The presence of a dataset that covers parametric space materials and process conditions in process-consistent manner is essential for the realization catalyst informatics. Here, an important piece progress demonstrated oxidative coupling methane. A high-throughput screening instrument developed enabling automatic performance evaluation 20 catalysts 216 reaction conditions. This affords methane comprised 12 708 data points 59 three successive operations. Based on variety visualization...

Abstract Catalysis research is on the verge of experiencing a paradigm shift regarding how catalysts are designed and characterized due to rise catalyst informatics. The details informatics reviewed where following three key concepts proposed: data, data design via science, platform. Additionally, progress opportunities within explored introduced. If field grows in appropriate manner, methods approaches taken for would be fundamentally altered, leading towards great advancement catalysis research.

Undiscovered perovskite materials for applications in capturing solar lights are explored through the implementation of data science. In particular, 15000 is analyzed where visualization reveals hidden trends and clustering data. Random forest classification within machine learning used order to predict band gap 18 physical descriptors revealed determine gap. With trained random forest, 9328 with potential cell predicted. The selected Li Na based predicted evaluated first principle...

Combinatorial catalyst design is hardly generalizable, and the empirical aspect of research has biased literature data toward accidentally found combinations. Here, 300 quaternary solid catalysts are randomly sampled from a materials space consisting 36,540 catalysts, their performance in oxidative coupling methane evaluated by high-throughput screening instrument. The obtained bias-free set analyzed to withdraw guidelines. Even with random sampling, 51 out provide C2 yield sufficiently...

Determining the manner in which crystal structures are formed is considered a great mystery within materials science. Potential solutions have possibility to be uncovered by revealing hidden patterns material data. Data science therefore implemented order link data structure. In particular, unsupervised and supervised machine learning techniques used where Gaussian mixture model employed understand structure of database while random forest classification predict As result, reveal descriptors...

Catalysts descriptors for representing catalytic activities have been challenging in regard to machine learning. Machine learning and catalyst big data generated from high-throughput experiments are combined explore the descriptors. Catalyst designed using physical quantities periodic table oxidative coupling of methane (OCM) reaction. unveils five key ethylene/ethane selectivity (C2s) OCM reaction, where predicted three catalysts high C2s values. Experiments confirm that proposed values...

An innovative web-based integrated catalyst informatics platform, Catalyst Acquisition by Data Science (CADS), is developed for use towards the discovery and design of catalysts.

The coupling of high-throughput calculations with catalyst informatics is proposed as an alternative way to design heterogeneous catalysts. High-throughput first-principles for the oxidative methane (OCM) reaction are designed and performed where 1972 surface planes CH4 CH3 calculated. Several catalysts OCM based on key elements that unveiled via data visualization network analysis. Among catalysts, several active such CoAg/TiO2, Mg/BaO, Ti/BaO found result in high C2 yield. Results...

Understanding the unique features of catalysts is a complex matter as it requires quantitative analysis with relatively large selection catalyst data. Here, each within oxidative methane coupling (OCM) reaction are investigated by combining data science and high throughput experimental Visualization high-throughput OCM reveals that there several groups based on their response against conditions. Unsupervised machine learning, in particular, Gaussian mixture model, classifies into six...

Materials and catalyst informatics are emerging fields that a result of shifts in terms how materials catalysts discovered the science catalysis. However, these not reaching their full potential due to issues related database creation curation. Issues such as lack uniformity, data selectivity, presence bias affect quality usefuless databases, especially when attempting search for descriptors. Without uniform rules frameworks, databases limited use outside intent creators database. Ontologies...

Newly discovered two-dimensional tin, named stanene, has been theoretically predicted and found to have unique electronic properties. Stanene is a buckled structure which could be key against chemical reactivity. Hence, the reactivity of stanene air pollutants NO, NO2, SO, SO2, CO, CO2 investigated within first principles calculations. The results showed that reactive those pollutants. Furthermore, dissociation activation energies over are lower than previously reported catalysts. physical...

Oxidation states of materials are characterized by the X-ray absorption near edge structure (XANES) region in spectroscopy (XAS).

Direct design of low temperature oxidative coupling methane catalysts is proposed <italic>via</italic> machine learning and data mining.

Designing high performance catalysts for the oxidative coupling of methane (OCM) reaction is often hindered by inconsistent catalyst data, which leads to difficulties in extracting information such as combinatorial effects elements upon well reaching yields beyond a particular threshold. In order investigate C2 more systematically, throughput experiments are conducted an effort mass-produce catalyst-related data way that provides consistency and structure. Graph theory applied visualize...

The prediction of the lattice constant binary body centered cubic crystals is performed in terms first principle calculations and machine learning. In particular, 1541 are calculated using density functional theory. Results from calculations, corresponding information periodic table, mathematically tailored data stored as a dataset. Data mining reveals seven descriptors which key to determining where contribution also discussed visualized. Support vector regression (SVR) technique...

Abstract In the oxidative coupling of methane (OCM), activation and suppression deep oxidation are in a persistent trade‐off relationship, catalyst design strategy that balances activity selectivity is desired. this study, we analyzed random dataset for OCM was earlier obtained by high‐throughput experimentation, extracted heuristics such as elements, supports, their combinations related to at low temperature selective formation C 2 compounds high temperature. The were used development. most...

Identifying details of chemical reactions is a challenging matter for both experiments and computations. Here, the reaction pathway in oxidative coupling methane (OCM) investigated using series experimental data science techniques which are analyzed variety visualization techniques. Data visualization, pairwise correlation, machine learning unveil relationships between conditions selectivities CO, CO2, C2H4, C2H6, H2 OCM reaction. More importantly, network constructed on basis scores...

An octagonal allotrope of two dimensional boron nitride is explored through first principles calculations. Calculations show that can be formed with a binding energy comparable to hexagonal nitride. In addition, found have band gap smaller than nitride, suggesting the possibility semiconductive attributes. Two also has ability layer physisorption. Defects present within lead toward introduction magnetic moment absence atoms. The presence defects render both and nitrides reactive against...

The thresholds among atomic clusters, nanoparticles, and the bulk state have been ambiguous. A potential solution is to determine cluster growth toward bulk, but this challenging with experiments computation. Data science proposed predict cluster-nanoparticle-bulk using Ag clusters as a prototype element. Supervised machine learning reveals that has nonlinear models where found accurately binding energy. Unsupervised discovers three groups (cluster, semiclusters, nanoparticles) linear...

The introduction of data science as a viable new approach to research has led toward the establishment materials informatics. However, issues relating infrastructure collection and organization in have hindered development Issues related quality, conflicting terminologies between subfields, inconsistent recording practices make it difficult share implement science. Furthermore, one can consider that scientific discoveries occurred via rules are unconsciously defined by scientist's mind,...

Square atomic configurations of two-dimensional gold and platinum are designed on the basis density functional theory calculations. Calculations reveal that Au9 Pt9 clusters form energetically stable perfect square structures. Combining alternating two allows for formation infinite sheets Au9, Pt9, Au18Pt18. The electronic state reveals Au18Pt18 retain metallic characteristics whereas also possess magnetic properties. In addition, multilayered up to five layers four layers, respectively,...

Multioutput support vector regression (SVR) is implemented to simultaneously predict the selectivities and CH4 conversion against experimental conditions in methane oxidation catalysts. The predictions unveil details of how each selectivity behaves catalyst. In particular, Mn–Na2WO4/SiO2, Ti–Na2WO4/SiO2, Pd–Na2WO4/SiO2, Na2WO 4/SiO2 are predicted, effects Mn, Ti, Pd unveiled. addition, trade-off points CO C2H6 identified for catalyst, leading maximization yield. Thus simultaneous prediction...