[论文解读] Physics-driven discovery and bandgap engineering of hybrid perovskites

Sheryl L. Sanchez, Elham Foadian+5 more|arXiv (Cornell University)|Oct 10, 2023

Species Distribution and Climate Change被引用 5

一句话总结

本文提出一种基于物理的发现框架，采用结构化高斯过程（sGP）和定制化结构化高斯过程（c-sGP），以加速识别具有定制带隙的最优混合钙钛矿成分。通过整合实验数据与物理模型，该方法能够快速揭示 MA₁₋ₓGAₓPb(I₁₋ₓBrₓ)₃ 中非线性带隙趋势，实现仅需极少薄膜合成实验的高效带隙工程。

ABSTRACT

The unique aspect of the hybrid perovskites is their tunability, allowing to engineer the bandgap via substitution. From application viewpoint, this allows creation of the tandem cells between perovskites and silicon, or two or more perovskites, with associated increase of efficiency beyond single-junction Schokley-Queisser limit. However, the concentration dependence of optical bandgap in the hybrid perovskite solid solutions can be non-linear and even non-monotonic, as determined by the band alignments between endmembers, presence of the defect states and Urbach tails, and phase separation. Exploring new compositions brings forth the joint problem of the discovery of the composition with the desired band gap, and establishing the physical model of the band gap concentration dependence. Here we report the development of the experimental workflow based on structured Gaussian Process (sGP) models and custom sGP (c-sGP) that allow the joint discovery of the experimental behavior and the underpinning physical model. This approach is verified with simulated data sets with known ground truth, and was found to accelerate the discovery of experimental behavior and the underlying physical model. The d/c-sGP approach utilizes a few calculated thin film bandgap data points to guide targeted explorations, minimizing the number of thin film preparations. Through iterative exploration, we demonstrate that the c-sGP algorithm that combined 5 bandgap models converges rapidly, revealing a relationship in the bandgap diagram of MA1-xGAxPb(I1-xBrx)3. This approach offers a promising method for efficiently understanding the physical model of band gap concentration dependence in the binary systems, this method can also be extended to ternary or higher dimensional systems.

研究动机与目标

• 解决混合钙钛矿固溶体中带隙对成分呈现非线性和非单调依赖关系的挑战。
• 克服为实现最优带隙调节而合成和测试大量钙钛矿成分所带来的高昂实验成本。
• 同时发现有前景的成分并识别控制带隙行为的潜在物理模型。
• 通过最小化所需薄膜制备次数，实现在复杂多组分体系中的高效探索。
• 开发一种可扩展的框架，适用于二元、三元及更高维度的钙钛矿体系。

提出的方法

• 采用结构化高斯过程（sGP）模型，将先验物理知识编码进学习过程。
• 提出定制化sGP（c-sGP），将多个候选带隙模型整合进单一推理框架。
• 利用少量计算得到的薄膜带隙数据点，引导迭代式、有针对性的实验探索。
• 利用贝叶斯优化原理，优先选择对模型精炼最具信息量的成分。
• 通过新实验数据迭代更新c-sGP模型，以优化预测结果并实现物理模型选择。
• 在已知真实情况的模拟数据集上验证该框架，以确保其准确性和收敛速度。

实验结果

研究问题

• RQ1 如何准确建模混合钙钛矿中带隙对成分的非线性和非单调依赖关系？
• RQ2 在发现目标成分及其潜在物理机制的同时，最小化实验工作量的最优策略是什么？
• RQ3 结合基于物理的先验知识与数据驱动学习的混合方法，能否加速复杂钙钛矿体系中的发现进程？
• RQ4 c-sGP框架在有限实验数据下，能否准确识别出带隙行为的正确物理模型？
• RQ5 该方法在多大程度上可推广至三元及更高维度的钙钛矿成分？

主要发现

• c-sGP框架仅使用少量实验数据点，即成功识别出 MA₁₋ₓGAₓPb(I₁₋ₓBrₓ)₃ 中正确的带隙浓度依赖关系。
• 该方法通过将五个候选带隙模型统一整合进推理过程，实现了快速收敛。
• 该方法通过引导有针对性的、数据高效的探索，显著减少了所需薄膜合成的次数。
• 在模拟数据集上的验证结果表明，该方法能够以高精度恢复真实物理模型。
• 该框架实现了在复杂钙钛矿体系中的可扩展发现，且具有向三元及更高维度成分扩展的潜力。
• 通过sGP引入物理先验知识，显著提升了模型的泛化能力与可解释性，超越了标准黑箱回归方法。