Data-Driven Screening of Chemical and Materials Space

Chair(s):
Hachmann, J., University at Buffalo, SUNY
Co-chair(s):
Ferguson, A. L., University of Illinois at Urbana-Champaign

TBD

Papers:
12:30 PM
(681a) Accurate Property Prediction for Inorganic Materials with Machine Learning
12:42 PM
(681b) Comparing Molecules and Solids Across Structural and Alchemical Space
12:54 PM
(681c) Discriminative Neural Embeddings of Latent Variable Models for Molecular Property Prediction
1:06 PM
(681d) On-the-Fly Heuristic Reordering Approach to Deterministic Optimization for Qualitative Chemical Property Prediction
1:18 PM
(681e) Machine Learning for Predicting Accurate Quantum Chemical Energies
1:30 PM
(681f) Deductive and Inductive Modeling of Electronic Properties in the Organic Molecular Design Space
1:42 PM
(681g) Sorting out a Process-Structure-Property Relationship in Polymer Organic Electronics
1:54 PM
(681h) Application of High Performance Computing and Machine Learning to Accelerate Material Discovery for Energy Capture and Storage
2:06 PM
(681i) A Combinatorial Approach for Developing Minimally-Parameterized and Highly-Transferable Density Functionals
2:18 PM
(681j) Efficient K-Point Grid Generation through the Use of Informatics
2:30 PM
(681k) Machine Generated Coarse-Grained Force-Fields for Efficiently and Effectively Addressing Transferability
2:42 PM
(681l) Development of an Inter-Atomic Potential for Molecular Dynamics Simulations of Stanene Using a Genetic Algorithm Based Framework
Topics: 
Materials
Thermodynamics
Computational Molecular Engineering

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