Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation

High-throughput calculation for the screening of formamidinium halide perovskite for solar cells

128 organic halide perovskites are systematically investigated using high-throughput first principles calculations where Ge and Sn-based materials are searched. The results revealed that all calculated materials exhibited exothermic reactions. Notably, a correlation between the heat of formation and X-site ions is identified. Six specific compounds, namely FA-Ge-I-I-I, FA-Sn-F-I-I, FA-Sn-Cl-I-I, FA-Sn-Br-Br-I, FA-Sn-Br-I-I, and FA-Sn-I-I-I, where FA stands for formamidinium, are found to have a bandgap ranging from 1.0 to 2.0 eV, characterized by a direct bandgap in their band structure. Electronic structure analysis indicated that the CBM (conduction band minimum) is influenced by the B-site p-orbital, while the VBM (valence band maximum) is influenced by the X-site p-orbitals. This study underscores the capability of high-throughput calculations to unveil hidden trends in perovskite materials, offering an effective approach for the exploration of promising perovskite materials.

Electrocatalytic Mechanisms for an Oxygen Evolution Reaction at a Rhombohedral Boron Monosulfide Electrode/Alkaline Medium Interface

Rhombohedral boron monosulfide (r-BS) with a layer stacking structure is a promising electrocatalyst for an oxygen evolution reaction (OER) within an alkaline solution. We investigated the catalytic mechanisms at the r-BS electrode/alkaline medium interface for an OER using hybrid solvation theory based on the first-principles method combined with classical solution theory. In this study, we elucidate the activities of the OER at the outermost r-BS sheet with and without various surface defects. The Gibbs free energies along the OER path indicate that the boron vacancies at the first and second layers of the r-BS surface (VB1 and VB2) can promote the OER. However, we found that the VB1 is easily occupied by the oxygen atom during the OER, degrading its electrocatalytic performance. In contrast, VB2 is suitable for the active site of the OER due to its structure stability. Next, we applied a bias voltage with the OER potential to the r-BS electrode. The bias voltage incorporates the positive excess surface charge into pristine r-BS and VB2, which can be understood by the relationship between the OER potential and potentials of zero charge at the r-BS electrode. Because the OH- ions are the starting point of the OER, the positively charged surface is kinetically favorable for the electrocatalyst owing to the attractive interaction with the OH- ions. Finally, we qualitatively discuss the flat-band potential at a semiconductor/alkaline solution interface. It suggests that p-type carrier doping could promote the catalytic performance of r-BS. These results explain the previous measurement of the OER performance with the r-BS-based electrode and provide valuable insights into developing a semiconductor electrode/water interface.

Exploring Metal Nanocluster Catalysts for Ammonia Synthesis Using Informatics Methods: A Concerted Effort of Bayesian Optimization, Swarm Intelligence, and First-Principles Computation

This paper details the use of computational and informatics methods to design metal nanocluster catalysts for efficient ammonia synthesis. Three main problems are tackled: defining a measure of catalytic activity, choosing the best candidate from a large number of possibilities, and identifying the thermodynamically stable cluster catalyst structure. First-principles calculations, Bayesian optimization, and particle swarm optimization are used to obtain a Ti8 nanocluster as a catalyst candidate. The N2 adsorption structure on Ti8 indicates substantial activation of the N2 molecule, while the NH3 adsorption structure suggests that NH3 is likely to undergo easy desorption. The study also reveals several cluster catalyst candidates that break the general trade-off that surfaces that strongly adsorb reactants also strongly adsorb products.

Toward the Golden Age of Materials Informatics: Perspective and Opportunities

Materials informatics is reaching the transition point and is evolving from its early stages of adoption and development and moving toward its golden age. Here, the transformation of the early stage of materials informatics toward the next level of materials informatics is explored. In particular, it has become crucial to be able to manipulate materials synthesis data, materials properties data, and materials characterization data. Through the use of ontology, material design and understanding can be carried out simultaneously in a whitebox manner. Here, a perspective on the ultimate goal of materials informatics along with potential key components is discussed.

Catalysts informatics: paradigm shift towards data-driven catalyst design

Designing catalysts is a challenging matter as catalysts are involved with various factors that impact synthesis, catalysts, reactor and reaction. In order to overcome these difficulties, catalysts informatics is proposed as an alternative way to design and understand catalysts. The underlying concept of catalysts informatics is to design the catalysts from trends and patterns found in catalysts data. Here, three key concepts are introduced: experimental catalysts database, knowledge extraction from catalyst data via data science, and a catalysts informatics platform. Methane oxidation is chosen as a prototype reaction for demonstrating various aspects of catalysts informatics. This work summarizes how catalysts informatics plays a role in catalyst design. The work covers big data generation via high throughput experiments, machine learning, catalysts network method, catalyst design from small data, catalysts informatics platform, and the future of catalysts informatics via ontology. Thus, the proposed catalysts informatics would help innovate how catalysts can be designed and understood.

Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis

The recent rise of computational, data-driven research has significant potential to accelerate materials discovery. Automated workflows and materials databases are being rapidly developed, contributing to high-throughput data of bulk materials that are growing in quantity and complexity, allowing for correlation between structural-chemical features and functional properties. In contrast, computational data-driven approaches are still relatively rare for nanomaterials discovery due to the rapid scaling of computational cost for finite systems. However, the distinct behaviors at the nanoscale as compared to the parent bulk materials and the vast tunability space with respect to dimensionality and morphology motivate the development of data sets for nanometric materials. In this review, we discuss the recent progress in data-driven research in two aspects: functional materials design and guided synthesis, including commonly used metrics and approaches for designing materials properties and predicting synthesis routes. More importantly, we discuss the distinct behaviors of materials as a result of nanosizing and the implications for data-driven research. Finally, we share our perspectives on future directions for extending the current data-driven research into the nano realm.