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Phase pure β-(Al x Ga 1−x ) 2 O 3 thin films are grown on (001) oriented β-Ga 2 O 3 substrates via metalorganic chemical vapor deposition. By systematically tuning the precursor molar flow rates, the epitaxial growth of coherently strained β-(Al x Ga 1−x ) 2 O 3 films is demonstrated with up to 25% Al compositions as evaluated by high resolution x-ray diffraction. The asymmetrical reciprocal space mapping confirms the growth of coherent β-(Al x Ga 1−x ) 2 O 3 films (x < 25%) on (001) β-Ga 2 O 3 substrates. However, the alloy inhomogeneity with local segregation of Al along the ([Formula: see text]) plane is observed from atomic resolution STEM imaging, resulting in wavy and inhomogeneous interfaces in the β-(Al x Ga 1−x ) 2 O 3 /β-Ga 2 O 3 superlattice structure. Room temperature Raman spectra of β-(Al x Ga 1−x ) 2 O 3 films show similar characteristics peaks as the (001) β-Ga 2 O 3 substrate without obvious Raman shifts for films with different Al compositions. Atom probe tomography was used to investigate the atomic level structural chemistry with increasing Al content in the β-(Al x Ga 1−x ) 2 O 3 films. A monotonous increase in chemical heterogeneity is observed from the in-plane Al/Ga distributions, which was further confirmed via statistical frequency distribution analysis. Although the films exhibit alloy fluctuations, n-type doping demonstrates good electrical properties for films with various Al compositions. The determined valence and conduction band offsets at β-(Al x Ga 1−x ) 2 O 3 /β-Ga 2 O 3 heterojunctions using x-ray photoelectron spectroscopy reveal the formation of type-II (staggered) band alignment. 

In this work, we develop and employ an accelerated design strategy using a machine learning algorithm to overcome the challenges for designing a new machinable glass ceramic. The trained machine learning model predicts the specific hardness value for numerous possibilities of processing conditions such as growth temperature and time. We report that the optimized growth parameters of 1200°C and 5 h achieve the highest machinability of 0.4 in the glass ceramic. Furthermore, we predicted the eight most promising candidates containing specific ratios of silicon, magnesium, aluminum, lithium, boron, potassium, barium, and oxygen. Combining machine learning with experimental data enables a systemic and rapid design of a ceramic material while capturing the underlying physics represented in the experimental data.