Equilibrist: A Browser-Based Platform for Fitting Equilibrium and Rate Constants from Optical and NMR Data

Area of Science:

• Chemical kinetics
• Chemical equilibrium analysis
• Computational chemistry

Background:

• Quantitative analysis of chemical systems is crucial for understanding reaction mechanisms.
• Existing tools may lack comprehensive statistical validation and parameter identifiability analysis.
• Accurate modeling requires robust methods for assessing chemical equilibria and kinetics.

Purpose of the Study:

• Introduce Equilibrist, a novel open-source platform for quantitative chemical analysis.
• Provide advanced statistical diagnostics for model selection and parameter estimation.
• Enhance the assessment of chemical models by moving beyond traditional R-squared and Hessian errors.

Main Methods:

• Utilizes a plain-text scripting language for defining chemical reactions and constraints.
• Implements five fitting modes including species concentration, NMR, and spectrophotometry.
• Employs L-BFGS-B and Nelder-Mead optimization methods.
• Integrates comprehensive statistical diagnostics: AIC, BIC, Akaike weights, F-test, residual analysis (Durbin-Watson, Shapiro-Wilk), and Q-Q plots.
• Features uncertainty quantification via bootstrap, jackknife, and Monte Carlo methods.
• Assesses parameter identifiability using RMSE profiles, eigen decomposition, correlation heatmaps, t-tests, and local sensitivity coefficients (ξ).

Main Results:

• Equilibrist provides a flexible platform for diverse chemical equilibrium and kinetic analyses.
• The integrated statistical layer offers robust model selection and validation tools.
• Advanced parameter identifiability analysis aids in understanding model limitations and parameter reliability.
• The platform facilitates a deeper assessment of chemical models compared to conventional methods.

Conclusions:

• Equilibrist empowers researchers with a comprehensive, free, and open-source tool for chemical system analysis.
• Its advanced diagnostics enable more rigorous evaluation of chemical models and parameter estimates.
• The platform promotes improved accuracy and reliability in quantitative chemical research.