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1. High-Throughput Computational Studies in Catalysis and Materials Research, and Their Impact on Rational Design

   https://doi.org/10.1142/9789811204555_0001  

   Afzal, Mohammad Atif; Hachmann, Johannes (May 2020, Handbook on Big Data and Machine Learning in the Physical Sciences, Vol 1: Big Data Methods in Experimental Materials Discovery)

   Kalidindi, Surya R.; Kalinin, Sergei V.; Lookman, Turab; Foster, Ian (Ed.)

   The discovery of new compounds and materials has a fundamental impact on industrial and economic development. The discovery process is increasingly supported by computational approaches as they provide efficient means to uncover promising targets. In the past two decades, we have witnessed tremendous growth in the drug discovery field due to the implementation of virtual high-throughput screening (HTPS) techniques. Recently, these techniques have been embraced in various materials applications, such as catalysis, energy materials, optoelectronics, photovoltaics, etc., thereby developing into a promising tool for the discovery of nextgeneration materials. In addition to the discovery of new materials, these HTPS studies provide a solid data foundation for rational design approaches as well as guidance for experimental partners. In this chapter, we review recent HTPS efforts undertaken for new materials for photovoltaics, gas separation, optical devices, and OLEDs. We also review HTPS projects for catalyst materials for various important reactions, such as the oxygen reduction reaction (ORR), oxygen evolution reaction (OER), hydrogen evolution reaction (HER), and carbon dioxide reduction reaction (CO2RR). 

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2. Machine Learning Prediction of Heat Capacity for Solid Inorganics

   https://doi.org/10.1007/s40192-018-0108-9  

   Kauwe, Steven K.; Graser, Jake; Vazquez, Antonio; Sparks, Taylor D. (May 2018, Integrating Materials and Manufacturing Innovation)

   Abstract Many thermodynamic calculations and engineering applications require the temperature-dependent heat capacity (Cp) of a material to be known a priori. First-principle calculations of heat capacities can stand in place of experimental information, but these calculations are costly and expensive. Here, we report on our creation of a high-throughput supervised machine learning-based tool to predict temperature-dependent heat capacity. We demonstrate that material heat capacity can be correlated to a number of elemental and atomic properties. The machine learning method predicts heat capacity for thousands of compounds in seconds, suggesting facile implementation into integrated computational materials engineering (ICME) processes. In this context, we consider its use to replace Neumann-Kopp predictions as a high-throughput screening tool to help identify new materials as candidates for engineering processes. Also promising is the enhanced speed and performance compared to cation/anion contribution methods at elevated temperatures as well as the ability to improve future predictions as more data are made available. This machine learning method only requires formula inputs when calculating heat capacity and can be completely automated. This is an improvement to common best-practice methods such as cation/anion contributions or mixed-oxide approaches which are limited in application to specific materials and require case-by-case considerations. 

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3. Data-Driven Discovery of Robust Materials for Photocatalytic Energy Conversion

   https://doi.org/10.1146/annurev-conmatphys-031620-100957  

   Singh, Arunima K.; Gorelik, Rachel; Biswas, Tathagata (March 2023, Annual Review of Condensed Matter Physics)

   The solar–to–chemical energy conversion of Earth-abundant resources like water or greenhouse gas pollutants like CO₂promises an alternate energy source that is clean, renewable, and environmentally friendly. The eventual large-scale application of such photo-based energy conversion devices can be realized through the discovery of novel photocatalytic materials that are efficient, selective, and robust. In the past decade, the Materials Genome Initiative has led to a major leap in the development of materials databases, both computational and experimental. Hundreds of photocatalysts have recently been discovered for various chemical reactions, such as water splitting and carbon dioxide reduction, employing these databases and/or data informatics, machine learning, and high-throughput computational and experimental methods. In this article, we review these data-driven photocatalyst discoveries, emphasizing the methods and techniques developed in the last few years to determine the (photo)electrochemical stability of photocatalysts, leading to the discovery of photocatalysts that remain robust and durable under operational conditions. 

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4. Machine Learning for the Discovery, Design, and Engineering of Materials

   https://doi.org/10.1146/annurev-chembioeng-092320-120230  

   Duan, Chenru; Nandy, Aditya; Kulik, Heather J. (June 2022, Annual Review of Chemical and Biomolecular Engineering)

   Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design. 

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5. Emerging Capabilities for the High-Throughput Characterization of Structural Materials

   https://doi.org/10.1146/annurev-matsci-080619-022100  

   Miracle, Daniel B.; Li, Mu; Zhang, Zhaohan; Mishra, Rohan; Flores, Katharine M. (July 2021, Annual Review of Materials Research)

   null (Ed.)

   Structural materials have lagged behind other classes in the use of combinatorial and high-throughput (CHT) methods for rapid screening and alloy development. The dual complexities of composition and microstructure are responsible for this, along with the need to produce bulk-like, defect-free materials libraries. This review evaluates recent progress in CHT evaluations for structural materials. High-throughput computations can augment or replace experiments and accelerate data analysis. New synthesis methods, including additive manufacturing, can rapidly produce composition gradients or arrays of discrete alloys-on-demand in bulk form, and new experimental methods have been validated for nearly all essential structural materials properties. The remaining gaps are CHT measurement of bulk tensile strength, ductility, and melting temperature and production of microstructural libraries. A search strategy designed for structural materials gains efficiency by performing two layers of evaluations before addressing microstructure, and this review closes with a future vision of the autonomous, closed-loop CHT exploration of structural materials. Expected final online publication date for the Annual Review of Materials Science, Volume 51 is August 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

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