Generating new molecules with graph grammar
An efficient machine-learning method uses chemical knowledge to create a learnable grammar with production rules to build synthesizable monomers and polymers.
Seeing an elusive magnetic effect through the lens of machine learning
An MIT team incorporates AI to facilitate the detection of an intriguing materials phenomenon that can lead to electronics without energy dissipation.
Scientists develop the largest, most detailed model of the early universe to date
Named after a goddess of the dawn, the Thesan simulation of the first billion years helps explain how radiation shaped the early universe.
Lawrence Udeigwe uses elegant math to understand complex systems of the brain
Martin Luther King Jr. Scholar bridges disciplines to translate vision into elegant math and neuroscience theory.
An “oracle” for predicting the evolution of gene regulation
Researchers create a mathematical framework to examine the genome and detect signatures of natural selection, deciphering the evolutionary past and future of non-coding DNA.
Using nature’s structures in wooden buildings
A new approach enables architects to use discarded tree forks as load-bearing joints in their structures.
Deep-learning technique predicts clinical treatment outcomes
A new methodology simulates counterfactual, time-varying, and dynamic treatment strategies, allowing doctors to choose the best course of action.
Mapping the depths of plasma physics
In his new lab, where he will study how plasma behaves in the universe, assistant professor of nuclear science and engineering Jack Hare draws inspiration from spelunking.
Research aims to mitigate chemical and biological airborne threats
Lincoln Laboratory leads a large-scale measurement campaign in New York City to improve air dispersion models and emergency protocols.
Shaking up earthquake research at MIT
Geophysicists Camilla Cattania and William Frank team up to explore the tectonics and fault mechanics behind earthquakes, and their associated hazards.
New computational tool predicts cell fates and genetic perturbations
The technique can help predict a cell’s path over time, such as what type of cell it will become.
Preparing global online learners for the clean energy transition
MIT Energy Initiative edX course asks students to rethink how we operate power systems.
Fast-tracking the search for energy-efficient materials
Doctoral candidate Nina Andrejević combines spectroscopy and machine learning techniques to identify novel and valuable properties in matter.
3 Questions: Anuradha Annaswamy on building smart infrastructures
Senior research scientist and her team are designing intelligent systems that could someday transform the way we travel and consume energy.
Computing for ocean environments
MIT ocean and mechanical engineers are using advances in scientific computing to address the ocean’s many challenges, and seize its opportunities.