结合神经网络的多体展开
The Many-Body Expansion Combined with Neural Networks
摘要：
碎裂方法，如多体展开（MBE）是一种常见的策略，通过将能量划分为一个层次的显着贡献递减来模拟大型系统。对于从头分子束外延来说，化学精确度所需的碎片数量仍然非常昂贵，无法与单点能量以外的力场近似方法竞争。随着分子束外延技术的发展，从头计算势能面的经验模型也得到了改进，特别是基于神经网络的非线性模型能够快速准确地再现从头计算势能面。虽然NN速度很快，但它们有自己的维数灾难;它们必须在化学空间的代表性样本上进行训练。在本文中，我们研究的MBE和NN的协同作用，并探讨他们的互补性。分子束外延提供了一种系统的方法来处理任意大小的系统，并智能地对化学空间进行采样。神经网络的减少，超过$10^6$的MBE的计算开销的一个因素，并重现从头计算的准确性没有专门的力场。我们表明，他们是非常普遍的，提供了相当的准确性与截然不同的化学嵌入。为了评估这一点，我们测试了一种新的化学嵌入，它可以被反转来预测具有所需特性的分子。
英文摘要：
Fragmentation methods such as the many-body expansion (MBE) are a common strategy to model large systems by partitioning energies into a hierarchy of decreasingly significant contributions. The number of fragments required for chemical accuracy is still prohibitively expensive for ab-initio MBE to compete with force field approximations for applications beyond single-point energies. Alongside the MBE, empirical models of ab-initio potential energy surfaces have improved, especially non-linear models based on neural networks (NN) which can reproduce ab-initio potential energy surfaces rapidly and accurately. Although they are fast, NNs suffer from their own curse of dimensionality; they must be trained on a representative sample of chemical space. In this paper we examine the synergy of the MBE and NN's, and explore their complementarity. The MBE offers a systematic way to treat systems of arbitrary size and intelligently sample chemical space. NN's reduce, by a factor in excess of $10^6$ the computational overhead of the MBE and reproduce the accuracy of ab-initio calculations without specialized force fields. We show they are remarkably general, providing comparable accuracy with drastically different chemical embeddings. To assess this we test a new chemical embedding which can be inverted to predict molecules with desired properties.
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