2024 AIChE Annual Meeting

Session: Machine Learning for Soft and Hard Materials II

Chair

Co-Chairs

Gissinger, J., University of Colorado-Boulder
Lee, E., University of Chicago
Bartel, C., University of California, Berkeley

Presentations

08:00 AM

(455a) Accelerating Ab Initio Calculations of Chemical Bonding and Equilibria at Material Interfaces Using Machine Learning

08:12 AM

(455b) Neural Network Wave Function Solver

08:24 AM

(455c) Accelerated Discovery of High-Performance Organic Electrode Materials for Battery Applications Using an Interpretable Machine Learning Framework

08:36 AM

(455d) Inverse Design of Materials with Globally Optimal Topology and Geometry through Mixed Integer Linear Programming (MILP)

08:48 AM

(455e) Catberta: Catalyst Energy Prediction and Feature Analysis through Language Models

09:00 AM

(169bv) Systematic Development of Machine Learning Interatomic Potentials for MAPbI3 Perovskites

09:12 AM

(455g) Machine-Learning Accelerated Simulation: Toward Design and Synthesis of Nanocarbon Materials

09:24 AM

(455h) Using Large Language Model to Collect and Analyze Metal-Organic Framework Property Dataset

09:36 AM

(455i) Data-Driven Acceleration of Materials Development: Leveraging AI/ML to Scalably Bridge the Gap from Models to Materials

09:48 AM

(455j) Simulation-Free, Two-Dimensional Histograms As Effective Adsorbent Representations for Machine-Learning Based Adsorption Predictions

10:00 AM

(455k) Representation Learning for All-Silica Zeolites: Model Representations, Transfer Learning, and Multi-Task Learning

10:12 AM

(455l) High-Throughput Simulations and Machine Learning for Adsorption Processes