Battery Electrolyte and Additive Development Services
CD ComputaBio provides computational electrolyte and additive development services covering solvent and salt screening, multi-component formulation optimization, solvation and ion-transport analysis, electrochemical-stability prediction, SEI/CEI additive evaluation, and experimental-data-guided candidate prioritization.
Our workflows help battery developers reduce formulation space and select test-ready electrolyte candidates for lithium-ion, lithium-metal, sodium-ion, high-voltage, fast-charging, and low-temperature systems.
Computational services for electrolyte and additive development
We do not provide a generic electrolyte-property report. Each project is designed around a practical decision: which components to test, which formulation to optimize, why a baseline is failing, or which additive is most likely to improve interfacial stability.
Clients may provide a component library, an existing formulation, an experimental dataset, a target battery chemistry, or only a defined performance objective.
Request a Project-Specific PlanElectrolyte Component Screening
Curate and rank solvents, co-solvents, diluents, lithium or sodium salts, and functional additives against electrochemical, transport, safety, and compatibility targets.
Multi-Component Formulation Design
Optimize solvent ratios, salt concentration, additive loading, and composition trade-offs using mixture-aware models and multi-objective ranking.
Solvation and Ion-Transport Analysis
Resolve ion coordination, solvent participation, contact-ion pairs, aggregates, diffusion behavior, residence times, and desolvation-related features.
Electrochemical Stability Prediction
Compare oxidation and reduction tendencies, redox descriptors, frontier orbitals, decomposition energetics, and formulation stability windows.
SEI and CEI Additive Evaluation
Assess preferential reduction or oxidation, electrode adsorption, decomposition pathways, and the likelihood of supporting protective interphase formation.
Experimental Data-Guided Optimization
Use conductivity, cycling, impedance, spectroscopy, safety, or formulation data to refine models and select the next most informative candidates.
Electrolyte systems
- Conventional carbonate electrolytes
- Ether-based electrolytes
- Fluorinated electrolyte systems
- High-concentration and localized high-concentration electrolytes
- Ionic-liquid and flame-retardant systems
Additive functions
- SEI-forming additives
- CEI-forming additives
- Gas-suppressing additives
- Acid and water scavengers
- Overcharge-protection and flame-retardant additives
Battery applications
- Lithium-ion batteries
- Lithium-metal and anode-free batteries
- Sodium-ion batteries
- High-voltage systems
- Fast-charging and low-temperature systems
Different starting points, one decision-focused workflow
The project scope is adapted to the client’s current data, development stage, and immediate experimental choice.
“We have many solvents, salts, or additives.”
We define filters, calculate relevant descriptors, rank the chemical space, and recommend a manageable first-round validation set.
“Our electrolyte works, but not well enough.”
We compare targeted composition changes, identify the dominant bottleneck, and prioritize modifications against the baseline formulation.
“We do not know why performance drops.”
We investigate solvation, transport, oxidation, reduction, adsorption, and decomposition hypotheses linked to the observed failure mode.
Why electrolyte development requires system-level analysis
Electrolyte performance emerges from interactions among solvents, salts, additives, electrodes, temperature, concentration, and cycling conditions. A component that improves one property may reduce another, making single-descriptor screening insufficient.
CD ComputaBio connects composition, molecular behavior, interface chemistry, and experimental constraints so that candidate ranking reflects the intended battery system rather than an isolated calculated property.
What we evaluate in an electrolyte formulation
Each ingredient class is assessed by its individual properties and by the way it changes the behavior of the full formulation.
Solvent Framework
Compare dielectric environment, viscosity, donor behavior, volatility, flammability, and compatibility with target electrodes.
Salt Selection
Assess dissociation, coordination, transport contribution, thermal behavior, corrosion risk, and decomposition tendency.
Additive Package
Identify film-forming, scavenging, flame-retardant, overcharge-protection, and interfacial functions.
Operating Context
Account for voltage, temperature, charge rate, electrode chemistry, loading, and expected lifetime.
Translate formulation goals into measurable questions
Instead of applying every method to every candidate, calculations are selected according to the uncertainty that blocks the next experiment.
Methods selected for the specific electrolyte decision
Modules can be used independently or connected into a staged screening workflow.
Solvent and Salt Screening
Curate and rank candidate components using physicochemical, electrochemical, safety, and compatibility criteria.
Formulation Optimization
Use mixture-aware models, experimental data, and multi-objective optimization to prioritize practical compositions.
Solvation Structure Analysis
Characterize ion coordination, solvent participation, aggregate formation, and concentration-dependent organization.
Ion Mobility Assessment
Compare diffusion-related behavior, conductivity descriptors, transference trends, and desolvation-relevant features.
Additive Function Prediction
Evaluate reduction or oxidation preference, adsorption, decomposition, and possible SEI or CEI contributions.
Experimental Learning
Convert cycling, conductivity, spectroscopy, or formulation results into improved prediction and candidate-selection models.
Match the method to the uncertainty
A credible electrolyte study often combines several levels of modeling, but each calculation should have a defined role in the final decision.
AI and QSPR Models
Rapid property prediction, candidate ranking, formulation optimization, active learning, and data-driven trade-off analysis.
Molecular Dynamics
Solvation, coordination, radial distributions, residence behavior, diffusion, aggregation, and concentration effects.
Quantum Chemistry and DFT
Redox behavior, electronic descriptors, reaction energetics, decomposition pathways, and additive mechanisms.
Interface and Reactive Modeling
Surface adsorption, early-stage decomposition hypotheses, interphase chemistry, and mechanistic validation where chemical reactions matter.
From formulation space to a testable electrolyte shortlist
The workflow is structured around progressive risk reduction rather than a fixed software sequence.
Define Targets
Set chemistry, voltage, temperature, rate, safety, and validation criteria.
Build the Space
Curate solvents, salts, additives, concentrations, and available data.
Screen
Apply rapid filters and predictive models to narrow the candidate set.
Resolve Mechanisms
Use MD, DFT, or interface calculations on high-value uncertainties.
Plan Validation
Deliver ranked formulations, controls, and recommended experiments.
What the client receives
Deliverables are adapted to the available data, scientific question, and intended validation stage.
Ranked Formulations
Prioritized solvents, salts, additives, concentrations, and mixture candidates.
Mechanistic Evidence
Solvation, transport, redox, adsorption, and decomposition interpretations.
Technical Data Package
Structures, descriptors, simulation outputs, plots, models, and method documentation.
Validation Strategy
Recommended experiments, controls, readouts, and next-round selection criteria.
Planning an electrolyte and additive project
Projects may begin from a defined formulation list, a component library, an experimental dataset, or a target performance profile.
Broad chemical space, limited data
Use descriptor-based filtering, literature-informed curation, AI ranking, and focused high-fidelity calculations to identify a manageable first test set.
Known baseline, clear performance gap
Compare targeted substitutions, concentration changes, and additive packages against the baseline using multi-objective and mechanistic analysis.
Promising formulation, unclear failure mode
Investigate solvation, interfacial reactivity, decomposition, transport, or temperature sensitivity to explain observed behavior.
Growing experimental dataset
Build a learning loop that updates candidate selection as new conductivity, cycling, spectroscopy, or safety data become available.
Common questions about electrolyte formulation modeling
What information is needed to start?
Useful inputs include battery chemistry, electrode materials, baseline electrolyte, candidate components, concentration ranges, operating voltage, temperature, charging conditions, measured data, and the experimental decision the project should support.
Can a project start without a large experimental dataset?
Yes. Early-stage projects can begin with a defined chemical space, public or client-approved data, physics-based descriptors, and targeted calculations. Model uncertainty should be reported clearly when data are limited.
Can you model concentrated or localized high-concentration electrolytes?
Yes. These systems require careful treatment of coordination, aggregation, composition, and sampling. The exact workflow depends on the salt, diluent, concentration range, and target property.
Do calculations replace electrolyte testing?
No. The purpose is to reduce the number of low-value experiments, clarify mechanisms, and prioritize informative validation rather than replace electrochemical and physicochemical testing.
Turn a broad electrolyte space into a focused experimental plan
Share your battery chemistry, formulation constraints, candidate list, or existing data with CD ComputaBio for a project-specific computational strategy.
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