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 have
been trained from scratch because learning additional data typically
requires retraining 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 key for our development of a lifelong machine learning
potential (lMLP). Uncertainty quantification can be exploited to transgress
a fixed, pretrained 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.