Physics-informed machine learning for inorganic scintillator discovery

Bright Innovations: Review of Next-Generation Advances in Scintillator Engineering

This review focuses on modern scintillators, the heart of ionizing radiation detection with applications in medical diagnostics, homeland security, research, and other areas. The conventional method to improve their characteristics, such as light output and timing properties, consists of improving in material composition and doping, etc., which are intrinsic to the material. On the contrary, we review recent advancements in cutting-edge approaches to shape scintillator characteristics via photonic and metamaterial engineering, which are extrinsic and introduce controlled inhomogeneity in the scintillator's surface or volume. The methods to be discussed include improved light out-coupling using photonic crystal (PhC) coating, dielectric architecture modification producing the Purcell effect, and meta-materials engineering based on energy sharing. These approaches help to break traditional bulk scintillators' limitations, e.g., to deal with poor light extraction efficiency from the material due to a typically large refractive index mismatch or improve timing performance compared to bulk materials. In the Outlook section, modern physical phenomena are discussed and suggested as the basis for the next generations of scintillation-based detectors and technology, followed by a brief discussion on cost-effective fabrication techniques that could be scalable.

Band-Edge Engineering To Eliminate Radiation-Induced Defect States in Perovskite Scintillators

Under radiative environments such as extended hard X- or γ-rays, degradation of scintillation performance is often due to irradiation-induced defects. To overcome the effect of deleterious defects, novel design mitigation strategies are needed to identify and design more resilient materials. The potential for band-edge engineering to eliminate the effect of radiation-induced defect states in rare-earth-doped perovskite scintillators is explored, taking Ce³⁺-doped LuAlO₃ as a model material system, using density functional theory (DFT)-based DFT + U and hybrid Heyd-Scuseria-Ernzerhof (HSE) calculations. From spin-polarized hybrid HSE calculations, the Ce³⁺ activator ground-state 4f position is determined to be 2.81 eV above the valence band maximum in LuAlO₃. Except for the oxygen vacancies which have a deep level inside the band gap, all other radiation-induced defects in LuAlO₃ have shallow defect states or are outside the band gap, that is, relatively far away from either the 5d¹ or the 4f Ce³⁺ levels. Finally, we examine the role of Ga doping at the Al site and found that LuGaO₃ has a band gap that is more than 2 eV smaller than that of LuAlO₃. Specifically, the lowered conduction band edge envelopes the defect gap states, eliminating their potential impact on scintillation performance and providing direct theoretical evidence for how band-edge engineering could be applied to rare-earth-doped perovskite scintillators.

Energy Gap-Refractive Index Relations in Perovskites

In this study, the energy gap-refractive index relations of perovskites are examined in detail. In general, the properties of perovskites are dependent on the structural reorganization and covalent nature of their octahedral cages. Based on this notion, a simple relation governing the energy gap and the refractive index is proposed for perovskites. The results obtained with this relation are in good accord with the literature values and are consistent with some well-established relations.

Emerging role of machine learning in light-matter interaction

Machine learning has provided a huge wave of innovation in multiple fields, including computer vision, medical diagnosis, life sciences, molecular design, and instrumental development. This perspective focuses on the implementation of machine learning in dealing with light-matter interaction, which governs those fields involving materials discovery, optical characterizations, and photonics technologies. We highlight the role of machine learning in accelerating technology development and boosting scientific innovation in the aforementioned aspects. We provide future directions for advanced computing techniques via multidisciplinary efforts that can help to transform optical materials into imaging probes, information carriers and photonics devices.

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. Therefore, 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.