Running quantum chemical simulations is often difficult and frustrating. Even skilled computational chemists waste hours fighting their software, and non-experts don’t even know where to start.
Using software for your research should be as easy as using Uber, Venmo, or ChatGPT. Go from hypothesis to results in mere minutes with our cloud-based quantum chemistry platform.
Just upload your molecule, choose your task and level of theory, and run your job.
Rowan currently supports Hartree-Fock and density-functional theory (DFT) through PySCF, semiempirical methods through xTB, and the AIMNet2 machine-learned interatomic potential.
You’ll never have to worry about long queues before upcoming deadlines again. We allocate a new computer in the cloud for every job, so every job starts within a minute or two.
Predict the acidity of small molecules with Rowan’s pKa workflow. Rowan uses AIMNet2, a machine-learned interatomic potential, to obtain fast and reasonable pKa values with minimal empiricism.
Quickly script your own workflow with Rowan’s convenient Python API. Output data is returned as a developer-friendly Python dictionary, making downstream analysis simple. And the API is fully integrated with the web backend, so jobs submitted from the web can be analyzed through Python (and vice versa).
import cctk
import rowan
rowan.api_key = "rowan-sk..."
client = rowan.Client()
# load molecule by name
molecule = cctk.Molecule.new_from_name("cyclobutane")
# run calculation remotely and return result
result = client.compute(
"calculation",
input_mol=molecule,
name="opt cyclobutane",
method="aimnet2_wb97md3",
tasks=["optimize", "charge"]
)
You can see presentation-quality graphs and figures even before a job is finished. No need to teach an experimental scientist how to open a coordinate file—just generate a shareable link for any calculation and send it over.
Rowan encrypts all your data in transit and at rest.
For confidential jobs, run Rowan’s Python API in delete_when_finished
mode or run the Rowan AMI in your own AWS account or virtual private cloud (awaiting AWS Marketplace approval).
Do you want to run DFT calculations occasionally, but you’re not sure if you can justify purchasing an expensive license? Rowan’s usage-based pricing model means you can pay as you go for the computational time you use. If your organization has consistent demand and wants more predictable costs, we have pricing plans for that, too.