2021 Annual Meeting

Session: Data-Driven Design and Modeling II

Chair

Co-Chairs

Yi He, Zhejiang University (Yuquan Campus)

Presentations

03:30 PM

(186a) Automated Solid State Electrolyte Conductivity Predictions Via Probability Density Analysis
Yan Yao, Karun Kumar Rao, Michael Nikolaou, Lars Grabow, Christopher M. Wolverton

03:45 PM

(186c) Bayesian Model Selection: Applying Parsimony to Build Better Molecular Models
Michael Shirts, Owen Madin, Richard A. Messerly, Simon Boothroyd

04:00 PM

(186e) Evaluating Polymer Stabilizer Performance Using Molecular Descriptors and Machine Learning on a Small Dataset
Aaron Liu, Rahul Venkatesh, Michael McBride, Elsa Reichmanis, J Carson Meredith, Martha Grover

04:15 PM

(186f) Machine-Learning-Guided Discovery of New Electrochemical Reactions
Andrew Zahrt

04:30 PM

(186g) A Deep Learning Potential to Study Large-Scale Anhydrous Proton Transport Systems
Siddarth Achar, Linfeng Zhang, Leonardo Bernasconi, Karl Johnson

04:45 PM

(186h) Leveraging Machine Learning Techniques to Identify Ionic Liquids Possessing High Ionic Conductivity
Pratik Dhakal

05:00 PM

(186j) High-Throughput Electrochemical Screening of Deep Eutectic Solvents for Use in Redox Flow Batteries
Maria Politi, Jaime Rodriguez, Lilo Pozzo

05:15 PM

(707f) Deep Learning for Morphology Detection of Self-Assembled Oligomers in Atomistic Simulations: Pointnet
Zhengyuan Shen, Yangzesheng Sun, Ke Luo, Mahesh Mahanthappa, Timothy P. Lodge, Joern Siepmann