Machine Learning for Designing Mixed Metal Halides for Efficient Ammonia Separation and Storage
Armando de Rezende, Mahdi Malmali, Pavlo O. Dral, Hans Lischka, Daniel Tunega, Adélia J. A. Aquino
Abstract
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 [MgxCa1–xClyBr2–y(NH3)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.