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A combination of quantum mechanics calculations with machine learning (ML)techniques can lead to a paradigm shift in our ability to predict materialsproperties from first principles. Here we show that on-the-fly training of aninteratomic potential described through moment tensors provides the sameaccuracy as state-of-the-art {\it ab inito} molecular dynamics in predictinghigh-temperature elastic properties of materials with two orders of magnitudeless computational effort. Using the technique, we investigate high-temperaturebcc phase of titanium and predict very weak, Elinvar, temperature dependence ofits elastic moduli, similar to the behavior of the so-called GUM Ti-basedalloys [T. Sato {\ it et al.}, Science {\bf 300}, 464 (2003)]. Given the factthat GUM alloys have complex chemical compositions and operate at roomtemperature, Elinvar properties of elemental bcc-Ti observed in the widetemperature interval 1100--1700 K is unique.

Elinvar effect in $\beta-$Ti simulated by on-the-fly trained moment tensor potential