65 citations · 274 across the 12 of their papers we have counts for
13 papers
Physics is the New Data
Sergei V. Kalinin, Maxim Ziatdinov, Bobby G. Sumpter +1
The rapid development of machine learning (ML) methods has fundamentally affected numerous applications ranging from computer vision, biology, and medicine to accounting and text a…
Symmetric Molecular Dynamics
Sam Cox, Andrew D. White
We derive a formulation of molecular dynamics that generates only symmetric configurations. We implement it for all 2D planar and 3D space groups. An atlas of 2D Lennard-Jones crys…
City-wide modeling of Vehicle-to-Grid Economics to Understand Effects of Battery Performance
Heta A. Gandhi, Andrew D. White
Vehicle-to-grid (V2G) is a promising approach to solve the problem of grid-level intermittent supply and demand mismatch, caused due to renewable energy resources, because it uses…
Natural Language Processing Models That Automate Programming Will Transform Chemistry Research and Teaching
Glen M. Hocky, Andrew D. White
Natural language processing models have emerged that can generate usable software and automate a number of programming tasks with high fidelity. These tools have yet to have an imp…
Iterative Symbolic Regression for Learning Transport Equations
Mehrad Ansari, Heta A. Gandhi, David G. Foster +1
Computational fluid dynamics (CFD) analysis is widely used in engineering. Although CFD calculations are accurate, the computational cost associated with complex systems makes it d…
Graph Neural Network Based Coarse-Grained Mapping Prediction
Zhiheng Li, Geemi P. Wellawatte, Maghesree Chakraborty +3
The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation. It is still an open question about what is optimal for this cho…
Reconstruction of the interatomic forces from dynamic Scanning Transmission Electron Microscopy data
M. Chakraborty, M. Ziatdinov, O. Dyck +3
We explore the possibility for the reconstruction of the generative physical models describing interactions between atomic units in solids from observational electron microscopy da…
Investigating Active Learning and Meta-Learning for Iterative Peptide Design
Rainier Barrett, Andrew D. White
Often the development of novel functional peptides is not amenable to high throughput or purely computational screening methods. Peptides must be synthesized one at a time in a pro…
Recent Advances in Maximum Entropy Biasing Techniques for Molecular Dynamics
Dilnoza B. Amirkulova, Andrew D. White
This review describes recent advances by the authors and others on the topic of incorporating experimental data into molecular simulations through maximum entropy methods. Methods…
Classifying Antimicrobial and Multifunctional Peptides with Bayesian Network Models
Rainier Barrett, Shaoyi Jiang, Andrew D White
Bayesian network models are finding success in characterizing enzyme-catalyzed reactions, slow conformational changes, predicting enzyme inhibition, and genomics. In this work, we…
Combining Enhanced Sampling with Experiment Directed Simulation of the GYG peptide
Dilnoza B Amirkulova, Andrew D White
Experiment directed simulation is a technique to minimally bias molecular dynamics simulations to match experimentally observed results. The method improves accuracy but does not a…
Encoding and Selecting Coarse-Grain Mapping Operators with Hierarchical Graphs
Maghesree Chakraborty, Chenliang Xu, Andrew D. White
Coarse grain (CG) molecular dynamics (MD) can simulate systems inaccessible to fine grain (FG) MD simulations. A CG simulation decreases the degrees of freedom by mapping atoms fro…
Improved Ab Initio Molecular Dynamics by Minimal Biasing with Experimental Data
Andrew D. White, Chris Knight, Glen M. Hocky +1
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…