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Thermodynamic rules for zeolite formation from machine learning based global optimization.

ABSTRACT: While the [TO₄] tetrahedron packing rule leads to millions of likely zeolite structures, there are currently only 252 types of zeolite frameworks reported after decades of synthetic efforts. The subtle synthetic conditions, e.g. the structure-directing agents, pH and the feed ratio, were often blamed for the limited zeolite types due to the complex kinetics. Here by developing machine learning global optimization techniques, we are now able to establish the global potential energy surface of a typical zeolite system, Si _x Al _y P _z O₂H _y-z with 12 T atoms (T: Si, Al and P) that is the general formula shared by CHA, ATS, ATO and ATV zeolite frameworks. After analyzing more than 10⁶ minima data, we identify thermodynamic rules on energetics and local bonding patterns for stable zeolites. These rules provide general guidelines to classify zeolite types and correlate them with synthesis conditions. The machine learning based atomistic simulation thus paves a new way towards rational design and synthesis of stable zeolite frameworks with desirable compositions.

SUBMITTER: Ma S

PROVIDER: S-EPMC8162439 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Thermodynamic rules for zeolite formation from machine learning based global optimization.

Ma Sicong S Shang Cheng C Wang Chuan-Ming CM Liu Zhi-Pan ZP

Chemical science 20200902 37

While the [TO<sub>4</sub>] tetrahedron packing rule leads to millions of likely zeolite structures, there are currently only 252 types of zeolite frameworks reported after decades of synthetic efforts. The subtle synthetic conditions, <i>e.g.</i> the structure-directing agents, pH and the feed ratio, were often blamed for the limited zeolite types due to the complex kinetics. Here by developing machine learning global optimization techniques, we are now able to establish the global potential ene ...[more]

PMID: 34094273

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Project description:BackgroundKinetic modeling is a powerful tool for understanding the dynamic behavior of biochemical systems. For kinetic modeling, determination of a number of kinetic parameters, such as the Michaelis constant (Km), is necessary, and global optimization algorithms have long been used for parameter estimation. However, the conventional global optimization approach has three problems: (i) It is computationally demanding. (ii) It often yields unrealistic parameter values because it simply seeks a better model fitting to experimentally observed behaviors. (iii) It has difficulty in identifying a unique solution because multiple parameter sets can allow a kinetic model to fit experimental data equally well (the non-identifiability problem).ResultsTo solve these problems, we propose the Machine Learning-Aided Global Optimization (MLAGO) method for Km estimation of kinetic modeling. First, we use a machine learning-based Km predictor based only on three factors: EC number, KEGG Compound ID, and Organism ID, then conduct a constrained global optimization-based parameter estimation by using the machine learning-predicted Km values as the reference values. The machine learning model achieved relatively good prediction scores: RMSE = 0.795 and R2 = 0.536, making the subsequent global optimization easy and practical. The MLAGO approach reduced the error between simulation and experimental data while keeping Km values close to the machine learning-predicted values. As a result, the MLAGO approach successfully estimated Km values with less computational cost than the conventional method. Moreover, the MLAGO approach uniquely estimated Km values, which were close to the measured values.ConclusionsMLAGO overcomes the major problems in parameter estimation, accelerates kinetic modeling, and thus ultimately leads to better understanding of complex cellular systems. The web application for our machine learning-based Km predictor is accessible at https://sites.google.com/view/kazuhiro-maeda/software-tools-web-apps , which helps modelers perform MLAGO on their own parameter estimation tasks.

Dataset Information

Thermodynamic rules for zeolite formation from machine learning based global optimization.

Publications

Thermodynamic rules for zeolite formation from machine learning based global optimization.

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