Machine-learning-assisted search for functional materials over extended chemical space

Korolev, Vadim V.; Mitrofanov, Artem; Елисеев, А.А.; Tkachenko, Valery

doi:10.1039/d0mh00881h

Cited by 17 publications

(17 citation statements)

References 66 publications

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“…One of the most promising approaches for new materials structure creation is deep generative machine learning models. [12][13][14][15][16][25][26][27] Both variational autoencoder (VAE) [14,15,26,27] and generative adversarial networks (GAN) [12,13,16,25] have been adapted for inverse design of inorganic materials with different crystal structure representations. A VAE model contains two parts: an encoder and a decoder.…”

Section: Introductionmentioning

confidence: 99%

High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks

Zhao

Al-Fahdi

et al. 2021

Advanced Science

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High-throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of sufficient and diverse materials in current materials repositories such as the open quantum materials database (OQMD). Recent progress in deep learning have enabled generative strategies that learn implicit chemical rules for creating hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structurally diverse, chemically valid, and stable materials. Here we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large scale generative design of novel cubic materials. When trained on 375 749 ternary materials from the OQMD database, the authors show that the model is able to not only rediscover most of the currently known cubic materials but also generate hypothetical materials of new structure prototypes. A total of 506 such materials have been verified by phonon dispersion calculation. Considering the importance of cubic materials in wide applications such as solar panels, the GAN model provides a promising approach to significantly expand existing materials repositories, enabling the discovery of new functional materials via screening. The new crystal structures discovered are freely accessible at www.carolinamatdb.org.

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Section: Introductionmentioning

confidence: 99%

High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks

Zhao

Al-Fahdi

et al. 2021

Advanced Science

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“…It is also possible to distinguish a group of algorithms based on various branches of artificial intelligence: evolutionary and genetic algorithms, which are widely used in materials science, 11,12 as well as approaches based on Bayesian formalism 13 that is of growing interest in chemistry. [14][15][16][17] Another wellknown solution is a distance geometry algorithm 18 (DG), for example, Experimental-Torsion basic Knowledge Distance Geometry method (ETKDG) 19 implemented in a popular RDKit library. 20 Finally, local optimization algorithms may be applied to fine-tune the results of the above-mentioned approaches on each global optimization step or at the end of the process.…”

Section: Introductionmentioning

confidence: 99%

“…This group of methods includes the Monte Carlo 10 and related approaches. It is also possible to distinguish a group of algorithms based on various branches of artificial intelligence: evolutionary and genetic algorithms, which are widely used in materials science, 11,12 as well as approaches based on Bayesian formalism 13 that is of growing interest in chemistry 14–17 . Another well‐known solution is a distance geometry algorithm 18 (DG), for example, Experimental‐Torsion basic Knowledge Distance Geometry method (ETKDG) 19 implemented in a popular RDKit library 20 .…”

Section: Introductionmentioning

confidence: 99%

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Tree Parzen estimator for global geometry optimization: A benchmark and database of experimental gas‐phase structures of organic molecules

Andreadi

Zankov²,

Karpov

et al. 2022

J Comput Chem

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Finding global and local minima on the potential energy surface is a key task for most studies in computational chemistry. Having a set of possible conformations for chemical structures and their corresponding energies, one can judge their chemical activity, understand the mechanisms of reactions, describe the formation of metal‐ligand and ligand‐protein complexes, and so forth. Despite the fact that the interest in various minima search algorithms in computational chemistry arose a while ago (during the formation of this science), new methods are still emerging. These methods allow to perform conformational analysis and geometry optimization faster, more accurately, or for more specific tasks. This article presents the application of a novel global geometry optimization approach based on the Tree Parzen Estimator method. For benchmarking, a database of small organic molecule geometries in the global minimum conformation was created, as well as a software package to perform the tests.

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“…One of the most promising approaches for new materials structure creation is deep generative machine learning models [12,14,15,16,25,26,13,27]. Both variational autoencoder (VAE) [15,14,26,27] and generative adversarial networks (GAN) [12,13,16,25] have been adapted for inverse design of inorganic materials with different crystal structure representations. A VAE model contains two parts: an encoder and a decoder [28,29,30].…”

Section: Introductionmentioning

confidence: 99%

High-throughput discovery of novel cubic crystal materials using deep generative neural networks

Zhao,

Al-Fahdi,

et al. 2021

Preprint

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High-throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of quantity and diversity of known materials deposited in the current materials repositories such as ICSD and OQMD. Recent progress in machine learning and especially deep learning have enabled a generative strategy that learns implicit chemical rules for creating chemically valid hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structurally diverse, chemically valid, and stable materials. Here we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large scale generation of novel cubic crystal structures. When trained on 375,749 ternary crystal materials from the OQMD database, we show that our model is able to not only rediscover most of the currently known cubic materials but also generate hypothetical materials of new structure prototypes. A total of 506 such new materials (all of them are either ternary or quarternary) have been verified by DFT based phonon dispersion stability check, several of which have been found to potentially have exceptional functional properties. Considering the importance of cubic materials in wide applications such as solar cells and lithium batteries, our GAN model provides a promising approach to significantly expand the current repository of materials, enabling the discovery of new functional materials via screening. The new crystal structures finally verified by DFT are freely accessible at Carolina Materials Database www.carolinamatdb.org.

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Machine-learning-assisted search for functional materials over extended chemical space

Abstract: Materials discovery is a grand challenge for modern materials science. In particular, inverse materials design is aimed at the accelerated search for materials with human-defined target properties. Unfortunately, this is...

Cited by 17 publications

References 66 publications

High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks

High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks

Tree Parzen estimator for global geometry optimization: A benchmark and database of experimental gas‐phase structures of organic molecules

High-throughput discovery of novel cubic crystal materials using deep generative neural networks

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