Machine learning to predict quasicrystals from chemical compositions

Liu, Chang; Fujita, Erina; Katsura, Yukari; Inada, Yuki; Ishikawa, Asuka; Tamura, Ryuji; Kimura, Kaoru; Yoshida, Ryo

doi:10.21203/rs.3.rs-240290/v1

Cited by 3 publications

(5 citation statements)

References 27 publications

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“…For a given 2D formula, CSPML first restricts the candidates to structures with the same compositional ratio (e.g., SiTiO 3 has a composition ratio of 1:1:3). The compositional descriptor of query formula and templates is then calculated by XenonPy [38]. XenonPy provides 58 physicochemical features for each element.…”

Section: Template Based 2d Materials Structure Predictionmentioning

confidence: 99%

Discovery of 2D materials using Transformer Network based Generative Design

Dong¹,

Song²,

Siriwardane³

et al. 2023

Preprint

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Two-dimensional (2D) materials have wide applications in superconductors, quantum, and topological materials. However, their rational design is not well established, and currently less than 6,000 experimentally synthesized 2D materials have been reported. Recently, deep learning, data-mining, and density functional theory (DFT)-based high-throughput calculations are widely performed to discover potential new materials for diverse applications. Here we propose a generative material design pipeline, namely material transformer generator(MTG), for large-scale discovery of hypothetical 2D materials. We train two 2D materials composition generators using self-learning neural language models based on Transformers with and without transfer learning. The models are then used to generate a large number of candidate 2D compositions, which are fed to known 2D materials templates for crystal structure prediction. Next, we performed DFT computations to study their thermodynamic stability based on energy-above-hull and formation energy. We report four new DFT-verified stable 2D materials with zero e-above-hull energies, including NiCl 4 , IrSBr, CuBr 3 , and CoBrCl. Our work thus demonstrates the potential of our MTG generative materials design pipeline in the discovery of novel 2D materials and other functional materials.

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Section: Template Based 2d Materials Structure Predictionmentioning

confidence: 99%

Discovery of 2D materials using Transformer Network based Generative Design

Dong¹,

Song²,

Siriwardane³

et al. 2023

Preprint

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“…We calculated the compositional descriptor, φ(C i ) ∈ R d , using XenonPy [35,36]. XenonPy is an open-source Python library for materials informatics that provides 58 physicochemical features for each element (Fig.…”

Section: Chemical Composition Descriptormentioning

confidence: 99%

Crystal structure prediction with machine learning-based element substitution

Kusaba,

Liu,

Yoshida

2022

Preprint

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The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of assembled atoms can be determined by optimizing the energy surface, which in turn can be evaluated using first-principles calculations. However, performing the iterative gradient descent on the potential energy surface using first-principles calculations is prohibitively expensive for complex systems, such as those with many atoms per unit cell. Here, we present a unique methodology for crystal structure prediction (CSP) that relies on a machine learning algorithm called metric learning. It is shown that a binary classifier, trained on a large number of already identified crystal structures, can determine the isomorphism of crystal structures formed by two given chemical compositions with an accuracy of approximately 96.4%. For a given query composition with an unknown crystal structure, the model is used to automatically select from a crystal structure database a set of template crystals with nearly identical stable structures to which element substitution is to be applied. Apart from the local relaxation calculation of the identified templates, the proposed method does not use ab initio calculations. The potential of this substation-based CSP is demonstrated for a wide variety of crystal systems.

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“…Recently, there has been a growing trend in various domains of materials science to use machine-learning techniques to accelerate the process of designing and creating new materials. Conventionally, machine learning models are used to rapidly perform high-throughput virtual screening across millions or billions of candidate materials that span an enormous search space [1][2][3][4][5] . In general, a model describes physicochemical, electronic, thermodynamic, or mechanical properties as a function of the input materials, which are given in various forms, such as small-or macro-molecules, crystalline systems, chemical or raw material compositions, and their mixtures.…”

Section: Introductionmentioning

confidence: 99%

“…In general, a model describes physicochemical, electronic, thermodynamic, or mechanical properties as a function of the input materials, which are given in various forms, such as small-or macro-molecules, crystalline systems, chemical or raw material compositions, and their mixtures. To put the task into a machine learning framework, such a non-numeric variable needs to be transformed into a fixed-length numeric vector called a descriptor, which represents the compositional or structural features of the given material [5][6][7][8][9][10][11][12][13][14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

“…Chemical composition can be considered as a set variable consisting of a variable number of element species and their contents. There are a large volume of previous studies on the representation of such compositional features [5][6][7][8][9] . A crystal structure is typically vectorized by encoding the local structural environments of each atom and the neighboring relations of constituent atoms in a unit cell [7][8][9]21 .…”

Section: Introductionmentioning

confidence: 99%

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Functional Output Regression for Machine Learning in Materials Science

Iwayama

Liu

et al. 2022

Preprint

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In recent years, there has been a rapid growth in the use of machine learning in materials science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a function of input descriptors that vectorize the compositional or structural features of any given material, such as molecules, chemical compositions, or crystalline systems. In machine learning of materials data, on the other hand, the output variable is often given as a function. For example, when predicting the optical absorption spectrum of a molecule, the output variable is a spectral function defined in the wavelength domain. Alternatively, in predicting the microstructure of a polymer nanocomposite, the output variable is given as an image from an electron microscope, which can be represented as a two- or three-dimensional function in the image coordinate system. In this study, we considered two unified frameworks to handle such multidimensional or functional output regressions, which are applicable to a wide range of predictive analyses in materials science. The first approach employs generative adversarial networks, which are known to exhibit outstanding performance in various computer vision tasks such as image generation, style transfer, and video generation. We also present another type of statistical modelling inspired by a statistical methodology referred to as functional data analysis. This is an extension of kernel regression to deal with functional outputs, and its simple mathematical structure makes it an effective modelling even for given data in limited supply. We demonstrate the proposed method through several case studies in materials science.

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Machine learning to predict quasicrystals from chemical compositions

Cited by 3 publications

References 27 publications

Discovery of 2D materials using Transformer Network based Generative Design

Discovery of 2D materials using Transformer Network based Generative Design

Crystal structure prediction with machine learning-based element substitution

Functional Output Regression for Machine Learning in Materials Science

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