Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape

Choudhary, Kamal; DeCost, Brian; Tavazza, Francesca

doi:10.1103/physrevmaterials.2.083801

Cited by 129 publications

(162 citation statements)

References 47 publications

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“…We now present a relatively chronological list used in recent materials research, which is considerable but not exhaustive. These include: bondorientational order parameters (BOP) [243]; Behler-Parrinello atom-centered symmetry functions (ACSF) [233,244], and its modified [245] and weighted (wACSF) [246] versions; Gaussian Approximation Potentials (GAP) [212,232] using smooth overlap of atomic positions (SOAP) [213] also extended for tensorial properties [247]; Coulomb matrix [248] and Bag of Bonds (BOB) [249], and the subsequent interatomic many body expansions (MBE) [250,251] like the so-called BAML (bonds, angles machine learning) [252] and fixed-size inverse distances [253]; metric fingerprints [238]; bispectrum [213]; atomic local frame (ALF) [254]; partial radial and angular distribution functions (PRDF, ADF) [255] and generalized radial distribution functions (GRDF) [224]; Fourier series of radial distribution functions [256]; force vectors representations [257]; spectral neighbor analysis potential (SNAP) [258]; permutation invariant polynomials [245]; particle densities [259]; angular Fourier series (AFS) [213]; topological polyhedra [260], Voronoi [261] and Voronoi-Dirichlet [262] tessellations; spherical harmonics [263]; histogram of distances, angles, or dihedral angles [264]; classical forcefield-inspired descriptors (CFID) [209]; graph-based such as Graph Approximated Energy (GRAPE) [265]; constant complexity descriptors based on Chebyshev polynomials [266]; symmetrized gradient-domain machine learning ...…”

Section: Representations and Descriptorsmentioning

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: Representations and Descriptorsmentioning

confidence: 99%

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

et al. 2019

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“…Another common strategy is a bottom‐up approach, whereby features are constructed from the local environment of each atom and combined at the crystal level. Such descriptors include atom‐centered symmetry functions (ACSF), bispectrum coefficients, smooth overlap of atomic positions (SOAP), moment tensors, classical force‐field‐inspired descriptors (CFID), etc. They benefit from the locality of target properties, e.g., energy can be divided into atomic energy.…”

Section: Featurizationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

401

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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“…The structural and electronic properties of different hybrid 2D materials were provided and various parameters for vdW heterostructures were screened. A machine learning model with force-filed-inspired descriptors in material screening for complex systems has been introduced and used to discover exfoliated 2D-layered materials 187 . An artificial neural network for titanium dioxide systems was trained based on the a DFT calculated database, where a novel quasi 2D titanium dioxide structure was revealed 188 .…”

Section: Navier-stokesmentioning

confidence: 99%

Multiscale computational understanding and growth of 2D materials: a review

et al. 2020

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The successful discovery and isolation of graphene in 2004, and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional (2D) materials that explore their growth, new atomic-scale physics, and potential device applications. This review aims to provide an overview of theoretical, computational, and machine learning methods and tools at multiple length and time scales, and discuss how they can be utilized to assist/guide the design and synthesis of 2D materials beyond graphene. We focus on three methods at different length and time scales as follows: (i) nanoscale atomistic simulations including density functional theory (DFT) calculations and molecular dynamics simulations employing empirical and reactive interatomic potentials; (ii) mesoscale methods such as phase-field method; and (iii) macroscale continuum approaches by coupling thermal and chemical transport equations. We discuss how machine learning can be combined with computation and experiments to understand the correlations between structures and properties of 2D materials, and to guide the discovery of new 2D materials. We will also provide an outlook for the applications of computational approaches to 2D materials synthesis and growth in general.npj Computational Materials (2020) 6:22 ; https://doi.

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Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape

Cited by 129 publications

References 47 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

A Critical Review of Machine Learning of Energy Materials

Multiscale computational understanding and growth of 2D materials: a review

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