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Chitre A, Querimit RCM, Rihm SD, Karan D, Zhu B, Wang K, Wang L, Hippalgaonkar K, Lapkin AA. Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset. Sci Data 2024; 11:728. [PMID: 38961122 PMCID: PMC11222379 DOI: 10.1038/s41597-024-03573-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 06/25/2024] [Indexed: 07/05/2024] Open
Abstract
Liquid formulations are ubiquitous yet have lengthy product development cycles owing to the complex physical interactions between ingredients making it difficult to tune formulations to customer-defined property targets. Interpolative ML models can accelerate liquid formulations design but are typically trained on limited sets of ingredients and without any structural information, which limits their out-of-training predictive capacity. To address this challenge, we selected eighteen formulation ingredients covering a diverse chemical space to prepare an open experimental dataset for training ML models for rinse-off formulations development. The resulting design space has an over 50-fold increase in dimensionality compared to our previous work. Here, we present a dataset of 812 formulations, including 294 stable samples, which cover the entire design space, with phase stability, turbidity, and high-fidelity rheology measurements generated on our semi-automated, ML-driven liquid formulations workflow. Our dataset has the unique attribute of sample-specific uncertainty measurements to train predictive surrogate models.
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Affiliation(s)
- Aniket Chitre
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, Singapore, 138602, Singapore
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Singapore, 138634, Singapore
| | - Robert C M Querimit
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Singapore, 138634, Singapore
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, 637459, Singapore
| | - Simon D Rihm
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, Singapore, 138602, Singapore
| | - Dogancan Karan
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, Singapore, 138602, Singapore
| | - Benchuan Zhu
- BASF Advanced Chemicals Co. Ltd., No. 300, Jiang Xin Sha Road, Pudong, Shanghai, 200137, China
| | - Ke Wang
- BASF Advanced Chemicals Co. Ltd., No. 300, Jiang Xin Sha Road, Pudong, Shanghai, 200137, China
| | - Long Wang
- BASF Advanced Chemicals Co. Ltd., No. 300, Jiang Xin Sha Road, Pudong, Shanghai, 200137, China
| | - Kedar Hippalgaonkar
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Singapore, 138634, Singapore.
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
| | - Alexei A Lapkin
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, Singapore, 138602, Singapore.
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Shen K, Nguyen M, Sherck N, Yoo B, Köhler S, Speros J, Delaney KT, Shell MS, Fredrickson GH. Predicting surfactant phase behavior with a molecularly informed field theory. J Colloid Interface Sci 2023; 638:84-98. [PMID: 36736121 DOI: 10.1016/j.jcis.2023.01.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 12/24/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
HYPOTHESIS The computational study of surfactants and self-assembly is challenging because 1) models need to reflect chemistry-specific interactions, and 2) self-assembled structures are difficult to equilibrate with conventional molecular dynamics. We propose to overcome these challenges with a multiscale simulation approach where relative entropy minimization transfers chemically-detailed information from all-atom (AA) simulations to coarse-grained (CG) models that can be simulated using field-theoretic methods. Field-theoretic simulations are not limited by intrinsic physical time scales like diffusion and allow for rigorous equilibration via free energy minimization. This approach should enable the study of properties that are difficult to obtain by particle-based simulations. SIMULATION WORK We apply this workflow to sodium dodecylsulfate. To ensure chemical fidelity we present an AA force field calibrated against interfacial tension experiments. We generate CG models from AA simulation trajectories and show that particle-based and field-theoretic simulations of the CG model reproduce AA simulations and experimental measurements. FINDINGS The workflow captures the complex balance of interactions in a multicomponent system ultimately described by an atomistic model. The resulting CG models can study complex 3D phases like double or alternating gyroids, and reproduce salt effects on properties like aggregation number and shape transitions.
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Affiliation(s)
- Kevin Shen
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara 93106, CA, United States; Materials Research Laboratory, University of California, Santa Barbara, Santa Barbara 93106, CA, United States.
| | - My Nguyen
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara 93106, CA, United States
| | - Nicholas Sherck
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara 93106, CA, United States
| | - Brian Yoo
- BASF Corporation, Tarrytown 10591, NY, United States
| | | | - Joshua Speros
- California Research Alliance (CARA) by BASF, Berkeley 94720, CA, United States
| | - Kris T Delaney
- Materials Research Laboratory, University of California, Santa Barbara, Santa Barbara 93106, CA, United States
| | - M Scott Shell
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara 93106, CA, United States.
| | - Glenn H Fredrickson
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara 93106, CA, United States; Materials Research Laboratory, University of California, Santa Barbara, Santa Barbara 93106, CA, United States; Department of Materials Engineering, University of California, Santa Barbara, Santa Barbara 93106, CA, United States.
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Zhu LT, Chen XZ, Ouyang B, Yan WC, Lei H, Chen Z, Luo ZH. Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c01036] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Li-Tao Zhu
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Xi-Zhong Chen
- Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, S1 3JD, U.K
| | - Bo Ouyang
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Wei-Cheng Yan
- School of Chemistry and Chemical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - He Lei
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Zhe Chen
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Zheng-Hong Luo
- Department of Chemical Engineering, School of Chemistry and Chemical Engineering, State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
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Yano J, Gaffney KJ, Gregoire J, Hung L, Ourmazd A, Schrier J, Sethian JA, Toma FM. The case for data science in experimental chemistry: examples and recommendations. Nat Rev Chem 2022; 6:357-370. [PMID: 37117931 DOI: 10.1038/s41570-022-00382-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/17/2022] [Indexed: 12/31/2022]
Abstract
The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to 'co-design' chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.
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Bai J, Cao L, Mosbach S, Akroyd J, Lapkin AA, Kraft M. From Platform to Knowledge Graph: Evolution of Laboratory Automation. JACS AU 2022; 2:292-309. [PMID: 35252980 PMCID: PMC8889618 DOI: 10.1021/jacsau.1c00438] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Indexed: 05/19/2023]
Abstract
High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.
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Affiliation(s)
- Jiaru Bai
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Liwei Cao
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Sebastian Mosbach
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
- Cambridge
Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, 138602 Singapore
| | - Jethro Akroyd
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
- Cambridge
Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, 138602 Singapore
| | - Alexei A. Lapkin
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
- Cambridge
Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, 138602 Singapore
| | - Markus Kraft
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
- Cambridge
Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower #05-05, 1 Create Way, 138602 Singapore
- School
of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, 637459 Singapore
- The
Alan Turing Institute, London NW1 2DB, United Kingdom
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Volk AA, Campbell ZS, Ibrahim MYS, Bennett JA, Abolhasani M. Flow Chemistry: A Sustainable Voyage Through the Chemical Universe en Route to Smart Manufacturing. Annu Rev Chem Biomol Eng 2022; 13:45-72. [PMID: 35259931 DOI: 10.1146/annurev-chembioeng-092120-024449] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Microfluidic devices and systems have entered many areas of chemical engineering, and the rate of their adoption is only increasing. As we approach and adapt to the critical global challenges we face in the near future, it is important to consider the capabilities of flow chemistry and its applications in next-generation technologies for sustainability, energy production, and tailor-made specialty chemicals. We present the introduction of microfluidics into the fundamental unit operations of chemical engineering. We discuss the traits and advantages of microfluidic approaches to different reactive systems, both well-established and emerging, with a focus on the integration of modular microfluidic devices into high-efficiency experimental platforms for accelerated process optimization and intensified continuous manufacturing. Finally, we discuss the current state and new horizons in self-driven experimentation in flow chemistry for both intelligent exploration through the chemical universe and distributed manufacturing. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Amanda A Volk
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA; , , , ,
| | - Zachary S Campbell
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA; , , , ,
| | - Malek Y S Ibrahim
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA; , , , ,
| | - Jeffrey A Bennett
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA; , , , ,
| | - Milad Abolhasani
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA; , , , ,
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Kontogeorgis GM, Jhamb S, Liang X, Dam-Johansen K. Computer-aided design of formulated products. Curr Opin Colloid Interface Sci 2022. [DOI: 10.1016/j.cocis.2021.101536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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