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Crystal Diffusion Variational Autoencoder for Periodic Material Generation

ICLR 2022 · Probabilistic and scientific modeling › Molecules and proteins · approach: Machine-learned interatomic potentials · 105 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use CDVAE instead of FTCP for reconstructionCrystal reconstruction · Perov-5RMSE: 0.0259 -> 0.0156Helped
Use CDVAE instead of FTCP for reconstructionCrystal reconstruction · Carbon-24RMSE: 0.2563 -> 0.1251Helped
Use CDVAE instead of FTCP for reconstructionCrystal reconstruction · MP-20RMSE: 0.1593 -> 0.0356Helped
Use CDVAE instead of FTCP for reconstructionCrystal reconstruction · Perov-5match rate (%): 99.34 -> 97.52Hurt
Use CDVAE instead of FTCP for reconstructionCrystal reconstruction · Carbon-24match rate (%): 62.28 -> 55.22Hurt
Use CDVAE instead of FTCP for reconstructionCrystal reconstruction · MP-20match rate (%): 69.89 -> 45.43Hurt
Use CDVAE instead of the strongest reported baselineCrystal generation · Perov-5COV-R (%): 73.92 -> 99.45Helped
Use CDVAE instead of the strongest reported baselineCrystal generation · Carbon-24COV-R (%): 0.00 -> 99.80Helped
Use CDVAE instead of the strongest reported baselineCrystal generation · MP-20COV-R (%): 41.93 -> 99.15Helped
Use CDVAE instead of the strongest reported baselineCrystal generation · MP-20COV-P (%): 99.74 -> 99.49No clear effect
Use CDVAE instead of FTCPProperty optimization · MP-20SR5: 0.02 -> 0.78Helped
Use CDVAE instead of FTCPProperty optimization · MP-20SR10: 0.04 -> 0.86Helped

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