Towards Experimental Handbooks in Catalysis

A bridge between trust and control: computational workflows meet automated battery cycling

Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We successfully interface off-the-shelf battery cycling hardware with the computational workflow management software AiiDA, allowing us to control experiments, while ensuring trust in the data by tracking its provenance. We design user interfaces compatible with this tooling, which span the inventory, experiment design, and result analysis stages. Other features, including monitoring of workflows and import of externally generated and legacy data are also implemented. Finally, the full software stack required for this work is made available in a set of open-source packages.

Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis

Synthesis protocol exploration is paramount in catalyst discovery, yet keeping pace with rapid literature advances is increasingly time intensive. Automated synthesis protocol analysis is attractive for swiftly identifying opportunities and informing predictive models, however such applications in heterogeneous catalysis remain limited. In this proof-of-concept, we introduce a transformer model for this task, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. Our model adeptly converts SAC protocols into action sequences, and we use this output to facilitate statistical inference of their synthesis trends and applications, potentially expediting literature review and analysis. We demonstrate the model's adaptability across distinct heterogeneous catalyst families, underscoring its versatility. Finally, our study highlights a critical issue: the lack of standardization in reporting protocols hampers machine-reading capabilities. Embracing digital advances in catalysis demands a shift in data reporting norms, and to this end, we offer guidelines for writing protocols, significantly improving machine-readability. We release our model as an open-source web application, inviting a fresh approach to accelerate heterogeneous catalysis synthesis planning.

Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation

Artificial intelligence (AI) can accelerate catalyst design by identifying key physicochemical descriptive parameters correlated with the underlying processes triggering, favoring, or hindering the performance. In analogy to genes in biology, these parameters might be called "materials genes" of heterogeneous catalysis. However, widely used AI methods require big data, and only the smallest part of the available data meets the quality requirement for data-efficient AI. Here, we use rigorous experimental procedures, designed to consistently take into account the kinetics of the catalyst active states formation, to measure 55 physicochemical parameters as well as the reactivity of 12 catalysts toward ethane, propane, and n-butane oxidation reactions. These materials are based on vanadium or manganese redox-active elements and present diverse phase compositions, crystallinities, and catalytic behaviors. By applying the sure-independence-screening-and-sparsifying-operator symbolic-regression approach to the consistent data set, we identify nonlinear property-function relationships depending on several key parameters and reflecting the intricate interplay of processes that govern the formation of olefins and oxygenates: local transport, site isolation, surface redox activity, adsorption, and the material dynamical restructuring under reaction conditions. These processes are captured by parameters derived from N₂ adsorption, X-ray photoelectron spectroscopy (XPS), and near-ambient-pressure in situ XPS. The data-centric approach indicates the most relevant characterization techniques to be used for catalyst design and provides "rules" on how the catalyst properties may be tuned in order to achieve the desired performance.

Tuning catalysis by surface-deposition of elements on oxidation catalysts via atomic layer deposition

This study on surface-modifications of bulk oxidation catalysts with sub-monolayers of POx, BOx and MnOxvia atomic layer deposition demonstrates this method to be a powerful tool for tuning the performance in selective oxidations of light alkanes.

Advancing Critical Chemical Processes for a Sustainable Future: Challenges for Industry and the Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis (FUNCAT)

Catalysis is involved in around 85 % of manufacturing industry and contributes an estimated 25 % to the global domestic product, with the majority of the processes relying on heterogeneous catalysis. Despite the importance in different global segments, the fundamental understanding of heterogeneously catalysed processes lags substantially behind that achieved in other fields. The newly established Max Planck-Cardiff Centre on the Fundamentals of Heterogeneous Catalysis (FUNCAT) targets innovative concepts that could contribute to the scientific developments needed in the research field to achieve net zero greenhouse gas emissions in the chemical industries. This Viewpoint Article presents some of our research activities and visions on the current and future challenges of heterogeneous catalysis regarding green industry and the circular economy by focusing explicitly on critical processes. Namely, hydrogen production, ammonia synthesis, and carbon dioxide reduction, along with new aspects of acetylene chemistry.

Quo Vadis Dry Reforming of Methane?—A Review on Its Chemical, Environmental, and Industrial Prospects

In recent years, the catalytic dry reforming of methane (DRM) has increasingly come into academic focus. The interesting aspect of this reaction is seemingly the conversion of CO2 and methane, two greenhouse gases, into a valuable synthesis gas (syngas) mixture with an otherwise unachievable but industrially relevant H2/CO ratio of one. In a possible scenario, the chemical conversion of CO2 and CH4 to syngas could be used in consecutive reactions to produce synthetic fuels, with combustion to harness the stored energy. Although the educts of DRM suggest a superior impact of this reaction to mitigate global warming, its potential as a chemical energy converter and greenhouse gas absorber has still to be elucidated. In this review article, we will provide insights into the industrial maturity of this reaction and critically discuss its applicability as a cornerstone in the energy transition. We derive these insights from assessing the current state of research and knowledge on DRM. We conclude that the entire industrial process of syngas production from two greenhouse gases, including heating with current technologies, releases at least 1.23 moles of CO2 per mol of CO2 converted in the catalytic reaction. Furthermore, we show that synthetic fuels derived from this reaction exhibit a negative carbon dioxide capturing efficiency which is similar to burning methane directly in the air. We also outline potential applications and introduce prospective technologies toward a net-zero CO2 strategy based on DRM.

FAIR data enabling new horizons for materials research

The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.

Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence

ABSTRACT

The performance in heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes (e.g., the different surface chemical reactions, and the dynamic restructuring of the catalyst material at reaction conditions). Modeling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Instead, we show here how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to model catalysis and determine the key descriptive parameters ("materials genes") reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of "clean data," containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression SISSO approach, we identify correlations between the few most relevant materials properties and their reactivity. This approach highlights the underlying physicochemical processes, and accelerates catalyst design.

IMPACT STATEMENT

Artificial intelligence (AI) accepts that there are relationships or correlations that cannot be expressed in terms of a closed mathematical form or an easy-to-do numerical simulation. For the function of materials, for example, catalysis, AI may well capture the behavior better than the theory of the past. However, currently the flexibility of AI comes together with a lack of interpretability, and AI can only predict aspects that were included in the training. The approach proposed and demonstrated in this IMPACT article is interpretable. It combines detailed experimental data (called "clean data") and symbolic regression for the identification of the key descriptive parameters (called "materials genes") that are correlated with the materials function. The approach demonstrated here for the catalytic oxidation of propane will accelerate the discovery of improved or novel materials while also enhancing physical understanding.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1557/s43577-021-00165-6.