Deep Learning Coordinate-Free Quantum Chemistry.
Deep Learning Coordinate-Free Quantum Chemistry.
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DOI：
10.1021/acs.jpca.1c04462
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发表时间：
2021-10-14
影响因子：
2.9
通讯作者：
Swamidass, S. Joshua
中科院分区：
文献类型：
--
作者：
Matlock, Matthew K.;Hoffman, Max;Le Dang, Na;Folmsbee, Dakota L.;Langkamp, Luke A.;Hutchison, Geoffrey R.;Kumar, Neeraj;Sarullo, Kathryn;Swamidass, S. Joshua
计算小分子和聚合物的量子化学性质可以为物理学家，化学家和生物学家在设计新材料，催化剂，生物探针和药物时提供有价值的见解。深度学习可以在密度泛函理论等常用方法所需的一小部分时间内准确计算量子化学性质。目前量子化学中的大多数深度学习方法开始都是从实验推导的分子结构或预先计算的原子坐标中获得几何信息。这些方法有许多有用的应用，但它们在时间和计算资源上可能是昂贵的。在这项研究中，我们证明了在没有几何信息的情况下，可以通过使用图形编码的深度学习在无坐标域中进行精确的量子化学计算。无坐标方法仅依赖于分子图，即原子和键的连接性，而不依赖于原子坐标或键距。我们还发现，图编码架构的选择大大影响这些方法的性能。这些图形编码架构的结构为探索量子力学中一个重要而突出的问题提供了机会：什么类型的量子化学性质可以由局部变量模型表示？我们发现Wave是一种局部变量模型，可以准确地计算量子化学性质，而图卷积架构需要全局变量。此外，局部变量Wave模型在具有大型相关系统的复杂分子上的性能优于全局变量图卷积模型。
Computing quantum chemical properties of small molecules and polymers can provide insights valuable into physicists, chemists, and biologists when designing new materials, catalysts, biological probes, and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of time required by commonly used methods such as density functional theory. Most current approaches to deep learning in quantum chemistry begin with geometric information from experimentally derived molecular structures or pre-calculated atom coordinates. These approaches have many useful applications, but they can be costly in time and computational resources. In this study, we demonstrate that accurate quantum chemical computations can be performed without geometric information by operating in the coordinate-free domain using deep learning on graph encodings. Coordinate-free methods rely only on molecular graphs, the connectivity of atoms and bonds, without atom coordinates or bond distances. We also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The structures of these graph-encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: what types of quantum chemical properties can be represented by local variable models? We find that Wave, a local variable model, accurately calculates the quantum chemical properties, while graph convolutional architectures require global variables. Furthermore, local variable Wave models outperform global variable graph convolution models on complex molecules with large, correlated systems.
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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