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.
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"
March 18, 2018
Heta gave an oral presentation at ACS Spring conference. The title of her talk was "Can Parked Cars and Carbon Taxes Create a Profit? The Economics of Vehicle-to-Grid Energy Storage for Peak Reduction"
September 27, 2018
Heta won the third place for her poster at New York Battery Energy Storage Technologies.
March 29, 2018
Prof. White has received an NSF Career award for his proposal "Multiscale Modeling of Peptide Self-Assembly with Experiment Directed Simulation" in the CBET Div Of Chem, Bioeng, Env, & Transp Sys
Abstract for Experiment Directed Simulation of Ab Initio Water:
Accounting for electrons and nuclei simultaneously is a powerful capability of ab initio molecular dynamics (AIMD). However, AIMD is often unable to accurately reproduce properties of systems such as water due to inaccuracies in the underlying electronic
density functionals. This shortcoming is often addressed by added empirical corrections and/or increasing the simulation temperature. We present here a maximum-entropy approach to directly incorporate limited experimental data
via a minimal bias. Biased AIMD simulations of water and an excess proton in water are shown to give significantly improved properties both for observables which were biased to match experimental data and for unbiased observables.
This approach also yields new physical insight into inaccuracies in the underlying density functional theory as utilized in the unbiased AIMD.