An evolutionary variational autoencoder for perovskite discovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><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> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><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> 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it