Computational model captures the elusive transition states of chemical reactions
Using generative AI, MIT chemists created a model that can predict the structures formed when a chemical reaction reaches its point of no return.
chemistry
MIT engineers develop a way to determine how the surfaces of materials behave
Using machine learning, the computational method can provide details of how materials work as catalysts, semiconductors, or battery components.
Learning the language of molecules to predict their properties
This AI system only needs a small amount of data to predict molecular properties, which could speed up drug discovery and material development.
Inaugural J-WAFS Grand Challenge aims to develop enhanced crop variants and move them from lab to land
Matt Shoulders will lead an interdisciplinary team to improve RuBisCO — the photosynthesis enzyme thought to be the holy grail for improving agricultural yield.
Mining the right transition metals in a vast chemical space
Computational chemists design better ways of discovering and designing materials for energy applications.
Five Artificial Intelligence (AI) Ideas for the Chemical Industry
Artificial intelligence, machine learning, and deep learning are now being used in the chemical industry.Machine Learning and Deep Learning are a growing and diverse fields of Artificial Intelligence (AI) which studies algorithms that are capable of au…