论文标题
$β-$ ti的elinvar效应，由训练有素的量张量张力模拟
Elinvar effect in $β-$Ti simulated by on-the-fly trained moment tensor potential
论文作者
论文摘要
量子力学计算与机器学习（ML）技术的组合可以导致我们从第一原理预测材料特性的能力。在这里，我们表明，通过矩张量描述的原子间电位的直通训练提供了与最先进的{\ it ab Inito}分子动力学相同的准确性，以预测材料的高温度弹性特性，其计算效果较小。使用该技术，我们研究了钛的高温BCC相，并预测其弹性模量非常弱，Elinvar，温度依赖性，类似于所谓的基于Gum Ti的合金的行为[T. T. sato {\ it等}，科学{\ bf 300}，464（2003）]。鉴于口香糖具有复杂的化学成分并在室温下运行，因此在宽温度间隔1100--1700 K中观察到的元素BCC-TI的Elinvar特性是独一无二的。
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.
