Machine Learning in Materials Science: What Can It Actually Do?
November 21, 2024
Machine learning has become a buzzword in all areas of science, and the hype around it can make it difficult to differentiate fact from (science) fiction. When it comes to researching new materials, some scientists believe we are now at the beginning of a second computational revolution at the hands of machine learning. But is that really true?
Materials science already went through a revolution in the 20th century with the advent of computational methods such as density functional theory (DFT), Monte Carlo simulations, and molecular dynamics.
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.