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 : Personalized and Precision Medicine (PPM) as a unique healthcare model through biodesign-inspired and upgraded business marketing to secure the human healthcare and biosafety
Sergey Suchkov, National Center for Human Photosynthesis, Aguascalientes, Mexico
Title : Eliminating implant failure in humans with nano chemistry: 30,000 cases and counting
Thomas J Webster, Brown University, United States
Title : Enhancing process efficiency and safety with advanced sensor technology
Susanne Naf Rudiger, Hamilton Bonaduz AG, Switzerland
Title : Solar Box Cooker dehydration, and relative humidity endpoint detection, of Lamiaceae culinary leaves on the island of Crete
Victor John Law, University College Dublin, Ireland
Title : Nuclear-enhanced photocatalysis: Ionizing radiation meets artificial photosynthesis for atom-efficient hydrogen production
Shree Niwas Chaturvedi, Centre for Aptitude Analysis and Talent Search, India
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