Deep learning enabled inorganic material generator

Pathak, Yashaswi; Juneja, Karandeep Singh; Varma, Girish; Ehara, Masahiro

doi:10.1039/d0cp03508d

Cited by 52 publications

(42 citation statements)

References 54 publications

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“…45,46 In recent years, Bayesian optimization has seen widespread applications in the field of chemistry, ranging from latent space optimization in molecular generation to reaction optimization for chemical synthesis. 20,35,47,48 There are two main components in Bayesian optimization, a surrogate function which is a statistical model that can be used to approximate the black box, and an acquisition function to determine the next points to the sample. In this work, Gaussian Process Regression (ExactGP) and Deep Gaussian Process (DeepGP) are used as surrogate functions in ExactMEMES and DeepMEMES variants, respectively, and expected improvement 49 is used as an acquisition function.…”

Section: Methodsmentioning

confidence: 99%

MEMES: Machine learning framework for Enhanced MolEcular Screening

et al. 2021

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Section: Methodsmentioning

confidence: 99%

MEMES: Machine learning framework for Enhanced MolEcular Screening

et al. 2021

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“…Reversibility means that the digital space is mapped bijectively to real molecules, while symmetry invariance means that the representations after rotation, translation, and permutation can be identified as the same molecule before these operations. Simplified molecular input line entry system (SMILES) strings [48,49] and molecular graphs [50] are among the most renowned representation schemes [Figure 3A].…”

Section: Representationmentioning

confidence: 99%

Generative models for inverse design of inorganic solid materials

Chen¹,

Zhang²,

Nie³

et al. 2021

JMI

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Overwhelming evidence has been accumulating that materials informatics can provide a novel solution for materials discovery. While the conventional approach to innovation relies mainly on experimentation, the generative models stemming from the field of machine learning can realize the long-held dream of inverse design, where properties are mapped to the chemical structures. In this review, we introduce the general aspects of inverse materials design and provide a brief overview of two generative models, variational autoencoder and generative adversarial network, which can be utilized to generate and optimize inorganic solid materials according to their properties. Reversible representation schemes for generative models are compared between molecular and crystalline structures, and challenges in regard to the latter are also discussed. Finally, we summarize the recent application of generative models in the exploration of chemical space with compositional and configurational degrees of freedom, and potential future directions are speculatively outlined.

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“…Saidi and coworkers [34] concatenated properties of the atoms and molecules forming ABX 3 perovskites to estimate the lattice constant, octahedral tilt angle, and even band gap energies using convolutional neural networks (CNNs); with these CNNs, hidden information was extracted from the 1D-input data. Pathak and coworkers [35] developed a strategy using conditional variational autoencoders to generate candidate materials that suit formation energy, energy per atom, and volume per atom constraints. Since the characterization of the materials was based on concatenated one-hot vectors, information concerning the structure was not necessary to generate the candidate materials.…”

Section: Introductionmentioning

confidence: 99%

Crystal-Site-Based Artificial Neural Networks for Material Classification

2021

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In materials science, crystal structures are the cornerstone in the structure–property paradigm. The description of crystal compounds may be ascribed to the number of different atomic chemical environments, which are related to the Wyckoff sites. Hence, a set of features related to the different atomic environments in a crystal compound can be constructed as input data for artificial neural networks (ANNs). In this article, we show the performance of a series of ANNs developed using crystal-site-based features. These ANNs were developed to classify compounds into halite, garnet, fluorite, hexagonal perovskite, ilmenite, layered perovskite, -o-tp- perovskite, perovskite, and spinel structures. Using crystal-site-based features, the ANNs were able to classify the crystal compounds with a 93.72% average precision. Furthermore, the ANNs were able to retrieve missing compounds with one of these archetypical structure types from a database. Finally, we showed that the developed ANNs were also suitable for a multitask learning paradigm, since the extracted information in the hidden layers linearly correlated with lattice parameters of the crystal structures.

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Deep learning enabled inorganic material generator

Abstract: Recent years have witnessed utilization of modern machine learning approaches for predicting properties of material using available datasets. However, to identify potential candidates for material discovery, one has to systematically...

Cited by 52 publications

References 54 publications

MEMES: Machine learning framework for Enhanced MolEcular Screening

MEMES: Machine learning framework for Enhanced MolEcular Screening

Generative models for inverse design of inorganic solid materials

Crystal-Site-Based Artificial Neural Networks for Material Classification

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