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The new, quaternary diamond-like semiconductor (DLS) Cu 4 MnGe 2 S 7 was prepared at high-temperature from a stoichiometric reaction of the elements under vacuum. Single crystal X-ray diffraction data were used to solve and refine the structure in the polar space group Cc. Cu 4 MnGe 2 S 7 features [Ge 2 S 7 ] 6− units and adopts the Cu 5 Si 2 S 7 structure type that can be considered a derivative of the hexagonal diamond structure. The DLS Cu 2 MnGeS 4 with the wurtz-stannite structure was similarly prepared at a lower temperature. The achievement of relatively phase-pure samples, confirmed by X-ray powder diffraction data, was nontrival as differential thermal analysis shows an incongruent melting behaviour for both compounds at relatively high temperature. The dark red Cu 2 MnGeS 4 and Cu 4 MnGe 2 S 7 compounds exhibit direct optical bandgaps of 2.21 and 1.98 eV, respectively. The infrared (IR) spectra indicate potentially wide windows of optical transparency up to 25 μm for both materials. Using the Kurtz–Perry powder method, the second-order nonlinear optical susceptibility, χ (2) , values for Cu 2 MnGeS 4 and Cu 4 MnGe 2 S 7 were estimated to be 16.9 ± 2.0 pm V −1 and 2.33 ± 0.86 pm V −1 , respectively, by comparing with an optical-quality standard reference material, AgGaSe 2 (AGSe). Cu 2 MnGeS 4 was found to be phase matchable at λ = 3100 nm, whereas Cu 4 MnGe 2 S 7 was determined to be non-phase matchable at λ = 1600 nm. The weak SHG response of Cu 4 MnGe 2 S 7 precluded phase-matching studies at longer wavelengths. The laser-induced damage threshold (LIDT) for Cu 2 MnGeS 4 was estimated to be ∼0.1 GW cm −2 at λ = 1064 nm (pulse width: τ = 30 ps), while the LIDT for Cu 4 MnGe 2 S 7 could not be ascertained due to its weak response. The significant variance in NLO properties can be reasoned using the results from electronic structure calculations. 

The authors showcase the potential of symbolic regression as an analytic method for use in materials research. First, the authors briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques. Next, the authors discuss industrial applications of symbolic regression and its potential applications in materials science. The authors then present two GPSR use-cases: formulating a transformation kinetics law and showing the learning scheme discovers the well-known Johnson–Mehl–Avrami–Kolmogorov form, and learning the Landau free energy functional form for the displacive tilt transition in perovskite LaNiO 3 . Finally, the authors propose that symbolic regression techniques should be considered by materials scientists as an alternative to other machine learning-based regression models for learning from data.