Chemistry Program
June 7th
- Session Topic
- 9:00-9:30
- 9:30-10:00
- 10:00-10:30
- break
- 11:00-11:30
- 11:30-12:00
- 12:00-12:30
- 12:30-1:30 - lunch/posters
- 1:30-2:30 - plenary
- Session Topic
- 2:30-3:00
- 3:00-3:30
- 3:30-4:00
- break
- 4:30-5:00
- 5:00-5:30
- 5:30-6:00
June 8th
- Session Topic - Predicting Molecular Properties
- 9:00-9:30 - Kieron Burke (UC Irvine) – “Using Machine Learning to Create New Density Functional Approximations”
- 9:30-10:00 - Johannes Hachmann ( Buffalo) – “Advancing Molecular Property Predictions and Design with Machine Learning”
- 10:00-10:30 - Noa Marom (Carnegie Mellon) – “Molecular Crystal Structure Prediction with GAtor and Genarris”
- break
- 11:00-11:30 - Le Song (Georgia Tech) – “Deep Graph Embedding for Molecular Property Prediction and Optimization”
- 11:30-12:00 - Deyu Lu (Brookhaven ) – “Application of Machine Learning in X-ray Absorption Spectroscopy”
- 12:00-12:30 - Olexandr Isayev (UNC) – “Neural Networks Learning Quantum Chemistry”
- 12:30-1:30 - lunch/posters
- 1:30-2:30 - plenary
- Session Topic - Molecular Design
- 2:30-3:00 - Geoff Hutchison ( Pittsburgh) – “Rapid Discovery of Molecular Materials - Combining Machine Learning and Evolutionary Algorithms”
- 3:00-3:30 - Joshua Schrier (Haverford) – “Data-Driven Approaches to Predicting Reaction Outcomes in Solid State Chemistry”
- 3:30-4:00 - Dmitry Zubarev (IBM)– "Bridging Gaps Between Computational and Experimental Aspects of the Fourth Paradigm"
- break
- 4:30-4:50 - Carlos Borca (Georgia Tech) – “CrystalLattE: Automated Computation of Benchmark-Level Lattice Energies of Molecular Crystals”
- 4:50-5:10 - David Sheen (NIST) – "Chemometric Analysis of Hydrocarbon Reference Materials for Certification as Aircraft Fuels"
- 5:10-5:30 - David Yaron (Carnegie Mellon) – “A Quantum Chemical Layer for Deep learning of Electronic Properties”
Posters
Mojtaba Haghighatlari (Buffalo) – “Software development and its application for predicting optical properties in molecular sspace: ChemML program suite”
Xi Chen (Brown) – “The application of machine learning in variational transition state theory”
Derek Metcalf (MSU) – “Using Bayesian neural networks to understand uncertainty in model neural network chemistry predictions”
Chen Qu (Emory) – “Assessing the Gaussian process approach in potential energy surface fitting”
Mohammad Atif Afzal (Buffalo) – “Harnessing virtual high-throughput screening and machine learning for the discovery of novel high-refractive-index polymers”
Holden Parks (CMU) – “Quantifying uncertainty in first-principles predictions of molecular vibrational frequencies with applications to machine learning”
Haichen Li (CMU) – “Using deep reinformcement learning to guide chemical reactions”
Michael Taylor (Pittsburgh) – “TBD”
Mariya Popova (UNC) – “De-novo drug design with deep reinforcement learning”
Christopher Kotke (CMU) – “TBD”
Run Li (Florida State) – “WITHDRAWN- Development of complex v2RDM driven relativistic CASSCF methods”
Timothy Rose (CMU) – “Evolutionary niching in the GAtor genetic algorithym for molecular crystal structure prediction”
Xingyu Liu (CMU) – “Accelerate searching for singlet fission materials: Feature selection and model construction for GW+BSE method with SISSO”
Joon-Yong Lee (PNNL) – "Deep Learning Benchmark Data for de novo Peptide Sequencing"