ai4chems/nist-logv-binary

NIST log-viscosity — base dataset + per-fit CSVs

A two-layer dataset:

1. points (HF dataset main payload) — the binary-only chemixhub nist-logV measurements plus a mixture_T_id join key. Immutable; one row per measurement. This is the data layer.
2. fits/<name>.csv — one CSV per fit, keyed on mixture_T_id. Each CSV holds whatever that fitter's results look like — fit parameters plus a small canonical set (pure_at_x0, pure_at_x1, nonlinearity). Multiple fits coexist; pushing a new fit doesn't touch the base data.

Built by mixtureml-data.

What's in the box

• points (HF dataset main payload, ~150K rows) — one row per measurement. Columns: mixture_T_id, value (log viscosity), T (K), P (bar), cmp_ids (length-2 list of chemixhub compound IDs), cmp_mole_fractions (length-2 list, aligned with cmp_ids).
• compounds.csv — chemixhub's compound_id → smiles map, filtered to the IDs that appear in points. Lets downstream consumers translate cmp_ids to SMILES without depending on the chemixhub repo.
• fits/rk_loocv.csv — the inaugural fit. One row per (mixture, integer-rounded T-bin), keyed on mixture_T_id. See the schema below.

Additional fits can be pushed alongside fits/rk_loocv.csv without disturbing anything else; each has its own filename.

Method: rk_loocv fit

Per binary mixture and per integer-rounded T-bin (≥ 4 measurement rows), fit the Redlich-Kister model:

y(x) = β_2·(1 − x) + β_1·x  +  x(1 − x) · Σᵢ aᵢ (2x − 1)ⁱ        for i = 0..p
       └──── ψ (ideal) ─────┘     └──────── δ (excess) ──────────┘

The polynomial degree p ∈ {0, 1, 2, 3} is selected per bin via leave-one-out cross-validation on the Predictive Average Relative Deviation (PARD):

PARD = (100 / N) · Σ_i |y_pred^(−i) − y_i| / |y_i|

Closed-form LOO via the OLS hat-matrix diagonal: r_loo_i = r_i / (1 − h_ii). The winning p minimizes PARD. This is the methodology of Ramírez-de-Santiago, Viscosity of Binary Liquid Mixtures, Ind. Eng. Chem. Res. 2024, 63, 22470−22480 (§2, eqs 3–5).

For RkLoocvFitter, pure_at_x0 and pure_at_x1 equal β_2 and β_1 exactly (the excess vanishes at x = 0 and x = 1 because of the x(1 − x) factor). nonlinearity is the L2 norm of the excess on [0, 1], computed analytically: √(a^T G a) where G_ij = ∫₀¹ x²(1−x)² (2x−1)^(i+j) dx.

Loading

import ast
from datasets import load_dataset
from huggingface_hub import hf_hub_download
import pandas as pd

REPO = "ai4chems/nist-logv-binary"

# Base data
points = load_dataset(REPO, split="train").to_pandas()
compounds = pd.read_csv(hf_hub_download(REPO, "compounds.csv", repo_type="dataset"))

# RK-LOOCV fit
rk = pd.read_csv(hf_hub_download(REPO, "fits/rk_loocv.csv", repo_type="dataset"))

# Sidecar cmp_ids columns come back as stringified lists — parse if needed:
rk["cmp_ids"] = rk["cmp_ids"].apply(ast.literal_eval)
rk["mixture_T_id"] = rk["mixture_T_id"].apply(ast.literal_eval)

# Join points ↔ fit on mixture_T_id (single column).
points["mixture_T_id_t"] = points["mixture_T_id"].apply(tuple)
rk["mixture_T_id_t"] = rk["mixture_T_id"].apply(tuple)
joined = points.merge(rk, on="mixture_T_id_t", how="left", suffixes=("", "_fit"))

# compound_id → SMILES lookup.
smiles = compounds.set_index("compound_id")["smiles"].to_dict()

Reconstructing per-row predictions

The fit CSV publishes the per-bin coefficients only; per-row predictions are recomputable in closed form:

import numpy as np

def rk_predict(row):
    """row from the joined frame: needs x_1, beta_1=pure_at_x1, beta_2=pure_at_x0, a_0..a_3."""
    x1_arr = np.array(row["cmp_mole_fractions"])
    c1 = min(row["cmp_ids"])
    x = float(x1_arr[list(row["cmp_ids"]).index(c1)])
    base = x * (1.0 - x)
    skew = 2.0 * x - 1.0
    excess = 0.0
    for i in range(4):
        a_i = row.get(f"a_{i}")
        if a_i is not None and not np.isnan(a_i):
            excess += a_i * (skew ** i)
    return row["pure_at_x1"] * x + row["pure_at_x0"] * (1.0 - x) + base * excess

nonlinearity and linear_span = pure_at_x1 − pure_at_x0 are likewise downstream computations.

Schema

points (HF dataset main payload)

• mixture_T_id — [cmp_id1_sorted, cmp_id2_sorted, round(T)]. The join key.
• value — log viscosity (chemixhub-canonical column name).
• T, P — temperature (K), pressure (bar).
• cmp_ids — length-2 list of chemixhub compound IDs.
• cmp_mole_fractions — length-2 list of mole fractions, aligned with cmp_ids.

fits/rk_loocv.csv

Bin metadata (duplicated so the CSV is self-contained):

• mixture_T_id, cmp_ids, T — keys.
• n_in_bin — number of measurement rows in this bin.
• T_min, T_max, P_min, P_max — actual spans of the bin's measurements (T_min and T_max typically straddle T).

Canonical fit columns (every fit CSV has these):

• pure_at_x0 — fitted curve at x = 0.
• pure_at_x1 — fitted curve at x = 1.
• nonlinearity — L2 norm of (curve − linear baseline) on [0, 1].
• minima_x, minima_value — argmin / min of the fitted curve on [0, 1]; endpoints are valid candidates. Never NaN.
• maxima_x, maxima_value — argmax / max of the fitted curve on [0, 1]; endpoints are valid candidates. Never NaN.

rk_loocv-specific columns:

• best_p — the LOOCV-selected RK degree (∈ {0, 1, 2, 3}).
• best_pard — the predictive ARD (%) at best_p (lower is better; cross-validated, so it tracks generalization).
• best_aard — the training-set ARD (%) at best_p (lower-bounded by PARD; closer to PARD ⇒ less overfitting headroom).
• a_0, a_1, a_2, a_3 — RK excess coefficients. Coefficients with index i > best_p are NaN.

Source

Built from chemixhub's processed_NISTlogV.csv (chemcognition-lab/chemixhub), which derives from the NIST log-viscosity benchmark. Compound IDs are those used by chemixhub.

Single-source filter: every (cmp_ids, round(T)) bin in the published data comes from a single chemixhub Ref_ID. Bins where the source data contained measurements from more than one reference were dropped at load time, before fitting. Different references reporting the same nominal measurement typically have systematic offsets, and pooling them would distort the RK fit.