Machine learning for heterogeneous catalyst design and discovery

Goldsmith, Bryan R.; Esterhuizen, Jacques A.; Liu, Jin‐Xun; Bartel, Christopher J.; Sutton, Christopher

doi:10.1002/aic.16198

Cited by 304 publications

(287 citation statements)

References 117 publications

Supporting

Mentioning

280

Contrasting

Unclassified

Order By: Relevance

“…Finding a catalyst with low overpotential and cost becomes essential in catalyst materials design. ML methods have been used in catalyst design since the 1990s, and are now a resurgence of interest and being applied more broadly to a large number of systems …”

Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

373

281

View full text Add to dashboard Cite

Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

show abstract

Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

373

281

View full text Add to dashboard Cite

show abstract

“…This study presents not only the advantages of ML but also the limitations and difficulties of ML for heterogeneous catalysis. The schemes proposed and results obtained provide a valuable contribution to establishing “catalysis Informatics.”…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data

et al. 2019

View full text Add to dashboard Cite

The literature provides insights for catalyst design and discovery. Effective analysis of reported data using machine learning (ML) methods offers the ability to gain valuable information. However, utilizing the literature in this way has obstacles such as lack of compositional overlaps, bias from prior published data, and low sample counts for many elements. The present study describes an ML approach that considers elemental features as input representations instead of inputting catalyst compositions directly. This ML method has the potential for catalyst discovery, including catalytic reactions with limited catalyst composition overlap in the available data. Oxidative coupling of methane (OCM), water gas shift (WGS), and CO oxidation reactions were chosen to confirm the effectiveness of the proposed method by analysis using several state‐of‐the‐art ML methods. Among the ML methods tested, gradient boosting regression with XGBoost (XGB) provided the best results, and prediction accuracy was improved by the proposed approach for all three reaction types. In addition, a quantitative value of “feature importance score” was calculated to evaluate the most influential input variables on catalyst performance. Finally, catalyst optimization was explored using ML as a “surrogate” model, and the top 20 promising candidate catalysts were identified for the OCM reaction based on the optimization. The advantages of ML in catalysis analysis as well as the difficulties and limitations originating from the complexity of heterogeneous catalysis were explored.

show abstract

“…Catalytic reactions involve multiple experimental factors and surface chemistries in a complex manner, making it difficult to create a complete model. However, the implementation of data science could potentially navigate and reveal such complex matter within catalysis as machine learning can treat multiple factors in high dimensions . In earlier years, neural network was implemented to treat heterogeneous catalysis where such data science techniques are proposed to be effective tools for simulating catalyst properties and the performance of solid materials .…”

Section: Introductionmentioning

confidence: 99%

Data Driven Determination of Reaction Conditions in Oxidative Coupling of Methane via Machine Learning

2019

View full text Add to dashboard Cite

The challenge in catalytic reactions lies within its complexity coming from high dimensional experimental factors. In order to solve such complexity, machine learning is implemented to treat experimental conditions in high dimensions. Oxidative coupling of methane, methane to C2 compounds (ethylene and ethane), is chosen as the prototype reaction where 156 data consisting of various experimental conditions is prepared. Machine learning reveals that the relationship between experimental conditions and C2 yield is non‐linear matter. In particular, extreme tree regression is found to accurately reproduce the experimental data. In addition, machine learning predictions can be a good indicator for designing experiments. Thus, machine learning can be a powerful approach towards understanding and determining experimental conditions in high dimension.

show abstract

Machine learning for heterogeneous catalyst design and discovery

Cited by 304 publications

References 117 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data

Data Driven Determination of Reaction Conditions in Oxidative Coupling of Methane via Machine Learning

Contact Info

Product

Resources

About