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 : Eliminating implant failure in humans with nano chemistry: 30,000 cases and counting
Thomas J Webster, Brown University, United States
Title : Nutrient and heavy metal loads from the Ribeiras to Coastal zones: A land-ocean continuum perspective in Madeira Island
Aracelis Del Carmen Narayan Rajnauth, University of Porto, Portugal
Title : Prospective polyoxometalate-based covalent organic framework heterogeneous catalysts
Arash Ebrahimi, Comenius University Bratislava, Slovakia, Slovenia
Title : De novo molecular design and bioactivity prediction of novel hexahydroquinolines as transmission-blocking PfCDPK4 inhibitors
Gbolahan O Oduselu, University of Ghana, Ghana
Title : Expanding and improve the 2D periodic law of Менделееь elements, and construct the 3D periodic law of elements
Zhongsheng Lee, Zhengzhou Commercial Technician College, China
Title : Advances in plasma-based radioactive waste treatment
Hossam A Gabbar, Ontario Tech University, Canada