
DOI: 10.1039/D1MH00495F

Autonomous reaction self-optimization using in-line high-field NMR spectroscopy

Nour El Sabbagh, Margherita Bazzoni, Yuliia Horbenko, Aurélie Bernard, Daniel Cortés-Borda, Patrick Giraudeau, François-Xavier Felpin and Jean-Nicolas Dumez

React. Chem. Eng., 2024, 9, 2599
DOI: 10.1039/D4RE00270A

A universal reactor platform for batch and flow: application to homogeneous and heterogeneous hydrogenation

Fanfu Guan, Nikil Kapur, Louise Sim, Connor J. Taylor, Jialin Wen, Xumu Zhang and A. John Blacker

React. Chem. Eng., 2020, 5, 1903
DOI: 10.1039/D0RE00061B

Accelerating reaction modeling using dynamic flow experiments, part 1: design space exploration

Peter Sagmeister, Christine Schiller, Peter Weiss, Klara Silber, Sebastian Knoll, Martin Horn, Christopher A. Hone, Jason D. Williams and C. Oliver Kappe

React. Chem. Eng., 2023, 8, 2818
DOI: 10.1039/D3RE00243H

Revisiting aromatic diazotization and aryl diazonium salts in continuous flow: highlighted research during 2001–2021

Jianli Chen, Xiaoxuan Xie, Jiming Liu, Zhiqun Yu and Weike Su

React. Chem. Eng., 2022, 7, 1247
DOI: 10.1039/D2RE00001F

Artificial neural networks and data fusion enable concentration predictions for inline process analytics

Peter Sagmeister, Robin Hierzegger, Jason D. Williams, C. Oliver Kappe and Stefan Kowarik

Digital Discovery, 2022, 1, 405
DOI: 10.1039/D2DD00006G

Automated flow and real-time analytics approach for screening functional group tolerance in heterogeneous catalytic reactions

Kevin Simon, Peter Sagmeister, Rachel Munday, Kevin Leslie, Christopher A. Hone and C. Oliver Kappe

Catal. Sci. Technol., 2022, 12, 1799
DOI: 10.1039/D2CY00059H

Expanding analytical horizons: 3D HPLC calibration surfaces for micromole scale self-optimizing flow reactors

Maëva Vallet, Daniel Cortés-Borda and François-Xavier Felpin

React. Chem. Eng., 2024, 9, 2262
DOI: 10.1039/D3RE00559C

Autonomous model-based experimental design for rapid reaction development

Sebastian Knoll, Clemens E. Jusner, Peter Sagmeister, Jason D. Williams, Christopher A. Hone, Martin Horn and C. Oliver Kappe

React. Chem. Eng., 2022, 7, 2375
DOI: 10.1039/D2RE00208F

Forgotten and forbidden chemical reactions revitalised through continuous flow technology

Arlene Bonner, Aisling Loftus, Alex C. Padgham and Marcus Baumann

Org. Biomol. Chem., 2021, 19, 7737
DOI: 10.1039/D1OB01452H

Sustainability of flow chemistry and microreaction technology

Volker Hessel, Sampurna Mukherjee, Sutanuka Mitra, Arunava Goswami, Nam Nghiep Tran, Francesco Ferlin, Luigi Vaccaro, Fariba Malekpour Galogahi, Nam-Trung Nguyen and Marc Escribà-Gelonch

Green Chem., 2024, 26, 9503
DOI: 10.1039/D4GC01882F

Assessing the impact of deviations in optimized multistep flow synthesis on the scale-up

M. K. Sharma, J. Raval, Gwang-Noh Ahn, Dong-Pyo Kim and A. A. Kulkarni

React. Chem. Eng., 2020, 5, 838
DOI: 10.1039/D0RE00025F

Use of open-source software platform to develop dashboards for control and automation of flow chemistry equipment

C. Johan van der Westhuizen, Jurie du Toit, Nicole Neyt, Darren Riley and Jenny-Lee Panayides

Digital Discovery, 2022, 1, 596
DOI: 10.1039/D2DD00036A

Dynamic sampling in autonomous process optimization

Melodie Christensen, Yuting Xu, Eugene E. Kwan, Michael J. Di Maso, Yining Ji, Mikhail Reibarkh, Alexandra C. Sun, Andy Liaw, Patrick S. Fier, Shane Grosser and Jason E. Hein

Chem. Sci., 2024, 15, 7160
DOI: 10.1039/D3SC06884F

Self-optimizing parallel millifluidic reactor for scaling nanoparticle synthesis

Lu Wang, Lanja R. Karadaghi, Richard L. Brutchey and Noah Malmstadt

Chem. Commun., 2020, 56, 3745
DOI: 10.1039/D0CC00064G

A streamlined synthesis of the neurosteroid 3β-methoxypregnenolone assisted by a statistical experimental design and automation

Valentina Mancino, Federico Croci, Anna Maria Lozza, Bruno Cerra and Antimo Gioiello

React. Chem. Eng., 2020, 5, 300
DOI: 10.1039/C9RE00353C

Multivariate analysis of inline benchtop NMR data enables rapid optimization of a complex nitration in flow

Peter Sagmeister, Johannes Poms, Jason D. Williams and C. Oliver Kappe

React. Chem. Eng., 2020, 5, 677
DOI: 10.1039/D0RE00048E

Meta optimization based on real-time benchmarking of multiple surrogate models for autonomous flow synthesis

Amirreza Mottafegh, Gwang-Noh Ahn and Dong-Pyo Kim

Lab Chip, 2023, 23, 1613
DOI: 10.1039/D2LC00938B

Continuous conversion of furfural to furfuryl alcohol by transfer hydrogenation catalyzed by copper deposited in a monolith reactor

Tiefu Li, Jiaming Liu, Zipeng Li, Peng Zhang, Yunlong Yao, Zhichao Sun, Yao Wang, Ying-Ya Liu and Anjie Wang

React. Chem. Eng., 2023, 8, 377
DOI: 10.1039/D2RE00363E

Accelerating reaction modeling using dynamic flow experiments, part 2: development of a digital twin

Klara Silber, Peter Sagmeister, Christine Schiller, Jason D. Williams, Christopher A. Hone and C. Oliver Kappe

React. Chem. Eng., 2023, 8, 2849
DOI: 10.1039/D3RE00244F

Development of a continuous-flow system with immobilized biocatalysts towards sustainable bioprocessing

Apisit Naramittanakul, Supacha Buttranon, Atitsa Petchsuk, Pimchai Chaiyen and Nopphon Weeranoppanant

React. Chem. Eng., 2021, 6, 1771
DOI: 10.1039/D1RE00189B

Automated multi-objective reaction optimisation: which algorithm should I use?

Pia Müller, Adam D. Clayton, Jamie Manson, Samuel Riley, Oliver S. May, Norman Govan, Stuart Notman, Steven V. Ley, Thomas W. Chamberlain and Richard A. Bourne

React. Chem. Eng., 2022, 7, 987
DOI: 10.1039/D1RE00549A

Energy-, time-, and labor-saving synthesis of α-ketiminophosphonates: machine-learning-assisted simultaneous multiparameter screening for electrochemical oxidation

Masaru Kondo, Akimasa Sugizaki, Md. Imrul Khalid, H. D. P. Wathsala, Kazunori Ishikawa, Satoshi Hara, Takayuki Takaai, Takashi Washio, Shinobu Takizawa and Hiroaki Sasai

Green Chem., 2021, 23, 5825
DOI: 10.1039/D1GC01583D

An automated computational approach to kinetic model discrimination and parameter estimation

Connor J. Taylor, Hikaru Seki, Friederike M. Dannheim, Mark J. Willis, Graeme Clemens, Brian A. Taylor, Thomas W. Chamberlain and Richard A. Bourne

React. Chem. Eng., 2021, 6, 1404
DOI: 10.1039/D1RE00098E

Reaction condition optimization for non-oxidative conversion of methane using artificial intelligence

Hyun Woo Kim, Sung Woo Lee, Gyoung S. Na, Seung Ju Han, Seok Ki Kim, Jung Ho Shin, Hyunju Chang and Yong Tae Kim

React. Chem. Eng., 2021, 6, 235
DOI: 10.1039/D0RE00378F

Automated self-optimization of continuous crystallization of nirmatrelvir API

Kakasaheb Y. Nandiwale, Robert P. Pritchard, Cameron T. Armstrong, Steven M. Guinness and Kevin P. Girard

React. Chem. Eng., 2024, 9, 2460
DOI: 10.1039/D4RE00272E

What can reaction databases teach us about Buchwald–Hartwig cross-couplings?

Martin Fitzner, Georg Wuitschik, Raffael J. Koller, Jean-Michel Adam, Torsten Schindler and Jean-Louis Reymond

Chem. Sci., 2020, 11, 13085
DOI: 10.1039/D0SC04074F

Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening

Natalie S. Eyke, William H. Green and Klavs F. Jensen

React. Chem. Eng., 2020, 5, 1963
DOI: 10.1039/D0RE00232A

Self-optimising continuous-flow hydrothermal reactor for nanoparticle synthesis

Cameron Jackson, Karen Robertson, Vitaliy Sechenyh, Thomas W. Chamberlain, Richard A. Bourne and Edward Lester

React. Chem. Eng., 2025, 10, 511
DOI: 10.1039/D4RE00339J

The rise of continuous flow biocatalysis – fundamentals, very recent developments and future perspectives

Piera De Santis, Lars-Erik Meyer and Selin Kara

React. Chem. Eng., 2020, 5, 2155
DOI: 10.1039/D0RE00335B

Ultra-fast and sustainable formal [3 + 3] cycloadditions enabled by mixed variable optimization on an automated micromole scale flow platform

Kouakou E. Konan, Aravind Senthil Vel, Abollé Abollé, Daniel Cortés-Borda and François-Xavier Felpin

React. Chem. Eng., 2023, 8, 2446
DOI: 10.1039/D3RE00272A

Continuous biphasic chemical processes in a four-phase segmented flow reactor

Amanda A. Volk, Robert W. Epps, Daniel Yonemoto, Felix N. Castellano and Milad Abolhasani

React. Chem. Eng., 2021, 6, 1367
DOI: 10.1039/D1RE00247C

Multi-objective Bayesian optimisation of a two-step synthesis of p-cymene from crude sulphate turpentine

Perman Jorayev, Danilo Russo, Joshua D. Tibbetts, Artur M. Schweidtmann, Paul Deutsch, Steven D. Bull and Alexei A. Lapkin

Chemical Engineering Science, 2022, 247, 116938
DOI: 10.1016/j.ces.2021.116938

Sampling and Analysis in Flow: The Keys to Smarter, More Controllable, and Sustainable Fine‐Chemical Manufacturing

Mathieu A. Morin, Wenyao (Peter) Zhang, Debasis Mallik and Michael G. Organ

Angew Chem Int Ed, 2021, 60, 20606
DOI: 10.1002/anie.202102009

Droplet microfluidic flow platforms for automated reaction screening and optimisation

Zara Arshad, A. John Blacker, Thomas W. Chamberlain, Nikil Kapur, Adam D. Clayton and Richard A. Bourne

Current Opinion in Green and Sustainable Chemistry, 2024, 48, 100940
DOI: 10.1016/j.cogsc.2024.100940

Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead!

Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras, Maarten K. Sabbe, Guy B. Marin and Kevin M. Van Geem

Engineering, 2023, 27, 23
DOI: 10.1016/j.eng.2023.02.019

Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis

Stefan P Schmid, Leon Schlosser, Frank Glorius and Kjell Jorner

Beilstein J. Org. Chem., 2024, 20, 2280
DOI: 10.3762/bjoc.20.196

Local reaction condition optimization via machine learning

Wenhuan Song and Honggang Sun

J Mol Model, 2025, 31
DOI: 10.1007/s00894-025-06365-0

Can a Simple Surrogate Model System Be Used to Develop a Continuous Flow Packed Bed Hydrogenation for a Complex Molecule?

Stefano Martinuzzi, Martin Mex, Jelena Milic, Christopher A. Hone and C. Oliver Kappe

Org. Process Res. Dev., 2025, 29, 363
DOI: 10.1021/acs.oprd.4c00411

Bayesian optimization with adaptive surrogate models for automated experimental design

Bowen Lei, Tanner Quinn Kirk, Anirban Bhattacharya, Debdeep Pati, Xiaoning Qian, Raymundo Arroyave and Bani K. Mallick

npj Comput Mater, 2021, 7
DOI: 10.1038/s41524-021-00662-x

NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

Yifan Wang, Tai-Ying Chen and Dionisios G. Vlachos

J. Chem. Inf. Model., 2021, 61, 5312
DOI: 10.1021/acs.jcim.1c00637

A Brief Introduction to Chemical Reaction Optimization

Connor J. Taylor, Alexander Pomberger, Kobi C. Felton, Rachel Grainger, Magda Barecka, Thomas W. Chamberlain, Richard A. Bourne, Christopher N. Johnson and Alexei A. Lapkin

Chem. Rev., 2023, 123, 3089
DOI: 10.1021/acs.chemrev.2c00798

Parametric effect of continuous synthesis of metal nanoparticles within microchannels and application: A review study

Gaurav Sharma, Susmita Das, Praveen Kumar and Urska Lavrencic Stangar

Chemical Engineering Research and Design, 2024, 209, 142
DOI: 10.1016/j.cherd.2024.07.058

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

Yunchao Xie, Kianoosh Sattari, Chi Zhang and Jian Lin

Progress in Materials Science, 2023, 132, 101043
DOI: 10.1016/j.pmatsci.2022.101043

Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt

Connor W. Coley, Natalie S. Eyke and Klavs F. Jensen

Angewandte Chemie, 2020, 132, 23054
DOI: 10.1002/ange.201909987

Throughput Optimization of Molybdenum Carbide Nanoparticle Catalysts in a Continuous Flow Reactor Using Design of Experiments

Lanja R. Karadaghi, Majed S. Madani, Emily M. Williamson, Anh T. To, Susan E. Habas, Frederick G. Baddour, Joshua A. Schaidle, Daniel A. Ruddy, Richard L. Brutchey and Noah Malmstadt

ACS Appl. Nano Mater., 2022, 5, 1966
DOI: 10.1021/acsanm.1c02916

Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform

Anirudh M. K. Nambiar, Christopher P. Breen, Travis Hart, Timothy Kulesza, Timothy F. Jamison and Klavs F. Jensen

ACS Cent. Sci., 2022, 8, 825
DOI: 10.1021/acscentsci.2c00207

Hybrid Flow-Batch Model for the Efficient Synthesis of 2-(Dimethylamino)-6-methylpyridin-4-ol

Juan A. Rincón, Antonio Navarro, María José Nieves-Remacha, José Eugenio de Diego, J. Craig Ruble, María Luz de la Puente, María Jesús Trigo and Brian A. Schaefer

Org. Process Res. Dev., 2022, 26, 891
DOI: 10.1021/acs.oprd.1c00264

From traditional to data-driven medicinal chemistry: A case study

Ryo Kunimoto, Jürgen Bajorath and Kazumasa Aoki

Drug Discovery Today, 2022, 27, 2065
DOI: 10.1016/j.drudis.2022.04.017

An integrated self-optimizing programmable chemical synthesis and reaction engine

Artem I. Leonov, Alexander J. S. Hammer, Slawomir Lach, S. Hessam M. Mehr, Dario Caramelli, Davide Angelone, Aamir Khan, Steven O’Sullivan, Matthew Craven, Liam Wilbraham and Leroy Cronin

Nat Commun, 2024, 15
DOI: 10.1038/s41467-024-45444-3

From Platform to Knowledge Graph: Evolution of Laboratory Automation

Jiaru Bai, Liwei Cao, Sebastian Mosbach, Jethro Akroyd, Alexei A. Lapkin and Markus Kraft

JACS Au, 2022, 2, 292
DOI: 10.1021/jacsau.1c00438

Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot

Robert W. Epps, Michael S. Bowen, Amanda A. Volk, Kameel Abdel‐Latif, Suyong Han, Kristofer G. Reyes, Aram Amassian and Milad Abolhasani

Advanced Materials, 2020, 32
DOI: 10.1002/adma.202001626

Rapid and Mild Lactamization Using Highly Electrophilic Triphosgene in a Microflow Reactor

Shinichiro Fuse, Keiji Komuro, Yuma Otake, Hisashi Masui and Hiroyuki Nakamura

Chemistry A European J, 2021, 27, 7525
DOI: 10.1002/chem.202100059

Towards the Standardization of Flow Chemistry Protocols for Organic Reactions

Christopher A. Hone and C. Oliver Kappe

Chemistry Methods, 2021, 1, 454
DOI: 10.1002/cmtd.202100059

Process intensification 4.0: A new approach for attaining new, sustainable and circular processes enabled by machine learning

Enrique A. López-Guajardo, Fernando Delgado-Licona, Alejandro J. Álvarez, Krishna D.P. Nigam, Alejandro Montesinos-Castellanos and Ruben Morales-Menendez

Chemical Engineering and Processing - Process Intensification, 2022, 180, 108671
DOI: 10.1016/j.cep.2021.108671

PAT-Enabled Automated Feedforward Control: An Application to the Continuous Manufacture of Apremilast

Hsiao-Wu Hsieh, Daniel J. Griffin, Anirudh M. K. Nambiar, Nandini Sarkar, Hamza Youssef Ismail, Kartik Saigal, Dongying Erin Shen, Nicole Goudas-Salomon, Rasangi Wimalasinghe, Alicia Zeng, Oliver R. Thiel and Matthew G. Beaver

Org. Process Res. Dev., 2024, 28, 2844
DOI: 10.1021/acs.oprd.4c00155

Black-box optimization algorithms for chemical synthesis and reaction condition optimization

Kexin Chen, Yaodong Yang, Bowen Wang, Jianye Hao, Guangyong Chen and Pheng Ann Heng

Sci. Sin.-Chim, 2023, 53, 26
DOI: 10.1360/SSC-2022-0118

Bayesian reaction optimization as a tool for chemical synthesis

Benjamin J. Shields, Jason Stevens, Jun Li, Marvin Parasram, Farhan Damani, Jesus I. Martinez Alvarado, Jacob M. Janey, Ryan P. Adams and Abigail G. Doyle

Nature, 2021, 590, 89
DOI: 10.1038/s41586-021-03213-y

Rapid and Mild One‐Flow Synthetic Approach to Unsymmetrical Sulfamides Guided by Bayesian Optimization

Naoto Sugisawa, Hiroki Sugisawa, Yuma Otake, Roman V. Krems, Hiroyuki Nakamura and Shinichiro Fuse

Chemistry Methods, 2021, 1, 484
DOI: 10.1002/cmtd.202100053

Immobilization strategies for heterogeneous catalysts in micro flow chemistry: Key techniques for efficient and sustainable chemical transformations

Xiao Chen, Mengmeng Fu, Jialun He, Jiayi Lu, Chensheng Wang, Danfeng Jiang, Yingguo Li and Chao Yu

Coordination Chemistry Reviews, 2025, 538, 216723
DOI: 10.1016/j.ccr.2025.216723

Machine Learning-Driven Optimization of Continuous-Flow Photoredox Amine Synthesis

Perman Jorayev, Sebastian Soritz, Simon Sung, Mohammed I. Jeraal, Danilo Russo, Alexandre Barthelme, Frédéric C. Toussaint, Matthew J. Gaunt and Alexei A. Lapkin

Org. Process Res. Dev., 2025, 29, 1411
DOI: 10.1021/acs.oprd.4c00533

Analytical Tools Integrated in Continuous-Flow Reactors: Which One for What?

Mireia Rodriguez-Zubiri and François-Xavier Felpin

Org. Process Res. Dev., 2022, 26, 1766
DOI: 10.1021/acs.oprd.2c00102

Advanced Real‐Time Process Analytics for Multistep Synthesis in Continuous Flow**

Peter Sagmeister, René Lebl, Ismael Castillo, Jakob Rehrl, Julia Kruisz, Martin Sipek, Martin Horn, Stephan Sacher, David Cantillo, Jason D. Williams and C. Oliver Kappe

Angewandte Chemie, 2021, 133, 8220
DOI: 10.1002/ange.202016007

The Medicinal Chemistry in the Era of Machines and Automation: Recent Advances in Continuous Flow Technology

Antimo Gioiello, Alessandro Piccinno, Anna Maria Lozza and Bruno Cerra

J. Med. Chem., 2020, 63, 6624
DOI: 10.1021/acs.jmedchem.9b01956

A Machine Learning‐Enabled Autonomous Flow Chemistry Platform for Process Optimization of Multiple Reaction Metrics

Mohammed I. Jeraal, Simon Sung and Alexei A. Lapkin

Chemistry Methods, 2021, 1, 71
DOI: 10.1002/cmtd.202000044

Advanced Real‐Time Process Analytics for Multistep Synthesis in Continuous Flow**

Peter Sagmeister, René Lebl, Ismael Castillo, Jakob Rehrl, Julia Kruisz, Martin Sipek, Martin Horn, Stephan Sacher, David Cantillo, Jason D. Williams and C. Oliver Kappe

Angew Chem Int Ed, 2021, 60, 8139
DOI: 10.1002/anie.202016007

Recent Advances in the Solid- and Solution-Phase Synthesis of Peptides and Proteins Using Microflow Technology

Hisashi Masui and Shinichiro Fuse

Org. Process Res. Dev., 2022, 26, 1751
DOI: 10.1021/acs.oprd.2c00074

Accelerated Chemical Reaction Optimization Using Multi-Task Learning

Connor J. Taylor, Kobi C. Felton, Daniel Wigh, Mohammed I. Jeraal, Rachel Grainger, Gianni Chessari, Christopher N. Johnson and Alexei A. Lapkin

ACS Cent. Sci., 2023, 9, 957
DOI: 10.1021/acscentsci.3c00050

Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization

Jin Da Tan, Balamurugan Ramalingam, Swee Liang Wong, Jayce Jian Wei Cheng, Yee-Fun Lim, Vijila Chellappan, Saif A. Khan, Jatin Kumar and Kedar Hippalgaonkar

J. Chem. Inf. Model., 2023, 63, 4560
DOI: 10.1021/acs.jcim.3c00504

Automating Synthetic Organic Electrochemistry: Concepts and Advancements

Eduardo Rial‐Rodríguez, C. Oliver Kappe and Gabriele Laudadio

Chemistry Methods, 2025
DOI: 10.1002/cmtd.202500104

Closed-Loop Model-Based Design of Experiments for Kinetic Model Discrimination and Parameter Estimation: Benzoic Acid Esterification on a Heterogeneous Catalyst

Conor Waldron, Arun Pankajakshan, Marco Quaglio, Enhong Cao, Federico Galvanin and Asterios Gavriilidis

Ind. Eng. Chem. Res., 2019, 58, 22165
DOI: 10.1021/acs.iecr.9b04089

Continuous Flow Upgrading of Selected C2–C6Platform Chemicals Derived from Biomass

Romaric Gérardy, Damien P. Debecker, Julien Estager, Patricia Luis and Jean-Christophe M. Monbaliu

Chem. Rev., 2020, 120, 7219
DOI: 10.1021/acs.chemrev.9b00846

Simultaneous Regulation of Molecular Weight Distribution and Composition through RDRP in a Continuous-Flow Reactor: A Kinetic Modeling

Haiyan Xue and Junpo He

Macromolecules, 2025, 58, 1169
DOI: 10.1021/acs.macromol.4c02183

Process analytical technologies and self-optimization algorithms in automated pharmaceutical continuous manufacturing

Peiwen Liu, Hui Jin, Yan Chen, Derong Wang, Haohui Yan, Mingzhao Wu, Fang Zhao and Weiping Zhu

Chinese Chemical Letters, 2024, 35, 108877
DOI: 10.1016/j.cclet.2023.108877

Multigram Synthesis of Chiral Cyclopropane Carboxylic Acids via Tandem Diastereoselective Wadsworth–Emmons Cyclopropanation-Hydrolysis in Continuous Flow

Carlos Mateos, Susana García-Cerrada, Pablo García-Losada and Oscar de Frutos

Org. Process Res. Dev., 2024, 28, 1896
DOI: 10.1021/acs.oprd.3c00444

Automated, Capsule-Based Suzuki–Miyaura Cross Couplings

Guillaume Coin, Tuo Jiang, Samuele Bordi, Paula L. Nichols, Jeffrey W. Bode and Benedikt M. Wanner

Org. Lett., 2024, 26, 2708
DOI: 10.1021/acs.orglett.3c01057

Data Management of Microscale Reaction Calorimeter Using a Modular Open-Source IoT-Platform

Timothy Aljoscha Frede, Constantin Weber, Tobias Brockhoff, Tassilo Christ, Denis Ludwig and Norbert Kockmann

Processes, 2023, 11, 279
DOI: 10.3390/pr11010279

Autonomous Multi‐Step and Multi‐Objective Optimization Facilitated by Real‐Time Process Analytics

Peter Sagmeister, Florian F. Ort, Clemens E. Jusner, Dominique Hebrault, Thomas Tampone, Frederic G. Buono, Jason D. Williams and C. Oliver Kappe

Advanced Science, 2022, 9
DOI: 10.1002/advs.202105547

Photochemical Synthesis of the Bioactive Fragment of Salbutamol and Derivatives in a Self‐Optimizing Flow Chemistry Platform

Romaric Gérardy, Anirudh M. K. Nambiar, Travis Hart, Prajwal T. Mahesh and Klavs F. Jensen

Chemistry A European J, 2022, 28
DOI: 10.1002/chem.202201385

2D Materials for Wearable Energy Harvesting

Meng Hao Lee and Wenzhuo Wu

Adv Materials Technologies, 2022, 7
DOI: 10.1002/admt.202101623

MicroCycle: An Integrated and Automated Platform to Accelerate Drug Discovery

Cara E. Brocklehurst, Eva Altmann, Corentin Bon, Holly Davis, David Dunstan, Peter Ertl, Carol Ginsburg-Moraff, Jonathan Grob, Daniel J. Gosling, Guillaume Lapointe, Alexander N. Marziale, Heinrich Mues, Marco Palmieri, Sophie Racine, Richard I. Robinson, Clayton Springer, Kian Tan, William Ulmer and René Wyler

J. Med. Chem., 2024, 67, 2118
DOI: 10.1021/acs.jmedchem.3c02029

Automated robotic platforms in design and development of formulations

Liwei Cao, Danilo Russo and Alexei A. Lapkin

AIChE Journal, 2021, 67
DOI: 10.1002/aic.17248

Data-science driven autonomous process optimization

Melodie Christensen, Lars P. E. Yunker, Folarin Adedeji, Florian Häse, Loïc M. Roch, Tobias Gensch, Gabriel dos Passos Gomes, Tara Zepel, Matthew S. Sigman, Alán Aspuru-Guzik and Jason E. Hein

Commun Chem, 2021, 4
DOI: 10.1038/s42004-021-00550-x

Cognitively Driven Autonomous Flow Chemistry for Producing On‐Demand Perovskite Quantum Dots Via Advanced Closed‐Loop Feedback Control

Thi Thuy Huong Nguyen, Hoang Khang Bui, Ju Yeon Im and Tae Seok Seo

Small Methods, 2025, 9
DOI: 10.1002/smtd.202400094

Continuous Flow Organophosphorus Chemistry

Romain Morodo, Pauline Bianchi and Jean‐Christophe M. Monbaliu

Eur J Org Chem, 2020, 2020, 5236
DOI: 10.1002/ejoc.202000430

Sampling and Analysis in Flow: The Keys to Smarter, More Controllable, and Sustainable Fine‐Chemical Manufacturing

Mathieu A. Morin, Wenyao (Peter) Zhang, Debasis Mallik and Michael G. Organ

Angewandte Chemie, 2021, 133, 20774
DOI: 10.1002/ange.202102009

Recent Developments in Process Digitalisation for Advanced Nanomaterial Syntheses

Diego Iglesias, Dina Haddad and Victor Sans

Chemistry Methods, 2022, 2
DOI: 10.1002/cmtd.202200031

Facile Generation of tert-Butoxycarbonyl Chloride Equivalent and Its Use in Microflow Reactor

Kana Miyamoto, Ren Okabe and Shinichiro Fuse

Org. Process Res. Dev., 2024, 28, 1971
DOI: 10.1021/acs.oprd.4c00009

Self-Optimizing Flow Reactors Get a Boost by Multitasking

Jason D. Williams and C. Oliver Kappe

ACS Cent. Sci., 2023, 9, 864
DOI: 10.1021/acscentsci.3c00548

Chemputation and the Standardization of Chemical Informatics

Alexander J. S. Hammer, Artem I. Leonov, Nicola L. Bell and Leroy Cronin

JACS Au, 2021, 1, 1572
DOI: 10.1021/jacsau.1c00303

Fabrication of a Monolith Reactor in a Copper Tube by Polymerization of Acetylene for Flow Catalysis

Tiefu Li, Ming He, Peng Zhang, Zhiquan Yu, Jiaming Liu, Yingya Liu, Yao Wang and Anjie Wang

Ind. Eng. Chem. Res., 2022, 61, 7852
DOI: 10.1021/acs.iecr.2c01159

Self-Driving Laboratories for Chemistry and Materials Science

Gary Tom, Stefan P. Schmid, Sterling G. Baird, Yang Cao, Kourosh Darvish, Han Hao, Stanley Lo, Sergio Pablo-García, Ella M. Rajaonson, Marta Skreta, Naruki Yoshikawa, Samantha Corapi, Gun Deniz Akkoc, Felix Strieth-Kalthoff, Martin Seifrid and Alán Aspuru-Guzik

Chem. Rev., 2024, 124, 9633
DOI: 10.1021/acs.chemrev.4c00055

Quid Pro Flow

Andrea Laybourn, Karen Robertson and Anna G. Slater

J. Am. Chem. Soc., 2023, 145, 4355
DOI: 10.1021/jacs.2c13670

Direct C−H Arylation of Indole‐3‐Acetic Acid Derivatives Enabled by an Autonomous Self‐Optimizing Flow Reactor

N. Vasudevan, Eric Wimmer, Elvina Barré, Daniel Cortés‐Borda, Mireia Rodriguez‐Zubiri and François‐Xavier Felpin

Adv Synth Catal, 2021, 363, 791
DOI: 10.1002/adsc.202001217

Autonomous chemistry: Navigating self-driving labs in chemical and material sciences

Oliver Bayley, Elia Savino, Aidan Slattery and Timothy Noël

Matter, 2024, 7, 2382
DOI: 10.1016/j.matt.2024.06.003

Multistep automated synthesis of pharmaceuticals

Gan Wang, Hwee Ting Ang, Srinivas Reddy Dubbaka, Patrick O’Neill and Jie Wu

Trends in Chemistry, 2023, 5, 432
DOI: 10.1016/j.trechm.2023.03.008

Enhancing Optimization of Mixed Variables on a Robotic Flow Platform: Integrating Statistical Filtering with Nelder–Mead and Bayesian Methods

Aravind Senthil Vel, Kouakou E. Konan, Daniel Cortés-Borda and François-Xavier Felpin

Org. Process Res. Dev., 2024, 28, 1597
DOI: 10.1021/acs.oprd.3c00238

Comparison of Derivative-Free Optimization: Energy Optimization of Steam Methane Reforming Process

Minsu Kim, Areum Han, Jaewon Lee, Sunghyun Cho, Il Moon, Jonggeol Na and Koteswara Raju Dhenuvakonda

International Journal of Energy Research, 2023, 2023, 1
DOI: 10.1155/2023/8868540

An Integrated Method of Bayesian Optimization and D-Optimal Design for Chemical Experiment Optimization

Xinye Huang, Shuyuan Zhang, Haoran Li and Tong Qiu

Processes, 2022, 11, 87
DOI: 10.3390/pr11010087

Cluster Preface: Integrated Synthesis Using Continuous-Flow Technologies

Shinichiro Fuse

Synlett, 2020, 31, 1878
DOI: 10.1055/s-0040-1706605

Data Science Guided Multiobjective Optimization of a Stereoconvergent Nickel-Catalyzed Reduction of Enol Tosylates to Access Trisubstituted Alkenes

Natalie P. Romer, Daniel S. Min, Jason Y. Wang, Richard C. Walroth, Kyle A. Mack, Lauren E. Sirois, Francis Gosselin, Daniel Zell, Abigail G. Doyle and Matthew S. Sigman

ACS Catal., 2024, 14, 4699
DOI: 10.1021/acscatal.4c00650

Autonomous Discovery in the Chemical Sciences Part I: Progress

Connor W. Coley, Natalie S. Eyke and Klavs F. Jensen

Angew Chem Int Ed, 2020, 59, 22858
DOI: 10.1002/anie.201909987

Digital Transformation in Materials Science: A Paradigm Change in Material's Development

Julian Kimmig, Stefan Zechel and Ulrich S. Schubert

Advanced Materials, 2021, 33
DOI: 10.1002/adma.202004940

A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization

Florian Wagner, Peter Sagmeister, Clemens E. Jusner, Thomas G. Tampone, Vidhyadhar Manee, Frederic G. Buono, Jason D. Williams and C. Oliver Kappe

Advanced Science, 2024, 11
DOI: 10.1002/advs.202308034

Fast Oxidative Coupling Giving Functionalized Biaryls via Organocuprates

Kazuhiro Okamoto, Ruka Ebisawa, Hiroki Nakayama and Aiichiro Nagaki

Eur J Org Chem, 2025
DOI: 10.1002/ejoc.202500072

Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis

Emily M. Williamson and Richard L. Brutchey

Inorg. Chem., 2023, 62, 16251
DOI: 10.1021/acs.inorgchem.3c02697

Navigating with chemometrics and machine learning in chemistry

Payal B. Joshi

Artif Intell Rev, 2023, 56, 9089
DOI: 10.1007/s10462-023-10391-w

Rapid Optimization of Photoredox Reactions for Continuous-Flow Systems Using Microscale Batch Technology

María González-Esguevillas, David F. Fernández, Juan A. Rincón, Mario Barberis, Oscar de Frutos, Carlos Mateos, Susana García-Cerrada, Javier Agejas and David W. C. MacMillan

ACS Cent. Sci., 2021, 7, 1126
DOI: 10.1021/acscentsci.1c00303

Automated synthesis of gadopentetate dimeglumine through solid-liquid reaction in femtosecond laser fabricated microfluidic chips

Difeng Yin, Yucen Li, Lingling Xia, Wenbo Li, Wei Chu, Jianping Yu, Miao Wu, Ya Cheng and Ming Hu

Chinese Chemical Letters, 2022, 33, 1077
DOI: 10.1016/j.cclet.2021.05.073