Machine Learning Assisted Discovery of Efficient MOFs for One-Step C2H4 Purification from Ternary C2H2/C2H4/C2H6 Mixtures

Purifying C2H4 from a mixture of C2H2/C2H4/C2H6 using a single adsorbent is crucial industrially. Yet, the challenge lies in their similar physicochemical properties, leading to low separation efficiency. Additionally, the lack of understanding regarding the structure–performance relationships hinders the development of high-performance metal–organic frameworks (MOFs). In this study, machine learning assisted high-throughput molecular simulation methods are employed to discover efficient MOFs for one-step C2H4 purification. The general material design strategies were proposed based on the analysis of 14,142 CoRE MOF simulation data. These include locking open metal sites, ensuring relative mass proportion of H atoms in the range of 2–4%, optimizing the largest cavity diameter to span 5–7 Å (ultramicropore), and fine-tuning φ within 0.5–0.6. Further using the computational insights obtained, 10 materials were identified with both C2H2/C2H4 and C2H6/C2H4 selectivities exceeding 3 from 137,953 hypothetical MOFs and 303,991 generated MOFs through additional molecular simulations. Our study not only provides screened and designed potential candidates for efficient one-step C2H4 purification from ternary C2H2/C2H4/C2H6 mixtures but also provides useful information for further performance improvement.