2021 Annual Meeting

Session: Data-Driven Design and Modeling I

Chair

Co-Chairs

Yi He, Zhejiang University (Yuquan Campus)

Presentations

12:30 PM

(118a) The Machine Learning Route to Accelerated Discovery and Inverse Design of Chemical and Materials Systems
Johannes Hachmann

01:00 PM

(118b) A Universal Framework for Featurization of Atomistic Systems
Andrew Medford, Ray Lei

01:15 PM

(118c) Transfer Learning Using Large-Scale ML Models for Catalyst/Molecular Datasets
Adeesh Kolluru, Muhammed Shuaibi, Zachary Ulissi

01:30 PM

(118d) Semi-Supervised Learning Detects Safe Islands Where Density Functional Theory Is Applicable for Chemical Discovery
Chenru Duan, Aditya Nandy, Fang Liu, Heather Kulik

01:45 PM

(118e) In silico discovery of Target Peptide Inhibitors for C. Diff toxins a and B
Sudeep Sarma, Xingqing Xiao, Stefano Menegatti, Nathan Crook, Scott Magness, Carol Hall

02:00 PM

(118f) You May Not Have Noticed, but Your Neural Network Did: Machine Learning from Simulated Enzyme Variants
Tucker Burgin, Jim Pfaendtner, David Beck

02:15 PM

(118g) Deep Reinforcement Machine Learning Driven Vaccine Design Against Highly Mutable Pathogens
Jonathan Faris, Brenden Peterson, Daniel Faissol, Kayla Sprenger

02:30 PM

(118h) Data-Driven Reconstruction of Molecular Folding Trajectories from Single-Molecule Experimental Measurements
Maximilian Topel, Andrew Ferguson

02:45 PM

(118i) High-Throughput Screening of Tribological Properties of Monolayer Films Using Molecular Dynamics and Machine Learning
Co Quach, Justin Gilmer, Christopher Iacovella, Peter Cummings, Clare McCabe