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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.

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.

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.

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 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...

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.