Training Algorithm Matters for the Performance of Neural Network Potential: A Case Study of Adam and the Kalman Filter Optimizers

no code implementations • 8 Sep 2021 • Yunqi Shao, Florian M. Dietrich, Carl Nettelblad, Chao Zhang

Here we compare the performance of two popular training algorithms, the adaptive moment estimation algorithm (Adam) and the Extended Kalman Filter algorithm (EKF), using the Behler-Parrinello neural network (BPNN) and two publicly accessible datasets of liquid water [Proc.

PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials

2 code implementations • 8 Oct 2019 • Yunqi Shao, Matti Hellström, Pavlin D. Mitev, Lisanne Knijff, Chao Zhang

Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physico-chemical properties of molecules and materials.

Computational Physics Disordered Systems and Neural Networks Chemical Physics