Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene

Rajan, Arunkumar Chitteth; Mishra, Avanish; Satsangi, Swanti; Vaish, Rishabh; Mizuseki, Hiroshi; Lee, Kwang-Ryeol; Singh, Abhishek K.

doi:10.1021/acs.chemmater.8b00686

Cited by 299 publications

(229 citation statements)

References 35 publications

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“…Roughly two classes of properties can be predicted, or classified, using machine learning methods: bandgaps and electronic conductivity. The former being widely explored by regression techniques, capable of presenting a numerical value for the gap [206,210,253,264,[452][453][454][455][456][457][458][459][460][461][462], or classification methods, which simply provide an answer to the question 'is this compound or material a metal?' [463].…”

Section: Electronic Propertiesmentioning

confidence: 99%

“…The use of a neural network to predict the bandgap of inorganic materials dates back to the end of the last century [464]. More recent examples can be found in the literature where the authors make use of both methods [262,461,465], first classifying the materials as metals or insulators/semiconductors and in the sequence, obtaining a prediction of the bandgap of the latter class, avoiding in this manner the nonphysical prediction of negative values of E g . Figure 19 shows a few examples of predictions of bandgaps using a variety of ML algorithms.…”

Section: Electronic Propertiesmentioning

confidence: 99%

“…Among those, many authors point out the requirement that descriptors should be invariant with respect to translations and rotations of atomic positions, as well as reordering of atomic indices. Popular descriptors with these properties can be categorized into few cases: structural data such as Coulomb matrices [248,457,470], molecular strings or graphs [264,455,457], and polymer fingerprinting [457][458][459]; simple atomic properties of the constituent species [460,462,466], and DFT-derived data, such as PBE/LDAlevel bandgaps and hybrid-level electronic density [206,272,452,456,461,471,472]. Frequently a combination of two or more classes of descriptors [453,457,460,473] as well as experimental data as features [272,465] is found in the literature.…”

Section: Electronic Propertiesmentioning

confidence: 99%

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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: Electronic Propertiesmentioning

confidence: 99%

Section: Electronic Propertiesmentioning

confidence: 99%

Section: Electronic Propertiesmentioning

confidence: 99%

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

et al. 2019

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“…It is noteworthy that a aNANt database containing the structural and electronic information of approximately 15,000 MXenes has recently been released [67]. The chemical formula considered in this database is MM XTT , where M/M is an early transition metal, X is C or N, and T/T stands for 14 different termination groups such as F, OH, CN, and SCN [68]. The electronic properties have been predicted with a combination of DFT and machine learning.…”

Section: Electronic Structuresmentioning

confidence: 99%

Recent advances in MXenes: From fundamentals to applications

Khazaei

Mishra

Venkataramanan

et al. 2019

Current Opinion in Solid State and Materials Science

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307

147

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The family of MAX phases and their derivative MXenes are continuously growing in terms of both crystalline and composition varieties. In the last couple of years, several breakthroughs have been achieved that boosted the synthesis of novel MAX phases with ordered double transition metals and, consequently, the synthesis of novel MXenes with a higher chemical diversity and structural complexity, rarely seen in other families of two-dimensional (2D) materials. Considering the various elemental composition possibilities, surface functional tunability, various magnetic orders, and large spin−orbit coupling, MXenes can truly be considered as multifunctional materials that can be used to realize highly correlated phenomena. In addition, owing to their large surface area, hydrophilicity, adsorption ability, and high surface reactivity, MXenes have attracted attention for many applications, e.g., catalysts, ion batteries, gas storage media, and sensors. Given the fast progress of MXene-based science and technology, it is timely to update our current knowledge on various properties and possible applications. Since many theoretical predictions remain to be experimentally proven, here we mainly emphasize the physics and chemistry that can be observed in MXenes and discuss how these properties can be tuned or used for different applications.

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“…required to investigate the effect of substrate surface energy, orbital radii and ionization energy on monolayer metal oxide coating stability on support metal oxides [11]. Band gaps are another energetic property of materials that depends on the composition and atomic ordering for which extensive information based on computation and experiment can provide deeper insight at a molecular level [12,13]. Such efforts have potential applications in guiding the search for new photoactive materials for photocatalysis [14].…”

Section: Introductionmentioning

confidence: 99%

High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning

Unke

Koner

Patra

et al. 2020

Mach. Learn.: Sci. Technol.

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An overview of computational methods to describe high-dimensional potential energy surfaces suitable for atomistic simulations is given. Particular emphasis is put on accuracy, computability, transferability and extensibility of the methods discussed. They include empirical force fields, representations based on reproducing kernels, using permutationally invariant polynomials, neural network-learned representations and combinations thereof. Future directions and potential improvements are discussed primarily from a practical, application-oriented perspective.

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Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene

Cited by 299 publications

References 35 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

Recent advances in MXenes: From fundamentals to applications

High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning

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