Technology Platform
multi-physics + multi-scale + Agentic AI
DeepChemiQ develops a cloud-native scientific AI platform that integrates DFT simulation, microkinetic modeling, CFD, machine learning, and agentic AI to accelerate catalyst discovery for clean hydrogen and chemical process innovation.
Closed-Loop Workflow
From data to autonomous discovery
Materials Database
DFT / MKM / CFD
AI Model Training
Agentic Planning
Candidate Generation
Validation
Core Modules
Multiscale modeling + AI automation
Atomic-scale catalyst modeling
DFT Simulation
High-throughput first-principles simulations are used to calculate adsorption energies, transition states, electronic structures, and surface reaction pathways.
Microkinetic modeling
MKM
Microkinetic models translate atomic-scale energetics into reaction rates, kinetic descriptors, hydrogen yield, and catalyst performance under realistic operating conditions.
Reactor and transport simulation
CFD
CFD connects catalyst behavior with reactor-scale transport, plasma environments, flow fields, temperature gradients, and process-level performance.
Predictive and generative models
AI / ML
Graph neural networks, diffusion models, active learning, and LLM-based planning agents support catalyst prediction, inverse design, and autonomous workflow optimization.
Agentic AI System
Planning, execution, evaluation, and memory
The platform is designed around multiple AI agents that plan simulation campaigns, execute computational workflows, evaluate performance, and store results in a searchable memory layer for continuous learning.
