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Rabitz H, Russell B, Ho TS. The Surprising Ease of Finding Optimal Solutions for Controlling Nonlinear Phenomena in Quantum and Classical Complex Systems. J Phys Chem A 2023; 127:4224-4236. [PMID: 37142303 DOI: 10.1021/acs.jpca.3c01896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
This Perspective addresses the often observed surprising ease of achieving optimal control of nonlinear phenomena in quantum and classical complex systems. The circumstances involved are wide-ranging, with scenarios including manipulation of atomic scale processes, maximization of chemical and material properties or synthesis yields, Nature's optimization of species' populations by natural selection, and directed evolution. Natural evolution will mainly be discussed in terms of laboratory experiments with microorganisms, and the field is also distinct from the other domains where a scientist specifies the goal(s) and oversees the control process. We use the word "control" in reference to all of the available variables, regardless of the circumstance. The empirical observations on the ease of achieving at least good, if not excellent, control in diverse domains of science raise the question of why this occurs despite the generally inherent complexity of the systems in each scenario. The key to addressing the question lies in examining the associated control landscape, which is defined as the optimization objective as a function of the control variables that can be as diverse as the phenomena under consideration. Controls may range from laser pulses, chemical reagents, chemical processing conditions, out to nucleic acids in the genome and more. This Perspective presents a conjecture, based on present findings, that the systematics of readily finding good outcomes from controlled phenomena may be unified through consideration of control landscapes with the same common set of three underlying assumptions─the existence of an optimal solution, the ability for local movement on the landscape, and the availability of sufficient control resources─whose validity needs assessment in each scenario. In practice, many cases permit using myopic gradient-like algorithms while other circumstances utilize algorithms having some elements of stochasticity or introduced noise, depending on whether the landscape is locally smooth or rough. The overarching observation is that only relatively short searches are required despite the common high dimensionality of the available controls in typical scenarios.
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Affiliation(s)
- Herschel Rabitz
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Benjamin Russell
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Tak-San Ho
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
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Fujinami M, Maekawara H, Isshiki R, Seino J, Yamaguchi J, Nakai H. Solvent Selection Scheme Using Machine Learning Based on Physicochemical Description of Solvent Molecules: Application to Cyclic Organometallic Reaction. BULLETIN OF THE CHEMICAL SOCIETY OF JAPAN 2020. [DOI: 10.1246/bcsj.20200045] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Mikito Fujinami
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Hiroki Maekawara
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Ryota Isshiki
- Department of Applied Chemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Junji Seino
- Waseda Research Institute for Science and Engineering (WISE), Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Junichiro Yamaguchi
- Department of Applied Chemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Hiromi Nakai
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Waseda Research Institute for Science and Engineering (WISE), Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Elements Strategy Initiative for Catalysts and Batteries (ESICB), Kyoto University, Katsura, Kyoto 615-8520, Japan
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Epps RW, Bowen MS, Volk AA, Abdel-Latif K, Han S, Reyes KG, Amassian A, Abolhasani M. Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e2001626. [PMID: 32495399 DOI: 10.1002/adma.202001626] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 04/07/2020] [Indexed: 05/28/2023]
Abstract
The optimal synthesis of advanced nanomaterials with numerous reaction parameters, stages, and routes, poses one of the most complex challenges of modern colloidal science, and current strategies often fail to meet the demands of these combinatorially large systems. In response, an Artificial Chemist is presented: the integration of machine-learning-based experiment selection and high-efficiency autonomous flow chemistry. With the self-driving Artificial Chemist, made-to-measure inorganic perovskite quantum dots (QDs) in flow are autonomously synthesized, and their quantum yield and composition polydispersity at target bandgaps, spanning 1.9 to 2.9 eV, are simultaneously tuned. Utilizing the Artificial Chemist, eleven precision-tailored QD synthesis compositions are obtained without any prior knowledge, within 30 h, using less than 210 mL of total starting QD solutions, and without user selection of experiments. Using the knowledge generated from these studies, the Artificial Chemist is pre-trained to use a new batch of precursors and further accelerate the synthetic path discovery of QD compositions, by at least twofold. The knowledge-transfer strategy further enhances the optoelectronic properties of the in-flow synthesized QDs (within the same resources as the no-prior-knowledge experiments) and mitigates the issues of batch-to-batch precursor variability, resulting in QDs averaging within 1 meV from their target peak emission energy.
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Affiliation(s)
- Robert W Epps
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, 27606, USA
| | - Michael S Bowen
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, 27606, USA
| | - Amanda A Volk
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, 27606, USA
| | - Kameel Abdel-Latif
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, 27606, USA
| | - Suyong Han
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, 27606, USA
| | - Kristofer G Reyes
- Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY, 14260, USA
| | - Aram Amassian
- Department of Material Science and Engineering, Organic and Carbon Electronics Laboratories (ORaCEL), North Carolina State University, Raleigh, NC, 27606, USA
| | - Milad Abolhasani
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, 27606, USA
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Fujinami M, Seino J, Nukazawa T, Ishida S, Iwamoto T, Nakai H. Virtual Reaction Condition Optimization based on Machine Learning for a Small Number of Experiments in High-dimensional Continuous and Discrete Variables. CHEM LETT 2019. [DOI: 10.1246/cl.190267] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Mikito Fujinami
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Junji Seino
- Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Takumi Nukazawa
- Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Shintaro Ishida
- Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Takeaki Iwamoto
- Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Hiromi Nakai
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Element Strategy Initiative for Catalysts and Batteries (ESICB), Kyoto University, Katsura, Kyoto 615-8520, Japan
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Freeze JG, Kelly HR, Batista VS. Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists. Chem Rev 2019; 119:6595-6612. [PMID: 31059236 DOI: 10.1021/acs.chemrev.8b00759] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In silico catalyst design is a grand challenge of chemistry. Traditional computational approaches have been limited by the need to compute properties for an intractably large number of possible catalysts. Recently, inverse design methods have emerged, starting from a desired property and optimizing a corresponding chemical structure. Techniques used for exploring chemical space include gradient-based optimization, alchemical transformations, and machine learning. Though the application of these methods to catalysis is in its early stages, further development will allow for robust computational catalyst design. This review provides an overview of the evolution of inverse design approaches and their relevance to catalysis. The strengths and limitations of existing techniques are highlighted, and suggestions for future research are provided.
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Affiliation(s)
- Jessica G Freeze
- Department of Chemistry , Yale University , New Haven , Connecticut 06520 , United States.,Energy Sciences Institute , Yale University , West Haven , Connecticut 06516 , United States
| | - H Ray Kelly
- Department of Chemistry , Yale University , New Haven , Connecticut 06520 , United States.,Energy Sciences Institute , Yale University , West Haven , Connecticut 06516 , United States
| | - Victor S Batista
- Energy Sciences Institute , Yale University , West Haven , Connecticut 06516 , United States.,Department of Chemistry , Yale University , P.O. Box 208107 , New Haven , Connecticut 06520 , United States
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Pan LJ, Tu JW, Ma HT, Yang YJ, Tian ZQ, Pang DW, Zhang ZL. Controllable synthesis of nanocrystals in droplet reactors. LAB ON A CHIP 2017; 18:41-56. [PMID: 29098217 DOI: 10.1039/c7lc00800g] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In recent years, a broad range of nanocrystals have been synthesized in droplet-based microfluidic reactors which provide obvious advantages, such as accurate manipulation, better reproducibility and reliable automation. In this review, we initially introduce general concepts of droplet reactors followed by discussions of their main functional regions including droplet generation, mixing of reactants, reaction controlling, in situ monitoring, and reaction quenching. Subsequently, the enhanced mass and heat transport properties are discussed. Next, we focus on research frontiers including sequential multistep synthesis, intelligent synthesis, reliable scale-up synthesis, and interfacial synthesis. Finally, we end with an outlook on droplet reactors, especially highlighting some aspects such as large-scale production, the integrated process of synthesis and post-synthetic treatments, automated droplet reactors with in situ monitoring and optimizing algorithms, and rapidly developing strategies for interfacial synthesis.
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Affiliation(s)
- Liang-Jun Pan
- Key Laboratory of Analytical Chemistry for Biology and Medicine (Ministry of Education), College of Chemistry and Molecular Science, Wuhan University, Wuhan 430072, People's Republic of China.
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Walker BE, Bannock JH, Nightingale AM, deMello JC. Tuning reaction products by constrained optimisation. REACT CHEM ENG 2017. [DOI: 10.1039/c7re00123a] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
An effective means of defining optimisation criteria for self-optimising criteria is described, applicable to situations where a compromise is sought between several competing objectives.
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Affiliation(s)
- Barnaby E. Walker
- Centre for Plastic Electronics
- Department of Chemistry
- Imperial College London
- London SW7 2AY
- UK
| | - James H. Bannock
- Centre for Plastic Electronics
- Department of Chemistry
- Imperial College London
- London SW7 2AY
- UK
| | - Adrian M. Nightingale
- Faculty of Engineering and the Environment
- University of Southampton
- Southampton SO17 1BJ
- UK
| | - John C. deMello
- Centre for Plastic Electronics
- Department of Chemistry
- Imperial College London
- London SW7 2AY
- UK
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