Leverage our computational resources to run off-the-shelf or customized simulation packages, manage simulation data and use optimal learning to efficiently perform in-silico optimization.

Computer simulations and models strengthen experimental results, but require a significant commitment to pursue, causing you to jump several hurdles:

  • Selecting and maintaining the most appropriate and up-to-date simulation software for your problem.
  • Acquiring high-performance or specialized computational resources to obtain timely results.
  • Setting-up, running, and visualizing simulations, which require knowledge of additional software and file formats.
  • Maintaining and managing simulation data, meta-data and a system of record.

Let us run simulations for you

We can model and simulate your systems, using our up-to-date computational resources and software, freeing up your time, money and human resources for use in actual lab science. We’ll help maintain simulation data and meta-data, ensuring reproducibility and an accurate system of record for your computational work.

The right software and hardware

Leverage our computational resources (a mix of modern CPU and GPU processors) to run the simulation software spanning a broad range of computational science including molecular dynamics, Monte Carlo methods, optimization, differential equation solvers and thermodynamic calculations.

  • Fully atomistic molecular dynamics simulations

    LAAMPS for general purpose MD simulation and GROMACS for biomolecular systems.

  • Kinetic/Dynamic Monte Carlo

    Proprietary software for fully atomistic simulations of crystalline materials capable of simulating time-scales of minutes and length-scales of microns.

Custom modeling

In addition to off-the-shelf simulation packages, we can develop theoretical and computational models for many systems. Our core expertise is in chemical kinetics and thermodynamics. Working with lab scientists on actual applications, we’ve developed or simulated models of:

  • Growth kinetics of carbon nanotubes.
  • Emulsion destabilization and controlled release of solute.
  • Nanoparticle formation via molecular beam and liquid droplet epitaxy.
  • Nanowire growth and kinking mechanisms

We’re also developing software to automatically learn chemical reaction networks and their associated kinetics.

In-silico optimization

Computer simulations and modeling naturally tie into our optimal learning optimization framework. Simulations require some user-supplied parameters including:

  • Control variables such as temperature, material flux or solute concentration.
  • Physical parameters such as energy barriers, reaction rates or force fields.
  • Experimental procedures and configurations like temperature ramps, substrate patterning or initial conditions.

When trying to match some objective, it is often not clear what these parameters should be, especially when working with new materials.

  • What temperature maximizes protein interaction without denaturing?
  • Which energy barriers should I use in simulations to most closely match my experimental results?

Simulations are expensive, so you cannot afford to iterate over all potential settings for these parameters in a brute-force way. Using optimal learning, we can guide you through a sequence of simulations designed to minimize the number of in-silico experiments needed to discover optimal parameter settings.

Work with us

Spend more time in the lab and not in front of a computer. We can tailor and run a simulation solution specific to your problem, help keep track of simulation data and efficiently discover the correct parameters for your simulations.