2024
|
Yoon, Ji Wei; Kumar, Adithya; Kumar, Pawan; Hippalgaonkar, Kedar; Senthilnath, J; Chellappan, Vijila Explainable machine learning to enable high-throughput electrical conductivity optimization and discovery of doped conjugated polymers KNOWLEDGE-BASED SYSTEMS, 295 , 2024, DOI: 10.1016/j.knosys.2024.111812. Abstract | BibTeX | Endnote @article{ISI:001237223200001,
title = {Explainable machine learning to enable high-throughput electrical conductivity optimization and discovery of doped conjugated polymers},
author = {Ji Wei Yoon and Adithya Kumar and Pawan Kumar and Kedar Hippalgaonkar and J Senthilnath and Vijila Chellappan},
doi = {10.1016/j.knosys.2024.111812},
times_cited = {0},
issn = {0950-7051},
year = {2024},
date = {2024-07-08},
journal = {KNOWLEDGE-BASED SYSTEMS},
volume = {295},
publisher = {ELSEVIER},
address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
abstract = {The combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identification of materials with cutting-edge properties. However, the measurement of certain physical quantities remains challenging to automate. Specifically, meticulous process control, experimentation and laborious measurements are required to achieve optimal electrical conductivity in doped polymer materials. We propose a ML approach, which relies on readily measured absorbance spectra, to accelerate the workflow associated with measuring electrical conductivity. The classification model accurately classifies samples with a conductivity >similar to 25 to 100 S/cm, achieving a maximum of 100 % accuracy rate. For the subset of highly conductive samples, we employed a regression model to predict their conductivities, yielding an impressive test R-2 value of 0.984. We tested the models with samples of the two highest conductivities (498 and 506 S/cm) and showed that they were able to correctly classify and predict the two extrapolative conductivities at satisfactory levels of errors. The proposed ML-assisted workflow results in an improvement in the efficiency of the conductivity measurements by 89 % of the maximum achievable using our experimental techniques. Furthermore, our approach addressed the common challenge of the lack of explainability in ML models by exploiting bespoke mathematical properties of the descriptors and ML model, allowing us to gain corroborated insights into the spectral influences on conductivity. Through this study, we offer an accelerated pathway for optimizing the properties of doped polymer materials while showcasing the valuable insights that can be derived from purposeful utilization of ML in experimental science.},
keywords = {},
pubstate = {published},
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The combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identification of materials with cutting-edge properties. However, the measurement of certain physical quantities remains challenging to automate. Specifically, meticulous process control, experimentation and laborious measurements are required to achieve optimal electrical conductivity in doped polymer materials. We propose a ML approach, which relies on readily measured absorbance spectra, to accelerate the workflow associated with measuring electrical conductivity. The classification model accurately classifies samples with a conductivity >similar to 25 to 100 S/cm, achieving a maximum of 100 % accuracy rate. For the subset of highly conductive samples, we employed a regression model to predict their conductivities, yielding an impressive test R-2 value of 0.984. We tested the models with samples of the two highest conductivities (498 and 506 S/cm) and showed that they were able to correctly classify and predict the two extrapolative conductivities at satisfactory levels of errors. The proposed ML-assisted workflow results in an improvement in the efficiency of the conductivity measurements by 89 % of the maximum achievable using our experimental techniques. Furthermore, our approach addressed the common challenge of the lack of explainability in ML models by exploiting bespoke mathematical properties of the descriptors and ML model, allowing us to gain corroborated insights into the spectral influences on conductivity. Through this study, we offer an accelerated pathway for optimizing the properties of doped polymer materials while showcasing the valuable insights that can be derived from purposeful utilization of ML in experimental science. - FNClarivate Analytics Web of Science
- VR1.0
- PTJ
- AUYoon, JW
Kumar, A
Kumar, P
Hippalgaonkar, K
Senthilnath, J
Chellappan, V
- AFJi Wei Yoon
Adithya Kumar
Pawan Kumar
Kedar Hippalgaonkar
J Senthilnath
Vijila Chellappan
- TIExplainable machine learning to enable high-throughput electrical conductivity optimization and discovery of doped conjugated polymers
- SOKNOWLEDGE-BASED SYSTEMS
- LAEnglish
- DTArticle
- DEExplainable Machine Learning; High -throughput Experimentation; Doped Conjugated Polymers; Data -driven Feature Selection; AI -accelerated Materials Discovery; AI -enabled Materials Optimization; Spectral Analysis
- IDBLENDS
- ABThe combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identification of materials with cutting-edge properties. However, the measurement of certain physical quantities remains challenging to automate. Specifically, meticulous process control, experimentation and laborious measurements are required to achieve optimal electrical conductivity in doped polymer materials. We propose a ML approach, which relies on readily measured absorbance spectra, to accelerate the workflow associated with measuring electrical conductivity. The classification model accurately classifies samples with a conductivity >similar to 25 to 100 S/cm, achieving a maximum of 100 % accuracy rate. For the subset of highly conductive samples, we employed a regression model to predict their conductivities, yielding an impressive test R-2 value of 0.984. We tested the models with samples of the two highest conductivities (498 and 506 S/cm) and showed that they were able to correctly classify and predict the two extrapolative conductivities at satisfactory levels of errors. The proposed ML-assisted workflow results in an improvement in the efficiency of the conductivity measurements by 89 % of the maximum achievable using our experimental techniques. Furthermore, our approach addressed the common challenge of the lack of explainability in ML models by exploiting bespoke mathematical properties of the descriptors and ML model, allowing us to gain corroborated insights into the spectral influences on conductivity. Through this study, we offer an accelerated pathway for optimizing the properties of doped polymer materials while showcasing the valuable insights that can be derived from purposeful utilization of ML in experimental science.
- C1[Yoon, Ji Wei; Senthilnath, J.] ASTAR, Inst Infocomm Res I2R, 1 Fusionopolis Way,21-01 Connexis South Tower, Singapore 138632, Singapore.
[Kumar, Adithya; Kumar, Pawan; Hippalgaonkar, Kedar; Chellappan, Vijila] ASTAR, Inst Mat Res & Engn, 2 Fusionopolis Way,08-03 Innovis, Singapore 138634, Singapore.
[Hippalgaonkar, Kedar] Nanyang Technol Univ, Mat Sci & Engn, 50 Nanyang Ave, Singapore 639798, Singapore.
[Chellappan, Vijila] Natl Univ Singapore, Inst Funct Intelligent Mat, Singapore 117544, Singapore - C3Agency for Science Technology & Research (A*STAR); A*STAR - Institute for Infocomm Research (I2R); Agency for Science Technology & Research (A*STAR); A*STAR - Institute of Materials Research & Engineering (IMRE); Nanyang Technological University; National University of Singapore; Institute for Functional Intelligent Materials (I-FIM)
- RPYoon, JW (corresponding author), ASTAR, Inst Infocomm Res I2R, 1 Fusionopolis Way,21-01 Connexis South Tower, Singapore 138632, Singapore
- FUAccelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research, Singapore [A1898b0043]
- FXThis study was supported by the Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research, Singapore under Grant No. A1898b0043.
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Low, Andre K Y; Mekki-Berrada, Flore; Gupta, Abhishek; Ostudin, Aleksandr; Xie, Jiaxun; Vissol-Gaudin, Eleonore; Lim, Yee-Fun; Li, Qianxiao; Ong, Yew Soon; Khan, Saif A; Hippalgaonkar, Kedar Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs NPJ COMPUTATIONAL MATERIALS, 10 (1), 2024, DOI: 10.1038/s41524-024-01274-x. Abstract | BibTeX | Endnote @article{ISI:001221677000002,
title = {Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs},
author = {Andre K Y Low and Flore Mekki-Berrada and Abhishek Gupta and Aleksandr Ostudin and Jiaxun Xie and Eleonore Vissol-Gaudin and Yee-Fun Lim and Qianxiao Li and Yew Soon Ong and Saif A Khan and Kedar Hippalgaonkar},
doi = {10.1038/s41524-024-01274-x},
times_cited = {0},
year = {2024},
date = {2024-05-13},
journal = {NPJ COMPUTATIONAL MATERIALS},
volume = {10},
number = {1},
publisher = {NATURE PORTFOLIO},
address = {HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY},
abstract = {The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. However, current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints. Here, we devise an Evolution-Guided Bayesian Optimization (EGBO) algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement (qNEHVI) optimizer; this not only solves for the Pareto Front (PF) efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space. The algorithm is developed together with a custom self-driving lab for seed-mediated silver nanoparticle synthesis, targeting 3 objectives (1) optical properties, (2) fast reaction, and (3) minimal seed usage alongside complex constraints. We demonstrate that, with appropriate constraint handling, EGBO performance improves upon state-of-the-art qNEHVI. Furthermore, across various synthetic multi-objective problems, EGBO shows significative hypervolume improvement, revealing the synergy between selection pressure and the qNEHVI optimizer. We also demonstrate EGBO's good coverage of the PF as well as comparatively better ability to propose feasible solutions. We thus propose EGBO as a general framework for efficiently solving constrained multi-objective problems in high-throughput experimentation platforms.},
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pubstate = {published},
tppubtype = {article}
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The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. However, current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints. Here, we devise an Evolution-Guided Bayesian Optimization (EGBO) algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement (qNEHVI) optimizer; this not only solves for the Pareto Front (PF) efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space. The algorithm is developed together with a custom self-driving lab for seed-mediated silver nanoparticle synthesis, targeting 3 objectives (1) optical properties, (2) fast reaction, and (3) minimal seed usage alongside complex constraints. We demonstrate that, with appropriate constraint handling, EGBO performance improves upon state-of-the-art qNEHVI. Furthermore, across various synthetic multi-objective problems, EGBO shows significative hypervolume improvement, revealing the synergy between selection pressure and the qNEHVI optimizer. We also demonstrate EGBO's good coverage of the PF as well as comparatively better ability to propose feasible solutions. We thus propose EGBO as a general framework for efficiently solving constrained multi-objective problems in high-throughput experimentation platforms. - FNClarivate Analytics Web of Science
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Li, QX
Ong, YS
Khan, SA
Hippalgaonkar, K
- AFAndre K Y Low
Flore Mekki-Berrada
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Jiaxun Xie
Eleonore Vissol-Gaudin
Yee-Fun Lim
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Yew Soon Ong
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- TIEvolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs
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- ABThe development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. However, current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints. Here, we devise an Evolution-Guided Bayesian Optimization (EGBO) algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement (qNEHVI) optimizer; this not only solves for the Pareto Front (PF) efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space. The algorithm is developed together with a custom self-driving lab for seed-mediated silver nanoparticle synthesis, targeting 3 objectives (1) optical properties, (2) fast reaction, and (3) minimal seed usage alongside complex constraints. We demonstrate that, with appropriate constraint handling, EGBO performance improves upon state-of-the-art qNEHVI. Furthermore, across various synthetic multi-objective problems, EGBO shows significative hypervolume improvement, revealing the synergy between selection pressure and the qNEHVI optimizer. We also demonstrate EGBO's good coverage of the PF as well as comparatively better ability to propose feasible solutions. We thus propose EGBO as a general framework for efficiently solving constrained multi-objective problems in high-throughput experimentation platforms.
- C1[Low, Andre K. Y.; Vissol-Gaudin, Eleonore; Hippalgaonkar, Kedar] Nanyang Technol Univ, Sch Mat Sci & Engn, Singapore 639798, Singapore.
[Low, Andre K. Y.; Lim, Yee-Fun; Hippalgaonkar, Kedar] ASTAR, Inst Mat Res & Engn IMRE, 2 Fusionopolis Way,Innovis 08-03, Singapore 138634, Singapore.
[Mekki-Berrada, Flore; Ostudin, Aleksandr; Xie, Jiaxun; Khan, Saif A.] Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 117585, Singapore.
[Gupta, Abhishek] Indian Inst Technol Goa, Sch Mech Sci, Ponda 403401, Goa, India.
[Lim, Yee-Fun] ASTAR, Inst Sustainabil Chem Energy & Environm ISCE2, 1 Pesek Rd, Singapore 627833, Singapore.
[Li, Qianxiao] Natl Univ Singapore, Dept Math, Singapore 119077, Singapore.
[Li, Qianxiao] Natl Univ Singapore, Inst Funct Intelligent Mat, Singapore 117544, Singapore.
[Ong, Yew Soon] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore - C3Nanyang Technological University; Agency for Science Technology & Research (A*STAR); A*STAR - Institute of Materials Research & Engineering (IMRE); National University of Singapore; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) Goa; Agency for Science Technology & Research (A*STAR); National University of Singapore; National University of Singapore; Institute for Functional Intelligent Materials (I-FIM); Nanyang Technological University
- RPHippalgaonkar, K (corresponding author), Nanyang Technol Univ, Sch Mat Sci & Engn, Singapore 639798, Singapore; Hippalgaonkar, K (corresponding author), ASTAR, Inst Mat Res & Engn IMRE, 2 Fusionopolis Way,Innovis 08-03, Singapore 138634, Singapore; Khan, SA (corresponding author), Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 117585, Singapore
- FUAME Programmatic Funds by the Agency for Science, Technology and Research [A1898b0043, A20G9b0135]; National Research Foundation (NRF), Singapore [NRF- NRFF13-2021-0005]; The 25th NRF CRP programme [EDUNC-33-18-279-V12]; NRF fellowship [NRF-NRFF13-2021-0011]; Ministry of Education, Singapore
- FXThe authors acknowledge funding from AME Programmatic Funds by the Agency for Science, Technology and Research under Grant No. A1898b0043 and No. A20G9b0135. KH also acknowledges funding from the National Research Foundation (NRF), Singapore under the NRF Fellowship (NRF-NRFF13-2021-0011). SAK and FMB also acknowledge funding from the 25th NRF CRP programme (NRF-CRP25-2020RS-0002). QL also acknowledges support from the NRF fellowship (project No. NRF- NRFF13-2021-0005) and the Ministry of Education, Singapore, under its Research Centre of Excellence award to the Institute for Functional Intelligent Materials (I-FIM, project No. EDUNC-33-18-279-V12).
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2023
|
Chen, Xiaoli; Soh, Beatrice W; Ooi, Zi-En; Vissol-Gaudin, Eleonore; Yu, Haijun; Novoselov, Kostya S; Hippalgaonkar, Kedar; Li, Qianxiao Constructing custom thermodynamics using deep learning NATURE COMPUTATIONAL SCIENCE, 4 (1), 2023, DOI: 10.1038/s43588-023-00581-5. Abstract | BibTeX | Endnote @article{ISI:001133735500001,
title = {Constructing custom thermodynamics using deep learning},
author = {Xiaoli Chen and Beatrice W Soh and Zi-En Ooi and Eleonore Vissol-Gaudin and Haijun Yu and Kostya S Novoselov and Kedar Hippalgaonkar and Qianxiao Li},
doi = {10.1038/s43588-023-00581-5},
times_cited = {0},
year = {2023},
date = {2023-12-29},
journal = {NATURE COMPUTATIONAL SCIENCE},
volume = {4},
number = {1},
publisher = {SPRINGERNATURE},
address = {CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND},
abstract = {One of the most exciting applications of artificial intelligence is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail. Here we develop a platform based on a generalized Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems directly from observations of their microscopic trajectories. Our method simultaneously constructs reduced thermodynamic coordinates and interprets the dynamics on these coordinates. We demonstrate its effectiveness by studying theoretically and validating experimentally the stretching of long polymer chains in an externally applied field. Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate. Our general methodology can be used to address a wide range of scientific and technological applications.},
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One of the most exciting applications of artificial intelligence is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail. Here we develop a platform based on a generalized Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems directly from observations of their microscopic trajectories. Our method simultaneously constructs reduced thermodynamic coordinates and interprets the dynamics on these coordinates. We demonstrate its effectiveness by studying theoretically and validating experimentally the stretching of long polymer chains in an externally applied field. Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate. Our general methodology can be used to address a wide range of scientific and technological applications. - FNClarivate Analytics Web of Science
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- AUChen, XL
Soh, BW
Ooi, ZE
Vissol-Gaudin, E
Yu, HJ
Novoselov, KS
Hippalgaonkar, K
Li, QX
- AFXiaoli Chen
Beatrice W Soh
Zi-En Ooi
Eleonore Vissol-Gaudin
Haijun Yu
Kostya S Novoselov
Kedar Hippalgaonkar
Qianxiao Li
- TIConstructing custom thermodynamics using deep learning
- SONATURE COMPUTATIONAL SCIENCE
- LAEnglish
- DTArticle
- IDPROFESSIONAL SPORTS; FOOTBALL CLUBS; BROWNIAN DYNAMICS; NEURAL-NETWORKS; IRREVERSIBLE-PROCESSES; FINANCIAL PERFORMANCE; DISCRIMINANT-ANALYSIS; UTILITY MAXIMIZATION; RECIPROCAL RELATIONS; COMPETITIVE BALANCE
- ABOne of the most exciting applications of artificial intelligence is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail. Here we develop a platform based on a generalized Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems directly from observations of their microscopic trajectories. Our method simultaneously constructs reduced thermodynamic coordinates and interprets the dynamics on these coordinates. We demonstrate its effectiveness by studying theoretically and validating experimentally the stretching of long polymer chains in an externally applied field. Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate. Our general methodology can be used to address a wide range of scientific and technological applications.
- C3National University of Singapore; National University of Singapore; Institute for Functional Intelligent Materials (I-FIM); Agency for Science Technology & Research (A*STAR); A*STAR - Institute of Materials Research & Engineering (IMRE); Nanyang Technological University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
- RPLi, QX (corresponding author), Natl Univ Singapore, Dept Math, Singapore, Singapore; Novoselov, KS (corresponding author), Natl Univ Singapore, Inst Funct Intelligent Mat, Singapore, Singapore; Hippalgaonkar, K (corresponding author), ASTAR, Inst Mat Res & Engn, Singapore, Singapore; Hippalgaonkar, K (corresponding author), Nanyang Technol Univ, Sch Mat Sci & Engn, Singapore, Singapore
- FXThis research is supported by the Ministry of Education, Singapore, under its Research Centre of Excellence award to the Institute for Functional Intelligent Materials (I-FIM, project no. EDUNC-33-18-279-V12 KSN). K.S.N. is grateful to the Royal Society (UK, grant number RSRP R 190000 KSN) for support. Q.L. acknowledges support from the National Research Foundation, Singapore, under the NRF fellowship (project no. NRF-NRFF13-2021-0005 QL). H.Y. acknowledges support from the National Natural Science Foundation of China under Grant No. 12171467 HY and 12161141017 HY. K.H., B.W.S. and Z.-E.O. acknowledge support from 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 KH. The computational work for this article was partially performed on resources of the National Supercomputing Centre, Singapore. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank C. Jie Leong for help with experimental preparation.
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Tan, Jin Da; Ramalingam, Balamurugan; Wong, Swee Liang; Cheng, Jayce Jian Wei; Lim, Yee-Fun; Chellappan, Vijila; Khan, Saif A A; Kumar, Jatin; Hippalgaonkar, Kedar Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization JOURNAL OF CHEMICAL INFORMATION AND MODELING, 63 (15), pp. 4560-4573, 2023, DOI: 10.1021/acs.jcim.3c00504. Abstract | BibTeX | Endnote @article{ISI:001027030200001,
title = {Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization},
author = {Jin Da Tan and Balamurugan Ramalingam and Swee Liang Wong and Jayce Jian Wei Cheng and Yee-Fun Lim and Vijila Chellappan and Saif A A Khan and Jatin Kumar and Kedar Hippalgaonkar},
doi = {10.1021/acs.jcim.3c00504},
times_cited = {2},
issn = {1549-9596},
year = {2023},
date = {2023-07-11},
journal = {JOURNAL OF CHEMICAL INFORMATION AND MODELING},
volume = {63},
number = {15},
pages = {4560-4573},
publisher = {AMER CHEMICAL SOC},
address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA},
abstract = {The skew and shape of the molecular weight distribution(MWD) ofpolymers have a significant impact on polymer physical properties.Standard summary metrics statistically derived from the MWD only providean incomplete picture of the polymer MWD. Machine learning (ML) methodscoupled with high-throughput experimentation (HTE) could potentiallyallow for the prediction of the entire polymer MWD without informationloss. In our work, we demonstrate a computer-controlled HTE platformthat is able to run up to 8 unique variable conditions in parallelfor the free radical polymerization of styrene. The segmented-flowHTE system was equipped with an inline Raman spectrometer and offlinesize exclusion chromatography (SEC) to obtain time-dependent conversionand MWD, respectively. Using ML forward models, we first predict monomerconversion, intrinsically learning varying polymerization kineticsthat change for each experimental condition. In addition, we predictentire MWDs including the skew and shape as well as SHAP analysisto interpret the dependence on reagent concentrations and reactiontime. We then used a transfer learning approach to use the data fromour high-throughput flow reactor to predict batch polymerization MWDswith only three additional data points. Overall, we demonstrate thatthe combination of HTE and ML provides a high level of predictiveaccuracy in determining polymerization outcomes. Transfer learningcan allow exploration outside existing parameter spaces efficiently,providing polymer chemists with the ability to target the synthesisof polymers with desired properties.},
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The skew and shape of the molecular weight distribution(MWD) ofpolymers have a significant impact on polymer physical properties.Standard summary metrics statistically derived from the MWD only providean incomplete picture of the polymer MWD. Machine learning (ML) methodscoupled with high-throughput experimentation (HTE) could potentiallyallow for the prediction of the entire polymer MWD without informationloss. In our work, we demonstrate a computer-controlled HTE platformthat is able to run up to 8 unique variable conditions in parallelfor the free radical polymerization of styrene. The segmented-flowHTE system was equipped with an inline Raman spectrometer and offlinesize exclusion chromatography (SEC) to obtain time-dependent conversionand MWD, respectively. Using ML forward models, we first predict monomerconversion, intrinsically learning varying polymerization kineticsthat change for each experimental condition. In addition, we predictentire MWDs including the skew and shape as well as SHAP analysisto interpret the dependence on reagent concentrations and reactiontime. We then used a transfer learning approach to use the data fromour high-throughput flow reactor to predict batch polymerization MWDswith only three additional data points. Overall, we demonstrate thatthe combination of HTE and ML provides a high level of predictiveaccuracy in determining polymerization outcomes. Transfer learningcan allow exploration outside existing parameter spaces efficiently,providing polymer chemists with the ability to target the synthesisof polymers with desired properties. - FNClarivate Analytics Web of Science
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Ramalingam, B
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Kumar, J
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- AFJin Da Tan
Balamurugan Ramalingam
Swee Liang Wong
Jayce Jian Wei Cheng
Yee-Fun Lim
Vijila Chellappan
Saif A A Khan
Jatin Kumar
Kedar Hippalgaonkar
- TITransfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization
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- IDRADICAL POLYMERIZATION; DRUG DISCOVERY; TERMINATION RATE; STYRENE; OPTIMIZATION; AUTOMATION; GENERATION; CHALLENGES; DEPENDENCE; EFFICIENCY
- ABThe skew and shape of the molecular weight distribution(MWD) ofpolymers have a significant impact on polymer physical properties.Standard summary metrics statistically derived from the MWD only providean incomplete picture of the polymer MWD. Machine learning (ML) methodscoupled with high-throughput experimentation (HTE) could potentiallyallow for the prediction of the entire polymer MWD without informationloss. In our work, we demonstrate a computer-controlled HTE platformthat is able to run up to 8 unique variable conditions in parallelfor the free radical polymerization of styrene. The segmented-flowHTE system was equipped with an inline Raman spectrometer and offlinesize exclusion chromatography (SEC) to obtain time-dependent conversionand MWD, respectively. Using ML forward models, we first predict monomerconversion, intrinsically learning varying polymerization kineticsthat change for each experimental condition. In addition, we predictentire MWDs including the skew and shape as well as SHAP analysisto interpret the dependence on reagent concentrations and reactiontime. We then used a transfer learning approach to use the data fromour high-throughput flow reactor to predict batch polymerization MWDswith only three additional data points. Overall, we demonstrate thatthe combination of HTE and ML provides a high level of predictiveaccuracy in determining polymerization outcomes. Transfer learningcan allow exploration outside existing parameter spaces efficiently,providing polymer chemists with the ability to target the synthesisof polymers with desired properties.
- C1[Tan, Jin Da; Ramalingam, Balamurugan; Wong, Swee Liang; Cheng, Jayce Jian Wei; Lim, Yee-Fun; Chellappan, Vijila; Kumar, Jatin; Hippalgaonkar, Kedar] Agcy Sci Technol & Res, Inst Mat Res & Engn, Singapore 138634, Singapore.
[Tan, Jin Da; Khan, Saif A. A.] Natl Univ Singapore, Integrat Sci & Engn Programme, Grad Sch, Singapore 119077, Singapore.
[Ramalingam, Balamurugan; Lim, Yee-Fun] Agcy Sci Technol & Res, Inst Sustainabil Chem Energy & Environm, Singapore 138665, Singapore.
[Wong, Swee Liang] Home Team Sci & Technol Agcy, Singapore 138507, Singapore.
[Khan, Saif A. A.] Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 117585, Singapore.
[Kumar, Jatin] Xinterra Pte Ltd, Singapore 068896, Singapore.
[Hippalgaonkar, Kedar] Nanyang Technol Univ, Dept Mat Sci & Engn, Singapore 639798, Singapore.
[Hippalgaonkar, Kedar] Natl Univ Singapore, Inst Funct Intelligent Mat, Singapore 117544, Singapore - C3Agency for Science Technology & Research (A*STAR); A*STAR - Institute of Materials Research & Engineering (IMRE); National University of Singapore; Agency for Science Technology & Research (A*STAR); National University of Singapore; Nanyang Technological University; National University of Singapore; Institute for Functional Intelligent Materials (I-FIM)
- RPKumar, J (corresponding author), Agcy Sci Technol & Res, Inst Mat Res & Engn, Singapore 138634, Singapore; Kumar, J (corresponding author), Xinterra Pte Ltd, Singapore 068896, Singapore; Hippalgaonkar, K (corresponding author), Nanyang Technol Univ, Dept Mat Sci & Engn, Singapore 639798, Singapore; Hippalgaonkar, K (corresponding author), Natl Univ Singapore, Inst Funct Intelligent Mat, Singapore 117544, Singapore
- FUAccelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research [A1898b0043]
- FXThe authors acknowledge funding from 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.
- NR73
- TC2
- Z92
- U15
- U215
- PUAMER CHEMICAL SOC
- PIWASHINGTON
- PA1155 16TH ST, NW, WASHINGTON, DC 20036 USA
- SN1549-9596
- J9J CHEM INF MODEL
- JIJ. Chem Inf. Model.
- PDJUL 11
- PY2023
- VL63
- BP4560
- EP4573
- DI10.1021/acs.jcim.3c00504
- PG14
- WCChemistry, Medicinal; Chemistry, Multidisciplinary; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications
- SCPharmacology & Pharmacy; Chemistry; Computer Science
- GAO8DT1
- UTWOS:001027030200001
- ER
- EF
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