background pattern

Desmond for Materials Science

High-performance molecular dynamics (MD) engine providing high scalability, throughput, and scientific accuracy

Desmond for Materials Science

Understand and predict key properties of materials with fast, accurate molecular dynamics

Desmond is a GPU-powered high-performance molecular dynamics (MD) engine for predicting bulk properties of materials, such as thermophysical properties, elastic constants, stress/strain relationships, diffusion coefficients, viscosity, persistence length, free energy of solvation, and more. Desmond also characterizes structure and properties in complex systems involving non-equilibrium systems as well as interfaces or self-assembled structures.

Comprehensive molecular dynamics capabilities

Speed time to market of new catalysts
Exceptional performance

Achieve exceptional throughput on commodity Linux clusters with both typical and high-end networks. Improve computing speed by 100x on general-purpose GPU (GPGPU) versus single CPU.

Superior accuracy
Superior accuracy

Constructed with a focus on numerical accuracy, stability, and rigor. Enables the simulation of large scale features of nanometers to micron size over time scales of picoseconds to microseconds.

Trusted energetics
Trusted energetics

Provides a robust framework for the calculation of energies and forces for atomistic and coarse grained force field models. Compatible with chemistries commonly used in both biomolecular and condensed-matter research.

Realistic simulations
Realistic simulations

Perform explicit solvent simulations with periodic boundary conditions using cubic, orthorhombic, truncated octahedron, rhombic dodecahedron, and arbitrary triclinic simulation boxes with careful attention to the efficient and accurate calculation of long-range electrostatics, and can be used to model explicit membrane systems, complex mixtures, polymers, and interfaces under various conditions.

Easy-to-use interface
Easy-to-use interface

Support automated simulation setup, including multistage MD simulations with built-in simulation protocols, prediction of equation of states (EOS) at multiple temperatures, and prediction of dynamic responses at non-equilibrium states. An intuitive interface provides intelligent default settings and allows for rapid setup of computational experiments. 

Powerful analysis tools
Powerful analysis tools

Visualize and examine computed results within the same MS Maestro modeling environment that connects to a comprehensive suite of modeling tools from quantum mechanics to machine learning.

Case Studies

Discover how Schrödinger technology is being used to solve real-world research challenges.

Molecular dynamics and coarse-grained simulations facilitate design new eco-friendly cosmetic formulations
Prediction of moisture adsorption and effects on amorphous starch
Molecular dynamics simulations accelerate the development and optimization of recyclable tire materials

Broad applications across materials science research areas

Get more from your ideas by harnessing the power of large-scale chemical exploration and accurate in silico molecular prediction.

Polymeric Materials
Complex Formulations
Energy Capture & Storage
Organic Electronics
Consumer Packaged Goods

Official NVIDIA Partner

Schrödinger has a strategic partnership with NVIDIA to optimize our computational drug discovery platform for NVIDIA GPU technology.

dark theme background

Related Products

Learn more about the related computational technologies available to progress your research projects.

MS Maestro

Complete modeling environment for your materials discovery


Modern, comprehensive force field for accurate molecular simulations


Efficient coarse-grained (CG) molecular dynamics (MD) simulations for large systems over long time scales

MS Morph

Efficient modeling tool for organic crystal habit prediction

MS Penetrant Loading

Molecular dynamics (MD) modeling for predicting water loading and small molecule gas adsorption capacity of a condensed system

MS Transport

Efficient molecular dynamics (MD) simulation tool for predicting liquid viscosity and diffusions of atoms and molecules


Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Materials Science
Molecular-scale exploration of mechanical properties and interactions of poly(lactic acid) with cellulose and chitin
Materials Science
Physics-based molecular modeling of biosurfactants
Life Science
Atomistic simulations of the Escherichia coli ribosome provide selection criteria for translationally active substrates
Materials Science
Redesigning an (R)-Selective Transaminase for the Efficient Synthesis of Pharmaceutical N-Heterocyclic Amines
Materials Science
The influence of axial fluorination of SubPc on the photoresponse performances of small-molecule organic photodiodes
Materials Science
Free charge photogeneration in a single component high photovoltaic efficiency organic semiconductor
Materials Science
QC and MD Modelling for Predicting the Electrochemical Stability Window of Electrolytes: New Estimating Algorithm
Materials Science
Algorithm for Theoretical Assessment of the Electrochemical Stability of Electrolytes in Lithium-Ion Batteries by the Example of LiBF4 in the EC/DMC Mixture
Materials Science
A molecular dynamics (MD) simulation of the solubility behaviours of cellulose in aqueous cuprammonium hydroxide solution
Materials Science
Pyrene-based chalcones as functional materials for organic electronics application

Training & Resources

Online certification courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.


Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.