ABSTRACT

The functionality of the materials used for energy applications is critically determined by the physical properties of small active regions such as dopants, dislocations, interfaces, grain boundaries, etc. The capability to manipulate and utilize the inevitable disorder in materials, whether due to the finite-dimensional defects (such as vacancies, dopants, grain boundaries) or due to the complete atomic randomness (as in amorphous materials), can bring innovation in designing energy materials. With the increase in computational material science capabilities, it is now possible to understand the complexity present in materials due to various degrees of disorder resulting in pathways required for optimizing their efficiencies. This article provides a critical overview of such computational advancements specifically for designing realistic materials with various types of disorders for sustainable energy applications such as catalysts and electrochemical devices. The ultimate goal is to gain a thorough knowledge of the traditional approaches (implemented via tools such as density functional theory, and molecular dynamics) as well as modern approaches such as machine learning that exist for modeling the disorder present in materials, thereby identify the future opportunities for energy materials design and discovery.

摘要

用于能源应用的材料的功能性主要取决于小的有源区的物理性质，例如掺杂剂，位错，界面，晶界等。无论是由于有限的有限，还是有能力操纵和利用材料中不可避免的无序性尺寸缺陷（例如空位，掺杂剂，晶界）或由于完全的原子随机性（例如在非晶材料中），可以带来能量材料设计方面的创新。随着计算材料科学能力的提高，现在可以理解由于各种程度的无序性而导致的材料复杂性，从而导致优化其效率所需的途径。本文提供了此类计算进展的重要概述，专门用于设计具有各种类型紊乱的现实材料以用于可持续能源应用，例如催化剂和电化学装置。最终目标是全面了解传统方法（通过诸如密度泛函理论和分子动力学等工具实施）以及现代方法（例如机器学习），这些方法可以对材料中存在的疾病进行建模，从而确定未来能源材料设计和发现的机会。