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This work summarizes how catalysts informatics plays a role in catalyst design.

Reaction networks of hydrocarbons are explored using first principles calculations, data science, and experiments.

The coupling of high-throughput calculations with catalyst informatics is proposed as an alternative way to design heterogeneous catalysts. High-throughput first-principles calculations for the oxidative coupling of methane (OCM) reaction are designed and performed where 1972 catalyst surface planes for the CH

Identification of catalysts is a difficult matter as catalytic activities involve a vast number of complex features that each catalyst possesses. Here, catalysis gene expression profiling is proposed from unique features discovered in catalyst data collected by high-throughput experiments as an alternative way of representing the catalysts. Combining constructed catalyst gene sequences with hierarchical clustering results in catalyst gene expression profiling where natural language processing is used to identify similar catalysts based on edit distance. In addition, catalysts with similar properties are designed by modifying catalyst genes where the designed catalysts are experimentally confirmed to have catalytic activities that are associated with their catalyst gene sequences. Thus, the proposed method of catalyst gene expressions allows for a novel way of describing catalysts that allows for similarities in catalysts and catalytic activity to be easily recognized while enabling the ability to design new catalysts based on manipulating chemical elements of catalysts with similar catalyst gene sequences.

Combinatorial catalyst design is hardly generalizable, and the empirical aspect of the research has biased the literature data toward accidentally found combinations. Here, 300 quaternary solid cat...

Multioutput support vector regression (SVR) is implemented to simultaneously predict the selectivities and the CH4 conversion against experimental conditions in methane oxidation catalysts. The predictions unveil the details of how each selectivity and CH4 conversion behaves in each catalyst. In particular, the selectivity and the CH4 conversion of Mn-Na2WO4/SiO2, Ti-Na2WO4/SiO2, Pd-Na2WO4/SiO2, and Na2WO 4/SiO2 are predicted, and the effects of Mn, Ti, and Pd are unveiled. In addition, the trade-off points of CO and C2H6 are identified for each catalyst, leading to maximization of the C2H6 yield. Thus the simultaneous prediction of the reaction trend with catalysts not only will help with the understanding of the catalyst activities but also will provide guidance for designing the experimental conditions.

Designing high performance catalysts for the oxidative coupling of methane (OCM) reaction is often hindered by inconsistent catalyst data, which often leads to difficulties in extracting information such as combinatorial effects of elements upon catalyst performance as well as difficulties in reaching yields beyond a particular threshold. In order to investigate C2 yields more systematically, high throughput experiments are conducted in an effort to mass-produce catalyst-related data in a way that provides more consistency and structure. Graph theory is applied in order to visualize underlying trends in the transformation of high-throughput data into networks, which are then used to design new catalysts that potentially result in high C2 yields during the OCM reaction. Transforming high-throughput data in this manner has resulted in a representation of catalyst data that is more intuitive to use and also has resulted in the successful design of a myriad of catalysts that elicit high C2 yields, several of which resulted in yields greater than those originally reported in the high-throughput data. Thus, transforming high-throughput catalytic data into catalyst design-friendly maps provides a new method of catalyst design that is more efficient and has a higher likelihood of resulting in high performance catalysts., Catalyst data created through high-throughput experimentation is transformed into catalyst knowledge networks, leading to a new method of catalyst design where successfully designed catalysts result in high C2 yields during the OCM reaction.

Direct design of low temperature oxidative coupling of methane (OCM) catalysts is proposed via machine learning and data mining. 58 OCM catalysts are experimentally synthesized and evaluated. The collected 58 sets of data are then classified by unsupervised machine learning in a multi-dimensional space where an active catalyst group for low temperature OCM is identified. Data mining then identifies the physical rule within the group. Catalysts satisfying such a physical rule are designed where 2 undiscovered low temperature OCM catalysts are found and experimentally validated. Thus, machine learning and data mining reveal the hidden physical rule behind the catalysis leading to the direct design of catalysts. Hence, machine learning and data mining open up the insight on a powerful strategy for designing catalysts.

Representing the chemical reaction is a challenging matter faced in chemistry due to the complex molecular interactions and difficulties faced when determining intermediate reactions that may occur throughout the reaction. Graph theory and network analysis are used with first-principles calculations and experiments to investigate possible intermediate reactions that may occur during a reaction; in this case, catalyst-free methane oxidation is chosen as the prototype reaction. Network analysis is used to help illuminate several key intermediate compounds that potentially appear throughout the course of the prototype reaction and the detailed mechanisms of methane oxidation while showing good agreement with experimental data. Presenting the chemical reaction as a network, therefore, makes it possible to link experimental and computational data in a space that accounts for the impact of intermediate reactions upon the outcome of the overall reaction, thereby making network analysis an alternative method for representing chemical reactions.

Understanding the unique features of catalysts is a complex matter as it requires quantitative analysis with a relatively large selection of catalyst data. Here, unique features of each catalyst within the oxidative methane of coupling (OCM) reaction are investigated by combining data science and high throughput experimental data. Visualization of high-throughput OCM data reveals that there are several groups of catalysts based on their response against experimental conditions. Unsupervised machine learning, in particular, the Gaussian mixture model, classifies the OCM catalysts into six groups based on similarity in catalytic activities. Data visualization and parallel coordinates unveil the unique catalytic features of each classified group. Each classified group is statistically analyzed where unique features of each group are defined in term of C

Identifying details of chemical reactions is a challenging matter for both experiments and computations. Here, the reaction pathway in oxidative coupling of methane (OCM) is investigated using a series of experimental data and data science techniques in which data are analyzed using a variety of visualization techniques. Data visualization, pairwise correlation, and machine learning unveil the relationships between experimental conditions and the selectivities of CO, CO2, C2H4, C2H6, and H2 in the OCM reaction. More importantly, the reaction network for the OCM reaction is constructed on the basis of the scores provided by machine learning and experimental data. In particular, the proposed reaction map not only contains the chemical compound but also contains experimental conditions. Thus, data-driven identification of chemical reactions can be achieved in principle via a series of experimental data, leading to more efficient experimental design and catalyst development.

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 platform provides three main functionalities: a repository for data sharing and publishing, an analytic workspace for exploratory visual analysis, and catalyst property prediction tools with pretrained machine learning models. Access to such a platform helps decrease barriers to entry faced by researchers in catalytic chemistry when attempting to apply catalyst informatics towards data by providing analytical and visualization tools that can be simultaneously applied and easily accessed within a central space, thereby helping the advancement of catalyst informatics. The developed platform allows researchers to upload and collect data onto the platform and conduct data analysis using a system of linked workspaces consisting of interactive visualization tools and machine learning tools that simultaneously update according to the researchers' actions in real time. The platform also provides a space for collaboration where researchers can choose to publish their uploaded data and resulting analyses to the platform for collaborations with other users and groups. As an example, CADS is applied towards oxidative coupling of methane (OCM) data where use of the platform tools reveals underlying patterns and trends that are otherwise hidden within the original data. Thus, the proposed platform contributes towards the advancement of catalyst informatics for both specialists and non-specialists.

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

Determining the manner in which crystal structures are formed is considered a great mystery within materials science. Potential solutions have the possibility to be uncovered by revealing hidden patterns within the material data. Data science is therefore implemented in order to link the material data to the crystal structure. In particular, unsupervised and supervised machine learning techniques are used where the Gaussian mixture model is employed in order to understand the data structure of the materials database while random forest classification is used to predict the crystal structure. As a result, the unsupervised and supervised machine learning techniques reveal descriptors for determining the crystal structure via the materials database. In addition, predicting atomic combinations from the crystal structure is also achieved using a trained machine where the first-principles calculations confirm the stability of predicted materials. Thus, one can consider that the estimation of the crystal structure can be achieved in principle via the combination of material data and machine learning, thereby leading to the advancement of crystal structure prediction.

Oxidation states of materials are characterized by the X-ray absorption near edge structure (XANES) region in X-ray absorption spectroscopy (XAS). However, the challenges in identifying oxide states are strong depending on the researcher’s judgment based on shift changes between measured XAS and reference spectra data. Here, automatic oxidation threshold recognition is performed using machine learning and experimental XAS spectra. In particular, a workflow from experimental data collection, data preprocessing and prediction using machine learning are proposed. 10 descriptors for recognizing the oxide state in XAS spectra are discovered. More importantly, the oxide states of unknown experimental XAS spectra are identified using a trained machine. The proposed approach thus allows for the machine learning to automatically recognize the oxidation threshold of a given XAS spectra without the presence of reference data, leading to the fast analysis of XAS spectra.

Single-atom-embedded bilayer graphene and two-dimensional hexagonal boron nitride are proposed in terms of first-principles calculations. In particular, a series of 68 different single atoms are em...

The introduction of data science as a viable new approach to research has led toward the establishment of materials informatics. However, issues relating to the infrastructure of data collection and organization in materials science have hindered the development of materials informatics. Issues related to data quality, conflicting terminologies between subfields, and inconsistent recording practices make it difficult to share data and implement data science. Furthermore, one can consider that scientific discoveries have occurred via the rules that are unconsciously defined by the scientist's mind, which has made scientific discovery an unintentional process. Here, ontology is proposed as a new way to structure databases as well as model scientific understandings of data. By implementing ontology during the database creation process, it not only becomes possible to define and visualize the experiences and knowledge held by researchers but also provides a way of creating a field-wide standard of defining data, the ability to incorporate data semantics, a method to increase the solid choice of descriptors for determining the materials' properties, and the space to merge databases in a more interactive and coherent manner. Ontology can also help improve database management by providing a way to incorporate new scientific discoveries into existing databases, which can have a positive effect on the search for new materials and material design.

The core lattice geometry of an aluminum phosphide (AlP) nanotube is altered from a hexagonal lattice to an octagonal lattice, and its effects on the electronic structure are investigated using first-principles calculations. The binding energy of the octagonal AlP nanotube is calculated to be −0.15 eV/atom, which denotes an exothermic reaction and results in the octagonal AlP nanotube being thermodynamically stable. Al and P atoms possess an average of 11.07 and 16.86 electrons, respectively, suggesting ionic bonding, while the atoms align to form alternating layers of elements within the nanotube wall. The electronic structure of the octagonal AlP nanotube suggests semiconductive properties of the nanotube. In addition, the presence of defects makes the nanotube more reactive against H, with an Al defect more reactive against H. By direct manipulation of the core lattice geometry and the purposeful introduction of defects, the conductivity and reactivity of an AlP nanotube can be tuned, and AlP nanotubes...

Undiscovered perovskite materials for applications in capturing solar lights are explored through the implementation of data science. In particular, 15000 perovskite materials data is analyzed where visualization of the data reveals hidden trends and clustering of data. Random forest classification within machine learning is used in order to predict the band gap of perovskite materials where 18 physical descriptors are revealed to determine the band gap. With trained random forest, 9328 perovskite materials with potential for applications in solar cell materials are predicted. The selected Li and Na based perovskite materials within predicted 9328 perovskite materials are evaluated with first principle calculations where 11 undiscovered Li(Na) based perovskite materials fall into the ideal band gap and formation energy ranges for solar cell applications. Thus, the implementation of data science accelerates the discovery of hidden perovskite materials and the approach can be applied to the materials scienc...

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

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 is challenging to determine with experiments and computation. Data science is proposed to predict atomic cluster growth and determine the cluster-nanoparticle-bulk thresholds using Ag clusters as a prototype element. Supervised machine learning reveals that Ag cluster growth has nonlinear models where nonlinear machine learning is found to accurately predict binding energy. Unsupervised machine learning discovers three groups (cluster, semiclusters, and nanoparticles) where linear regression is used to predict the binding energy in each group. Furthermore, machine learning reveals the linear relationship between binding energy and the surface-to-volume ratio of Ag nanoparticles. This allows for a binding energy estimation of large Ag nanoparticles and a revelation of how Ag nanoparticles grow toward the bulk. Thus, data science is proposed as a powerful tool for determining cluster growth and thresholds for clusters, nanoparticles, and bulk states.

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

Key descriptors representing the desorption temperature of ammonia borane against hydrides and chlorides are investigated and applied to machine learning. The desorption temperature of ammonia borane with hydrides and chlorides is experimentally measured and a database is constructed. The database indicates a correlation between the desorption temperature and electronegativity. A support vector machine with selected descriptors from the database reveals the optimal amounts of CuCl2 and AgCl needed for decreasing the desorption temperature of ammonia borane. Prediction of the electronegativity that would achieve the desorption temperature is also revealed where the crystal structure is also found to be a key descriptor. Thus, electronegativity and crystal structures are revealed to be key descriptors that predict the desorption temperature of ammonia borane with a chemical compound. This would essentially lead towards finding the optimal amount of dopants and ideal dopants with a minimum number of experiments.

The design of a two dimensional material is performed using first principle calculations by utilizing Mg 7 clusters in a periodic manner. In particular, placing the Mg 7 clusters in a hexagonal shape enables the formation of two dimensional Mg 7 . Calculations reveal the formation of two dimensional Mg 7 would occur in exothermic matter where the bond between each Mg 7 is considered to be due to physisorption. In addition, the electronic structure of Mg 7 clusters and two dimensional Mg 7 shows a bandgap of 0.8 eV at Fermi level. This suggests that two dimensional Mg 7 has a potential to be an alternative semiconductor material.

Materials and catalyst informatics are emerging fields that are a result of shifts in terms of how materials and catalysts are discovered in the fields of materials science and catalysis. However, these fields are not reaching their full potential due to issues related to database creation and curation. Issues such as lack of uniformity, data selectivity, and the presence of bias affect the quality and usefuless of materials databases, especially when attempting to search for materials descriptors. Without uniform rules and frameworks, databases are limited in use outside of the intent of the creators of the database. Ontologies are therefore investigated as a means of redesigning the way materials and catalysts databases are designed and created. In particular, an ontology consisting of information found within the periodic table as well as commonly used related data is constructed and applied toward the search for materials descriptors. Additional ontologies are also developed for two databases-a database consisting of computational data related to perovskites and a database consisting of experimental data related to oxidative coupling of methane (OCM) catalysts-in order to investigate the impact of merging ontologies.

Hydrophobic and antioxidant effects of two dimensional materials Sn, SnSb, InSb, and InSn are investigated with the implementation of first principles calculations. The stable atomic configurations of two dimensional SnSb, InSb, and InSn are determined where the structures are buckled. Adsorption of water and oxygen molecules over such surfaces reveals that SnSb (with Sb as the top surface) particularly has an outstanding hydrophobic effect while InSb (with Sb as the top surface) has a high antioxidant effect. The physical origin rests on the charge transfer where electrons are transferred from O to H in H2O, resulting in an increase of polar states between H and O, thereby leading to weak H2O adsorption on SnSb. On the other hand, there is no charge transfer between O2 and InSb (with Sb as the top surface) while O2 adsorption is involved with large electron transfer from the surface to oxygen in other surfaces. Thus, two dimensional SnSb and InSb offer hydrophobic and antioxidant properties, respectively, where such properties would be appreciated for designing surface coatings and electric circuits.

Adsorption of common molecules over two-dimensional Au9, Pt9, and Au18Pt18 is investigated with implementation of first-principles calculations. In general, it is found that Pt9 and Au18Pt18 exhibit low adsorption energies where Au18Pt18 preserves the structural integrity of the molecule and surface. In particular, adsorption of molecules onto Au18Pt18 frequently results in low adsorption energies and high reactivity with minor surface reconstruction of Au18Pt18 and average bond lengths of molecules. The decrease in adsorption energy can be attributed to the presence of platinum, while gold can be considered responsible for structural stability. In addition, molecule dissociation is observed in the cases of H2, HCl, CH4, SO, and SO2 when Pt atoms are involved. Thus, two-dimensional Au9, Pt9, and Au18Pt18 show low adsorption energies against common molecules, reflecting adsorption energies observed in small Au and Pt clusters. These results demonstrate that Au18Pt18 can successfully utilize the low adsorption energies associated with platinum while preserving the integrity of the surface structure using gold atoms, making it possible to adsorb desired molecules using select areas of the Au18Pt18 surface.

Newly discovered two-dimensional tin, named stanene, has been theoretically predicted and found to have unique electronic properties. Stanene is found to have a buckled structure which could be a key structure against chemical reactivity. Hence, the reactivity of stanene against key air pollutants NO, NO2, SO, SO2, CO, and CO2 is investigated within first principles calculations. The results showed that stanene is reactive against those air pollutants. Furthermore, the dissociation activation energies of those pollutants over stanene are lower than previously reported catalysts. The physical origin of low dissociation barriers rests in the charge transfer from stanene to those pollutants, resulting in bond weakening. Hence, one can predict that unique reactivities of stanene offer low temperature trapping and dissociation of air pollutants.