npj:钙钛矿结构和电子性质—分层卷积神经网络机器学习
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实现商业上可行的太阳能电力对于确保长期经济增长和减轻气候变化的影响至关重要。金属卤化物钙钛矿(MHP),尤其MAPbI3 (MA=CH3NH3),是目前研究最多的太阳能电池材料,其功率转换效率(PCE)约为25.2%,超过了目前商业化的太阳能电池,如多晶硅(c-Si,21.3%)、碲化镉(CdTe,22.1%)和铜铟镓硒(CIGS,22.3%)。但是,与传统的太阳能电池材料相比,MHP的主要优点是它们易于大规模合成且成本相对较低。此外,其在可见光区域的吸收系数>3.0×104cm−1 、激子结合能低所导致的自由电子和空穴量子产率高、长的电子-空穴扩散长度、电子良性点和晶界缺陷。MHP是串联太阳能电池,能将宽带隙的“顶部电池”与窄带隙材料(如硅)耦合为“底部电池”。鉴于晶体硅具有1.1 eV的带隙,需要具有1.75 eV带隙的材料才能使两个结的电流匹配。当前的研究重点是探寻成本低廉、稳定且无铅的MHPs单个吸收器或串联太阳能电池最佳材料,但材料设计的化学空间仍然过于宽泛,需更有效的搜索方法来寻找不同带隙范围的钙钛矿结构。
使用易于计算的描述符准确估算带隙;
克服目标值分布不平衡的小型数据集问题,即目标值的某些范围部分可能样本太多,而其他部分可能样本太少或没有;
使用简单的ML方法,这些方法在计算上并不苛求,并且相对容易理解和控制。
Machine-learning structural and electronic properties of metal halide perovskites using a hierarchical convolutional neural network
Wissam A. Saidi, Waseem Shadid & Ivano E. Castelli
The development of statistical tools based on machine learning (ML) and deep networks is actively sought for materials design problems. While structure-property relationships can be accurately determined using quantum mechanical methods, these first-principles calculations are computationally demanding, limiting their use in screening a large set of candidate structures. Herein, we use convolutional neural networks to develop a predictive model for the electronic properties of metal halide perovskites (MHPs) that have a billions-range materials design space. We show that a well-designed hierarchical ML approach has a higher fidelity in predicting properties of the MHPs compared to straight-forward methods. In this architecture, each neural network element has a designated role in the estimation process from predicting complex features of the perovskites such as lattice constant and octahedral till angle to narrowing down possible ranges for the values of interest. Using the hierarchical ML scheme, the obtained root-mean-square errors for the lattice constants, octahedral angle and bandgap for the MHPs are 0.01 Å, 5°, and 0.02 eV, respectively. Our study underscores the importance of a careful network design and a hierarchical approach to alleviate issues associated with imbalanced dataset distributions, which is invariably common in materials datasets.
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