The inverse design of new materials with desired properties is the ultimate goal of materials research, but demonstrating such a possibility for inorganic solid-state materials has been challenging, due partly to the invertibility of representation.Here, we demonstrate that the generative model using invertible image-based representation yields accurate reconstruction performance and can successfully rediscover experimentally known vanadium oxides. The model predicts several completely new compositions and polymorphs of vanadium oxides that are metastable and may be synthesizable.
Achieving the 2016 Paris agreement goal of limiting global warming below 2 °C and securing a sustainable energy future require materials innovations in renewable energy technologies. Machine learning has demonstrated many successes to accelerate the discovery renewable energy materials.
The
constant demand for novel functional materials calls for efficient
strategies to accelerate the materials discovery, and crystal structure
prediction is one of the most fundamental tasks along that direction.
In addressing this challenge, generative models can offer new opportunities
since they allow for the continuous navigation of chemical space via
latent spaces. In this work, we employ a crystal representation that
is inversion-free based on unit cell and fractional atomic coordinates
and build a generative adversarial network for crystal structures.
The proposed model is applied to generate the Mg–Mn–O
ternary materials with the theoretical evaluation of their photoanode
properties for high-throughput virtual screening (HTVS). The proposed
generative HTVS framework predicts 23 new crystal structures with
reasonable calculated stability and band gap. These findings suggest
that the generative model can be an effective way to explore hidden
portions of the chemical space, an area that is usually unreachable
when conventional substitution-based discovery is employed.
The binding site and energy is an invaluable descriptor in high-throughput screening of catalysts, as it is accessible and correlates with the activity and selectivity. Recently, comprehensive binding energy prediction machine-learning models have been demonstrated and promise to accelerate the catalyst screening. Here, we present a simple and versatile representation, applicable to any deeplearning models, to further accelerate such process. Our approach involves labeling the binding site atoms of the unrelaxed bare surface geometry; hence, for the model application, density functional theory calculations can be completely removed if the optimized bulk structure is available as is the case when using the Materials Project database. In addition, we present ensemble learning, where a set of predictions is used together to form a predictive distribution that reduces the model bias. We apply the labeled site approach and ensemble to crystal graph convolutional neural network and the ∼40 000 data set of alloy catalysts for CO 2 reduction. The proposed model applied to the data set of unrelaxed structures shows 0.116 and 0.085 eV mean absolute error, respectively, for CO and H binding energy, better than the best method (0.13 and 0.13 eV) in the literature that requires costly geometry relaxations. The analysis of the model parameters demonstrates that the model can effectively learn the chemical information related to the binding site.
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