Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds

Kim, Kyoungdoc; Ward, Logan; He, Jiangang; Krishna, Amar; Agrawal, Ankit; Wolverton, Christopher

doi:10.1103/physrevmaterials.2.123801

Cited by 81 publications

(80 citation statements)

References 55 publications

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“…A recent study by Kim et al 237 used the same method for the discovery of quaternary Heusler compounds and identified 53 new stable structures. The model was trained for different datasets (complete open quantum materials database, 80 only the quaternary Heusler compounds, etc.).…”

Section: Component Predictionmentioning

confidence: 99%

Recent advances and applications of machine learning in solid-state materials science

et al. 2019

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One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure-property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

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Section: Component Predictionmentioning

confidence: 99%

Recent advances and applications of machine learning in solid-state materials science

et al. 2019

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“…The prediction of crystal structures and their stability [399,400] has also been performed for several materials such as perovskites [287,[401][402][403], superhard materials [404], bcc materials and Fe alloys [405], binary alloys [406], phosphor hosts [407], Heuslers [408,409], catalysts [410], amorphous carbon [411], high-pressurehydrogen-compressor materials [412], binary intermetallic compounds with transition metals [413], and multicomponent crystalline solids [414]. An atomic-position independent descriptor was able to reach a MAE of 70 meV/atom for formation energy predictions of a diverse dataset of more than 85 000 materials [415].…”

Section: Discovery Energies and Stabilitymentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. characterized by its diverse and huge volume, usually ranging from terabytes to petabytes of data, being created in or near real-time. Such data is found either structured and unstructured in nature, and is exhaustive, usually aiming to capture entire populations in a scalable manner [7]. Simple tasks represent challenges in this scale: capture, curation, storage, search, sharing, analysis, and visualization of the data cannot be accomplished without the proper tools. Thus, it can be effectively summarized by the popular 'five V's': volume, velocity, variety, veracity, and value, shown in figure 3(right). A related sixth V is visualization, although not exclusive to Big Data, which requires different techniques to handle data with various characteristics.Striving to tackle the challenges imposed by Big Data, the field of Data Science has arisen. It is largely interdisciplinary being a combination of mathematics and statistics, computer science and programming, and domain knowledge for problem definition and solving, as shown in figure 3(left). Its objective is, roughly speaking, to deal with the whole process of data production, cleaning, preparation, and finally, analysis. Data science encompasses areas such as Big Data, which deals with large volumes of data, and data mining, which relates to analysis processes to discover patterns and extract knowledge from data, part of the so-called Knowledge Discovery in Databases (KDD).The analysis process within Data Science is challenging, as the techniques are very different from traditional static and rigid datasets, generated and analyzed under a predetermined hypothesis. The distinction from traditional data is based on the larger abundance, exhaustivity, and variety of Big Data. It is also much more dynamic, messy and uncertain, being highly relational [7]. Recently, the possibility of overcoming such a challenge slowly sta...

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“…However, the list of all possible materials for such scrutiny is immense. Machine learning (ML) guides in narrowing down the search on which further experimentation or computational study can be made [45]. It helps to find patterns (linear or non-linear) and building up models from a given dataset which can be used for prediction.…”

Section: Prediction Of New Double Perovskitesmentioning

confidence: 99%

Double perovskites with 3d and 4d/5d transition metals: compounds with promises

Saha‐Dasgupta

2020

Mater. Res. Express

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Double perovskites, which can host two different transition metal cations at the B-sites of its perovskite derived structure, provide the possibility to explore the interplay of localized 3d transition metals and the relatively delocalized 4d or 5d transition metals within the same structure. This interplay gives rise to extraordinary magnetic properties. In this overview article, we summarize our recent computational efforts in understanding the curious properties of magnetic double perovskites with 3d and 4d/5d transition metal ions, make predictions on new functionalities in known compounds of this family, engineering functionalities via chemical modifications/doping and prediction on a new set of magnetic 3d-4d/5d double perovkite compounds, which may be synthesized to explore and expand further this interesting family.

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Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds

Cited by 81 publications

References 55 publications

Recent advances and applications of machine learning in solid-state materials science

Recent advances and applications of machine learning in solid-state materials science

From DFT to machine learning: recent approaches to materials science–a review

Double perovskites with 3d and 4d/5d transition metals: compounds with promises

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