Screening stable and metastable ABO3 perovskites using machine learning and the materials project

Liu, Haiying; Cheng, Jiucheng; Dong, Hua; Feng, Jianguang; Pang, Beili; Tian, Ziya; Ma, Shuai; Xia, Fengjin; Zhang, Chunkai; Dong, Lifeng

doi:10.1016/j.commatsci.2020.109614

Cited by 61 publications

(52 citation statements)

References 33 publications

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“…presented. In 2020, Liu et al 62 screened stable and metastable ABO 3 perovskites using ML and the materials project based on the dataset of 397 ABO 3 compounds (Fig. 5a).…”

Section: Applications Of Machine Learning In Perovskite Materialsmentioning

confidence: 99%

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Machine learning for perovskite materials design and discovery

et al. 2021

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The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.

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“…presented. In 2020, Liu et al 62 screened stable and metastable ABO 3 perovskites using ML and the materials project based on the dataset of 397 ABO 3 compounds (Fig. 5a).…”

Section: Applications Of Machine Learning In Perovskite Materialsmentioning

confidence: 99%

“…Reproduced with permission from ref. 62 . Copyright Elsevier 2020 b Workflow for the ABO 3 cubic perovskite 65 .…”

Section: Applications Of Machine Learning In Perovskite Materialsmentioning

confidence: 99%

Machine learning for perovskite materials design and discovery

et al. 2021

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“…[ 14 ] Specifically, for perovskite structures, relevant studies implementing machine learning have been rapidly increasing. [ 17–22 ] For example, Kaneko et al. combined machine learning with Monte Carlo methods, for the stable anion ordering of large supercells of the BaNbO 2 N perovskite without expensive first‐principles calculations, to reduce computational costs by 99.9% compared to DFT‐based methods.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, 331 perovskite materials were found, of which 37 were stable and 13 were metastable. [ 21 ] Although CH 3 NH 3 PbI 3 ‐based HOIP solar cells have seen significant increases in their efficiency, research has been conducted to replace them due to intrinsic instability. A new HOIP perovskite was found using the random forest algorithm.…”

Section: Introductionmentioning

confidence: 99%

Synthesizable Double Perovskite Oxide Search via Machine Learning and High‐Throughput Computational Screening

Kim

Min

2021

Advcd Theory and Sims

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Double perovskite structures have great potential for applications in batteries, lighting devices, and energy‐harvesting materials. In this study, the synthesizability of ABB′O3 double perovskite materials is predicted using machine learning. The machine learning algorithms are validated by performing high‐throughput computational screening. First, material properties extracted from the Materials Project database are used as training data to develop models to predict the formation energy and convex hull energy of general inorganic materials. A regression model predicts the formation energy of general inorganic materials with a high accuracy; an R‐squared value equal to 0.98 and a root‐mean‐square error of 0.175 eV atom−1 are recorded. In addition, a classification accuracy for the convex hull energy of 0.77 is calculated, with an F1‐score of 0.771, in a separate model. Both models are employed to estimate the possible synthesizability of 11 763 ABB′O3 structures and their accuracy is further validated by performing first‐principles calculations, whose classification accuracy for the convex hull energy reaches an accuracy of 0.646, with an F1‐score of 0.733. The constructed surrogate model, as well as the materials database, can guide the discovery of synthesizable double perovskite oxide structures.

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“…Knowledge stored in these repositories has then been mined to screen materials for diverse applications (Zhang et al, 2018;Singh et al, 2019;Zhang et al, 2019). In addition, machine learning models have also been trained using data from these repositories to predict properties of novel materials (Ahmad et al, 2018;Xie and Grossman, 2018;Ye et al, 2018;Joshi et al, 2019;Liu et al, 2020). In a recent article, Sorkun et al (2020) identified several potential 2D materials for photocatalytic water splitting, CO 2 reduction, and N 2 reduction by training AI models on the computational 2D materials database and using the predictions from these models to screen a vast chemical space obtained by systematic elemental substitution in 2D material prototypes.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Discovery of 2D Materials for Solar Water Splitting

2021

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Hydrogen economy, wherein hydrogen is used as the fuel in the transport and energy sectors, holds significant promise in mitigating the deleterious effects of global warming. Photocatalytic water splitting using sunlight is perhaps the cleanest way of producing the hydrogen fuel. Among various other factors, widespread adoption of this technology has mainly been stymied by the lack of a catalyst material with high efficiency. 2D materials have shown significant promise as efficient photocatalysts for water splitting. The availability of open databases containing the “computed” properties of 2D materials and advancements in deep learning now enable us to do “inverse” design of these 2D photocatalysts for water splitting. We use one such database (Jain et al., ACS Energ. Lett. 2019, 4, 6, 1410–1411) to build a generative model for the discovery of novel 2D photocatalysts. The structures of the materials were converted into a 3D image–based representation that was used to train a cell, a basis autoencoder and a segmentation network to ascertain the lattice parameters as well as position of atoms from the images. Subsequently, the cell and basis encodings were used to train a conditional variational autoencoder (CVAE) to learn a continuous representation of the materials in a latent space. The latent space of the CVAE was then sampled to generate several new 2D materials that were likely to be efficient photocatalysts for water splitting. The bandgap of the generated materials was predicted using a graph neural network model while the band edge positions were obtained via empirical correlations. Although our generative modeling framework was used to discover novel 2D photocatalysts for water splitting reaction, it is generic in nature and can be used directly to discover novel materials for other applications as well.

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Screening stable and metastable ABO3 perovskites using machine learning and the materials project

Cited by 61 publications

References 33 publications

Machine learning for perovskite materials design and discovery

Machine learning for perovskite materials design and discovery

Synthesizable Double Perovskite Oxide Search via Machine Learning and High‐Throughput Computational Screening

Data-Driven Discovery of 2D Materials for Solar Water Splitting

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