rowan

name	rowan
description	Cloud quantum chemistry platform with Python SDK. Run geometry optimization, conformer generation, torsional scans, and energy minimization (DFT/semiempirical), and retrieve properties (dipole, partial charges, frontier orbitals) — no local QC software or HPC needed.
license	Proprietary

rowan

Overview

Rowan is a cloud quantum chemistry platform that exposes DFT and semiempirical calculations through a Python SDK (rowan). Submit calculations (geometry optimization, conformer generation, torsional scans, single-point energies) from Python scripts or Jupyter notebooks, and retrieve results — energies, geometries, partial charges, frontier orbital energies — without managing Gaussian, ORCA, or Psi4 installations. Rowan handles job queuing, execution, and storage. A free tier is available for academic and exploratory use.

When to Use

• Geometry optimization of small molecules: Getting accurate equilibrium geometries for drug candidates, fragments, or building blocks using DFT.
• Conformer generation with energy ranking: Generating and optimizing multiple conformers to identify the lowest-energy conformation for docking or property prediction.
• Torsional potential scans: Mapping the energy profile along a rotatable bond to understand conformational preferences.
• Quantum mechanical property calculation: Computing dipole moments, partial charges (Mulliken, ESP), HOMO/LUMO energies, and electrostatic potential surfaces.
• Energy minimization before docking: Refining ligand geometries before input to structure-based docking tools (DiffDock, AutoDock Vina).
• Comparing isomer stability: Calculating relative energies of tautomers, stereoisomers, or constitutional isomers.
• For large-scale conformer screening (>1000 molecules), use RDKit's ETKDGv3 + MMFF (force field level, no cloud cost).
• For protein-scale quantum mechanics/molecular mechanics (QM/MM), specialized packages like ORCA + CP2K are needed.

Prerequisites

• Python packages: rowan (official Python SDK)
• Account: Free account at https://rowan.chem.ucla.edu/ (academic) or https://rowanquantum.com/
• API key: Set ROWAN_API_KEY environment variable after account creation
• Data requirements: Molecular structures as SMILES strings or XYZ coordinate blocks

pip install rowan

# Set API key (add to .bashrc or .env)
export ROWAN_API_KEY="your_api_key_here"

Quick Start

import rowan

# Authenticate (uses ROWAN_API_KEY environment variable automatically)
client = rowan.RowanClient()

# Run geometry optimization of aspirin at GFN2-xTB level
job = client.compute(
    smiles="CC(=O)Oc1ccccc1C(=O)O",
    method="gfn2-xtb",
    tasks=["optimize"],
)
print(f"Job ID: {job.id}, Status: {job.status}")

# Wait for completion and retrieve energy
result = client.wait(job.id)
print(f"Energy: {result.energy:.6f} Hartree")
print(f"Optimized geometry atoms: {len(result.geometry.atoms)}")

Core API

Module 1: Client Initialization and Authentication

import rowan
import os

# Option 1: automatic (reads ROWAN_API_KEY env variable)
client = rowan.RowanClient()

# Option 2: explicit key
client = rowan.RowanClient(api_key=os.environ["ROWAN_API_KEY"])

print(f"Authenticated as: {client.user.email}")
print(f"Organization: {client.user.organization}")

Module 2: Geometry Optimization

Optimize a molecular geometry to the nearest local minimum.

import rowan

client = rowan.RowanClient()

# GFN2-xTB semiempirical (fast, good for conformer screening)
job_xtb = client.compute(
    smiles="CCc1ccc(cc1)NC(=O)C",    # paracetamol
    method="gfn2-xtb",
    tasks=["optimize"],
)
result_xtb = client.wait(job_xtb.id)
print(f"xTB optimized energy: {result_xtb.energy:.6f} Hartree")
print(f"Geometry: {len(result_xtb.geometry.atoms)} atoms")

# DFT optimization: B3LYP/6-31G* (accurate, slower)
job_dft = client.compute(
    smiles="CCc1ccc(cc1)NC(=O)C",
    method="b3lyp",
    basis_set="6-31g*",
    tasks=["optimize"],
    solvent="water",               # implicit solvent (SMD model)
)
result_dft = client.wait(job_dft.id)
print(f"B3LYP/6-31G* energy (water): {result_dft.energy:.6f} Hartree")
print(f"Dipole moment: {result_dft.dipole_moment:.3f} Debye")

Module 3: Conformer Generation

Generate multiple 3D conformers and rank by energy.

import rowan
import pandas as pd

client = rowan.RowanClient()

# Generate and optimize 10 conformers at GFN2-xTB level
job = client.compute(
    smiles="CC(C)CC1=CC=C(C=C1)C(C)C(=O)O",  # ibuprofen
    method="gfn2-xtb",
    tasks=["conformers"],
    n_conformers=10,
)
result = client.wait(job.id)

# Rank conformers by relative energy
conformers = result.conformers
df = pd.DataFrame([
    {"conformer_id": i,
     "energy_hartree":    c.energy,
     "rel_energy_kcal":   (c.energy - min(c2.energy for c2 in conformers)) * 627.509}
    for i, c in enumerate(conformers)
]).sort_values("rel_energy_kcal")

print(df.to_string(index=False))
print(f"\nLowest energy conformer ID: {df.iloc[0]['conformer_id']}")

Module 4: Torsional Scan

Map energy as a function of a dihedral angle.

import rowan
import numpy as np
import matplotlib.pyplot as plt

client = rowan.RowanClient()

# Scan the C-C=C-C dihedral of butene
job = client.compute(
    smiles="CC=CC",             # but-2-ene
    method="gfn2-xtb",
    tasks=["torsion_scan"],
    torsion_atoms=[0, 1, 2, 3],  # atom indices defining dihedral
    n_scan_points=36,             # 36 points = 10-degree steps
)
result = client.wait(job.id)

angles  = [point.angle  for point in result.torsion_scan]
energies = [point.energy for point in result.torsion_scan]
rel_e = [(e - min(energies)) * 627.509 for e in energies]  # convert to kcal/mol

fig, ax = plt.subplots(figsize=(7, 4))
ax.plot(angles, rel_e, "o-", color="steelblue")
ax.set_xlabel("Dihedral angle (degrees)")
ax.set_ylabel("Relative energy (kcal/mol)")
ax.set_title("Torsional potential: but-2-ene C-C=C-C dihedral")
plt.tight_layout()
plt.savefig("torsion_scan.png", dpi=150)
print("Torsion scan saved -> torsion_scan.png")

Module 5: Single-Point Properties

Compute electronic properties at a fixed geometry (HOMO/LUMO, partial charges, ESP).

import rowan

client = rowan.RowanClient()

# Single-point DFT: get frontier orbital energies and partial charges
job = client.compute(
    smiles="c1ccccc1N",          # aniline
    method="b3lyp",
    basis_set="6-311+g**",
    tasks=["single_point", "partial_charges", "orbitals"],
)
result = client.wait(job.id)

print(f"HOMO energy: {result.homo_energy:.4f} eV")
print(f"LUMO energy: {result.lumo_energy:.4f} eV")
print(f"HOMO-LUMO gap: {result.lumo_energy - result.homo_energy:.4f} eV")
print(f"Dipole moment: {result.dipole_moment:.3f} Debye")

# Partial charges (Mulliken)
for i, (atom, charge) in enumerate(zip(result.geometry.atoms, result.partial_charges)):
    print(f"  Atom {i} ({atom.symbol}): {charge:+.4f} e")

Module 6: Batch Job Submission

Submit multiple molecules concurrently.

import rowan
import pandas as pd
from concurrent.futures import ThreadPoolExecutor, as_completed

client = rowan.RowanClient()

smiles_list = [
    ("aspirin",      "CC(=O)Oc1ccccc1C(=O)O"),
    ("caffeine",     "Cn1cnc2c1c(=O)n(c(=O)n2C)C"),
    ("paracetamol",  "CC(=O)Nc1ccc(O)cc1"),
    ("ibuprofen",    "CC(C)Cc1ccc(cc1)C(C)C(=O)O"),
]

def submit_and_wait(name, smiles):
    job = client.compute(smiles=smiles, method="gfn2-xtb", tasks=["optimize"])
    result = client.wait(job.id)
    return {"name": name, "smiles": smiles, "energy": result.energy}

results = []
with ThreadPoolExecutor(max_workers=4) as executor:
    futures = {executor.submit(submit_and_wait, n, s): n for n, s in smiles_list}
    for future in as_completed(futures):
        results.append(future.result())

df = pd.DataFrame(results)
df.to_csv("batch_energies.csv", index=False)
print(df)

Key Parameters

Parameter	Module	Default	Range / Options	Effect
method	compute	required	"gfn2-xtb", "b3lyp", "wb97x-d", "mp2"	QC method; xTB is fastest, DFT is accurate, MP2 for correlation
basis_set	compute (DFT)	method-dependent	"6-31g*", "6-311+g**", "def2-svp", "def2-tzvp"	Basis set; larger = more accurate and slower
tasks	compute	required	["optimize"], ["single_point"], ["conformers"], ["torsion_scan"]	What calculation to run
n_conformers	conformers task	10	1–100	Number of conformers to generate and optimize
n_scan_points	torsion_scan	36	12–72	Number of dihedral scan points (360/n gives step size)
solvent	compute	None	"water", "dmso", "methanol", etc.	Implicit SMD solvation model
torsion_atoms	torsion_scan	required	list of 4 atom indices	Defines the dihedral angle for the scan

Best Practices

1. Use GFN2-xTB for screening, DFT for final characterization: xTB is 100–1000x faster than DFT and adequate for conformer ranking and geometry exploration. Switch to B3LYP or wB97X-D for accurate energetics, HOMO/LUMO gaps, and publishable results.

2. Always optimize geometry before computing properties: Single-point calculations on unrelaxed geometries (e.g., from SMILES 3D embedding) give unreliable energies and electronic properties. Run tasks=["optimize"] first.

3. Set solvent for biologically relevant molecules: Gas-phase energies often differ dramatically from solution-phase for charged or highly polar molecules. Use solvent="water" for drug candidates evaluated in aqueous conditions.

4. Poll job status for long DFT calculations: Use client.wait() with a timeout or poll with client.get_job(job_id).status in long-running batch workflows to avoid blocking.

5. Export optimized geometries as XYZ for reuse: Save the optimized geometry to avoid re-running costly DFT jobs when different property calculations are needed on the same structure.

   with open("optimized.xyz", "w") as f:
       f.write(result.geometry.to_xyz())
   

Common Workflows

Workflow 1: Conformer Search and Property Calculation

Goal: Find the lowest-energy conformer of a drug candidate and compute its electronic properties.

import rowan
import pandas as pd

client = rowan.RowanClient()
smiles = "CC(C)Cc1ccc(cc1)C(C)C(=O)O"   # ibuprofen

# Step 1: Generate and rank conformers at fast xTB level
conf_job = client.compute(
    smiles=smiles, method="gfn2-xtb", tasks=["conformers"], n_conformers=20
)
conf_result = client.wait(conf_job.id)
lowest_conf = min(conf_result.conformers, key=lambda c: c.energy)
print(f"Lowest conformer energy: {lowest_conf.energy:.6f} Hartree")

# Step 2: Refine with DFT and compute properties
dft_job = client.compute(
    xyz=lowest_conf.to_xyz(),      # use optimized xTB geometry as DFT start
    method="b3lyp",
    basis_set="6-31g*",
    tasks=["optimize", "partial_charges", "orbitals"],
    solvent="water",
)
dft_result = client.wait(dft_job.id)

print(f"B3LYP/6-31G* energy (water): {dft_result.energy:.6f} Hartree")
print(f"HOMO-LUMO gap: {dft_result.lumo_energy - dft_result.homo_energy:.4f} eV")
print(f"Dipole moment: {dft_result.dipole_moment:.3f} Debye")

Workflow 2: Relative Stability of Tautomers

Goal: Compare the energies of two tautomers to determine which is more stable in water.

import rowan

client = rowan.RowanClient()

tautomers = {
    "keto":   "CC(=O)CC(=O)C",     # acetylacetone keto form
    "enol":   "CC(=O)C=C(O)C",     # acetylacetone enol form
}

energies = {}
for name, smiles in tautomers.items():
    job = client.compute(
        smiles=smiles,
        method="b3lyp",
        basis_set="6-311+g**",
        tasks=["optimize"],
        solvent="water",
    )
    result = client.wait(job.id)
    energies[name] = result.energy
    print(f"{name}: {result.energy:.6f} Hartree")

delta_e_hartree = energies["enol"] - energies["keto"]
delta_e_kcal    = delta_e_hartree * 627.509
print(f"\nDelta E (enol - keto): {delta_e_kcal:.2f} kcal/mol")
print(f"More stable form: {'enol' if delta_e_kcal < 0 else 'keto'}")

Expected Outputs

• result.energy — total electronic energy in Hartree
• result.geometry — optimized 3D geometry (atoms + coordinates)
• result.dipole_moment — scalar dipole moment in Debye
• result.homo_energy, result.lumo_energy — frontier orbital energies in eV
• result.partial_charges — list of per-atom charges in elementary charge units
• result.conformers — list of conformer objects with individual energies
• result.torsion_scan — list of (angle, energy) scan points

Common Recipes

Recipe: Quick Single-Point Energy

When to use: Compare relative energies of two conformers or reaction intermediates in one call.

import rowan

client = rowan.Client()

smiles_list = ["CC(=O)O", "C(=O)(O)C"]  # Acetic acid, two representations
jobs = []
for smi in smiles_list:
    job = client.compute(
        molecule=rowan.Molecule.from_smiles(smi),
        theory_level="gfn2-xtb",
        task="single_point",
    )
    jobs.append(job)

# Collect energies
for smi, job in zip(smiles_list, jobs):
    result = client.wait(job)
    print(f"{smi}: energy = {result.energy:.6f} Hartree")

Recipe: Check Running Job Status

When to use: Monitor a long-running optimization or conformer search without blocking.

import rowan, time

client = rowan.Client()
job = client.compute(
    molecule=rowan.Molecule.from_smiles("c1ccccc1"),
    theory_level="gfn2-xtb",
    task="optimize",
)
print(f"Job ID: {job.id}")

# Poll every 10 seconds (non-blocking)
for _ in range(30):
    status = client.status(job)
    print(f"Status: {status}")
    if status in ("completed", "failed"):
        break
    time.sleep(10)

result = client.get(job)
print(f"Final energy: {result.energy:.6f} Hartree")

Troubleshooting

Problem	Cause	Solution
AuthenticationError	API key not set or expired	Set ROWAN_API_KEY env variable; regenerate key at rowanquantum.com
Job stays in queued status	High server load on free tier	Wait or upgrade to paid tier; free tier may queue during peak hours
SCF not converged error	DFT self-consistent field failed	Try a smaller basis set; use method="gfn2-xtb" first to get a better starting geometry
ValueError: invalid SMILES	Malformed SMILES string	Validate with Chem.MolFromSmiles(smiles) using RDKit before submission
High energy after optimization	Geometry stuck in local minimum	Generate multiple conformers with tasks=["conformers"] and pick the lowest
Missing result.homo_energy	Orbital calculation not requested	Add "orbitals" to the tasks list

References

• Rowan Documentation — official SDK and API reference
• Rowan Python SDK (GitHub) — source code and examples
• GFN2-xTB paper: Bannwarth et al. (2019), J. Chem. Theory Comput. — xTB semiempirical method used by Rowan
• B3LYP functional reference: Becke (1993), J. Chem. Phys. — foundational DFT method