2025 AIChE Annual Meeting

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

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.

Chair

Co-Chairs

Yaxin An, Virginia Tech (Virginia Polytechnic Institute)
Rituparna Samanta, The University of Texas at Austin

Presentations

08:00 AM

(58a) Identification of Highly Selective Antifungal Peptides Containing Non-Canonical Amino Acids with Iterative Machine Learning
Joshua Richardson, Douglas Chang, Myung-Ryul Lee, David M. Lynn, Sean P. Palecek, Reid Van Lehn

08:15 AM

(58b) Steering Retrosynthetic Planning Towards Chemical Engineering - a Multi-Objective Approach
Friedrich Hastedt, Klaus Hellgardt, Sophia N. Yaliraki, Dongda Zhang, Antonio del Rio Chanona

08:30 AM

(58c) Iterative Optimization of Plastic-Binding Peptides for Remediating Microplastic Pollution
Michael Bergman, Carol Hall

08:45 AM

(58d) Deep Graph Kernel Learning for Material and Atomic Level Uncertainty Quantification in Adsorption Energy Prediction
Osman Mamun, Shuwen Yue

09:00 AM

(58e) Physics-Informed Machine Learning Model for Phase Equilibrium Prediction in Multicomponent Systems
Yue Yang, Shiang-Tai Lin

09:15 AM

(58f) Data-Driven Strategy Selection for Multi-Fidelity Modeling in Autonomous Molecular Discovery
Kevin P. Greenman, Temujin Orkhon, William Green, Rafael Gomez-Bombarelli

09:30 AM

(58g) Evaluating the Accuracy of Machine Learning Interatomic Potentials in Calculating Elastic Properties
Kareem Abdelmaqsoud, Luis Barroso-Luque, Muhammed Shuaibi, John Kitchin

09:45 AM

(58h) Inverse Design of Non-PFAS Surfactants Via Pre-Trained Property Prediction Models and Reinforcement Learning-Guided Combinatorial Chemistry
Yejin Hong, Mingyeong Jeon, Daeun Shin, Haeyeon Choi, Jonggeol Na

10:00 AM

(58i) Data-Driven Structural Selection for Efficient Training of Machine Learning Force Fields on Metal Nanoparticles
Huaizhong Zhang, Kristen Fichthorn

10:15 AM

(58j) Maximizing the Data Efficiency of Experimental Planning Algorithms for Quantifying Material Structure-Property Relationships
Quinn Gallagher, Michael Webb