Symmetric Gradient Domain Machine Learning (sGDML)
This is a highly optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) force field model. It is able to faithfully reproduce detailed global potential energy surfaces (PES) for small- and medium-sized molecules from a limited number of user-provided reference calculations.
- S. Chmiela, A. Tkatchenko, H. E. Sauceda, I. Poltavsky, K. T. Schütt, K.-R. Müller Machine Learning of Accurate Energy-Conserving Molecular Force Fields, Sci. Adv. 3(5), e1603015, 2017.
- S. Chmiela, H. E. Sauceda, K.-R. Müller, A. Tkatchenko Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields, Nat. Commun. 9, 3887, 2018.
SchNetPack aims to provide accessible atomistic neural networks that can be trained and applied out-of-the-box, while still being extensible to custom atomistic architectures.
Bayesian optimization has been proven as an effective tool in accelerating scientific discovery. A standard implementation (e.g., scikit-learn), however, can accommodate only small training data. COMBO is highly scalable due to an efficient protocol that employs Thompson sampling, random feature maps, one-rank Cholesky update and automatic hyperparameter tuning.
T. Ueno, T.D. Rhone, Z. Hou, T. Mizoguchi and K. Tsuda, COMBO: An Efficient Bayesian Optimization Library for Materials Science, Materials Discovery, 2016, ISSN 2352-9245.