2023 AIChE Annual Meeting

Session: Machine Learning for Soft and Hard Materials II

Chair

Co-Chairs

Johannes Hachmann, University at Buffalo, SUNY
Chris Bartel, University of Minnesota

Presentations

12:30 PM

(474a) Predicting Activation Pathway of Integrin Using Generative Adversarial Networks and Targeted Molecular Dynamics Simulations
Siva Dasetty, Tamara C. Bidone, Gregory A. Voth, Andrew Ferguson

12:42 PM

(474b) Using Deep Learning to Accelerate Molecular Simulations of RNA Folding
Heng Ma, Ayush Gupta, Arvind Ramanathan, Gul Zerze

12:54 PM

(474c) Machine Learning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns
Usman Abbas, Qing Shao, Yuxuan Zhang, Joseph Tapia, Jian Shi, Jin Chen

01:06 PM

(474d) High-Throughput Experimentation and Compositional Screening of Polymer-Based Semiconductor/Insulator Blends for Organic Device Applications
Aaron Liu, Martha Grover, Rahul Venkatesh, Elsa Reichmanis, Carson Meredith, Haoqun Zhao

01:18 PM

(474e) Decoding Optical Responses of Contact-Printed Arrays of Liquid Crystals Using Machine Learning: Detection of Aqueous Amphiphiles with Enhanced Sensitivity and Selectivity
Shiyi Qin, Reid Van Lehn, Fengrui Wang, Claribel Acevedo-Velez, David Lynn, Victor Zavala

01:30 PM

(474f) Uni-MOF: A Universal Material Representationlearning Framework for Metal-Organic Frameworks
Jingqi Wang, Diannan Lu, Jianzhong Wu, Jiapeng Liu, Hongshuai Wang, Musen Zhou, Guolin Ke, Linfeng Zhang, Zhifeng Gao

01:42 PM

(474g) Shapelet-Based Defect Identification and Classification for Nanostructure Surface Imaging
Nasser M. Abukhdeir, Matthew P. Tino

01:54 PM

(474h) Predicting Pair Correlation Functions of Soft and Hard Materials Using Machine Learning
Kumar Ayush, Tarak Patra

02:06 PM

(474i) Exploring the Impact of Training Data Distributions on the Accuracy of Machine Learning Force Fields
Orlando A. Mendible Barreto

02:18 PM

(474j) Training Accurate and Physically Meaningful Machine Learning Force Fields for Water and Understanding Their Transferability
Tristan Maxson, Tibor Szilvasi

02:30 PM

(474k) Learning Interatomic Forces from Experimental Measurements of Fluid Structure
Brennon L. Shanks, Michael P. Hoepfner, Harry W. Sullivan

02:42 PM

(474l) Multi-Input E(n)-Graph Neural Networks for Learning Molecular Interaction Properties
Kieran Nehil-Puleo