Interfacial Stability and Degradation Prediction Services
Identify unstable electrode–electrolyte interactions, clarify likely decomposition pathways, and connect molecular changes with capacity loss, impedance growth, gas generation, and shortened cycle life. CD ComputaBio combines AI, molecular simulation, quantum chemistry, and experimental data analysis to support mechanism-based battery development.
Translate an observed aging signal into a testable molecular or materials-level explanation.
Loss of cyclable ions, active material, electrolyte, or accessible reaction sites.
Thickening interphases, blocked transport pathways, contact loss, or resistive by-products.
Solvent, salt, additive, binder, or surface reactions that produce volatile species.
Mechanical separation, unstable surface chemistry, dendritic growth, or repeated interphase repair.
Interfacial degradation is rarely explained by one descriptor
Battery aging can begin with an unfavorable adsorption event, electron transfer, bond cleavage, ion depletion, structural reconstruction, or mechanical loss of contact. These processes can occur simultaneously and may change with voltage, temperature, current density, state of charge, and cycle number.
Our studies are designed around the client's experimental observation and decision. Rather than reporting a single stability score, we evaluate competing mechanisms, identify the most plausible degradation drivers, and recommend the next materials, formulation, or validation step.
Typical questions we help resolve
Computational support across the interface aging pathway
The service scope can begin from a known failure mode, a candidate formulation, an electrode structure, cycling data, or a specific interface hypothesis.
Electrode–Electrolyte Compatibility Assessment
Evaluate adsorption, surface affinity, charge transfer, solvent orientation, ion coordination, and early-stage reaction susceptibility at selected electrode surfaces.
Electrolyte and Additive Decomposition Prediction
Compare oxidation, reduction, bond-cleavage, proton-transfer, and radical-mediated pathways for solvents, salts, additives, and their coordinated complexes.
SEI and CEI Formation Mechanism Analysis
Investigate precursor selection, surface reaction order, decomposition products, interphase composition, and conditions associated with protective or resistive film growth.
Surface Reconstruction and Coating Stability
Assess surface terminations, defects, dopants, coatings, lattice changes, transition-metal migration, and chemical compatibility under relevant operating conditions.
Lithium Plating and Dendrite-Related Risk Analysis
Study desolvation, ion depletion, nucleation preference, local electric-field effects, surface heterogeneity, and interface conditions associated with nonuniform deposition.
AI-Based Aging and Degradation Modeling
Use cycling, formulation, materials, and operating-condition data to identify degradation signatures, predict performance trends, and rank the variables most strongly associated with aging.
Model the interface that controls your battery system
The same electrolyte can behave differently against different surface compositions, states of charge, defects, coatings, and electrode architectures. Project models are therefore built around the actual interface rather than an isolated molecule alone.
From an aging signal to a testable intervention
Each project is organized around the mechanism that must be clarified and the next experimental decision that must be made.
Define the Failure Signal
Establish the chemistry, interface, operating condition, baseline, and measurable performance loss.
Build Interface Models
Prepare electrode surfaces, coatings, defects, electrolyte species, coordination states, and relevant structures.
Test Mechanisms
Compare adsorption, transport, redox, decomposition, structural, and mechanical hypotheses.
Rank Degradation Drivers
Integrate calculations and available data to identify likely causes, trade-offs, and uncertainty.
Recommend Validation
Propose materials changes, formulation changes, controls, analytical readouts, and the next test set.
Outputs that can guide the next experimental round
Deliverables are configured around the client's system, available inputs, and required level of mechanistic detail.
Interface Stability Assessment
Comparative ranking of surfaces, formulations, additives, coatings, or operating conditions by predicted interface risk.
Degradation Pathway Analysis
Proposed reaction sequences, intermediates, products, energy profiles, structural changes, and supporting evidence.
Structures and Simulation Results
Prepared models, optimized geometries, trajectories when applicable, calculated descriptors, plots, and method documentation.
Validation and Mitigation Strategy
Recommended controls, characterization readouts, formulation changes, candidate priorities, and next-round testing criteria.
Match the analysis to the degradation problem
Project scope is selected according to the observed failure mode and the intervention available to the development team.
Mechanistic modeling shaped around a practical battery decision
Interface projects often span multiple length scales and evidence types. CD ComputaBio can combine physics-based modeling, AI analysis, and client data without forcing every project into the same fixed workflow.
Interface-Specific Models
Models can include the actual surface, state of charge, defect, coating, electrolyte, and operating environment.
Multiple Evidence Levels
Molecular simulation, quantum chemistry, materials modeling, AI, and experimental data can be combined when needed.
Competing Mechanisms Compared
The analysis can evaluate alternative degradation routes rather than presenting one unsupported explanation.
Experiment-Ready Recommendations
Final outputs emphasize candidate priority, validation readouts, controls, and the most informative next experiment.
Planning an interface stability and degradation study
What information is needed to start an interface degradation project?
Useful inputs include the electrode and electrolyte compositions, operating voltage and temperature, formation and cycling conditions, observed failure signal, available characterization data, and the material or formulation decision the study should support. A project can also start from a proposed interface or a shortlist of candidates.
Can you predict the exact composition of an SEI or CEI?
Interphase composition depends on reaction kinetics, local environment, impurities, surface condition, and cycling history. Computation is most effective for comparing likely precursor reactions, products, formation tendencies, and competing mechanisms. Experimental characterization remains important for confirming the resulting interphase.
When are reactive simulations required?
Reactive or ab initio molecular dynamics may be useful when bond formation and cleavage must be observed directly. They are not automatically required for every project. Lower-cost calculations, classical molecular dynamics, and targeted quantum chemistry may provide a more efficient answer when the reaction space is already reasonably defined.
Can proprietary cycling and formulation data be used?
Yes. Client-provided formulations, structures, operating conditions, electrochemical results, characterization data, and negative results can be incorporated into project-specific analyses under an agreed confidentiality framework.
Can this service compare several additives or coatings?
Yes. Candidate additives, surface terminations, dopants, coatings, binders, or formulation conditions can be compared using a staged workflow. Rapid descriptors can first reduce the search space, after which higher-fidelity calculations can be applied to the most relevant candidates.
Turn an unexplained aging signal into a testable mechanism
Share your battery chemistry, interface of interest, operating conditions, and available data. Our scientists will help define a project that clarifies degradation risk and prioritizes the next materials or formulation decision.
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