01
Grand-Canonical DFT
Model electrified interfaces, applied-potential effects, solvation, adsorbate energetics, and transition states for electrochemical reaction pathways.
Application · Electrocatalysis
Multiscale electrocatalysis design powered by AI and physics.
DeepChemIQ supports electrocatalytic innovation from atomistic mechanism discovery to reactor-scale performance prediction. We combine grand-canonical DFT, microkinetic modeling, CFD, and AI-guided catalyst design to accelerate chemical R&D.
CO₂
e⁻
CO*
C–N
Modeling Stack
GC-DFT × MKM × CFD × AI
Connect surface chemistry, electrochemical operating conditions, transport, and reactor behavior in one integrated development workflow.
Capabilities
A complete computational platform for electrocatalytic systems.
02
Microkinetic Modeling
Convert atomistic energetics into Faradaic efficiency, current density, product selectivity, rate-control analysis, and operating-window predictions.
03
Electrochemical CFD
Evaluate reactor-scale transport, flow fields, concentration gradients, local pH, electrolyte effects, mass-transfer limitations, and cell performance.
04
AI-Guided Catalyst Design
Use descriptors, active learning, and physics-informed models to prioritize catalysts, interfaces, and reaction conditions before experimental validation.
Published Study Cases
Validated methods across carbon, nitrogen, and interface chemistry.
These publications serve as representative case studies for DeepChemIQ’s broader electrocatalysis platform. The approach is chemistry-flexible and can be adapted to new catalysts, reaction networks, electrolytes, and reactor designs.
CO-to-Acetate Electroreduction
Active learning, GC-DFT, and MKM were integrated to identify CH* binding as a descriptor for acetate selectivity and guide Cu-based bimetallic catalyst discovery.
CO₂ Reactive Capture & Conversion
Ligand–metal interfaces were used to enhance CO₂ activation and C–C coupling, demonstrating how organic–inorganic interfaces can tune activity and selectivity.
C–N Coupling to Urea
DFT and MKM revealed CO* and NH₂OH as key intermediates for electrochemical urea formation from CO₂ and nitrate over Cu-based MOFs.
Homogeneous & Heterogeneous Catalysis
The same modeling logic can be extended to molecular catalysts, ligand-modified surfaces, MOFs, metals, alloys, and hybrid catalytic interfaces.
Application Areas
Designed for diverse electrocatalytic innovation.
CO₂ and CO electroreduction
Reactive capture and conversion
C–N coupling to urea and organonitrogen products
Homogeneous electrocatalysis
Heterogeneous catalyst interfaces
MOF, alloy, ligand, and molecular catalyst design
Development Workflow
From mechanism to catalyst and reactor design.
We help teams move beyond trial-and-error by linking electronic structure, kinetic selectivity, transport phenomena, and process-relevant operating conditions.
1
Define the electrochemical target reaction and reactor environment
2
Build reaction networks for desired and competing pathways
3
Compute potential-dependent energetics with GC-DFT
4
Translate atomistic data into MKM and sensitivity analysis
5
Use CFD to quantify transport, local pH, and cell-scale performance
6
Prioritize catalysts, interfaces, and operating conditions for validation
Collaborate with DeepChemIQ
Need to design or optimize an electrocatalytic process?
We work with academic, startup, and industrial partners on CO₂ conversion, reactive capture, C–N coupling, homogeneous catalysis, heterogeneous catalysis, CFD, and AI-guided catalyst discovery.
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