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 & valeroyl ribofuranoside esters.
Hasinul Babu, University of Chittagong, Bangladesh
Title : Prospective polyoxometalate-based covalent organic framework heterogeneous catalysts
Arash Ebrahimi, Comenius University in Bratislava, Slovenia
Title : Utilizing Generative AI for Interactive Borane Modeling: Insights from Wade's Rule in Undergraduate Education
Mai Yan Yuen, The University of Hong Kong, Hong Kong
Title : Molecularly imprinted polymer-bimetallic nanoparticle based electrochemical sensor for dual detection of phenol iosmers micopollutants in water
Melkamu Biyana Regasa, Wollega University, Ethiopia