The pure and representative types of disordered platinum nanoparticles from machine learning

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

Theory of Anisotropic Metal Nanostructures

A significant challenge in the development of functional materials is understanding the growth and transformations of anisotropic colloidal metal nanocrystals. Theory and simulations can aid in the development and understanding of anisotropic nanocrystal syntheses. The focus of this review is on how results from first-principles calculations and classical techniques, such as Monte Carlo and molecular dynamics simulations, have been integrated into multiscale theoretical predictions useful in understanding shape-selective nanocrystal syntheses. Also, examples are discussed in which machine learning has been useful in this field. There are many areas at the frontier in condensed matter theory and simulation that are or could be beneficial in this area and these prospects for future progress are discussed.

Unsupervised machine learning discovers classes in aluminium alloys

Aluminium (Al) alloys are critical to many applications. Although Al alloys have been commercially widespread for over a century, their development has predominantly taken a trial-and-error approach. Furthermore, many discrete studies regarding Al alloys, often application specific, have precluded a broader consolidation of Al alloy classification. Iterative label spreading (ILS), an unsupervised machine learning approach, was used to identify the different classes of Al alloys, drawing from a specifically curated dataset of 1154 Al alloys (including alloy composition and processing conditions). Using ILS, eight classes of Al alloys were identified based on a comprehensive feature set under two descriptors. Further, a decision tree classifier was used to validate the separation of classes.

Predicting the effective thermal conductivity of unfrozen soils with various water contents based on artificial neural network

The effective thermal conductivity of soils is a crucial parameter for many applications such as geothermal engineering, environmental science, and agriculture and engineering. However, it is pretty challenging to accurately determine it due to soils' complex structure and components. In the present study, the influences of different parameters, including silt content (m_si), sand content (m_sa), clay content (m_cl), quartz content (m_qu), porosity, and water content on the effective thermal conductivity of soils, were firstly analyzed by the Pearson correlation coefficient. Then different artificial neural network (ANN) models were developed based on the 465 groups of thermal conductivity of unfrozen soils collected from the literature to predict the effective thermal conductivity of soils. Results reveal that the parameters ofm_si,m_sa,m_cl, andm_quhave a relatively slight influence on the effective thermal conductivity of soils compared to the water content and porosity. Although the ANN model with six parameters has the highest accuracy, the ANN model with two input parameters (porosity and water content) could predict the effective thermal conductivity well with acceptable accuracy andR²= 0.940. Finally, a correlation of the effective thermal conductivity for different soils was proposed based on the large number of results predicted by the two input parameters ANN-based model. This correlation has proved to have a higher accuracy without assumptions and uncertain parameters when compared to several commonly used existing models.