Predicting redox potentials is a cornerstone of electrochemical research. Whether you are designing electrolytes, screening additives, or modeling catalysts, accurate redox data can reveal how molecules behave under realistic electrochemical conditions. Yet, the process is often complex, requiring careful setup of density functional theory calculations (DFT) calculations, reference corrections, and thermodynamic cycles.

Compular Lab streamlines this entire workflow through its new Electrochemical Stability module. Built on a robust DFT methodology and a rigorous thermodynamic cycle, it takes you from a molecular structure from our molecule bank or even just a SMILES string to compute reduction and oxidation potentials in a single automated workflow. We provide a straightforward method for users to contribute new, novel molecules to the molecule bank, allowing exclusive addition of unique entries.

DFT Methodology Grounded in Thermodynamics

At the heart of the module lies a DFT-based approach that computes redox potentials via a well-defined thermodynamic cycle. This ensures that the computed values are consistent, reproducible, and physically meaningful across different systems. All key steps – geometry optimization, solvation corrections, and reference energy alignment are automatically handled by Compular Lab, minimizing user intervention without sacrificing accuracy.

Unlike conventional DFT workflows that demand manual setup and tuning, Compular Lab automates every stage of the calculation. Users can simply input molecular structures, select a reference, and run high-accuracy redox predictions with just a few clicks.

We have also benchmarked a range of density functionals across multiple solvents and additives, ensuring reliable predictions that capture solvent-specific effects, a critical factor for electrolyte systems.

Use case: Screening Additives for SEI and CEI Formation

One of the most powerful applications of this workflow is additive screening. The Electrochemical Stability module allows users to:

• Compute and compare reduction/oxidation potentials for large sets of molecules
• Rank additives according to their electrochemical stability
• Identify promising candidates for SEI (Solid Electrolyte Interphase) and CEI (Cathode Electrolyte Interphase) formation

This enables rapid, data-driven discovery of additives that balance stability, reactivity, and performance without manual guesswork or weeks of computational setup.