FCHL再访：更快、更准确的量子机器学习
FCHL revisited: faster and more accurate quantum machine learning
摘要：
我们介绍了FCHL 19表示的原子环境中的分子或凝聚相系统。基于FCHL 19的机器学习模型能够以毫秒级的化学准确度预测查询化合物的原子力和能量。FCHL 19是我们之前工作的修订版[Faber et al. 2018]，其中表示被离散化，并且使用Monte Carlo优化对单个特征进行了严格优化。结合包含元素筛选的高斯核函数，分别在训练数分钟和数小时后，在QM 7 b和QM 9数据集上达到能量学习的化学准确性。该模型还显示出良好的性能，在凝聚相中的非键相互作用的一组水簇的MAE结合能误差小于0.1千卡/摩尔/分子的训练后，3,200个样本。对于MD 17数据集上的强制学习，我们的优化模型同样显示了最先进的准确性，回归量基于高斯过程回归。当修改后的FCHL 19表示与操作符量子机器学习回归器相结合时，可以在每个原子仅几毫秒内预测力和能量。本文提出的模型是快速和轻量级的，足以用于一般的化学问题，以及分子动力学模拟。
英文摘要：
We introduce the FCHL19 representation for atomic environments in molecules or condensed-phase systems. Machine learning models based on FCHL19 are able to yield predictions of atomic forces and energies of query compounds with chemical accuracy on the scale of milliseconds. FCHL19 is a revision of our previous work [Faber et al. 2018] where the representation is discretized and the individual features are rigorously optimized using Monte Carlo optimization. Combined with a Gaussian kernel function that incorporates elemental screening, chemical accuracy is reached for energy learning on the QM7b and QM9 datasets after training for minutes and hours, respectively. The model also shows good performance for non-bonded interactions in the condensed phase for a set of water clusters with an MAE binding energy error of less than 0.1 kcal/mol/molecule after training on 3,200 samples. For force learning on the MD17 dataset, our optimized model similarly displays state-of-the-art accuracy with a regressor based on Gaussian process regression. When the revised FCHL19 representation is combined with the operator quantum machine learning regressor, forces and energies can be predicted in only a few milliseconds per atom. The model presented herein is fast and lightweight enough for use in general chemistry problems as well as molecular dynamics simulations.
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