使用虚拟现实中生成的基于片段的数据训练原子神经网络
Training atomic neural networks using fragment-based data generated in virtual reality
摘要：
理解和设计分子结构的能力依赖于对能量作为原子坐标的函数的准确描述。在这里，我们概述了一个新的范式，用于获得能量函数的多维分子系统，其中涉及到生成数据的低维系统在虚拟现实（VR），然后有效地训练原子神经网络（ANN）。这产生了高质量的数据，为特定领域内的超维空间的兴趣，表征分子的势能面（PES）。我们展示了这种方法的实用性，通过收集数据VR训练人工神经网络的化学反应，涉及少于8个重原子。这种策略使我们能够预测更高维系统的能量，例如包含近100个原子的系统。在仅包含15 K几何结构的数据集上训练，该方法产生约2 kcal/mol的平均绝对误差。这是第一次使用如此小的数据集生成用于大活性自由基的ANN-PES。我们的研究结果表明，VR可以智能地管理高质量的数据，从而加速学习过程。
英文摘要：
The ability to understand and engineer molecular structures relies on having accurate descriptions of the energy as a function of atomic coordinates. Here we outline a new paradigm for deriving energy functions of hyperdimensional molecular systems, which involves generating data for low-dimensional systems in virtual reality (VR) to then efficiently train atomic neural networks (ANNs). This generates high quality data for specific areas of interest within the hyperdimensional space that characterizes a molecule's potential energy surface (PES). We demonstrate the utility of this approach by gathering data within VR to train ANNs on chemical reactions involving fewer than 8 heavy atoms. This strategy enables us to predict the energies of much higher-dimensional systems, e.g. containing nearly 100 atoms. Training on datasets containing only 15K geometries, this approach generates mean absolute errors around 2 kcal/mol. This represents one of the first times that an ANN-PES for a large reactive radical has been generated using such a small dataset. Our results suggest VR enables the intelligent curation of high-quality data, which accelerates the learning process.
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。
