基于嵌套变分自编码器的新型外推复合算法
Proposing Novel Extrapolative Compounds by Nested Variational Autoencoders
摘要：
材料信息学（MI），它使用人工智能和数据分析技术，以提高材料开发的效率，正在吸引越来越多的工业界的兴趣。其主要应用之一是快速开发新的高性能化合物。最近，已经提出了几种深度生成模型来建议预期满足所需性能的候选化合物。然而，它们通常存在需要大量实验数据集进行训练以达到足够准确性的问题。在实际情况下，通常最多只能积累大约1000个实验数据。因此，作者提出了一种嵌套两个变分自编码器（VAE）的深度生成模型。外部VAE使用大规模公共数据学习化合物的结构特征，而内部VAE从小规模实验数据学习外部VAE的潜变量与性质之间的关系。为了生成超出训练数据范围的高性能化合物，作者还提出了一种损失函数，该函数放大了内部VAE的潜变量分量与材料性能之间的相关性。结果表明，该损失函数有助于提高产生高性能候选的概率。此外，作为与化学工业中的实际客户的验证测试的结果，证实了与常规方法相比，所提出的方法在将实验次数减少到$1/4$方面是有效的。
英文摘要：
Materials informatics (MI), which uses artificial intelligence and data analysis techniques to improve the efficiency of materials development, is attracting increasing interest from industry. One of its main applications is the rapid development of new high-performance compounds. Recently, several deep generative models have been proposed to suggest candidate compounds that are expected to satisfy the desired performance. However, they usually have the problem of requiring a large amount of experimental datasets for training to achieve sufficient accuracy. In actual cases, it is often possible to accumulate only about 1000 experimental data at most. Therefore, the authors proposed a deep generative model with nested two variational autoencoders (VAEs). The outer VAE learns the structural features of compounds using large-scale public data, while the inner VAE learns the relationship between the latent variables of the outer VAE and the properties from small-scale experimental data. To generate high performance compounds beyond the range of the training data, the authors also proposed a loss function that amplifies the correlation between a component of latent variables of the inner VAE and material properties. The results indicated that this loss function contributes to improve the probability of generating high-performance candidates. Furthermore, as a result of verification test with an actual customer in chemical industry, it was confirmed that the proposed method is effective in reducing the number of experiments to $1/4$ compared to a conventional method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
