2021 Annual Meeting

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

Chair

Co-Chair

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

Presentations

08:00 AM

(290e) Deriving Descriptors and Physical Understanding from Data in Catalysis Research
Levchenko, S.

08:12 AM

(290g) Inverse Design of Nanoparticles Using Charge Transfer Properties and Multi-Target Machine Learning
Li, S., Barnard, A.

08:24 AM

(364f) Design of Optoelectronic Self-Assembling ?-Conjugated Peptides Via Combined Experimental-Computational Multi-Fidelity Active Learning
Shmilovich, K., Panda, S. S., Tovar, J. D., Ferguson, A.

08:36 AM

(275a) A Data-Driven Approach to Understanding and Predicting the Early Formation of the Solid-Liquid Electrolyte Interphase
Persson, K.

08:48 AM

(290c) Building a Universal Data Science Workflow for Monodentate Organophosphorus Ligands
dos Passos Gomes, G., Gensch, T., Friederich, P., Peters, E., Pollice, R., Jorner, K., Nigam, A., Lindner-D'Addario, M., Sigman, M., Aspuru-Guzik, A., Gaudin, T.

09:00 AM

(51f) From Brownian Dynamics Simulations and Experimental Observations of Colloidal Suspensions to Data-Driven Observables and Effective Sdes with Manifold Learning
Evangelou, N., Wichrowski, N., Crabtree, E., Dietrich, F., Bello-Rivas, J., Yeh, A., Zhang, J., Bevan, M. A., Kevrekidis, I. G.

09:12 AM

(203g) A Data-Driven Approach to Disentangled Parametrization of Relations, with an Application to Multisite Phosphorylation
Evangelou, N., Wichrowski, N. J., Kevrekidis, G., Dietrich, F., Kooshkbaghi, M., McFann, S., Shvartsman, S. Y., Kevrekidis, I. G.

09:24 AM

(364h) Predicting Chemical Adsorption Onto Montmorillonite Clays through a Combination of Experimental Isotherms, Minimalistic Simulations and Data-Driven Models
Wang, M., Beykal, B., Orr, A., Ganesh, H. S., Hearon, S. E., Pistikopoulos, E., Phillips, T. D., Tamamis, P.

09:36 AM

(290f) Machine Learning Electronic Structure for Heterogeneous Catalysis
Fung, V., Sumpter, B. G., Ganesh, P.

09:48 AM

(51b) Machine Learning the Quantum-Chemical Properties of Metal-Organic Frameworks for Accelerated Materials Discovery with a New Electronic Structure Database
Iyer, S., Ray, D., Rosen, A., Yao, Z., Aspuru-Guzik, A., Gagliardi, L., Notestein, J., Snurr, R.

10:00 AM

(290d) Bayesian Parameter Estimation for Connecting Theory & Predictions to Experiments
Savara, A.

10:12 AM

(290h) Towards Development of Novel Heterogeneous Catalysts Using Extrapolative Machine Learning Methods
Toyao, T., Shimizu, K. I.