Automated Molecular and Materials Discovery: Integrating Machine Learning, Simulation, and Experiment | AIChE

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Automated Molecular and Materials Discovery: Integrating Machine Learning, Simulation, and Experiment

Chair(s)

Co-chair(s)

Bilodeau, C., University of Virginia

We invite contributions that focus on the use of high-throughput experimentation, computational modeling and simulation, and/or artificial intelligence, machine learning, and data science to advance the goal of autonomous, closed-loop molecular and materials discovery and engineering.

Presentations

8:00 AM
(361a) Efficient Data-Driven Discovery of Novel Innate Immunomodulators Using Machine Learning-Guided High Throughput Screening
8:15 AM
(361b) Automated, Hybrid Model-Driven pH Adjustment of Viscous Liquid Formulations
8:30 AM
(361c) Autonomous Construction of Nanomaterial Phase Maps Using High-Throughput X-Ray Scattering
8:45 AM
(361d) Pareto Optimization to Accelerate Multi-Property Virtual Screening
9:00 AM
(361e) Quantumpioneer: Self-Evolving Machine for High-Throughput Automated Potential Energy Surface Exploration and Closed-Loop Chemical Reactivity Discovery
9:15 AM
(361f) Design of Peptide-Based Drugs for Treating Clostridioides Difficile Infection
9:30 AM
(361g) From Platform to Knowledge Graph: Distributed Self-Driving Laboratories
9:45 AM
(361h) Discovering Novel Materials Using Combinatorial Chemistry-Based Reinforcement Learning
10:00 AM
(361i) Integrated Computational and Experimental Design of Selective Lanthanide Binding Peptides
10:15 AM
(361j) Integrating Automation and Machine Learning for Accelerated Closed-Loop Molecular Discovery

Topics 

Computational Molecular Engineering
Materials
Fundamentals (Chemical Engineering Theory)

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Individuals

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

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