Proton-coupled electron transfer rate theory from first principles: multi-channel vibronic rates with Monte Carlo uncertainty quantification, independently validated against pyPCET to 0.1% agreement on benchmark model systems.
Proton-coupled electron transfer (PCET) is the mechanism behind enzyme catalysis, photosynthesis, respiration, and fuel cells. The kinetic isotope effect (KIE) — how much reaction rates change when hydrogen is replaced by deuterium — reveals the quantum mechanical tunneling contribution.
Predicting KIE accurately requires expensive DFT/QM calculations (hours per system) or empirical fitting. No existing tool provides rapid, general KIE prediction from molecular parameters alone.
Molecular Hessian matrices (force constants) are symmetric positive-definite. The V+/V− decomposition separates vibrational mode structure (shape) from coupling strength (magnitude), enabling geometric feature extraction.
A novel application of Riemannian geometry to molecular kinetics. The implementation reproduces pyPCET reference rates to 0.1%, and Monte Carlo uncertainty quantification reports honest 50–100% rate uncertainties from parameter sensitivity.
Drug design requires understanding how enzymes accelerate reactions. KIE reveals whether proton tunneling is significant.
Fuel cells and artificial photosynthesis depend on PCET. Optimizing catalysts requires accurate rate prediction.
Mitochondrial electron transport, DNA repair, and antioxidant mechanisms all involve PCET chemistry.
Independent numerical validation against pyPCET; enzyme benchmark withdrawn pending provenance re-verification of literature rate data.
Independent implementation, BIP model systems
Monte Carlo UQ from parameter sensitivity
398 core + 6 API tests
The proton sits in a double-well potential between donor and acceptor. Its quantum wavefunction tunnels through the barrier — heavier deuterium tunnels far less, producing the kinetic isotope effect.
FC overlap
FC overlap
H/D ≈ KIE driver
Reactant and product free-energy parabolas intersect at the transition state. The proton tunnels below the classical barrier — the shaded region shows the tunneling shortcut.
Activation energy
Marcus region
Crossing point
Visualize how vibrational normal modes modulate the donor-acceptor tunneling distance. Select a mode to see how different motions affect proton transfer.
C···O separation
Tunneling probability
150 cm⁻¹
The donor (C) and acceptor (O) oscillate toward and away from each other, modulating the tunneling distance. This low-frequency gating mode is the primary determinant of proton tunneling rates.
Try the PCET Engine live. Toggle to “Live” to hit the real API.
Compute PCET rate from molecular parameters. SLO-1 wild-type example.
Screen mutations with just variant names and measured rates. No crystal structures or QM needed.
Screen directed evolution libraries computationally before wet-lab synthesis. Rank 1,000 variants in seconds, synthesize the top 50.
Saves: $50K–$500K per screening round in wet-lab costs
Predict cytochrome P450 PCET rates for drug candidates. Identify metabolic soft spots from molecular parameters, not just binding poses.
Complements: Simcyp, GastroPlus, ADMET tools
Optimize fuel cell catalysts, electrolyzer electrodes, and industrial biocatalysts. Electrochemical PCET with overpotential-dependent rates.
Markets: Hydrogen, fuel cells, water treatment
Consulting: $15K–$50K per enzyme optimization study. We calibrate, screen, and deliver ranked candidates. Learn more
Free academic tier: 100 calls/month, single-channel vibronic rates. No credit card required.
import requests
response = requests.post(
"https://pcet.omnisciences.io/v1/rate/parameters",
json={
"donor_acceptor_distance": 2.7,
"driving_force": -0.2,
"reorganization_energy": 1.1,
"coupling": 0.01,
"temperature": 300,
"proton_donor_acceptor_distance": 0.95,
"isotope": "H"
}
)
data = response.json()
print(f"Rate: {data['rate_constant']:.1f} s⁻¹")
print(f"KIE: {data['kie_factor']:.1f}")