  Machine learning potentials (MLPs) trained on accurate quantum chemical data
can retain the high accuracy, while inflicting little computational demands. On
the downside, they need to be trained for each individual system. In recent
years, a vast number of MLPs has been trained from scratch because learning
additional data typically requires to train again on all data to not forget
previously acquired knowledge. Additionally, most common structural descriptors
of MLPs cannot represent efficiently a large number of different chemical
elements. In this work, we tackle these problems by introducing
element-embracing atom-centered symmetry functions (eeACSFs) which combine
structural properties and element information from the periodic table. These
eeACSFs are a key for our development of a lifelong machine learning potential
(lMLP). Uncertainty quantification can be exploited to transgress a fixed,
pre-trained MLP to arrive at a continuously adapting lMLP, because a predefined
level of accuracy can be ensured. To extend the applicability of an lMLP to new
systems, we apply continual learning strategies to enable autonomous and
on-the-fly training on a continuous stream of new data. For the training of
deep neural networks, we propose the continual resilient (CoRe) optimizer and
incremental learning strategies relying on rehearsal of data, regularization of
parameters, and the architecture of the model.
