Research
Peer-reviewed science behind DeepChemiQ.
DeepChemiQ is built on high-impact research in computational chemistry, catalysis, artificial intelligence, and autonomous materials discovery.
Nature
Family Journals
JACS
High-Impact Chemistry
Featured Publications
Selected high-impact papers
Nature Chemical Engineering2025
Interpretable Machine Learning-Guided Plasma Catalysis for Hydrogen Production
AI + Plasma Catalysis
Nature Communications2025
Active Learning-Guided Catalyst Design for Selective Acetic Acid Production
Active Learning + Electrocatalysis
Journal of the American Chemical Society2025
Diffusion Model-Guided Inverse Design of Bimetallic Catalysts
Generative AI
JACS Au2025
Integrating Physical Principles with Machine Learning for Field-Enhanced Catalysis
Physics-Informed ML
ACS Catalysis2025
Multiscale Simulation Guided Electric Field-Enhanced Ammonia Catalytic Cracking
DFT + MKM
Angewandte Chemie2017
Catalytic Reaction Rates Controlled by Metal Oxidation State: C−H Bond Cleavage in Methane over Nickel-Based Catalysts
Multi-scale Simulation for Fuel cell
Journal of the American Chemical Society2023
Electrochemical C–N Bond Formation within Boron Imidazolate Cages Featuring Single Copper Sites
Multi-scale simulation guided discovery
Research Themes
Scientific areas
AI for Catalysis
DFT Simulation
Microkinetic Modeling
Plasma Catalysis
Autonomous Materials Discovery
Physics-Informed ML
Interested in scientific collaboration?
DeepChemiQ collaborates with academic groups, industrial R&D teams, and government-funded research programs in AI-driven chemistry and catalyst discovery.
Contact DeepChemiQ