Design rules for defect-tolerant photovoltaics

对可再生能源的需求不断增加，英国政府的目标是在2050年之前将碳排放量减少80%。太阳能发电是最有前途的可再生能源技术，由于太阳能提供的巨大能量。大多数商业上可用的太阳能电池板-基于晶体硅-相对有效，但制造成本昂贵。因此，对于同样有效但具有较低材料和加工成本的替代光伏吸收剂存在显著的兴趣。寻找新型太阳能吸收器的一种方法是使用量子力学计算。事实上，许多决定光伏性能的特性--比如可见光吸收的强度--都可以相对容易地计算出来。许多研究已经利用了这一点，通过寻找新的太阳能吸收剂的基础上，完全电子和光学性能。不幸的是，这种方法通常会产生许多误报，其中材料被预测为有效，但在实践中表现不佳。当不考虑晶体缺陷的行为时，这些缺点经常产生。这些缺陷，称为点缺陷，通过限制最大可获得的电压和电流，在光伏器件中起着至关重要的作用。然而，预测缺陷对光伏性能的影响迄今为止被证明是棘手的，并且仅在少数系统中实现。通过了解控制缺陷形成的基本因素，我们可以设计出抵抗其影响的新材料。其中缺陷不显著影响光伏性能的材料被称为“缺陷容忍”。由于难以计算缺陷的影响，导致缺陷容限的结构和化学性质尚未得到很好的理解。然而，计算工作流程软件的最新进展意味着现在可以自动计算复杂的属性。这个项目将开发一个自动计算工作流程，以确定材料是否是缺陷容忍。通过将工作流程应用于数百种材料并分析趋势，我们可以提取导致缺陷容限的结构-性能关系。我们还可以使用这些信息来开发机器学习模型，用于预测缺陷的影响，而无需执行任何计算。由于许多其他应用也依赖于点缺陷的形成-例如热电和量子计算机-我们的计算数据将引起科学界的广泛兴趣。因此，我们将使计算结果作为一个在线数据库的缺陷properties.一个先进的理解的因素，控制缺陷容限将使下一代光伏材料的合理设计。降低成本的光伏发电将促进太阳能的采用，并为清洁能源革命铺平道路。

There is increasing demand for renewable energy, as highlighted by the UK government's aim of reducing carbon emissions by 80% before 2050. Solar power is the most promising renewable technology due to the enormous amount of energy the sun can provide. Most commercially available solar panels - based on crystalline silicon - are relatively efficient but expensive to manufacture. Accordingly, there is significant interest in alternative photovoltaic absorbers that are just as efficient but have lower materials and processing costs. One route to finding novel solar absorbers is using quantum-mechanical computations. Indeed, many of the properties that determine photovoltaic performance - such as the strength of visible light absorption - can be calculated relatively easily. Many studies have taken advantage of this by searching for new solar absorbers based solely on electronic and optical properties. Unfortunately, this approach generally gives rise to many false positives where materials are predicted as efficient but perform poorly in practice. These shortcomings often result when the behaviour of crystal imperfections is not considered. These imperfections, called point-defects, play a crucial role in photovoltaic devices by limiting the maximum obtainable voltage and current. However, predicting the effects of defects on photovoltaic performance has so far proved tricky and has only been achieved for a select few systems.By gaining an understanding of the fundamental factors that control defect formation we can design new materials that are resistant to their effects. Materials in which defects do not significantly affect photovoltaic performance are called "defect tolerant". Due to the difficulty of calculating the impact of defects, the structural and chemical properties that give rise to defect tolerance are not well understood. However, recent advances in computational workflow software means it is now possible to automate the calculation of complex properties. This project will develop an automatic computational workflow to determine whether a material is defect tolerant. By applying the workflow to many hundreds of materials and analysing the trends, we can extract the structure-property relationships that give rise to defect tolerance. We can also use this information to develop machine learning models for predicting the impact of defects without needing to perform any calculations. As many other applications also rely on the formation of point-defects - such as thermoelectrics and quantum computers - our calculated data will be of broad interest to the scientific community. We will therefore make the results available as an online database of computed defect properties.An advanced understanding of the factors that govern defect tolerance will enable the rational design of the next generation of photovoltaic materials. Photovoltaics with reduced cost will facilitate the adoption of solar power and pave the way for a revolution in clean energy.