Guidarelli Mattioli Francesco, Sciortino Francesco, Russo John
Sapienza University of Rome, Piazzale Aldo Moro 2, 00185 Rome, Italy.
J Chem Phys. 2023 Mar 14;158(10):104501. doi: 10.1063/5.0139245.
We present a neural network (NN) potential based on a new set of atomic fingerprints built upon two- and three-body contributions that probe distances and local orientational order, respectively. Compared with the existing NN potentials, the atomic fingerprints depend on a small set of tunable parameters that are trained together with the NN weights. In addition to simplifying the selection of the atomic fingerprints, this strategy can also considerably increase the overall accuracy of the network representation. To tackle the simultaneous training of the atomic fingerprint parameters and NN weights, we adopt an annealing protocol that progressively cycles the learning rate, significantly improving the accuracy of the NN potential. We test the performance of the network potential against the mW model of water, which is a classical three-body potential that well captures the anomalies of the liquid phase. Trained on just three state points, the NN potential is able to reproduce the mW model in a very wide range of densities and temperatures, from negative pressures to several GPa, capturing the transition from an open random tetrahedral network to a dense interpenetrated network. The NN potential also reproduces very well properties for which it was not explicitly trained, such as dynamical properties and the structure of the stable crystalline phases of mW.
我们提出了一种基于一组新的原子指纹的神经网络（NN）势，该指纹基于分别探测距离和局部取向序的两体和三体贡献构建。与现有的NN势相比，原子指纹依赖于一小组与NN权重一起训练的可调参数。除了简化原子指纹的选择外，这种策略还可以显著提高网络表示的整体准确性。为了解决原子指纹参数和NN权重的同时训练问题，我们采用了一种退火协议，该协议逐步循环学习率，显著提高了NN势的准确性。我们针对水的mW模型测试了网络势的性能，mW模型是一种经典的三体势，能够很好地捕捉液相的异常现象。仅在三个状态点上进行训练，NN势就能在非常宽的密度和温度范围内（从负压到几吉帕）重现mW模型，捕捉从开放随机四面体网络到密集互穿网络的转变。NN势还能很好地重现其未明确训练的性质，如动力学性质和mW稳定晶相的结构。
