Integrated computational chemistry system for catalysts design

Ammal, S. Salai Cheettu; Takami, Seiichi; Kubo, Momoji; Miyamoto, Akira

doi:10.1007/bf02745544

Computer‐Aided Design of Solid Catalysts

Holeňa

¹

,

Baerns

²

2008

Handbook of Heterogeneous Catalysis

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The sections in this article are Introduction Theoretical Methods Rational Methods for the Design of Catalytic Experiments The Statistical Design of Experiments Optimization Methods for Empirical Objective Functions Evolutionary Methods Feature 1 Feature 2 Feature 3 Feature 4 Other Stochastic Methods Deterministic Methods Group 1 Methods Group 2 Methods Group 3 Methods Concluding Remarks Data Analysis and Data Mining Statistical Methods: An Overview Artificial Neural Networks Concluding Remarks Conclusions

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Troubles in the Systematic Prediction of Transition Metal Thermochemistry with Contemporary Out-of-the-Box Methods

Minenkov

¹

,

Chermak

²

,

Cavallo

³

2016

J. Chem. Theory Comput.

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The recently developed DLPNO-CCSD(T) method and seven popular DFT functionals (B3LYP, M06, M06L, PBE, PBE0, TPSS, and TPSSh) with and without an empirical dispersion term have been tested to reproduce 111 gas phase reaction enthalpies involving 11 different transition metals. Our calculations, corrected for both relativistic effects and basis set incompleteness, indicate that most of the methods applied with default settings perform with acceptable accuracy on average. Nevertheless, our calculations also evidenced unexpected and nonsystematic large deviations for specific cases. For group 12 metals (Zn, Cd, Hg), most of the methods provided mean unsigned errors (MUE) less than 5.0 kcal/mol, with DLPNO-CCSD(T) and PBE methods performing excellently (MUE lower 2.0 kcal/mol). Problems started with group 4 metals (Ti and Zr). The best performer for Zr complexes with MUE of 1.8 kcal/mol, PBE0-D3, provides MUE larger than 8 kcal/mol for Ti. DLPNO-CCSD(T) provides a reasonable MUE of 3.3 kcal/mol for Ti reactions but gives MUE a larger than 14.4 kcal/mol for Zr complexes, with all the larger deviations for reactions involving ZrF4. Large and nonsystematic errors have been obtained for group 6 metals (Mo and W), for eight reactions containing Fe, Cu, Nb, and Re complexes. Finally, for the whole set of 111 reactions, the DLPNO-CCSD(T), B3LYP-D3, and PBE0-D3 methods turned out to be the best performers, all providing MUE below 5.0 kcal/mol. Since DFT results cannot be systematically improved and large nonsystematic deviations of 20-30 kcal/mol were obtained even for best performers, our results indicate that current DFT methods are still unable to provide robust predictions in transition metal thermochemistry, at least for the functionals explored in this work. The same conclusion holds for both DLPNO-CCSD(T) and canonical CCSD(T) methods when used entirely as out-of-the-box. However, if careful investigation of core correlation is performed, relativistic effects are properly included and the quality of the reference wave function is properly checked, CCSD(T) methods can still provide good quality results that might even be used to validate DFT methods due to paucity of accurate thermodynamic data for realistic-sized transition metal complexes.

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Bayesian Framework for Building Kinetic Models of Catalytic Systems

Hsu¹,

Stamatis²,

Caruthers³

et al. 2009

Ind. Eng. Chem. Res.

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Recent advances in statistical procedures, coupled with the availability of high performance computational resources and the large mass of data generated from high throughput screening, have enabled a new paradigm for building mathematical models of the kinetic behavior of catalytic reactions. A Bayesian approach is used to formulate the model building problem, estimate model parameters by Monte Carlo based methods, discriminate rival models, and design new experiments to improve the discrimination and fidelity of the parameter estimates. The methodology is illustrated with a typical, model building problem involving three proposed Langmuir−Hinshelwood rate expressions. The Bayesian approach gives improved discrimination of the three models and higher quality model parameters for the best model selected as compared to the traditional methods that employ linearized statistical tools. This paper describes the methodology and its capabilities in sufficient detail to allow kinetic model builders to evaluate and implement its improved model discrimination and parameter estimation features.

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Integrated computational chemistry system for catalysts design

Cited by 4 publications

References 42 publications

Computer‐Aided Design of Solid Catalysts

Computer‐Aided Design of Solid Catalysts

Troubles in the Systematic Prediction of Transition Metal Thermochemistry with Contemporary Out-of-the-Box Methods

Bayesian Framework for Building Kinetic Models of Catalytic Systems

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