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autoparasweep

bnsl

Simualtes the assembly of binary nanoparticle superlattices (BNSL)

capsid-souffle

data4ml

contains datasets for training and testing ML models

energyinsights

jadhaolab-website

Jadhao Lab Website

mlflow

nanobind

polyvalent nanoparticle binding simulator

nanoconfinement-cpmd

nanoconfinement-md

This code allows users to simulate ions confined between material surfaces that are nanometers apart, and extract the associated ionic structure.

np-assembly-lab

Simulates assembly of nanoparticles and oppositely-charged linkers under different physiological conditions. Outputs are structural information such as pair distribution functions.

np-electrostatics-lab

This code computes the density distribution of ions near a spherical nanoparticle in biological environments

np-shape-lab

The shape of nanoparticles determines their ability to interact with biological systems in nanomedicine applications. This framework simulates the shape deformation of deformable patchy nanoparticles for a broad variety of nanoparticle material properties and solution conditions. Molecular dynamics based simulated annealing is used to minimize the energy of the nanoparticle for a given set of material and solution parameters. Users can input control parameters such as the maximum surface charge, surface patch size, stretching modulus, and bending modulus, as well as control the solution ionic strength (salt concentration) changing the electrostatic drive to deform. Changing patch size parameter tunes the surface charge from a small value close to 0 to the maximum surface charge selected by the user. Suggested values of patch size are 0.25, 0.5, 0.75, 0.9 to observe a wide range of shape transitions. After running the simulation, in various output tabs, users can view the snapshots of the nanoparticle shape at various stages of the energy minimization process. Plots of the minimization of the electrostatic energy and increase in the area of the nanoparticle are also available (both these quantities are normalized by the associated values of the initial spherical conformation). Volume of the nanoparticle is conserved, representative of a finite amount of cargo. The range of charge, elasticities, and salt concentrations enable users to observe deformation into varios equilibrium shapes including bowls, hemispheres, discs, and rods, and egg-like conformations. The app is ran using hybrid MPI/OMP parallelized C++ codes and Python post-processing and app deployment. After the simulation, the following data files can be downloaded: final image of the shape of the container (PNG), movie of the entire deformation process (LAMMPS output format for viewing in Ovito or VMD), raw area data (in units of the radius of the sphere), and raw energy data including electrostatic energy variation with time (in kB T).

rnn-md

Code for our paper "Simulating Molecular Dynamics with Large Timesteps using Recurrent Neural Networks"