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Efficient broadband near‐infrared (NIR) emitting materials with an emission peak centered above 830 nm are crucial for smart NIR spectroscopy‐based technologies. However, the development of these materials remains a significant challenge. Herein, a series of design rules rooted in computational methods and empirical crystal‐chemical analysis is applied to identify a new Cr³⁺‐substituted phosphor. The compound GaTaO₄:Cr³⁺emerged from this study is based on the material's high structural rigidity, suitable electronic environment, and relatively weak electron–phonon coupling. Irradiating this new phosphor with 460 nm blue light generates a broadband NIR emission (λ_em,max = 840 nm) covering the 700–1100 nm region of the electromagnetic spectrum with a full width at half maximum of 140 nm. The phase has a high internal quantum yield of 91% and excellent thermal stability, maintaining 85% of the room temperature emission intensity at 100 °C. Fabricating a phosphor‐converted light‐emitting diode device shows that the new compound generates an intense NIR emission (178 mW at 500 mA) with photoelectric efficiency of 6%. This work not only provides a new material that has the potential for next‐generation high‐power NIR applications but also highlights a set of design rules capable of developing highly efficient long‐wavelength broadband NIR materials.

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 for 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.

An efficient broadband NIR garnet-type Mg₃Gd₂Ge₃O₁₂:Cr³⁺phosphor with relatively long emission wavelength was developed, which demonstrates an excellent performance in NIR pc-LED applications.