MINERALCO: A Seven-Parameter Atomic Architecture Framework for Mineral Phase Intelligence — Equation of State Modelling, Lattice Thermodynamics & Mantle Phase Transition Prediction

MINERALCO (Mineral Intelligence Network for Equation-of-state Research, Atomic Lattice COmputation) is an open-source Python framework for the systematic characterisation of mineral behaviour under the extreme pressure and temperature conditions prevailing throughout Earth's crust, mantle, and core (0.1 MPa to 363 GPa; 300 K to 6,000 K). The framework introduces the Crystal Intelligence Septuplet (CIS) — a unified seven-parameter system comprising: (1) Bulk Modulus K0, (2) Lattice Parameters a, b, c, (3) Pressure Derivative K', (4) Crystal Symmetry Sy, (5) Thermal Expansion Coefficient alpha, (6) Gruneisen Parameter gamma, and (7) Specific Volume Vs — that fully parameterises the third-order Birch-Murnaghan equation of state and the Mie-Gruneisen thermal pressure correction for any mineral accessible within Earth. MINERALCO was validated against 3,200 experimental P-V-T data points for 47 mantle and core minerals, achieving a mean RMS volumetric prediction error of 0.19% (target: less than or equal to 0.30%) and correctly predicting phase transition onset for 43 of 47 benchmark minerals (91.5%), including all four major seismic discontinuity-producing transitions (410 km, 520 km, 660 km, D'' layer). The Symmetry-Stiffness Paradigm is confirmed: crystal symmetry class Sy is a statistically significant predictor of pressure derivative K' (Pearson r = -0.88, p less than 0.001). Thermodynamic and phonon Gruneisen parameters are self-consistent to r2 = 0.971 across 28 minerals. The bridgmanite seven-parameter CIS predicts PREM seismic velocities at 660 km depth to within 0.4% for VP and less than 0.1% for VS without any empirical adjustment. Software: pip install mineralco | GitHub: github.com/gitdeeper9/mineralco | GitLab: gitlab.com/gitdeeper9/mineralco | Dashboard: mineralco.netlify.app | OSF: osf.io/4xghc | OSF Registration DOI: 10.17605/OSF.IO/VG7ET | Submitted to: Physics and Chemistry of Minerals (Springer), March 2026.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-13
DOI
https://doi.org/10.5281/zenodo.19009597
Primary Topic
High-pressure geophysics and materials
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

MINERALCO: A Seven-Parameter Atomic Architecture Framework for Mineral Phase Intelligence — Equation of State Modelling, Lattice Thermodynamics & Mantle Phase Transition Prediction

Samir Baladi
Zenodo (CERN European Organization for Nuclear Research)
High-pressure geophysics and materials
article

MINERALCO: A Seven-Parameter Atomic Architecture Framework for Mineral Phase Intelligence — Equation of State Modelling, Lattice Thermodynamics & Mantle Phase Transition Prediction

Samir Baladi
article en

Abstract

MINERALCO (Mineral Intelligence Network for Equation-of-state Research, Atomic Lattice COmputation) is an open-source Python framework for the systematic characterisation of mineral behaviour under the extreme pressure and temperature conditions prevailing throughout Earth's crust, mantle, and core (0.1 MPa to 363 GPa; 300 K to 6,000 K). The framework introduces the Crystal Intelligence Septuplet (CIS) — a unified seven-parameter system comprising: (1) Bulk Modulus K0, (2) Lattice Parameters a, b, c, (3) Pressure Derivative K', (4) Crystal Symmetry Sy, (5) Thermal Expansion Coefficient alpha, (6) Gruneisen Parameter gamma, and (7) Specific Volume Vs — that fully parameterises the third-order Birch-Murnaghan equation of state and the Mie-Gruneisen thermal pressure correction for any mineral accessible within Earth. MINERALCO was validated against 3,200 experimental P-V-T data points for 47 mantle and core minerals, achieving a mean RMS volumetric prediction error of 0.19% (target: less than or equal to 0.30%) and correctly predicting phase transition onset for 43 of 47 benchmark minerals (91.5%), including all four major seismic discontinuity-producing transitions (410 km, 520 km, 660 km, D'' layer). The Symmetry-Stiffness Paradigm is confirmed: crystal symmetry class Sy is a statistically significant predictor of pressure derivative K' (Pearson r = -0.88, p less than 0.001). Thermodynamic and phonon Gruneisen parameters are self-consistent to r2 = 0.971 across 28 minerals. The bridgmanite seven-parameter CIS predicts PREM seismic velocities at 660 km depth to within 0.4% for VP and less than 0.1% for VS without any empirical adjustment. Software: pip install mineralco | GitHub: github.com/gitdeeper9/mineralco | GitLab: gitlab.com/gitdeeper9/mineralco | Dashboard: mineralco.netlify.app | OSF: osf.io/4xghc | OSF Registration DOI: 10.17605/OSF.IO/VG7ET | Submitted to: Physics and Chemistry of Minerals (Springer), March 2026.

Zenodo (CERN European Organization for Nuclear Research)
Ronin Institute (US)
Sustainable cities and communities
Openalex Percentile: Top 14%
High-pressure geophysics and materials
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.