Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs)

• Published in: ACS combinatorial science Vol. 19; no. 10; pp. 640 - 645
• Main Authors: Pardakhti, Maryam, Moharreri, Ehsan, Wanik, David, Suib, Steven L, Srivastava, Ranjan
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 09.10.2017
• Subjects: Adsorption; Algorithms; Computer Simulation; Machine Learning; Metals - chemistry; Organometallic Compounds - chemistry; Software; computational screening; machine learning; metal−organic frameworks; methane adsorption; predictive modeling

Using molecular simulation for adsorbent screening is computationally expensive and thus prohibitive to materials discovery. Machine learning (ML) algorithms trained on fundamental material properties can potentially provide quick and accurate methods for screening purposes. Prior efforts have focus...