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  1. Abstract

    The budding field of materials informatics has coincided with a shift towards artificial intelligence to discover new solid-state compounds. The steady expansion of repositories for crystallographic and computational data has set the stage for developing data-driven models capable of predicting a bevy of physical properties. Machine learning methods, in particular, have already shown the ability to identify materials with near ideal properties for energy-related applications by screening crystal structure databases. However, examples of the data-guided discovery of entirely new, never-before-reported compounds remain limited. The critical step for determining if an unknown compound is synthetically accessible is obtaining the formation energy and constructing the associated convex hull. Fortunately, this information has become widely available through density functional theory (DFT) data repositories to the point that they can be used to develop machine learning models. In this Review, we discuss the specific design choices for developing a machine learning model capable of predicting formation energy, including the thermodynamic quantities governing material stability. We investigate several models presented in the literature that cover various possible architectures and feature sets and find that they have succeeded in uncovering new DFT-stable compounds and directing materials synthesis. To expand access to machine learning models formore »synthetic solid-state chemists, we additionally presentMatLearn. This web-based application is intended to guide the exploration of a composition diagram towards regions likely to contain thermodynamically accessible inorganic compounds. Finally, we discuss the future of machine-learned formation energy and highlight the opportunities for improved predictive power toward the synthetic realization of new energy-related materials.

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  2. Free, publicly-accessible full text available November 16, 2023
  3. There is a significant need to identify cyan-emitting phosphors capable of filling the “cyan-gap” (480–520 nm) in full-visible-spectrum phosphor-converted white light-emitting diodes (pc-wLEDs). Here, a new broadband cyan-emitting phosphor that enables addressing of this challenge is reported. The compound, Ba 2 CaB 2 Si 4 O 14 :Ce 3+ , presents a bright cyan emission peaking at 478 nm with a large full width at half maximum of 142 nm (6053 cm −1 ), and minimal thermal quenching. The photoluminescence properties originate from Ce 3+ residing at two different crystallographic sites, a [BaO 9 ] distorted elongated square pyramid and a [CaO 6 ] trigonal prism. This combination results in an efficient, broad emission covering the blue to green region of the visible spectrum. Fabricating a simple dichromatic ultraviolet ( λ ex = 370 nm) pumped pc-wLED using Ba 2 CaB 2 Si 4 O 14 :Ce 3+ along with a commercially available red phosphor demonstrates full-visible-spectrum white light with high color rendering index ( R a > 90) and tunable correlated color temperature, showing the potential of this material for achieving high-quality LED-based lighting.