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 : Synthesis of chitosan composite of metal organic framework for the adsorption of dyes, kinetic and thermodynamic approach
Tooba Saeed, University of Peshawar, Pakistan
Title : Synthesis, ADMET, PASS, molecular docking, and dynamics simulation investigation of novel octanoyl glucoopyranosides and valeroyl ribofuranoside esters.
Hasinul Babu, University of Chittagong, Bangladesh
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
Title : Nature meets innovation: Green synthesis of nanoparticles using plant extracts and ionic liquids for a sustainable future
Azeez A Barzinjy, Soran University, Iraq