Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved ...processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.
Materials science is undergoing a revolution, generating valuable new materials such as flexible solar panels, biomaterials and printable tissues, new catalysts, polymers, and porous materials with ...unprecedented properties. However, the number of potentially accessible materials is immense. Artificial evolutionary methods such as genetic algorithms, which explore large, complex search spaces very efficiently, can be applied to the identification and optimization of novel materials more rapidly than by physical experiments alone. Machine learning models can augment experimental measurements of materials fitness to accelerate identification of useful and novel materials in vast materials composition or property spaces. This review discusses the problems of large materials spaces, the types of evolutionary algorithms employed to identify or optimize materials, and how materials can be represented mathematically as genomes, describes fitness landscapes and mutation operators commonly employed in materials evolution, and provides a comprehensive summary of published research on the use of evolutionary methods to generate new catalysts, phosphors, and a range of other materials. The review identifies the potential for evolutionary methods to revolutionize a wide range of manufacturing, medical, and materials based industries.
Halide perovskites have attracted enormous attention due to their potential applications in optoelectronics and photocatalysis. However, concerns over their instability, toxicity, and unsatisfactory ...efficiency have necessitated the development of lead-free all-inorganic halide perovskites. A major challenge in designing efficient halide perovskites for practical applications is the lack of effective methods for producing nanocrystals with precise size and shape control. In this work, a layered perovskite, Cs4ZnSb2Cl12 (CZS), is found from calculations to exhibit size- and facet-dependent optoelectronic properties in the nanoscale, and thus, a colloidal method is used to synthesize the CZS nanoparticles with size-tunable morphologies: zero- (nanodots), one- (nanowires and nanorods), two- (nanoplates), and three-dimensional (nanopolyhedra). The growth kinetics of the CZS nanostructures, along with the effects of surface ligands, reaction temperature, and time were investigated. The optoelectronic properties of the nanocrystals varied with size due to quantum confinement effects and with shape due to anisotropy within the crystals and the exposure of specific facets. These properties could be modulated to enhance the visible-light photocatalytic performance for toluene oxidation. In particular, the 9.7 nm CZS nanoplates displayed a toluene to benzaldehyde conversion rate of 1893 μmol g–1 h–1 (95% selectivity), 500 times higher than the bulk synthesized CZS, and comparable with the reported photocatalysts. This study demonstrates the integration of theoretical calculations and synthesis, revealing an approach to the design and fabrication of novel, high-performance colloidal perovskite nanocrystals for optoelectronic and photocatalytic applications.
One favorable situation for spins to enter the long-sought quantum spin liquid (QSL) state is when they sit on a kagome lattice. No consensus has been reached in theory regarding the true ground ...state of this promising platform. The experimental efforts, relying mostly on one archetypal material ZnCu_{3}(OH)_{6}Cl_{2}, have also led to diverse possibilities. Apart from subtle interactions in the Hamiltonian, there is the additional degree of complexity associated with disorder in the real material ZnCu_{3}(OH)_{6}Cl_{2} that haunts most experimental probes. Here we resort to heat transport measurement, a cleaner probe in which instead of contributing directly, the disorder only impacts the signal from the kagome spins. For ZnCu_{3}(OH)_{6}Cl_{2}, we observed no contribution by any spin excitation nor obvious field-induced change to the thermal conductivity. These results impose strong constraints on various scenarios about the ground state of this kagome compound: while certain quantum paramagnetic states other than a QSL may serve as natural candidates, a QSL state, gapless or gapped, must be dramatically modified by the disorder so that the kagome spin excitations are localized.
Preventing biological contamination (biofouling) is key to successful development of novel surface and nanoparticle-based technologies in the manufacturing industry and biomedicine. Protein ...adsorption is a crucial mediator of the interactions at the bio - nano -materials interface but is not well understood. Although general, empirical rules have been developed to guide the design of protein-resistant surface coatings, they are still largely qualitative. Herein we demonstrate that this knowledge gap can be addressed by using machine learning approaches to extract quantitative relationships between the material surface chemistry and the protein adsorption characteristics. We illustrate how robust linear and non-linear models can be constructed to accurately predict the percentage of protein adsorbed onto these surfaces using lysozyme or fibrinogen as prototype common contaminants. Our computational models could recapitulate the adsorption of proteins on functionalised surfaces in a test set with an r
of 0.82 and standard error of prediction of 13%. Using the same data set that enabled the development of the Whitesides rules, we discovered an extension to the original rules. We describe a workflow that can be applied to large, consistently obtained data sets covering a broad range of surface functional groups and protein types.
Machine learning (ML), which is becoming an increasingly popular tool in various scientific fields, also shows the potential to aid in the screening of materials for diverse applications. In this ...study, the computation-ready experimental (CoRE) metal-organic framework (MOF) data set for which the O
and N
uptakes, self-diffusivities, and Henry's constants were calculated was used to fit the ML models. The obtained models were subsequently employed to predict such properties for a hypothetical MOF (hMOF) data set and to identify structures having a high O
/N
selectivity at room temperature. The performance of the model on known entries indicated that it would serve as a useful tool for the prediction of MOF characteristics with
correlations between the true and predicted values typically falling between 0.7 and 0.8. The use of different descriptor groups (geometric, atom type, and chemical) was studied; the inclusion of all descriptor groups yielded the best overall results. Only a small number of entries surpassed the performance of those in the CoRE MOF set; however, the use of ML was able to present the structure-property relationship and to identity the top performing hMOFs for O
/N
separation based on the adsorption and diffusion selectivity.
Neural networks have generated valuable Quantitative Structure‐Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in ...sophistication and many of their initial problems have been overcome by modern mathematical techniques. QSAR studies have almost always used so‐called “shallow” neural networks in which there is a single hidden layer between the input and output layers. Recently, a new and potentially paradigm‐shifting type of neural network based on Deep Learning has appeared. Deep learning methods have generated impressive improvements in image and voice recognition, and are now being applied to QSAR and QSAR modelling. This paper describes the differences in approach between deep and shallow neural networks, compares their abilities to predict the properties of test sets for 15 large drug data sets (the kaggle set), discusses the results in terms of the Universal Approximation theorem for neural networks, and describes how DNN may ameliorate or remove troublesome “activity cliffs” in QSAR data sets.
Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of ...biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water models have been developed, and it remains a challenge to find a single water model that accurately reproduces all experimental properties of water simultaneously. Here, we report a comprehensive comparison of structural and dynamic properties of 30 commonly used 3-point, 4-point, 5-point, and polarizable water models simulated using consistent settings and analysis methods. For the properties of density, coordination number, surface tension, dielectric constant, self-diffusion coefficient, and solvation free energy of methane, models published within the past two decades consistently show better agreement with experimental values compared to models published earlier, albeit with some notable exceptions. However, no single model reproduced all experimental values exactly, highlighting the need to carefully choose a water model for a particular study, depending on the phenomena of interest. Finally, machine learning algorithms quantified the relationship between the water model force field parameters and the resulting bulk properties, providing insight into the parameter–property relationship and illustrating the challenges of developing a water model that can accurately reproduce all properties of water simultaneously.
Polymers are an important class of materials with vast arrays of physical and chemical properties and have been widely used in many applications and industrial products. Although there have been many ...successful polymer design studies, the pace of materials discovery research can be accelerated to meet the high demand for new, functional materials. With the advanced development of artificial intelligence, the use of machine learning has shown great potential in data-driven design and the discovery of polymers to date. Several polymer datasets have been compiled, allowing robust machine learning models to be trained and provide accurate predictions of various polymer properties. Such models are useful for screening promising candidate polymers with high-performing properties prior to lab synthesis. In this review, we focus on the most critical components of polymer design using molecular descriptors and machine learning algorithms. A summary of existing polymer databases is provided, and the different categories of polymer descriptors are discussed in detail. The application of these descriptors in machine learning studies of polymer design is critically reviewed, leading to a discussion of the challenges, opportunities, and future perspectives for polymer research using these advanced computational tools.
Molecular descriptors and machine learning are useful tools for extracting structure-property relationships from large, complex polymer data, and accelerating the design of novel polymers with tailored functionalities.
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