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.
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Using generative AI, MIT chemists created a model that can predict the structures formed when a chemical reaction reaches its point of no return.
Using machine learning, the computational method can provide details of how materials work as catalysts, semiconductors, or battery components.
A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.
With the PockEngine training method, machine-learning models can efficiently and continuously learn from user data on edge devices like smartphones.
Computer vision enables contact-free 3D printing, letting engineers print with high-performance materials they couldn’t use before.
Researchers coaxed a family of generative AI models to work together to solve multistep robot manipulation problems.
By focusing on causal relationships in genome regulation, a new AI method could help scientists identify new immunotherapy techniques or regenerative therapies.
Researchers use synthetic data to improve a model’s ability to grasp conceptual information, which could enhance automatic captioning and question-answering systems.
“Lightning” system connects photons to the electronic components of computers using a novel abstraction, creating the first photonic computing prototype to serve real-time machine-learning inference requests.
With a new technique, a robot can reason efficiently about moving objects using more than just its fingertips.