A twin convolutional neural network is proposed to predict the pressure and temperature-dependent sorption of gases, vapors, and supercritical fluids in amorphous polymers, using spatial electron density distribution. These distributions are obtained as 3D tensors (images) from DFT calculations. The dataset, compiled from over 250 literature sources, comprises nearly 15 000 experimental measurements of 79 gases’ uptakes (0.01–50 wt%) in 102 different polymers. These measurements, spanning pressures from 1 × 10⁻³ to 7 × 10² bar and temperatures from 233 to 508 K, include nearly 500 solvent–polymer systems, ranging from low-pressure sorption in membrane glassy polymers to high-pressure solubility of supercritical fluids in molten polymers. The irreducible mean absolute percentage error (MAPE) is estimated to be around 20%, with a brief discussion on the sources of data variability. In 150 epochs, the model achieved a 32% MAPE on a test set of 1600 measurements concerning 22 polymers not previously encountered by the model.

中文翻译：

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使用卷积神经网络根据电子密度预测气体、蒸汽和超临界流体在无定形聚合物中的溶解度

提出了一种双卷积神经网络，利用空间电子密度分布来预测无定形聚合物中气体、蒸汽和超临界流体的压力和温度依赖性吸附。这些分布是通过 DFT 计算以 3D 张量（图像）形式获得的。该数据集由 250 多个文献来源汇编而成，包含 102 种不同聚合物中 79 种气体吸收量 (0.01–50 wt%) 的近 15,000 项实验测量结果。这些测量的压力范围为 1 × 10 ^-3至 7 × 10 ² bar，温度范围为 233 至 508 K，包括近 500 个溶剂-聚合物系统，范围从膜玻璃状聚合物的低压吸附到超临界的高压溶解度。熔融聚合物中的流体。不可约平均绝对百分比误差 (MAPE) 估计约为 20%，并简要讨论了数据变异性的来源。在 150 个 epoch 中，该模型在 1600 次测量的测试集上实现了 32% 的 MAPE，这些测量涉及该模型以前未遇到的 22 种聚合物。