Using an Autoencoder for Dimensionality Reduction in Quantum Dynamics

机译：使用自动编码器减少量子动力学中的维数

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A key step in performing quantum dynamics for a chemical system is the reduction of dimensionality to allow a numerical treatment. Here, we introduce a machine learning approach for the (semi)autornatic construction of reactive coordinates. After generating a meaningful data set from trajectory calculations, we train an autoencoder to find a low-dimensional set of non-linear coordinates for use in molecular quantum dynamics. We compare the wave packet dynamics of proton transfer reactions in both linear and non-linear coordinate spaces and find significant improvement for physical properties like reaction timescales.

机译：对化学系统执行量子动力学的关键步骤是降低维数以允许进行数值处理。在这里，我们介绍一种用于反应坐标的（半）自动构造的机器学习方法。通过轨迹计算生成有意义的数据集后，我们训练自动编码器以找到用于分子量子动力学的非线性低坐标集。我们在线性和非线性坐标空间中比较了质子转移反应的波包动力学，发现物理性质如反应时间尺度有了显着改善。

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《International conference on artificial neural networks》|2019年|783-787|共5页
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Sebastian Reiter; Thomas Schnappinger; Regina de Vivie-Riedle;
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Quantum dynamics; Machine learning; Autoencoder; Dimensionality reduction;

机译：量子动力学;机器学习;自动编码器降维;

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Using an Autoencoder for Dimensionality Reduction in Quantum Dynamics

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