Materials science and engineering
Materials science and engineering

Materials science and engineering

Algorithms Artificial Intelligence Awards, honors and fellowships Biology chemistry DMSE Electrical engineering and computer science (EECS) Graduate, postdoctoral Lincoln Laboratory Materials science and engineering MIT Schwarzman College of Computing MIT-Harvard Center for Ultracold Atoms Physics Quantum Computing Quantum physics Research Research Laboratory of Electronics School of Engineering School of Science

MIT Quantum Initiative launches postdoctoral fellowship program

The Institute welcomes its first cohort of QMIT Fellows this fall to advance interdisciplinary quantum research.

Artificial Intelligence Chemical engineering chemistry Computer science and technology Department of Energy (DoE) DMSE machine learning Materials science and engineering National Science Foundation (NSF) Nuclear science and engineering Physics Research School of Engineering School of Science Semiconductors

AI helps design new materials that work in the real world

The “CrysVCD” tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs.

Agriculture Artificial Intelligence chemistry Cleaner industry Computer modeling DMSE Emissions Food Industry machine learning Materials science and engineering Nuclear science and engineering Pollution Research School of Engineering School of Science Sustainability

Paving the way for greener ammonia production

New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that’s essential to fertilizer and other products.

Artificial Intelligence Batteries Computer modeling DMSE Electric vehicles Energy storage Lithium-ion Materials science and engineering MIT.nano National Science Foundation (NSF) Nuclear science and engineering Renewable energy Research School of Engineering Transportation

Solving the solvent problem

By focusing on electrolytes, MIT scientists are making sodium-metal batteries a more practical energy storage option.

Artificial Intelligence Chemical engineering chemistry Computer modeling Computer science and technology DMSE machine learning Materials science and engineering Research School of Engineering

A better way to model the behavior of metal alloys

MIT researchers’ approach captures subtle atomic patterns, improving predictions of material properties.

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Sixteen new START.nano companies are developing hard-tech solutions with the support of MIT.nano

Startup accelerator program grows to over 30 companies, almost half of them with MIT pedigrees.

Artificial Intelligence Batteries Computer modeling Department of Energy (DoE) DMSE Electrical engineering and computer science (EECS) machine learning Manufacturing Materials science and engineering Nanoscience and nanotechnology National Science Foundation (NSF) Nuclear science and engineering Research School of Engineering Semiconductors Solar

MIT researchers use AI to uncover atomic defects in materials

A new model measures defects that can be leveraged to improve materials’ mechanical strength, heat transfer, and energy-conversion efficiency.

Artificial Intelligence Biology Civil and environmental engineering Computational biology Health sciences and technology Materials science and engineering Mechanical engineering MIT-IBM Watson AI Lab Proteins Research School of Engineering Synthetic biology

MIT engineers design proteins by their motion, not just their shape

An AI model generates novel proteins based on how they vibrate and move, opening new possibilities for dynamic biomaterials and adaptive therapeutics.

Algorithms Artificial Intelligence Astronomy and astrophysics chemistry Computer science and technology Data Faculty IDSS Interview Laboratory for Nuclear Science machine learning Materials science and engineering Mathematics MIT Schwarzman College of Computing National Science Foundation (NSF) Physics Research School of Science

3 Questions: On the future of AI and the mathematical and physical sciences

Professor Jesse Thaler describes a vision for a two-way bridge between artificial intelligence and the mathematical and physical sciences — one that promises to advance both.

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Accelerating science with AI and simulations

Associate Professor Rafael Gómez-Bombarelli has spent his career applying AI to improve scientific discovery. Now he believes we are at an inflection point.