Machine learning
Table of Contents


2023 - On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials

2023 - Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning

2023 - Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

2023 - Fast uncertainty estimates in deep learning interatomic potentials

2022 - Exploring chemical and conformational spaces by batch mode deep active learning

2020 - Methods for comparing uncertainty quantifications for material property predictions

Transfer learning

2021 - PIP delta learning from DFT to CCSD(T)

The Δ-machine learning is represented as a equation: $V_{low \rightarrow high} = V_{low} + \Delta V_{high-low}$. - $V_{low}$ is a fitted potential at low-level theory like DFT - $\Delta V_{high-low}$ is a the energy difference between high-level theory like couple cluster and the low-level theory

The goal is to fit a high-level potential with a small amount of training data. The direct fitting $V_{high}$ requires a lot of data and computational resources. It would save computational costs during the accumulation of training data if one trains on $V_{low}$ on many low-level data and learns $\Delta V_{high-low}$ by a few high-level data. The assumption here is that $\Delta V_{high-low}$ is less influenced by nuclear coordinations than $V_{low}$.