The ignition sensitivity of ammonium perchlorate (APC) and ammonium periodate (API) was analyzed in terms of crystalline structure, thermal and mechanical properties, and electronic structure using density functional theory (DFT) calculations. API is more rigid, with a higher bulk modulus (K) of 25.87 GPa compared with 21.42 GPa for APC. On the other hand, the shear moduli (G) are similar, 9.75 GPa for API and 9.42 GPa for APC. With higher bulk moduli and similar shear moduli, API will experience more shear than compression in situations such as friction. Also, API presents slightly more lateral deformation than APC, with Poisson’s ratio (ν) of 0.333, compared with 0.308 for APC, and contributes to a less consistent deformation in terms of the crystal lattice. A less stable lattice structure will contribute to greater ignition sensitivity of API compared with APC. The electronic density of states (DOS) analysis showed that API also has a more ignition sensitive profile with a band gap of a semiconductor type, Δg = 2.92 eV, while APC is a typical insulator with a band gap of Δg = 6.21 eV. The analysis of the electronic structure coupled with overall higher anisotropy (shown by calculated elastic constants) could induce ignition of API in a solid phase, whereas the greater stability of APC results in a multiphase ignition mechanism. Results shown here demonstrate important properties that influence the safe handling and use of energetic materials. The observed similarities in structural, mechanical, and thermodynamic properties of API and APC and the considerably large differences in electronic properties indicate that the latter is the key to the higher ignition sensitivity of API.

Metal halide salts have been proposed as promising absorbents for high-temperature separation of ammonia in the intensified Haber−Bosch process. A challenge for the widespread application of metal halides in such applications is the regeneration of absorbents, which requires energy-intensive temperature and pressure swings. New mixed metal halides could be prepared from the pure metal halides, offering a huge number of combinations that could be used to optimize the operating costs. To achieve this goal, knowledge of the deammoniation reaction energies is needed to direct the swing cycles efficiently. Computational methods offer excellent opportunities to obtain these data in an economic way. This work presents a combined density functional theory (DFT) and machine learning (ML) approach to manage these large amounts of mixing possibilities by efficiently and accurately predicting the required deammoniation reaction energies. Mixed metal halides (MMHs) of Mg, Ca, Cl, and Br [Mg x Ca 1−x Cl y Br 2−y (NH 3 ) n , x = 0−1, y = 0− 2, and n = 6, 2, 1 and 0] were selected with deammoniation steps n following the coordination numbers 6 → 2→ 1 → 0. To construct a data-efficient and computationally inexpensive ML approach, we (i) developed an efficient interpolation scheme based on DFT calculations for the structures of the MMHs based on those of the pure compounds, (ii) performed fixed-structure DFT calculations for creating training and test sets, and (iii) analyzed the importance of the features to be used in the ML procedure. Remarkably, our ML approach required a very small training set of 45 cases from a total of 4096 cases per deammoniation reaction step to achieve satisfactory predictions with chemical accuracy. Deammoniation energies were calculated with a standard deviation of better than ±0.1 kcal/mol for the step 6 → 2 and up to ±0.8 kcal/mol for the step 1 → 0. This approach has a potential to be applicable to a broad range of metal halides composed of metals from alkaline earth elements and/or 3d and 4d metals.

Harnessing aluminum oxidation energy requires navigating the particle’s passivation shell composed of alumina. The shell is a barrier to aluminum oxidation but can also exothermically react with halogenated species and...