Generative models for inverse design of inorganic solid materials

Chen, Litao; Zhang, Wentao; Nie, Zhiwei; Li, Shunning; Pan, Feng

doi:10.20517/jmi.2021.07

Cited by 15 publications

(12 citation statements)

References 68 publications

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“…In this sense, physics-based regularization can assist. Physics-based deep learning can also aid in inverse design problems, a challenging but important task 84,85 . On the flip side, deep Learning using Graph Neural Nets and symbolic regression (stochastically building symbolic expressions) has even been used to "discover" symbolic equations from data that capture known (and unknown) physics behind the data 86 , i.e., to deep learn a physics model rather than to use a physics model to constrain DL.…”

Section: Scientific Machine Learningmentioning

confidence: 99%

Recent advances and applications of deep learning methods in materials science

et al. 2022

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Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.

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Section: Scientific Machine Learningmentioning

confidence: 99%

Recent advances and applications of deep learning methods in materials science

et al. 2022

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“…have been applied [ 39 ]. To navigate chemical space, three methodologies can be used for materials identification ( Figure 2 ): (1) high-throughput virtual screening; (2) global optimization; and (3) generative models [ 40 , 41 ].…”

Section: Inverse Designmentioning

confidence: 99%

“…Many exciting developments have been well-established by Noh et al [ 79 ]. Chen et al [ 40 ] has reviewed the generative models for inverse design of inorganic solid material. Zunger [ 35 ] discussed the inverse design of solid-state materials with target functionalities very comprehensively.…”

Section: Application In Materials Designmentioning

confidence: 99%

Inverse Design of Materials by Machine Learning

Wang

Wang²,

Chen

2022

Materials

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It is safe to say that every invention that has changed the world has depended on materials. At present, the demand for the development of materials and the invention or design of new materials is becoming more and more urgent since peoples’ current production and lifestyle needs must be changed to help mitigate the climate. Structure-property relationships are a vital paradigm in materials science. However, these relationships are often nonlinear, and the pattern is likely to change with length scales and time scales, posing a huge challenge. With the development of physics, statistics, computer science, etc., machine learning offers the opportunity to systematically find new materials. Especially by inverse design based on machine learning, one can make use of the existing knowledge without attempting mathematical inversion of the relevant integrated differential equation of the electronic structure but by using backpropagation to overcome local minimax traps and perform a fast calculation of the gradient information for a target function concerning the design variable to find the optimizations. The methodologies have been applied to various materials including polymers, photonics, inorganic materials, porous materials, 2-D materials, etc. Different types of design problems require different approaches, for which many algorithms and optimization approaches have been demonstrated in different scenarios. In this mini-review, we will not specifically sum up machine learning methodologies, but will provide a more material perspective and summarize some cut-edging studies.

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“…Deep generative frameworks employing neural networks (NNs) have proven to be an invaluable tool in this context . Notably, these comprise a large zoo of approaches, including variational autoencoders (VAEs), − ,,,− generative adversarial networks (GANs), ,,− ,, and reinforcement learning (RL). , Given this wide range of approaches, it is a priori difficult to decide which method should be used for a new inverse design task.…”

Section: Introductionmentioning

confidence: 99%

Assessing Deep Generative Models in Chemical Composition Space

et al. 2022

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The computational discovery of novel materials has been one of the main motivations behind research in theoretical chemistry for several decades. Despite much effort, this is far from a solved problem, however. Among other reasons, this is due to the enormous space of possible structures and compositions that could potentially be of interest. In the case of inorganic materials, this is exacerbated by the combinatorics of the periodic table since even a single-crystal structure can in principle display millions of compositions. Consequently, there is a need for tools that enable a more guided exploration of the materials design space. Here, generative machine learning models have recently emerged as a promising technology. In this work, we assess the performance of a range of deep generative models based on reinforcement learning, variational autoencoders, and generative adversarial networks for the prototypical case of designing Elpasolite compositions with low formation energies. By relying on the fully enumerated space of 2 million main-group Elpasolites, the precision, coverage, and diversity of the generated materials are rigorously assessed. Additionally, a hyperparameter selection scheme for generative models in chemical composition space is developed.

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Generative models for inverse design of inorganic solid materials

Cited by 15 publications

References 68 publications

Recent advances and applications of deep learning methods in materials science

Recent advances and applications of deep learning methods in materials science

Inverse Design of Materials by Machine Learning

Assessing Deep Generative Models in Chemical Composition Space

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