Electronegativity Balance

The Electronegativity Balance app filters formulas or CIF structures by combining an oxidation-state assignment with a Pauling electronegativity ordering check.

Inputs

• Formula or formula list.
• CSV/TXT files with formulas in the first column or line-delimited formulas.
• Multiple CIF files, ZIP archives, TAR archives, TAR.GZ, or TGZ archives.
• Oxidation-state method selector.

Methods

The default method is the Oxi.matr.io oxidation-state model. It predicts a charge-balanced oxidation-state assignment using the electrochemical-series model, then checks whether positive oxidation-state species are less electronegative than negative oxidation-state species.

Other available methods:

• SMACT Inclusive OS: broad high-recall oxidation-state enumeration. Passes if any charge-neutral assignment also satisfies the electronegativity ordering.
• pymatgen oxidation-state guess: uses Composition.oxi_state_guesses() and checks the first assignment.
• BERTOS oxidation-state prediction: uses the installed composition-based deep-learning OS model when available.
• TOSS structure-based prediction: uses CIF structure context when available.
• Compare all methods: reports each method side by side and marks pass_any and pass_consensus.

For CIF uploads, formula-based methods extract the reduced formula with pymatgen. TOSS uses the actual CIF structure.

Outputs

• electronegativity-results.csv
• electronegativity-passing-cifs.zip for CIF/archive uploads
• Method label, oxidation-state assignment, net charge, charge-neutrality flag, electronegativity-balance flag, status, and failure reason
• Oxi.matr.io likelihood and mapped potential when using the default model
• TOSS site oxidation-state details when using TOSS

Use

Use this app as a chemical plausibility screen after formula generation or before higher-cost stability, synthesis, or structure validation workflows. For structure batches, upload CIF files or a CIF archive and download only the CIFs that pass the selected screen.

Limits

This is a heuristic screen, not a proof of stability or synthesizability. It does not fully represent metallic bonding, covalency, mixed anion chemistry, disorder, partial occupancies, redox flexibility, or coordination-site preferences. TOSS depends on a local TOSS installation and model artifacts.

Example

SrTiO3
BaZrO3
NaCl
FeO

Default method: pnas_electrochemical.

Acknowledgements

This app uses the installed pymatgen toolkit for formula and CIF handling. Electronegativity Balance uses the Oxi.matr.io oxidation-state model by default, with SMACT Inclusive OS, pymatgen oxidation-state guesses, BERTOS, and TOSS available as selectable alternatives.

Mueller, Tim, Joseph Montoya, Weike Ye, Xiangyun Lei, Linda Hung, Jens Hummelshøj, Michael Puzon, Daniel Martinez, Chris Fajardo, and Rachel Abela. "An electrochemical series for materials." Proceedings of the National Academy of Sciences 121, no. 38 (2024): e2320134121.

Fu, Nihang, Jeffrey Hu, Ying Feng, Gregory Morrison, Hans-Conrad zur Loye, and Jianjun Hu. "Composition based oxidation state prediction of materials using deep learning language models." Advanced Science 10, no. 28 (2023): 2301011.

Yin, Yue, and Hai Xiao. "Oxidation states in solids from data-driven paradigms." Chemical Science 16, no. 42 (2025): 19917-19928.