An evolutionary variational autoencoder for perovskite discovery

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 A2BB′X6 and AA′BB′X6 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 (doi.org/10.17172/NOMAD/2023.05.31-1) and are made openly available to interested users

Oxide Dispersion Strengthened Nickel Based Alloys via Spark Plasma Sintering

Oxide dispersion strengthened (ODS) nickel based alloys were developed via mechanical milling and spark plasma sintering (SPS) of Ni–20Cr powder with additional dispersion of 1.2 wt% Y2O3 powder. Furthermore, 5 wt% Al2O3 was added to Ni–20Cr–1.2Y2O3 to provide composite strengthening in the ODS alloy. The effects of milling times, sintering temperature, and sintering dwell time were investigated on both mechanical properties and microstructural evolution. A high number of annealing twins was observed in the sintered microstructure for all the milling times. However, longer milling time contributed to improved hardness and narrower twin width in the consolidated alloys. Higher sintering temperature led to higher fraction of recrystallized grains, improved density and hardness. Adding 1.2 wt% Y2O3 to Ni–20Cr matrix significantly reduced the grain size due to dispersion strengthening effect of Y2O3 particles in controlling the grain boundary mobility and recrystallization phenomena. The strengthening mechanisms at room temperature were quantified based on both experimental and analytical calculations with a good agreement. A high compression yield stress obtained at 800 °C for Ni–20Cr–1.2Y2O3–5Al2O3 alloy was attributed to a combined effect of dispersion and composite strengthening

Modelling and assessment of carbon fiber reinforced aluminum matrix composites and their laminate squeeze casting fabrication

The equibiaxial bend behavior of laminate carbon fiber fabric reinforced aluminum matrix composites is modelled and assessed. Analytical modelling and finite element analysis are comparatively investigated to study the mechanical properties, with particular focus on the elastic modulus and flexural strength. The investigation allows evaluating how far the experimental results deviate from idealized assumptions of the models, which provides insight into the composite quality and the effectiveness of the used laminate squeeze casting technique. Specifically, discrepancies shed light on the interlaminate and fiber-matrix interface bond as well as on the stability of the laminate layers during fabrication. The two model approaches are in good agreement with differences below 8%. Moreover, the models agree with experimental data in predicting an overall improvement in properties with increasing carbon fiber content up to 4.89 vol%. Overall, the composite samples outperform the model predictions, which indicates good interface bonding. However, microstructure investigations also indicate that the outperformance is partly caused by a shifting of the carbon fibers during squeeze casting closer to the later bend tensile loaded surface due to their lower density compared to aluminum. The result is higher load bearing capacity of the composites than estimated by the models that assume perfectly symmetrical composite structure. The experimental outperformance in ultimate flexural strength vanishes at higher carbon fiber contents. This is due to imperfect interlaminate and fiber-matrix interfaces where some defects such as pores, carbides and oxide particles tend to locate, leading to damage initiation and potentially interface failure

DataSheet1_An evolutionary variational autoencoder for perovskite discovery.PDF

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 A2BB′X6 and AA′BB′X6 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 (doi.org/10.17172/NOMAD/2023.05.31-1) and are made openly available to interested users.</p