Quantum-chemical insights from deep tensor neural networks.
Quantum-chemical insights from deep tensor neural networks.
复制标题
DOI：
10.1038/ncomms13890
复制
发表时间：
2017-01-09
影响因子：
16.6
通讯作者：
Tkatchenko A
中科院分区：
文献类型：
--
作者：
Schütt KT;Arbabzadah F;Chmiela S;Müller KR;Tkatchenko A
从数据中学习导致了许多学科的范式转变，包括Web，文本和图像搜索，语音识别以及生物信息学。机器学习能否在理解量子多体系统方面实现类似的突破？在这里，我们开发了一种有效的深度学习方法，可以在空间和化学上解析分子系统的量子力学观测值。我们将多体哈密顿的概念与专门设计的深度张量神经网络统一起来，这导致了中等大小分子的组成和构型化学空间中的尺寸扩展和一致准确（1 kcal mol−1）预测。作为化学相关性的一个例子，该模型揭示了芳香环的稳定性分类。我们的模型在预测分子中的原子能和局部化学势、可靠的异构体能量和具有特殊电子结构的分子方面的进一步应用表明了机器学习在揭示复杂量子化学系统方面的潜力。机器学习是一种越来越流行的分析数据和进行预测的方法。在这里，作者开发了一个“深度学习”框架，用于定量预测和定性理解化学系统的量子力学观测值，超越了训练数据中包含的平凡属性。
Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems. Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning' framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical systems, beyond properties trivially contained in the training data.