A combination of quantum mechanics calculations with machine learning (ML)
techniques can lead to a paradigm shift in our ability to predict materials
properties from first principles. Here we show that on-the-fly training of an
interatomic potential described through moment tensors provides the same
accuracy as state-of-the-art {\it ab inito} molecular dynamics in predicting
high-temperature elastic properties of materials with two orders of magnitude
less computational effort. Using the technique, we investigate high-temperature
bcc phase of titanium and predict very weak, Elinvar, temperature dependence of
its elastic moduli, similar to the behavior of the so-called GUM Ti-based
alloys [T. Sato {\ it et al.}, Science {\bf 300}, 464 (2003)]. Given the fact
that GUM alloys have complex chemical compositions and operate at room
temperature, Elinvar properties of elemental bcc-Ti observed in the wide
temperature interval 1100--1700 K is unique.Comment: 15 pages, 4 figure
