固体材料频率相关光谱的预测：一种多输出与多保真度机器学习方法
Prediction of Frequency-Dependent Optical Spectrum for Solid Materials: A Multi-Output & Multi-Fidelity Machine Learning Approach
AI总结：
该研究提出基于深度图神经网络的多输出多保真度机器学习方法，可直接从晶体结构预测宽频段复值介电函数，结合迁移学习等策略解决高保真数据稀缺问题，为光电子等领域的材料快速筛选提供工具。
摘要：
频率相关光谱在从材料表征到光电子学和能量收集的广泛应用中至关重要。基于密度泛函理论（DFT）数据训练的数据驱动替代模型，在保留其化学精度的同时，有效缓解了DFT的可扩展性限制，加快了材料发现的速度。然而，当前主流的机器学习（ML）研究通常聚焦于带隙等标量性质，忽略了光谱的复杂性。在本研究中，我们采用深度图神经网络（GNNs），直接从晶体结构预测红外、可见光和紫外光谱范围内的频率相关复值介电函数。我们探索了多种用于介电函数多输出光谱表示的架构，并利用迁移学习、保真度嵌入等多种多保真度学习策略，解决高保真DFT数据稀缺带来的挑战。此外，我们对关键的太阳能电池吸收效率指标进行了建模，结果表明，通过在频率相关吸收系数的学习中引入学习偏置来整合这些参数，能够提升这些参数的学习效果。本研究表明，利用多输出和多保真度机器学习技术，可实现从晶体结构准确预测光谱，为在宽频谱范围内快速筛选光电子学、光学传感和太阳能应用的材料提供了一种通用工具。
英文摘要：
The frequency-dependent optical spectrum is pivotal for a broad range of applications, from material characterization to optoelectronics and energy harvesting. Data-driven surrogate models, trained on density functional theory (DFT) data, have effectively alleviated the scalability limitations of DFT while preserving its chemical accuracy, expediting material discovery. However, prevailing machine learning (ML) efforts often focus on scalar properties such as the band gap, overlooking the complexities of optical spectra. In this work, we employ deep graph neural networks (GNNs) to predict the frequency-dependent complex-valued dielectric function across the infrared, visible, and ultraviolet spectra directly from crystal structures. We explore multiple architectures for multi-output spectral representation of the dielectric function and utilize various multi-fidelity learning strategies, such as transfer learning and fidelity embedding, to address the challenges associated with the scarcity of high-fidelity DFT data. Additionally, we model key solar cell absorption efficiency metrics, demonstrating that learning these parameters is enhanced when integrated through a learning bias within the learning of the frequency-dependent absorption coefficient. This study demonstrates that leveraging multi-output and multi-fidelity ML techniques enables accurate predictions of optical spectra from crystal structures, providing a versatile tool for rapidly screening materials for optoelectronics, optical sensing, and solar energy applications across an extensive frequency spectrum.
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