Pilania, Ghanshyam — Page 3 | Engineering Papers

DOE OSTI2019

KBaTeBiO 6 : A Lead-Free, Inorganic Double-Perovskite Semiconductor for Photovoltaic Applications

Solar cells made up of lead-halide perovskites have shown a remarkable increase in power conversion efficiency; however, they are plagued with instability issues that combined with the toxicity of lead have led to a search for new semiconductors made up of heavy and non-toxic metals such as bismuth. In this paper, we report on a new, inorganic, double perovskite oxide semiconductor: KBaTeBiO 6 , which has an experimental indirect band gap of 1.5 eV and shows excellent stability. We combined data analytics and high throughput density-functional-theory calculations to search through thousands of hypothetical inorganic double perovskite oxides containing bismuth and predict KBaTeBiO 6 as a promising photovoltaic material, which was subsequently synthesized using a wet-chemistry route. The calculated effective mass of the charge carriers and the spectroscopic limited maximum efficiency for KBaTeBiO 6 is comparable to the best performing Bi-halide double perovskites. We have fabricated photoelectrochemical solar cells with KBaTeBiO 6 nanoparticles as the active absorber layer, which show promising performance. Our work demonstrates the untapped potential of inorganic Bi-based double perovskite oxides— that offer the ability to change both the cation combination and their stoichiometry to achieve desired electronic properties—as exciting, benign and stable alternatives to lead-halide perovskites for various semiconducting applications.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia

Cost versus accuracy trade-offs are frequently encountered in materials science and engineering, where a particular property of interest can be measured/computed at different levels of accuracy or fidelity. Naturally, the most accurate measurement is also the most resource and time intensive, while the inexpensive quicker alternatives tend to be noisy. In such situations, a number of machine learning (ML) based multifidelity information fusion (MFIF) strategies can be employed to fuse information accessible from varying sources of fidelity and make predictions at the highest level of accuracy. In this work, we perform a comparative study on traditionally employed single-fidelity and three MFIF strategies, namely, (1) Δ-learning, (2) low-fidelity as a feature, and (3) multifidelity cokriging (CK) to compare their relative prediction accuracies and efficiencies for accelerated property predictions and high throughput chemical space explorations. We perform our analysis using a dopant formation energy data set for hafnia, which is a well-known high- k material and is being extensively studied for its promising ferroelectric, piezoelectric, and pyroelectric properties. We use a dopant formation energy data set of 42 dopants in hafnia—each studied in six different hafnia phases—computed at two levels of fidelities to find merits and limitations of these ML strategies. The findings of this work indicate that the MFIF based learning schemes outperform the traditional SF machine learning methods, such as Gaussian process regression and CK provides an accurate, inexpensive and flexible alternative to other MFIF strategies. While the results presented here are for the case study of hafnia, they are expected to be general. Furthermore, materials discovery problems that involve huge chemical space explorations can be studied efficiently (or even made feasible in some situations) through a combination of a large number of low-fidelity and a few high-fidelity measurements/computations, in conjunction with the CK approach.

36 MATERIALS SCIENCE↗