Prof. White gave an invited talk titled “Computational design of peptide-based materials with maximum entropy molecular simulation and data-driven modeling” at the International Symposium on Material Informatics held at the University
of Tokyo, Japan.
October 28-November 3rd, 2018
Heta, Rainier, and Dr. White will be giving poster presentations at AICHE. Dilnoza, Heta, Maghesree, Rainier, and Dr. White are giving oral presentations at AICHE
June 10, 2018
Dilnoza and Maghesree gave presentations at Midwestern Thermodynamics and Statistical Mechanics Conference. Dilnoza's presentation was named "Experiment Directed Simulations and Enhanced Sampling". Maghesree's talk was titled "Hierarchical
Graph Based Approach for Encoding Coarse-Grain Mapping Operators"
January 3, 2019
The augmented-reality system built by Heta, Rainier, Tayfun, and Prof. White has been moved to a new ChemE AR/VR lab in Hopeman Hall.
Dilnoza and Maghesree won travel grant to attend MDAnalysis workshop and hackathon at Northwestern.
July 23, 2018
Prof. White and co-PI Chenliang Xu have received an NSF award for "Applying Video Segmentation to Coarse-grain Mapping Operators in Molecular Simulations" in the Chemical Theory, Models and Computational Methods program.
Abstract for Encoding and Selecting Coarse-Grain Mapping Operators with Hierarchical Graphs:
Coarse-grained (CG) molecular dynamics (MD) can simulate systems inaccessible to fine-grained (FG) MD simulations. A CG simulation decreases the degrees of freedom by mapping atoms from an FG representation into agglomerate CG particles. The FG to CG
mapping is not unique. Research into systematic selection of these mappings is challenging due to their combinatorial growth with respect to the number of atoms in a molecule. Here we present a method of reducing the total count
of mappings by imposing molecular topology and symmetry constraints. The count reduction is illustrated by considering all mappings for nearly 50 000 molecules. The resulting number of mapping operators is still large, so we introduce
a novel hierarchical graphical approach which encodes multiple CG mapping operators. The encoding method is demonstrated for methanol and a 14-mer peptide. With the test cases, we show how the encoding can be used for automated
selection of reasonable CG mapping operators.