Machine Learning for Soft and Hard Materials II | AIChE

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Machine Learning for Soft and Hard Materials II

Chair(s)

Co-chair(s)

Hachmann, J., University at Buffalo, SUNY
Bartel, C., University of Minnesota

Machine Learning for Soft and Hard Materials II

Presentations

12:30 PM
(474a) Predicting Activation Pathway of Integrin Using Generative Adversarial Networks and Targeted Molecular Dynamics Simulations
12:42 PM
(474b) Using Deep Learning to Accelerate Molecular Simulations of RNA Folding
12:54 PM
(474c) Machine Learning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns
1:06 PM
(474d) High-Throughput Experimentation and Compositional Screening of Polymer-Based Semiconductor/Insulator Blends for Organic Device Applications
1:18 PM
(474e) Decoding Optical Responses of Contact-Printed Arrays of Liquid Crystals Using Machine Learning: Detection of Aqueous Amphiphiles with Enhanced Sensitivity and Selectivity
1:30 PM
(474f) Uni-MOF: A Universal Material Representationlearning Framework for Metal-Organic Frameworks
1:42 PM
(474g) Shapelet-Based Defect Identification and Classification for Nanostructure Surface Imaging
1:54 PM
(474h) Predicting Pair Correlation Functions of Soft and Hard Materials Using Machine Learning
2:06 PM
(474i) Exploring the Impact of Training Data Distributions on the Accuracy of Machine Learning Force Fields
2:18 PM
(474j) Training Accurate and Physically Meaningful Machine Learning Force Fields for Water and Understanding Their Transferability
2:30 PM
(474k) Learning Interatomic Forces from Experimental Measurements of Fluid Structure
2:42 PM
(474l) Multi-Input E(n)-Graph Neural Networks for Learning Molecular Interaction Properties

Topics 

Computational Molecular Engineering

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Individuals

AIChE Pro Members $150.00
AIChE Emeritus Members $105.00
AIChE Graduate Student Members Free
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AIChE Explorer Members $225.00
Non-Members $225.00

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