How Can Active Machine Learning Aid Kinetic Model Generation, and Why Should We Care?

Yannick Ureel , Maarten R. Dobbelaere , Istvan Lengyel , Maarten K. Sabbe , Kevin M. Van Geem

Engineering ›› 2025, Vol. 52 ›› Issue (9) : 14 -18.

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Engineering ›› 2025, Vol. 52 ›› Issue (9) :14 -18. DOI: 10.1016/j.eng.2025.08.009
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How Can Active Machine Learning Aid Kinetic Model Generation, and Why Should We Care?
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Yannick Ureel, Maarten R. Dobbelaere, Istvan Lengyel, Maarten K. Sabbe, Kevin M. Van Geem. How Can Active Machine Learning Aid Kinetic Model Generation, and Why Should We Care?. Engineering, 2025, 52 (9) : 14-18 DOI:10.1016/j.eng.2025.08.009

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References

[1]

Hill CG, Root TW. Introduction to chemical engineering kinetics and reactor design. 2nd ed. Hoboken: John Wiley & Sons, Inc.; 2014.

[2]

Van de Vijver R, Vandewiele NM, Bhoorasingh PL, Slakman BL, Seyedzadeh Khanshan F, Carstensen HH, et al. Automatic mechanism and kinetic model generation for gas- and solution-phase processes: a perspective on best practices, recent advances, and future challenges. Int J Chem Kinet 2015; 47 (4):199-231.

[3]

Simonin JP. On the comparison of pseudo-first order and pseudo-second order rate laws in the modeling of adsorption kinetics. Chem Eng J 2016; 300:254-63.

[4]

Ji W, Deng S. Autonomous discovery of unknown reaction pathways from data by chemical reaction neural network. Chem A Eur J 2021; 125(4):1082-92.

[5]

Burnham AK, Dinh LN. A comparison of isoconversional and model-fitting approaches to kinetic parameter estimation and application predictions. J Therm Anal Calorim 2007; 89(2):479-90.

[6]

Xiong Q, Jutan A. Grey-box modelling and control of chemical processes. Chem Eng Sci 2002; 57(6):1027-39.

[7]

Ding J, Xu N, Nguyen MT, Qiao Q, Shi Y, He Y, et al. Machine learning for molecular thermodynamics. Chin J Chem Eng 2021; 31:227-39.

[8]

Olsson F. A literature survey of active machine learning in the context of natural language processing. Kista: Swedish Institute of Computer Science; 2009.

[9]

Settles B. Active learning. Cham: Springer; 2012.

[10]

Frazier PI. A tutorial on Bayesian optimization. 2018. arXiv:1807.02811.

[11]

Rasmussen CE, Williams CKI. Gaussian processes for machine learning. Cambridge: The MIT Press; 2006.

[12]

Li Q, Chen H, Koenig BC, Deng S. Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification. Phys Chem Chem Phys 2023; 25(5):3707-17.

[13]

Nemani V, Biggio L, Huan X, Hu Z, Fink O, Tran A, et al. Uncertainty quantification in machine learning for engineering design and health prognostics: a tutorial. Mech Syst Sig Process 2023; 205:110796.

[14]

Tyralis H, Papacharalampous G. A review of predictive uncertainty estimation with machine learning. Artif Intell Rev 2024; 57(4):94.

[15]

Häse F, Roch LM, Kreisbeck C, Aspuru-Guzik A. Phoenics: a Bayesian optimizer for chemistry. ACS Cent Sci 2018; 4(9):1134-45.

[16]

Zhang Y, Lee AA. Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning. Chem Sci 2019; 10 (35):8154-63.

[17]

Szymanski NJ, Rendy B, Fei YX, Kumar RE, He TJ, Milsted D, et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 2023; 624(7990):86-91.

[18]

Shields BJ, Stevens J, Li J, Parasram M, Damani F, Alvarado JIM, et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature 2021; 590 (7844):89-96.

[19]

Burger B, Maffettone PM, Gusev VV, Aitchison CM, Bai Y, Wang X, et al. A mobile robotic chemist. Nature 2020; 583(7815):237-41.

[20]

Dürholt JP, Asche TS, Kleinekorte J, Mancino-Ball G, Schiller B, Sung S, et al. BoFire: Bayesian optimization framework intended for real experiments. 2024. arXiv:2408.05040.

[21]

Ureel Y, Dobbelaere MR, Ouyang Y, De Ras K, Sabbe MK, Marin GB, et al. Active machine learning for chemical engineers: a bright future lies ahead! Engineering 2022; 27:23-30.

[22]

Melnikov AA, Poulsen Nautrup H, Krenn M, Dunjko V, Tiersch M, Zeilinger A, et al. Active learning machine learns to create new quantum experiments. Proc Natl Acad Sci USA 2018; 115(6):1221-6.

[23]

Eyke NS, Green WH, Jensen KF. Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening. React Chem Eng 2020; 5(10):1963-72.

[24]

Ureel Y, Dobbelaere MR, Akin O, Varghese RJ, Pernalete CG, Thybaut JW, et al. Active learning-based exploration of the catalytic pyrolysis of plastic waste. Fuel 2022; 328:125340.

[25]

Seifrid M, Pollice R, Aguilar-Granda A, Chan ZM, Hotta K, Ser CT, et al. Autonomous chemical experiments: challenges and perspectives on establishing a self-driving lab. Acc Chem Res 2022; 55(17):2454-66.

[26]

Tom G, Schmid SP, Baird SG, Cao Y, Darvish K, Hao H, et al. Self-driving laboratories for chemistry and materials science. Chem Rev 2024; 124 (16):9633-732.

[27]

Kuddusi Y, Dobbelaere MR, Van Geem KM, Züttel A. Accelerated design of nickel-cobalt based catalysts for CO2 hydrogenation with human-in-the-loop active machine learning. Cat Sci Technol 2024; 14(21):6307-20.

[28]

Zuluaga M, Sergent G, Krause A, Püschel M. Active learning for multi-objective optimization. In: Dasgupta S, McAllester D, editors. Proceedings of the 30th International Conference on International Conference on Machine Learning; 2013 Jun 16-21; Atlanta, GA, USA. New York City: Journal of Machine Learning Research Inc.; 2013. p. 462-70.

[29]

Unsleber JP, Reiher M. The exploration of chemical reaction networks. Annu Rev Phys Chem 2020; 71:121-42.

[30]

Gao CW, Allen JW, Green WH, West RH. Reaction mechanism generator: automatic construction of chemical kinetic mechanisms. Comput Phys Commun 2016; 203:212-25.

[31]

Vandewiele NM, Van Geem KM, Reyniers MF, Marin GB. Genesys: kinetic model construction using chemo-informatics. Chem Eng J 2012; 207-208:526-38.

[32]

Ji W, Richter F, Gollner MJ, Deng S. Autonomous kinetic modeling of biomass pyrolysis using chemical reaction neural networks. Combust Flame 2022; 240:111992.

[33]

Wilson ZT, Sahinidis NV. The ALAMO approach to machine learning. Comput Chem Eng 2017; 106:785-95.

[34]

Sandoval IO, Zhang D, Hellgardt K, Kuok Mimi Hii K, del Rio Chanona EA. Automated kinetic model discovery—a methodological framework. In: Kokossis AC, Georgiadis MC, Pistikopoulos E, editors. Computer aided chemical engineering, volume 52. Amsterdam: Elsevier; 2023. p. 33-8.

[35]

Brunton SL, Proctor JL, Kutz JN. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc Natl Acad Sci USA 2016; 113(15):3932-4397.

[36]

Hoffmann M, Fröhner C, Noé F. Reactive SINDy: discovering governing reactions from concentration data. J Chem Phys 2019; 150(2):025101.

[37]

Hunter WG, Reiner AM. Designs for discriminating between two rival models. Technometrics 1965; 7(3):307-23.

[38]

Schwaab M, Luiz Monteiro J, Carlos PJ. Sequential experimental design for model discrimination: taking into account the posterior covariance matrix of differences between model predictions. Chem Eng Sci 2008; 63(9): 2408-19.

[39]

Box GEP, Hill WJ. Discrimination among mechanistic models. Technometrics 1967; 9(1):57-71.

[40]

Froment GF. Model discrimination and parameter estimation in heterogeneous catalysis. AIChE J 1975; 21(6):1041-57.

[41]

Thybaut JW, Marin GB. Single-event MicroKinetics: catalyst design for complex reaction networks. J Catal 2013; 308:352-62.

[42]

Thybaut JW, Marin GB, Baron GV, Jacobs PA, Martens JA. Alkene protonation enthalpy determination from fundamental kinetic modeling of alkane hydroconversion on Pt/H-(US)Y-zeolite. J Catal 2001; 202(2):324-39.

[43]

Park TY, Froment GF. A hybrid genetic algorithm for the estimation of parameters in detailed kinetic models. Comput Chem Eng 1998; 22(Suppl 1): S103-10.

[44]

Snoek J, Larochelle H, Adams RP. Practical Bayesian optimization of machine learning algorithms. In: Pereira F, Burges CJ, Bottou L, Weinberger KQ, editors. Advances in neural information processing systems 25: 26th Annual Conference on Neural Information Processing Systems 2012; 2012 Dec 3-6; Lake Tahoe, NV, USA. Red Hook: Curran Associates, Inc.; 2012. p. 2951-9.

[45]

Berkenkamp F, Krause A, Schoellig AP. Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics. Mach Learn 2023; 112(10):3713-47.

[46]

Park S, Na J, Kim M, Lee JM. Multi-objective Bayesian optimization of chemical reactor design using computational fluid dynamics. Comput Chem Eng 2018; 119:25-37.

[47]

Na J, Kshetrimayum KS, Lee U, Han C. Multi-objective optimization of microchannel reactor for Fischer-Tropsch synthesis using computational fluid dynamics and genetic algorithm. Chem Eng J 2017; 313:1521-34.

[48]

Wang X, Jin Y, Schmitt S, Olhofer M. Recent advances in Bayesian optimization. ACM Comput Surv 2023; 55(13s):1-36.

[49]

Kasiraju S, Wang Y, Bhandari S, Singh AR, Vlachos DG. A Python tool for parameter estimation of "black box" macro- and micro-kinetic models with Bayesian optimization—petBOA. Comput Phys Commun 2025; 306:109358.

[50]

Ureel Y, Tomme L, Sabbe MK, Van Geem KM. Genesys-Cat: automatic microkinetic model generation for heterogeneous catalysis with improved Bayesian optimization. Cat Sci Technol 2025; 15(3):750-64.

[51]

Kurilovich AA, Alexander CT, Pazhetnov EM, Stevenson KJ. Active learningbased framework for optimal reaction mechanism selection from microkinetic modeling: a case study of electrocatalytic oxygen reduction reaction on carbon nanotubes. Phys Chem Chem Phys 2020; 22(8):4581-91.

[52]

Bach FR. Active learning for misspecified generalized linear models. In: Schölkopf B, Platt J, Hoffman T, editors. Advances in neural information processing systems 19: 20th Annual Conference on Neural Information Processing Systems 2006; 2006 Dec 4-7; Vancouver, BC, Canada. Red Hook: Curran Associates, Inc.; 2006. p. 65-72.

[53]

Green WH. Moving from postdictive to predictive kinetics in reaction engineering. AIChE J 2020; 66(11):e17059.

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