The White lab hosted a four-day workshop for high school students where they a chance to learn about molecular chemistry and visualize simulations in VR. This workshop was funded by the NSF and took place as a part of the Upward
Bound program at the UofR.
May 23, 2019
Maghesree won the first prize for her lightening talk at the RAMP 2019 Symposium. Congratulations Maghesree! Earlier this year, in April, she won the best poster award at the Chemistry Chemical Engineering Poster Social organized
by the Chemistry Graduate Association.
May 12, 2019
Dilnoza, Maghesree and Rainier won travel grants to attend Machine learning in Science and Engineering (MLSE) Conference happening at Georgia Tech on June 10-12, 2019.
March 26, 2019
Prof. White was awarded the G. Graydon Curtis '58 and Jane W. Curtis Award for Nontenured Faculty Teaching.
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.
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.
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.