AUTHOR=Chenebuah Ericsson Tetteh , Nganbe Michel , Tchagang Alain Beaudelaire 

TITLE=An evolutionary variational autoencoder for perovskite discovery

JOURNAL=Frontiers in Materials

VOLUME=10

YEAR=2023

URL=https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2023.1233961

DOI=10.3389/fmats.2023.1233961

ISSN=2296-8016

ABSTRACT=<p>Machine learning (ML) techniques emerged as viable means for novel materials discovery and target property determination. At the vanguard of discoverable energy materials are perovskite crystalline materials, which are known for their robust design space and multifunctionality. Previous efforts for simulating the discovery of novel perovskites via ML have often been limited to straightforward tabular-dataset models and compositional phase-field representations. Therefore, the present study makes a contribution in expanding ML capability by demonstrating the efficacy of a new deep evolutionary learning framework for discovering stable and functional inorganic materials that adopts the complex <inline-formula id="inf1"><mml:math id="m1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi>B</mml:mi><mml:msup><mml:mi>B</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:msub><mml:mi>X</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and <inline-formula id="inf2"><mml:math id="m2" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>A</mml:mi><mml:msup><mml:mi>A</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:mi>B</mml:mi><mml:msup><mml:mi>B</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:msub><mml:mi>X</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> double perovskite stoichiometries. The model design is called the Evolutionary Variational Autoencoder for Perovskite Discovery (EVAPD), which is comprised of a semi-supervised variational autoencoder (SS-VAE), an evolutionary-based genetic algorithm, and a one-to-one similarity analytical model. The genetic algorithm performs adaptive metaheuristic search operations for finding the most theoretically stable candidates emerging from a target-learnable latent space of the generative SS-VAE model. The integrated similarity analytical model assesses the deviation in three-dimensional atomic coordination between newly generated perovskites and proven standards, and as such, recommends the most promising and experimentally feasible candidates. Using Density Functional Theory (DFT), the novel perovskites are subjected to thorough variable-cell optimization and property determination. The current study presents 137 new perovskite materials generated by the proposed EVAPD model and identifies potential candidates for photovoltaic and optoelectronic applications. The new materials data are archived at NOMAD repository (<ext-link ext-link-type="uri" xlink:href="http://doi.org/10.17172/NOMAD/2023.05.31-1" xmlns:xlink="http://www.w3.org/1999/xlink">doi.org/10.17172/NOMAD/2023.05.31-1</ext-link>) and are made openly available to interested users.</p>