The Use of Artificial Intelligence and Machine Learning in Chemical Simulations
November 25, 2024
AI and machine learning are revolutionizing chemical simulations, enabling faster and more accurate chemical properties prediction and analysis of chemical reactions. While traditional methods struggle with complex structure-activity relationships, machine learning models leverage data analysis to optimize chemical structure, predict reactivity, and enhance predictive modeling.
Techniques such as molecular fingerprints, Graph Neural Networks, and supervised learning improve accuracy, reducing reliance on trial-and-error approaches and accelerating computational efficiency.
How can AI reshape the future of chemical simulations? Read more to find out.
Unlock The Full Article For Free!
Get AccessDive Deeper
Recommended Articles
Machine Learning Force Fields: Transforming Molecular Dynamics Simulations
Machine learning force fields (MLFFs) are transforming molecular dynamics simulations by increasing the accuracy and reducing the computational costs of classical simulation techniques, enabling more accurate, efficient simulations across materials science, chemistry and biology.
Solid-State Batteries: The Next Frontier for Battery R&D
Solid-state batteries may one day outperform lithium-ion batteries, offering higher energy density, improved safety, and longer lifespans. However, there are still major challenges to overcome before this technology can compete in the global market.
The Solid-Electrolyte Interphase: A Challenge for Battery R&D
Despite the impact the solid electrolyte interphase (SEI) has on the performance of modern batteries, studying it remains a major challenge in battery R&D.