18 Experimental and Computational Approaches to Accelerate and Discover Inorganic Materials

Monday, November 16, 2020: 8:00 AM - 9:00 AM
Materials Engineering and Sciences Division (08) (PreRecorded+)

Description:
This session focuses on accelerated discovery and synthesis of inorganic materials for targeted applications through high-throughput screening using experimental, computational or AI/machine-learning techniques.

Sponsor:
Inorganic Materials
Chair:
Peng Bai Email: pengbai@umass.edu
Co-Chair:
Satish Nune Email: satish.nune@pnnl.gov


(18a) Predictive Modeling of Adsorption and Transport in Zeolites: From High-Throughput Screening to First Principles Simulations
J. Ilja Siepmann, Peng Bai, Yangzesheng Sun, Evgenii Fetisov, Mansi S. Shah, Robert F. DeJaco, Tyler R. Josephson and Michael Tsapatsis


(18b) Machine Learning-Aided Discovery and Design of Metal-Organic Frameworks for Sour Gas Sweetening
Eun Hyun Cho, Xuepeng Deng and Li-Chiang Lin


(18c) Towards Automated and Comprehensive Exploration of the Methane Physisorption Space Using an Iterative Mutation Search Algorithm
Paul Boone and Christopher E. Wilmer


(18d) A Recommender System to Match Metal-Organic Frameworks with Gases
Arni Sturluson, Grant McConachie, Melanie Huynh, Samuel Hough, Xiaoli Fern, Daniel W. Siderius and Cory Simon


(18e) Camd: An AI-Accelerated Iterative Materials Discovery Platform
Joseph Montoya and Muratahan Aykol


(18g) Evaluating Candidates for New Zeolites with Machine Learning
Benjamin Helfrecht, Rocio Semino, Giovanni Pireddu, Scott M. Auerbach and Michele Ceriotti


(18h) Surface Segregation across Ternary Alloy Composition Space
Chunrong Yin, Zhitao Guo and Andrew J. Gellman
See more of this Group/Topical: Materials Engineering and Sciences Division
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