5th Edition of
Chemistry World Conference
June 02-04, 2025 | Rome, Italy
AI & Machine Learning in Chemistry
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.
Yong Xiao Wang
Albany Medical College, United States
Hossam A Gabbar
Ontario Tech University, Canada
Stanislaw Dzwigaj
Sorbonne Université, France
Haibo Ge
Texas Tech University, United States
Thomas J Webster
Hebei University of Technology, China
Kyeongjae Cho
University of Texas at Dallas, United States
Fu Ming Tao
California State University, United StatesSubmit your abstract Today
Title : Nanostructured biodevices based on carbon nanotubes and glyconanoparticles for bioelectrocatalytic applications
Serge Cosnier, Silesian University of Technology, Poland
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 : Supramolecular nano chemistries: Fighting viruses, inhibiting bacteria and growing tissues
Thomas J Webster, Hebei University of Technology, China
Title : Chemical engineering of vanadium, titanium or chromium zeolites for application in environmental catalysis
Stanislaw Dzwigaj, Sorbonne Université, France
Title : Distal functionalization via transition metal catalysis
Haibo Ge, Texas Tech University, United States