Advancements in AI & machine learning in chemistry are revolutionizing research by enhancing reaction predictions, material design, and drug discovery. Machine learning models analyze vast chemical datasets, accelerating compound screening and reaction optimization. AI-driven simulations reduce trial-and-error in experiments, making chemical processes more efficient and sustainable. Neural networks and predictive algorithms improve molecular modeling, enabling precise identification of novel compounds. Automation in data analysis streamlines workflows in spectroscopy, chromatography, and synthesis. The integration of AI with experimental chemistry fosters innovation across multiple fields, from pharmaceuticals to green chemistry. As computational power grows, machine learning continues to reshape chemical research, driving faster discoveries and smarter, data-driven solutions.
Title : Carbon capture and storage: The impact of impurities in CO2 streams
Andy Brown, Progressive Energy Ltd, United Kingdom
Title : Rational design of battery cathode materials
Kyeongjae Cho, University of Texas at Dallas, United States
Title : Pharmaceutical chemistry studies of novel biologics and drugs for chronic obstructive pulmonary disease
Yong Xiao Wang, Albany Medical College, United States
Title : Advances in plasma-based waste treatment for sustainable communities
Hossam A Gabbar, Ontario Tech University, Canada
Title : Nanostructured biodevices based on carbon nanotubes and glyconanoparticles for bioelectrocatalytic applications
Serge Cosnier, Silesian University of Technology, Poland
Title : Chemical engineering of vanadium and tantalum zeolites for application in environmental catalysis
Stanislaw Dzwigaj, Sorbonne Universite, France