Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Abstract

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.

Porous Nanoarchitectures of Nonprecious Metal Borides: From Controlled Synthesis to Heterogeneous Catalyst Applications

“…However, due to the complexity of the composition, proper compositions and structures are extremely challenging to experimentally determine under the synthesis conditions based on only chemical intuition from a human . An efficient strategy is to combine experiments with data science and artificial intelligence through machine learning, which has great potential for accelerating catalysis research and the rapid exploration of materials chemistry . In addition, although DFT calculations have been used to predict reaction intermediates and catalyst active sites for designing efficient catalysts, such theoretical models are still primarily based on the classic theory for crystalline noble-metal catalysts.…”

“…151 An efficient strategy is to combine experiments with data science and artificial intelligence through machine learning, which has great potential for accelerating catalysis research and the rapid exploration of materials chemistry. 152 In addition, although DFT calculations have been used to predict reaction intermediates and catalyst active sites for designing efficient catalysts, such theoretical models are still primarily based on the classic theory for crystalline noble-metal catalysts. Furthermore, the amorphous state caused by B poses a challenge for developing theoretical and computational models.…”

Probing the properties of molecules and complex materials using machine learning

“…Working with colleagues from RMIT, we recently reviewed the application of ML methods to the modelling and design of these types of industrially important catalysts. [76] ML methods have been shown to be useful for leveraging a relatively small number of accurate but computationally expensive Grand Canonical Monte Carlo (GCMC) calculations into a much larger number of porous materials. The GCMC calculations can reliably predict the loading of gases, and using these data to train ML models provides a rapid method of estimating loading capacities of large porous materials datasets.…”

Trendbericht Theoretische Chemie 2022: Maschinelles Lernen für elektronisch angeregte Zustände

“…Auch in der theoretischen Chemie brachte es schon Fortschritte, beispielsweise bei der Entwicklung neuer Medikamente 1) oder Photokatalysatoren. 2) Die Frage "Welcher Trend … ist aufgekommen, den Sie so nicht erwartet haben?" oder "In welchem Gebiet erwarten Sie … die größten Entwicklungen?"…”