论文标题
第四代高维神经网络电势具有精确的静电，包括非局部电荷转移
A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer
论文作者
论文摘要
机器学习潜力已成为许多领域原子模拟的重要工具，从化学通过分子生物学到材料科学。但是，大多数已建立的方法都依赖于本地属性，因此无法考虑到电子结构的全局变化，这是由于远程电荷传输或不同的电荷状态所致。在这项工作中，我们通过引入第四代高维神经网络电位来克服这一局限性，该高维神经网络电位结合了采用环境依赖性原子电位和精确原子能的电荷平衡方案。该方法能够正确地描述任意系统中的全球电荷分布，它产生了很大的能量，并大大扩展了现代机器学习潜力的适用性。对于一系列代表化学和材料科学中典型场景的系统，这些系统被当前方法错误地描述了，而第四代神经网络电位与电子结构计算非常吻合。
Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the established methods, however, rely on local properties and are thus unable to take global changes in the electronic structure into account, which result from long-range charge transfer or different charge states. In this work we overcome this limitation by introducing a fourth-generation high-dimensional neural network potential that combines a charge equilibration scheme employing environment-dependent atomic electronegativities with accurate atomic energies. The method, which is able to correctly describe global charge distributions in arbitrary systems, yields much improved energies and substantially extends the applicability of modern machine learning potentials. This is demonstrated for a series of systems representing typical scenarios in chemistry and materials science that are incorrectly described by current methods, while the fourth-generation neural network potential is in excellent agreement with electronic structure calculations.
