非平衡条件下物相选择的基本物理问题研究

Increasing technological demand for materials with new or enhanced functionality calls for expanding the set of all accessible structures and chemistries. Non-equilibrium synthesis methods such as rapid quenching and thin-film/cluster sputtering or deposition have become more and more widely used experimental techniques in development of novel materials. Many metastable structures have been produced by such non-equilibrium synthesis methods. The formation of such metastable structures may be triggered/selected by varying the initial processing techniques, and would also be complicated by their transient nature, small grain sizes, and the fact that they often contain disordered defects. Machine learning and material informatics approaches are usually based on structures from the crystal database. Existing databases lack key metastable structures as well as kinetic information in spite of their important roles in dictating the phase selection and transformation pathways during the non-equilibrium synthesis. Therefore, it is important for theoretical/computational approaches to go beyond the zero-temperature, ground-state structural predictions, and extend the studies to model the phase selection and phase formation under realistic experimental synthesis conditions. Knowledge about the stable and metastable phases at finite temperatures and phase selection/formation at various non-equilibrium synthesis conditions (e.g., cooling rate) from fast computational studies are very valuable for aiding experiments in the control of the phase transformation pathways to produce new materials with desirable properties. However, determining the structures at finite temperature and the phase selection/formation pathways as the function of chemical compositions and synthesis procedures remain a notable challenge for theory and computation. Control of the phase selections and formation under far-from equilibrium conditions is much less explored both computationally and experimentally. Herein lies the fundamental motivation for the current investigation, in which we target the rich domain of metallic liquids that exhibit a dynamical arrest, permitting access to highly undercooled regimes where multiphase crystallization pathways become accessible. We propose to extend the zero-temperature structure prediction by an adaptive generic algorithm (AGA) to a more comprehensive computational approach that can combine the structure and kinetic determination at finite temperatures with materials-informatics/machine-learning to elucidate the relationship between high temperature structures and phase selection pathways.

现代科技的发展对材料的性能提出了更多高标准的要求。非平衡合成方法如快速淬火、薄膜溅射沉积等已经成为被广泛使用的实验技术手段来制备非平衡材料。这些亚稳材料的形成可以通过改变初始处理方式来触发或选择，并且由于它们的瞬态性质，小晶粒尺寸以及它们通常包含无序缺陷的事实而变得复杂。非平衡条件下的相选择在理论计算和实验上都很少被探索，现有的基于机器学习和材料信息学方法的材料计算设计缺乏关键的亚稳相结构及动力学信息。本项目拟在前期工作的基础上，以典型的金属过冷液体为研究体系，通过第一性原理和分子动力学方法研究组分、冷却速度等对液态和玻璃态局域原子结构和动力学行为的影响，并结合新近发展的自适应基因算法和原子团簇校正方法系统研究激冷过程中亚稳相的形成，深入分析其结构与形态，阐明亚稳相形成的结构条件、动力学条件和热力学机理，从而为实现通过控制相变转化路径来制备具有特殊性能的新材料提供理论依据。

现代科技的发展对材料的性能提出了更多高标准的要求。非平衡合成方法如快速淬火、薄膜溅射沉积等已经成为被广泛使用的实验技术手段来制备非平衡材料。这些亚稳材料的形成可以通过改变初始处理方式来触发或选择，并且由于它们的瞬态性质，小晶粒尺寸以及它们通常包含无序缺陷的事实而变得复杂。非平衡条件下的相选择在理论计算和实验上都很少被探索，现有的基于机器学习和材料信息学方法的材料计算设计缺乏关键的亚稳相结构及动力学信息。本项目在申请人前期在金属液体和玻璃态研究工作的基础上，以典型的金属过冷液体为研究体系，通过第一性原理和分子动力学方法研究组分、冷却速度等对液态和玻璃态局域原子结构和动力学行为的影响，并结合课题组发展的自适应基因算法和原子团簇校正方法系统研究激冷过程中亚稳相的形成，深入分析其结构与形态，阐明亚稳相形成的结构条件、动力学条件和热力学机理，从而为实现通过控制相变转化路径来制备具有特殊性能的新材料提供理论依据。在该项目的资助下，我们揭示了金属过冷液态和玻璃态的中程序与玻璃形成能力的关联性，深入研究了非晶体系中soft particles的动力学行为和结构无序的特性，提出了新的关于soft particles的描述参数，该描述参数有望在后续和机器学习方法结合来研究非平衡过程中相选择的机理。此外，我们和实验组合作，采用相分离的方法成功实现了单质金属玻璃钯（Pd）纳米颗粒的合成，并研究了其对氢的吸附储存能力，发现非晶Pd 纳米粒子的体积变化相比晶体材料要小很多，从而使得非晶Pd 在氢的储存和分离有潜在的应用前景。本项目共发表论文22篇，培养了2名博士生和2硕士。