Rowan now has a Fukui index & global electrophilicity workflow! Quickly predict, design, and modulate the reactivity of your small molecule. Read the blog post.

User-centered quantum chemistry

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.

Rowan ... has the potential to democratise access to specialist quantum chemistry tools that could massively accelerate drug discovery.

Laksh Aithani
Founder & CEO, CHARM Therapeutics

No input files

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.

A screenshot of Rowan's job submission page.

No waiting for jobs to run

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.

A screenshot of Rowan's job submission page.

Fast pKa prediction

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.

A screenshot of a pKa prediction in Rowan.

Rowan is now my first port of call for QM modelling. The attention paid to pragmatic and benchmarked methods means the software suite is practical to incorporate into our workflows.

Lewis Martin
CSO, OpenBench

Convenient Python API

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(
    name="opt cyclobutane",
    tasks=["optimize", "charge"]

View and share results immediately

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.

A screenshot of a jobs results page on Rowan.

State-of-the-art security measures

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).

A diagram of Rowan's infrastructure.

No upfront costs

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.


Run Rowan through our web platform or Python API.
2¢ per credit
+ 500 free credits
(One credit is one minute of CPU time.)
Get started →


Connect with us to discuss a deployment strategy and pricing that works for your organization!
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Rowan is backed by Pillar VC through the Harvard $1M Moonshot competition.

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