T3 Virtual Talks: Applications of Data Science to Molecules and Materials | AIChE

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T3 Virtual Talks: Applications of Data Science to Molecules and Materials

Chair(s)

Co-chair(s)

Rangarajan, S., Lehigh University - Dept of Chem & Biomolecular

Presentations

8:00 AM
(290e) Deriving Descriptors and Physical Understanding from Data in Catalysis Research
8:12 AM
(290g) Inverse Design of Nanoparticles Using Charge Transfer Properties and Multi-Target Machine Learning
8:24 AM
(364f) Design of Optoelectronic Self-Assembling ?-Conjugated Peptides Via Combined Experimental-Computational Multi-Fidelity Active Learning
8:36 AM
(275a) A Data-Driven Approach to Understanding and Predicting the Early Formation of the Solid-Liquid Electrolyte Interphase
8:48 AM
(290c) Building a Universal Data Science Workflow for Monodentate Organophosphorus Ligands
9:00 AM
(51f) From Brownian Dynamics Simulations and Experimental Observations of Colloidal Suspensions to Data-Driven Observables and Effective Sdes with Manifold Learning
9:12 AM
(203g) A Data-Driven Approach to Disentangled Parametrization of Relations, with an Application to Multisite Phosphorylation
9:24 AM
(364h) Predicting Chemical Adsorption Onto Montmorillonite Clays through a Combination of Experimental Isotherms, Minimalistic Simulations and Data-Driven Models
9:36 AM
(290f) Machine Learning Electronic Structure for Heterogeneous Catalysis
9:48 AM
(51b) Machine Learning the Quantum-Chemical Properties of Metal-Organic Frameworks for Accelerated Materials Discovery with a New Electronic Structure Database
10:00 AM
(290d) Bayesian Parameter Estimation for Connecting Theory & Predictions to Experiments
10:12 AM
(290h) Towards Development of Novel Heterogeneous Catalysts Using Extrapolative Machine Learning Methods

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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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