Machine learning made easy for optimizing chemical reactions

Hein, Jason E.

doi:10.1038/d41586-021-00209-6

Cited by 15 publications

(7 citation statements)

References 9 publications

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“…Bayesian optimization, the iterative response global optimization algorithm, has demonstrated exceptional performance for tuning machine learning models by optimizing the hyperparameters of models. [ 21 ] We applied Bayesian optimization to explore the hyperparameter space using fivefold cross‐validation with GPyOpt library, through which RMSE were minimized (Note S2 and Figure S5, Supporting Information). For each independent experiment, the statistical mean values for each experiment were used to build a dataset.…”

Section: Resultsmentioning

confidence: 99%

Microfluidic Screening‐Assisted Machine Learning to Investigate Vertical Phase Separation of Small Molecule:Polymer Blend

et al. 2022

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Solution‐based thin‐film processing is a widely utilized technique for the fabrication of various devices. In particular, the tunability of the ink composition and coating condition allows precise control of thin‐film properties and device performance. Despite the advantage of having such tunability, the sheer number of possible combinations of experimental parameters render it infeasible to efficiently optimize device performance and analyze the correlation between experimental parameters and device performance. In this work, a microfluidic screening‐embedded thin‐film processing technique is developed, through which thin‐films of varying ratios of small molecule semiconductor:polymer blend are simultaneously generated and screened in a time‐ and resource‐efficient manner. Moreover, utilizing the thin‐films of varying combinations of experimental parameters, machine learning models are trained to predict the transistor performance. Gaussian Process Regression (GPR) algorithms tuned by Bayesian optimization shows the best predictive accuracy amongst the trained models, which enables narrowing down of the combinations of experimental parameters and investigation of the degree of vertical phase separation under the predicted parameter space. The technique can serve as a guideline for elucidating the underlying complex parameter‐property‐performance correlations in solution‐based thin‐film processing, thereby accelerating the optimization of various thin‐film devices in the future.

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Section: Resultsmentioning

confidence: 99%

Microfluidic Screening‐Assisted Machine Learning to Investigate Vertical Phase Separation of Small Molecule:Polymer Blend

et al. 2022

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“…In addition, another type of metabolic pathway we can examine is the production pathways; their modeling would allow the development of an optimized overall process. In fact, another study revealed that ML methods can accelerate the optimization of chemical synthesis (Hein, 2021). As stated before, we do not need to enlarge this dataset, which is composed of records of the various parameters of an industrial-scale penicillin fermentation process.…”

Section: Non-linear Machine Learning Methods Are Efficient For Flux P...mentioning

confidence: 99%

Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model

Lo-Thong-Viramoutou

Charton

Cadet³

et al. 2022

Front. Artif. Intell.

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The use of machine learning (ML) in life sciences has gained wide interest over the past years, as it speeds up the development of high performing models. Important modeling tools in biology have proven their worth for pathway design, such as mechanistic models and metabolic networks, as they allow better understanding of mechanisms involved in the functioning of organisms. However, little has been done on the use of ML to model metabolic pathways, and the degree of non-linearity associated with them is not clear. Here, we report the construction of different metabolic pathways with several linear and non-linear ML models. Different types of data are used; they lead to the prediction of important biological data, such as pathway flux and final product concentration. A comparison reveals that the data features impact model performance and highlight the effectiveness of non-linear models (e.g., QRF: RMSE = 0.021 nmol·min−1 and R2 = 1 vs. Bayesian GLM: RMSE = 1.379 nmol·min−1 R2 = 0.823). It turns out that the greater the degree of non-linearity of the pathway, the better suited a non-linear model will be. Therefore, a decision-making support for pathway modeling is established. These findings generally support the hypothesis that non-linear aspects predominate within the metabolic pathways. This must be taken into account when devising possible applications of these pathways for the identification of biomarkers of diseases (e.g., infections, cancer, neurodegenerative diseases) or the optimization of industrial production processes.

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“…This will make it easier to deeply understand the basic mechanism, and provide an opportunity to discover a new reaction. Machine learning techniques make it possible to identify laws from data. − Therefore, machine learning is used to build data error analysis and calibration models for improving the precision and confidence of experimental data, achieving effective detection of weak reactions.…”

Section: Introductionmentioning

confidence: 99%

Weak Reaction Scatterometry of Plasmonic Resonance Light Scattering with Machine Learning

Peng

Luo

et al. 2021

Anal. Chem.

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Weak reactions are usually overlooked due to weak detectable features and susceptibility to interference from noise signals. Strategies for detecting weak reactions are essential for exploring reaction mechanisms and exploiting potential applications. Machine learning has recently been successfully used to identify patterns and trends in the data. Here, it is demonstrated that machine learning-based techniques can offer accurate local surface plasmon resonance (LSPR) scatterometry by improving the precision of the plasmonic scattering imaging in weak chemical reactions. Dark-field microscopy (DFM) imaging technique is the most effective method for high-sensitivity plasmonic nanoparticles LSPR scatterometry. Unfortunately, deviations caused by the instrument and operating errors are inevitable, and it is difficult to effectively detect the presence of weak reactions. Thus, introducing a machine learning calibration model to automatically calibrate the scattering signal of the nanoprobe in the reaction process can greatly improve the confidence of LSPR scatterometry under DFM imaging and allow DFM imaging to effectively monitor unobvious or weak reactions. By this approach, the weak oxidation of silver nanoparticles (AgNPs) in water by dissolved oxygen was successfully monitored. Moreover, a trivial reaction between AgNPs and mercury ions was detected in a dilute mercury solution with a concentration greater than 1.0 × 10 −10 mol/L. These results suggest the great potential of the combination of LSPR scatterometry and machine learning as a method for imaging analysis and intelligent sensing.

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Machine learning made easy for optimizing chemical reactions

Abstract: Debilitating gut pain is common, but the underlying cause is often unclear. It emerges that gut infection triggers localized immune responses that cause normally innocuous foods to be perceived as harmful, leading to persistent pain. See p.151

Cited by 15 publications

References 9 publications

Microfluidic Screening‐Assisted Machine Learning to Investigate Vertical Phase Separation of Small Molecule:Polymer Blend

Microfluidic Screening‐Assisted Machine Learning to Investigate Vertical Phase Separation of Small Molecule:Polymer Blend

Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model

Weak Reaction Scatterometry of Plasmonic Resonance Light Scattering with Machine Learning

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