Bandgaps of noble and transition metal/ZIF-8 electro/catalysts: a computational study

Baghban, Alireza Akbarzadeh; Habibzadeh, Sajjad; Ashtiani, Farzin Zokaee

doi:10.1039/d0ra02943b

Cited by 20 publications

(14 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead of searching a hyperplane, where the distance to the closest data points is maximized, the distance between the SVR hyperplane and most data points is no more than the tolerance margin, ξ, with ξ being determined by the required accuracy of the model. Both SVC and SVR can be used for catalytic activity prediction and simplification of DFT calculations. ,− For example, Baghban et al shortened the DFT calculation time for determining the bandgap of zeolitic imidazolate frameworks (ZIFs) using an SVM model, providing a simple approach for the design of ZIF-based electrocatalysts …”

Section: Machine Learning Modelingmentioning

confidence: 99%

“…87,187−190 For example, Baghban et al shortened the DFT calculation time for determining the bandgap of zeolitic imidazolate frameworks (ZIFs) using an SVM model, providing a simple approach for the design of ZIF-based electrocatalysts. 191 The SVM, a nonlinear algorithm, provides a global solution to the feature−property relation, shows good generation ability, and is insensitive to outliers. Nevertheless, training of the SVM model is very slow on a large data set, and there is no general rule for the selection of kernel functions.…”

Section: Support Vector Machinesmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

et al. 2022

View full text Add to dashboard Cite

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/ photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/ photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

show abstract

Section: Machine Learning Modelingmentioning

confidence: 99%

Section: Support Vector Machinesmentioning

confidence: 99%

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Moreover, machine learning approaches have recently been applied in different areas such as chemistry, catalyst, energy, chemical processes, etc. 44 – 49 . Support vector machine (SVM) 50 , artificial neural network (ANN) 51 , 52 , fuzzy logic system (FLS) 53 , and adaptive neuro-fuzzy inference system (ANFIS) 54 , 55 are the most familiar categories of machine learning which can be optimized by different optimization algorithms such as particle swarm optimization (PSO) 48 , genetic algorithm (GA) 56 , gray wolf optimization (GWO) 46 , imperialist competitive algorithm (ICA) 57 , teaching learning-based optimization (TLBO) 58 , etc.…”

Section: Introductionmentioning

confidence: 99%

On the evaluation of hydrogen evolution reaction performance of metal-nitrogen-doped carbon electrocatalysts using machine learning technique

Baghban

Habibzadeh

Ashtiani

2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

Single-atom catalysts (SACs) introduce as a promising category of electrocatalysts, especially in the water-splitting process. Recent studies have exhibited that nitrogen-doped carbon-based SACs can act as a great HER electrocatalyst. In this regard, Adaptive Neuro-Fuzzy Inference optimized by Gray Wolf Optimization (GWO) method was used to predict hydrogen adsorption energy (ΔG) obtained from density functional theory (DFT) for single transition-metal atoms including Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Zr, Nb, Mo, Tc, Ru, Rh, Pd, Ag, Cd, Hf, Ta, W, Re, Os, Ir, Pt, and Au embedded in N-doped carbon of different sizes. Various descriptors such as the covalent radius, Zunger radius of the atomic d-orbital, the formation energy of the single-atom site, ionization energy, electronegativity, the d-band center from − 6 to 6 eV, number of valence electrons, Bader charge, number of occupied d states from 0 to − 2 eV, and number of unoccupied d states from 0 to 2 eV were chosen as input parameters based on sensitivity analysis. The R-squared and MSE of the developed model were 0.967 and 0.029, respectively, confirming its great accuracy in determining hydrogen adsorption energy of metal/NC electrocatalysts.

show abstract

“…26,27 Recently, several chemical engineering problems have been widely studied by ML methods. [28][29][30][31][32][33][34] In 2018, the inuence of several parameters (specic surface area, I D /I G ratio, calculated pore size, doping element, and voltage window) on the capacitance was studied via an articial neutron network (ANN) method. 35 Recently Su et al investigated the effect of porous carbon-based materials and working potential ranges on EDL capacitance by using various ML models.…”

Section: Introductionmentioning

confidence: 99%