A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction

Jensen, Zach; Kim, Edward; Kwon, Sung Youn; Gani, Terry Z. H.; Román‐Leshkov, Yuriy; Moliner, Manuel; Corma, Avelino; Olivetti, Elsa

doi:10.1021/acscentsci.9b00193

Cited by 202 publications

(169 citation statements)

References 80 publications

(119 reference statements)

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“…Furthermore, the vast majority of databases are commercial products requiring a license, and programmatic application programming interfaces (APIs) for large‐scale data access are rarely implemented. A large fraction of experimental data are only available in journal publications, though recent successes in text mining offer a potential solution to this conundrum . Finally, major efforts are underway in high‐throughput/combinatorial experiments that can generate large experimental materials database with diverse properties …”

Section: Data Collectionmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

373

281

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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.

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Section: Data Collectionmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

373

281

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“…In contrast, running the advanced methods and models of modern QC requires several days in a supercomputer. ML approaches have already proven successful in different elds of chemistry, [51][52][53] with a strong focus on materials science [54][55][56][57][58][59][60][61] and drug discovery. [62][63][64][65][66][67][68] In other areas, including organic synthesis, [69][70][71][72][73] and theoretical [74][75][76][77][78][79][80][81] and inorganic 82,83 chemistry, the use of ML is rapidly growing.…”

Section: Introductionmentioning

confidence: 99%

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

et al. 2020

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“…Leveraging recent advances in natural language processing and text markup parsing tools, we recently developed a tool in collaboration with Olivetti and co-workers to automatically extract synthesis information and trends from zeolite journal articles (see Figure 9). 51 Specifically, our pipeline automatically located, extracted, and organized zeolite synthesis data from both the tables and main text of thousands of articles. We validated the accuracy of the extracted data using a subset of articles related to the preparation of germanium-containing zeolites for which the pipeline accurately identified the complex relationships between the synthesis parameters and resulting topology.…”

Section: 3-literature Data Extractionmentioning

confidence: 99%

Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery

2019

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CONSPECTUS: Zeolites are microporous crystalline materials with well-defined cavities and pores, which can be prepared under different pore topologies and chemical compositions. Their preparation is typically defined by multiple interconnected variables (e.g., reagent sources, molar ratios, aging treatments, reaction time and temperature, among others), but unfortunately their distinctive influence, particularly on the nucleation and crystallization processes, is still far from being understood. Thus, the discovery and/or optimization of specific zeolites is closely related to the exploration of the parametric space through trial-and-error methods, generally by studying the influence of each parameter individually. In the past decade, machine learning (ML) methods have rapidly evolved to address complex problems involving highly nonlinear or massively combinatorial processes that conventional approaches cannot solve. Considering the vast and interconnected multiparametric space in zeolite synthesis, coupled with our poor understanding of the mechanisms involved in their nucleation and crystallization, the use of ML is especially timely for improving zeolite synthesis. Indeed, the complex space of zeolite synthesis requires drawing inferences from incomplete and imperfect information, for which ML methods are very well-suited to replace the intuition-based approaches traditionally used to guide experimentation. In this Account, we contend that both existing and new ML approaches can provide the "missing link" needed to complete the traditional zeolite synthesis workflow used in our quest to rationalize zeolite synthesis. Within this context, we have made important efforts on developing ML tools in different critical areas, such as (1) data-mining tools to process the large amount of data generated using high-throughput platforms; (2) novel complex algorithms to predict the formation of energetically stable hypothetical zeolites and guide the synthesis of new zeolite structures; (3) new "ab initio" organic structure directing agent predictions to direct the synthesis of hypothetical or known zeolites; (4) an automated tool for nonsupervised data extraction and classification from published research articles. ML has already revolutionized many areas in materials science by enhancing our ability to map intricate behavior to process variables, especially in the absence of well-understood mechanisms. Undoubtedly, ML is a burgeoning field with many future opportunities for further breakthroughs to advance the design of molecular sieves. For this reason, this Account includes an outlook of future research directions based on current challenges and opportunities. We envision this Account will become a hallmark reference for both well-established and new researchers in the field of zeolite synthesis.

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A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction

Cited by 202 publications

References 80 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery

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