Identifying an efficient, thermally robust inorganic phosphor host via machine learning

Zhuo, Ya; Tehrani, Aria Mansouri; Oliynyk, Anton O.; Duke, Anna; Brgoch, Jakoah

doi:10.1038/s41467-018-06625-z

Cited by 252 publications

(181 citation statements)

References 64 publications

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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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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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“…Other applications of GPSR in materials and molecular systems are in areas where supervised machine learning has already demonstrated usefulness in providing new insight or solutions. While machine learning has produced many impressive results, 5,17,74 it is commonly understood that ML models exhibit a trade-off between performance on prediction metrics and the ability to explain the predictions of a model due to the complexity of state-of-the-art models like deep neural networks or gradient boosted decision trees. GPSR offers a middle ground with comparable performance but with the added ability to read and directly interpret the output function.…”

Section: B Opportunities In Materials Sciencementioning

confidence: 99%

Symbolic regression in materials science

2019

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We showcase the potential of symbolic regression as an analytic method for use in materials research. First, we briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques. Next, we discuss industrial applications of symbolic regression and its potential applications in materials science. We then present two GPSR use-cases: formulating a transformation kinetics law and showing the learning scheme discovers the well-known Johnson-Mehl-Avrami-Kolmogorov (JMAK) form, and learning the Landau free energy functional form for the displacive tilt transition in perovskite LaNiO3. Finally, we propose that symbolic regression techniques should be considered by materials scientists as an alternative to other machine-learning-based regression models for learning from data.

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“…Similar methods have been applied to the design of lead‐free organic–inorganic hybrid perovskite, monoatomic catalysts, light‐emitting diode (LED), organic light‐emitting diode (OLED), and other key materials. The latter two methods have also been verified by experiments.…”

Section: Ai Applications For Materials Science and Engineeringmentioning

confidence: 99%

Artificial Intelligence to Power the Future of Materials Science and Engineering

Sha

Guo

Qing

et al. 2020

Advanced Intelligent Systems

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Artificial intelligence (AI) has received widespread attention over the last few decades due to its potential to increase automation and accelerate productivity. In recent years, a large number of training data, improved computing power, and advanced deep learning algorithms are conducive to the wide application of AI, including material research. The traditional trial‐and‐error method is inefficient and time‐consuming to study materials. Therefore, AI, especially machine learning, can accelerate the process by learning rules from datasets and building models to predict. This is completely different from computational chemistry where a computer is only a calculator, using hard‐coded formulas provided by human experts. Herein, the application of AI in material innovation is reviewed, including material design, performance prediction, and synthesis. The realization details of AI techniques and advantages over conventional methods are emphasized in these applications. Finally, the future development direction of AI is expounded from both algorithm and infrastructure aspects.

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Identifying an efficient, thermally robust inorganic phosphor host via machine learning

Cited by 252 publications

References 64 publications

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

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

Symbolic regression in materials science

Artificial Intelligence to Power the Future of Materials Science and Engineering

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