Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts

MonteCat: A Basin-Hopping-Inspired Catalyst Descriptor Search Algorithm for Machine Learning Models

Proposing relevant catalyst descriptors that can relate the information on a catalyst's composition to its actual performance is an ongoing area in catalyst informatics, as it is a necessary step to improve our understanding on the target reactions. Herein, a small descriptor-engineered data set containing 3289 descriptor variables and the performance of 200 catalysts for the oxidative coupling of methane (OCM) is analyzed, and a descriptor search algorithm based on the workflow of the Basin-hopping optimization methodology is proposed to select the descriptors that better fit a predictive model. The algorithm, which can be considered wrapper in nature, consists of the successive generation of random-based modifications to the descriptor subset used in a regression model and adopting them depending on their effect on the model's score. The results are presented after being tested on linear and Support Vector Regression models with average cross-validation r2 scores of 0.8268 and 0.6875, respectively.

Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis

The empirical aspect of descriptor design in catalyst informatics, particularly when confronted with limited data, necessitates adequate prior knowledge for delving into unknown territories, thus presenting a logical contradiction. This study introduces a technique for automatic feature engineering (AFE) that works on small catalyst datasets, without reliance on specific assumptions or pre-existing knowledge about the target catalysis when designing descriptors and building machine-learning models. This technique generates numerous features through mathematical operations on general physicochemical features of catalytic components and extracts relevant features for the desired catalysis, essentially screening numerous hypotheses on a machine. AFE yields reasonable regression results for three types of heterogeneous catalysis: oxidative coupling of methane (OCM), conversion of ethanol to butadiene, and three-way catalysis, where only the training set is swapped. Moreover, through the application of active learning that combines AFE and high-throughput experimentation for OCM, we successfully visualize the machine's process of acquiring precise recognition of the catalyst design. Thus, AFE is a versatile technique for data-driven catalysis research and a key step towards fully automated catalyst discoveries.

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.

High-throughput materials screening algorithm based on first-principles density functional theory and artificial neural network for high-entropy alloys

This work introduced the high-throughput phase prediction of PtPd-based high-entropy alloys via the algorithm based on a combined Korringa-Kohn-Rostoker coherent potential approximation (KKR-CPA) and artificial neural network (ANN) technique. As the first step, the KKR-CPA was employed to generate 2,720 data of formation energy and lattice parameters in the framework of the first-principles density functional theory. Following the data generation, 15 features were selected and verified for all HEA systems in each phase (FCC and BCC) via ANN. The algorithm exhibited high accuracy for all four prediction models on 36,556 data from 9139 HEA systems with 137,085 features, verified by R² closed to unity and the mean relative error (MRE) within 5%. From this dataset comprising 5002 and 4137 systems of FCC and BCC phases, it can be realized based on the highest tendency of HEA phase formation that (1) Sc, Co, Cu, Zn, Y, Ru, Cd, Os, Ir, Hg, Al, Si, P, As, and Tl favor FCC phase, (2) Hf, Ga, In, Sn, Pb, and Bi favor BCC phase, and (3) Ti, V, Cr, Mn, Fe, Ni, Zr, Nb, Mo, Tc, Rh, Ag, Ta, W, Re, Au, Ge, and Sb can be found in both FCC and BCC phases with comparable tendency, where all predictions are in good agreement with the data from the literature. Thus, the combination of KKR-CPA and ANN can reduce the computational cost for the screening of PtPd-based HEA and accurately predict the structure, i.e., FCC, BCC, etc.

Fast-Decoding Algorithm for Electrode Processes at Electrified Interfaces by Mean-Field Kinetic Model and Bayesian Data Assimilation: An Active-Data-Mining Approach for the Efficient Search and Discovery of Electrocatalysts

The microscopic origins of the activity and selectivity of electrocatalysts has been a long-lasting enigma since the 19th century. By applying an active-data-mining approach, employing a mean-field kinetic model and a statistical approach of Bayesian data assimilation, we demonstrate here a fast decoding to extract key properties in the kinetics of complicated electrode processes from current-potential profiles in experimental and literary data. As the proof-of-concept, kinetic parameters on the four-electron oxygen reduction reaction in the 0.1 M HClO4 solution (ORR: O2 + 4e- + 4H+ → 2H2O) of various platinum-based single-crystal electrocatalysts are extracted from our own experiments and third-party literature to investigate the microscopic electrode processes. Furthermore, data assimilation of the mean-field ORR model and experimental data is performed based on Bayesian inference for the inductive estimation of kinetic parameters, which sheds light on the dynamic behavior of kinetic parameters with respect to overpotential. This work shows that a fast-decoding algorithm based on a mean-field kinetic model and Bayesian data assimilation is a promising data-driven approach to extract key microscopic features of complicated electrode processes and therefore will be an important method toward building up advanced human-machine collaborations for the efficient search and discovery of high-performance electrochemical materials.

High-Throughput Screening and Literature Data Driven Machine Learning Assisting Investigation of Multi-component La2O3-based Catalysts for Oxidative Coupling of Methane

Multi-component La2O3-based catalysts for oxidative coupling of methane (OCM) were designed based on high-throughput screening (HTS) and literature datasets with multi-output machine learning (ML) approaches including random forest regression (RFR),...