Generalized neural-network representation of high-dimensional potential-energy surfaces
Generalized neural-network representation of high-dimensional potential-energy surfaces
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DOI：
10.1103/physrevlett.98.146401
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发表时间：
2007-04-06
影响因子：
8.6
通讯作者：
Parrinello, Michele
中科院分区：
文献类型：
--
作者：
Behler, Joerg;Parrinello, Michele
对化学过程的准确描述往往需要使用诸如密度泛函理论（DFT）这类计算要求较高的方法，这使得对大型体系进行长时间模拟变得不可行。在这篇快报中，我们介绍了一种新型的DFT势能面神经网络表示方法，它能给出任意大小体系中所有原子位置的能量和力，并且比DFT快几个数量级。该方法的高精度在块状硅上得到了验证，并与经验势和DFT进行了比较。这种方法具有通用性，可应用于所有类型的周期性和非周期性体系。
The accurate description of chemical processes often requires the use of computationally demanding methods like density-functional theory (DFT), making long simulations of large systems unfeasible. In this Letter we introduce a new kind of neural-network representation of DFT potential-energy surfaces, which provides the energy and forces as a function of all atomic positions in systems of arbitrary size and is several orders of magnitude faster than DFT. The high accuracy of the method is demonstrated for bulk silicon and compared with empirical potentials and DFT. The method is general and can be applied to all types of periodic and nonperiodic systems.