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Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases

机译:使用机器学习和材料属性数据库筛选无机卤化物植物的散装和表面DFT调查

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In the recent past, there has been proliferation in high-throughput density functional theory and data-driven explorations of materials motivated by a need to reduce physical testing and costly computations for materials discovery. This has, in conjunction with the development of open-access materials property databases, encouraged accelerated and more streamlined discovery and screening of technologically relevant materials. In this work, we report our results on the screening and DFT studies of one such class of materials, i.e. ABX(3) inorganic halide perovskites (A, B and X representing the monovalent, divalent and halide ions respectively) using a coupled machine-learning (ML) and density functional theory (DFT) approach. Utilizing the support vector machine algorithm, we predict the formability of 454 inorganic halide compounds in the perovskite phase. Compounds with a formation probability P >= 0.8 are further checked for thermodynamic stability in at least one of these three open materials databases - Materials Project (MP), Automatic FLOW for Materials Discovery (AFLOW) and Open Quantum Materials Database (OQMD). The shortlisted candidate perovskites are then considered for DFT computations. Taking input geometries from MP's structure predictor, the optimized lattice parameters and computed band gaps (BG) for all screened compounds are compared with predictions across all databases. Subsequently, detailed studies on low index surfaces are presented for two halide perovksites - RbSnCl3 and RbSnBr3 - having band-gaps in the favourable range for photovoltaics (PV). Different possible (100), (110) and (111) surface terminations are investigated for each of these compositions and the atomic relaxations, surface energies and electronic band structures are reported for each termination. To the best of our knowledge, no surface studies have been reported in the literature for any of the halide perovskites present in our database. These studies, therefore, are an important step towards gaining a fundamental understanding of the interfacial properties of perovskites, which can help facilitate further breakthroughs in the PV technology.
机译:最近一段时间以来,一直在通过必要减少对材料的发现物理测试和昂贵的计算激励材料的高通量密度泛函理论和数据驱动的探索扩散。这有,在开放获取的材料性能数据库的开发相结合,鼓励加速和更加简化的发现和技术相关的物料的筛分。在这项工作中,我们报道了我们的筛选结果和一种这样的一类材料的DFT研究,即,ABX(3)无机卤化物钙钛矿(A,B和X代表分别单价,二价和卤化物离子),使用耦合机器学习(ML)和密度泛函理论(DFT)的方法。利用支持向量机算法,我们预测的454种无机卤化物化合物的可成形性的钙钛矿相。材料项目(MP),自动流量为材料发现(AFLOW)和开放量子材料数据库(OQMD) - 与产生概率P> = 0.8在这些三个开放材料数据库中的至少一个被进一步检查热力学稳定性的化合物。然后,入围候选人钙钛矿被认为是DFT运算。以输入几何从MP的结构预测,优化的晶格参数和计算的带隙(BG)的所有筛选的化合物与在所有数据库的预测比较。随后,低折射率表面上详细的研究提出了用于2个卤化物perovksites - RbSnCl3和RbSnBr3 - 具有在优选范围的带隙为光伏(PV)。不同的可能的(100),(110)和(111)面终端进行了研究对每个这些组合物和原子松弛,表面能和电子能带结构被报告每个终止。据我们所知,没有表面的研究已经在文献中报道对任何存在于我们的数据库中的卤化物钙钛矿。这些研究,因此,朝着获得钙钛矿的界面特性,它可以帮助促进光伏技术的进一步突破,从根本上了解的重要一步。

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    TCS Res Tata Res Dev &

    Design Ctr 54-B Hadapsar Ind Estate Pune 411013 Maharashtra India;

    TCS Res Tata Res Dev &

    Design Ctr 54-B Hadapsar Ind Estate Pune 411013 Maharashtra India;

    TCS Res Tata Res Dev &

    Design Ctr 54-B Hadapsar Ind Estate Pune 411013 Maharashtra India;

    TCS Res Tata Res Dev &

    Design Ctr 54-B Hadapsar Ind Estate Pune 411013 Maharashtra India;

    TCS Res Tata Res Dev &

    Design Ctr 54-B Hadapsar Ind Estate Pune 411013 Maharashtra India;

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  • 正文语种 eng
  • 中图分类 物理学;化学;
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