Physical properties,
as closed-form equations.
Drop a σ-profile in, get a property out — melting point, solubility, hydration free energy, logP, Hansen parameters. No trained model, no black box: every kernel is a closed-form ridge equation over quantum-chemical descriptors, on a ¼-integer exponent grid, validated on scaffold-blind held-out chemistry.
Closed-form, not trained
ŷ = β₀ + Σ βⱼ·z(fⱼ^pⱼ), solved by a single Cholesky solve. No SGD, no learnable weights, no hyperparameters. We say 'fit' and 'solve', never 'train'.
Quantum inputs only
The 9-dim feature vector comes from one DFT SCF per molecule (wB97M-V/def2-SVP/C-PCM). No empirical group contributions, no fitted correction tables.
Honest by construction
Every kernel carries its 25% scaffold-blind lift and a plain verdict — A.IMPROVE or B.UNDERFIT. We show the corrected numbers, including the ones that went down.
Featured kernels
All 12 kernels →MNSol · Water
Hydration free energy ΔG_hyd
Bradley · Tm
Melting point
MNSol · Hexadecane
Non-polar solvation ΔG_solv
Aqueous · logP
Partition coefficient logP (cosolvent)
HSP · δ_h
Hansen H-bonding parameter
We publish the corrections that hurt.
Our melting-point kernel was published at r = 0.826. A later audit found a hydrogen-bond classification bug inflating it — aromatic π-clouds miscounted as donors. The honest, corrected value is r = 0.655. We changed it everywhere. Finding and reporting your own bugs is the whole point of a lab.
Read all the findings →Have your own property data?
Upload a set of mfsig σ-profiles with measured values and we can fit a kernel against it — same rigor gate, same honest verdict.
Upload your mfsig dataset