2022 Annual Meeting

Session: Applications of Data Science in Molecular Sciences I

Chair

Hachmann, J., University at Buffalo, SUNY

Co-Chairs

Zhao, Q., Northeastern University

Presentations

12:30 PM

(152a) Combining functional group and Graph Neural Networks Towards Interpretable Molecular Property Models

12:45 PM

(152b) Evaluation of Sampling Algorithms to Explore the Latent Space Created By Deep Generative Models

01:00 PM

(152c) Automatic Creation of Molecular Substructure Descriptors for Estimation of Pure Compound Properties

01:15 PM

(152d) Data-Driven Chemical Property Models for Energetic Materials Using Transfer Learning

01:30 PM

(152e) Machine-Learning a Solution for Reactive Simulations of Complex Chemical Systems

01:45 PM

(152f) Capturing Molecular Interactions in Graph Neural Networks: A Case Study in Multi-Component Phase Equilibrium

02:00 PM

(152g) A Statistical Analysis of Computational Hydrogen Bond Features in Hydrophobic Non-Ion Deep Eutectic Solvents and Non-Deep Eutectic Solvents

02:15 PM

(152h) Predicting Temperature-Dependent Activity Coefficients of Ionic Liquid-Solute Systems through Graph-Based Machine Learning

02:30 PM

(152i) Property Prediction of Amine-Functionalized Ionic Liquids for Multi-Scale Carbon Capture Design and Optimization

02:45 PM

(152j) Machine-Learning Enabled Screening of MOFs for Ion Selective Membranes