# Computer chips

Published articles for Computer chips.

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## AI helps design new materials that work in the real world

DevFeed: [AI helps design new materials that work in the real world](<https://devfeed.tech/articles/ai-helps-design-new-materials-that-work-in-the-real-world-37941.md>)

Original publisher: [Read original article](<https://news.mit.edu/2026/ai-helps-design-new-materials-that-work-in-real-world-0826>)

Author: Zach Winn | MIT News

Published: 2026-08-26T09:00:00Z

Content type: news

Language: en

Sources: [MIT AI News](<https://devfeed.tech/sources/mit-ai-news.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Framework](<https://devfeed.tech/topics/framework.md>), [Simulation and Design](<https://devfeed.tech/topics/simulation-and-design.md>), [Crystal](<https://devfeed.tech/topics/crystal.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [bowen-yu](<https://devfeed.tech/tags/bowen-yu.md>), [chemical-engineering](<https://devfeed.tech/tags/chemical-engineering.md>), [chemistry](<https://devfeed.tech/tags/chemistry.md>), [computer-chips](<https://devfeed.tech/tags/computer-chips.md>), [computer-science-and-technology](<https://devfeed.tech/tags/computer-science-and-technology.md>), [crystal](<https://devfeed.tech/tags/crystal.md>), [crysvcd](<https://devfeed.tech/tags/crysvcd.md>), [data-centers](<https://devfeed.tech/tags/data-centers.md>), [department-of-energy-doe](<https://devfeed.tech/tags/department-of-energy-doe.md>), [diffusion-models](<https://devfeed.tech/tags/diffusion-models.md>), [dmse](<https://devfeed.tech/tags/dmse.md>), [hao-tang](<https://devfeed.tech/tags/hao-tang.md>), [heather-kulik](<https://devfeed.tech/tags/heather-kulik.md>), [ju-li](<https://devfeed.tech/tags/ju-li.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [materials-design](<https://devfeed.tech/tags/materials-design.md>), [materials-discovery](<https://devfeed.tech/tags/materials-discovery.md>), [materials-science-and-engineering](<https://devfeed.tech/tags/materials-science-and-engineering.md>), [mingda-li](<https://devfeed.tech/tags/mingda-li.md>), [mouyang-cheng](<https://devfeed.tech/tags/mouyang-cheng.md>), [national-science-foundation-nsf](<https://devfeed.tech/tags/national-science-foundation-nsf.md>), [nuclear-science-and-engineering](<https://devfeed.tech/tags/nuclear-science-and-engineering.md>), [paper](<https://devfeed.tech/tags/paper.md>), [physics](<https://devfeed.tech/tags/physics.md>), [research](<https://devfeed.tech/tags/research.md>), [school-of-engineering](<https://devfeed.tech/tags/school-of-engineering.md>), [school-of-science](<https://devfeed.tech/tags/school-of-science.md>), [semiconductors](<https://devfeed.tech/tags/semiconductors.md>), [weiliang-luo](<https://devfeed.tech/tags/weiliang-luo.md>), [weiwei-xie](<https://devfeed.tech/tags/weiwei-xie.md>), [yongqiang-cheng](<https://devfeed.tech/tags/yongqiang-cheng.md>)

### AI overview

MIT researchers developed CrysVCD, a framework that applies chemistry-based valence constraints before material generation to improve the stability of generated designs. In tests, it achieved high lattice-dynamics stability in nearly 70 percent of computational material generations and supported targeting properties such as high thermal conductivity and high dielectric constant.

### Source excerpt

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

## New chip could help tiny robots traverse complex environments

DevFeed: [New chip could help tiny robots traverse complex environments](<https://devfeed.tech/articles/new-chip-could-help-tiny-robots-traverse-complex-environments-37974.md>)

Original publisher: [Read original article](<https://news.mit.edu/2026/new-chip-could-help-tiny-robots-traverse-complex-environments-0623>)

Author: Adam Zewe | MIT News

Published: 2026-06-23T04:00:00Z

Content type: news

Language: en

Sources: [MIT AI News](<https://devfeed.tech/sources/mit-ai-news.md>)

Topics: [Hardware](<https://devfeed.tech/topics/hardware.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [3D](<https://devfeed.tech/topics/3d.md>), [navigation](<https://devfeed.tech/topics/navigation.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Green Software](<https://devfeed.tech/topics/green-software.md>), [Computer science](<https://devfeed.tech/topics/computer-science.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [aeronautical-and-astronautical-engineering](<https://devfeed.tech/tags/aeronautical-and-astronautical-engineering.md>), [algorithms](<https://devfeed.tech/tags/algorithms.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [assembly](<https://devfeed.tech/tags/assembly.md>), [augmented-and-virtual-reality](<https://devfeed.tech/tags/augmented-and-virtual-reality.md>), [autonomous-vehicles](<https://devfeed.tech/tags/autonomous-vehicles.md>), [computer-chips](<https://devfeed.tech/tags/computer-chips.md>), [computer-science-and-technology](<https://devfeed.tech/tags/computer-science-and-technology.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [electrical-engineering-and-computer-science-eecs](<https://devfeed.tech/tags/electrical-engineering-and-computer-science-eecs.md>), [energy](<https://devfeed.tech/tags/energy.md>), [energy-efficiency](<https://devfeed.tech/tags/energy-efficiency.md>), [gleanmer](<https://devfeed.tech/tags/gleanmer.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inside](<https://devfeed.tech/tags/inside.md>), [laboratory-for-information-and-decision-systems-lids](<https://devfeed.tech/tags/laboratory-for-information-and-decision-systems-lids.md>), [low-power](<https://devfeed.tech/tags/low-power.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [memory](<https://devfeed.tech/tags/memory.md>), [mit-schwarzman-college-of-computing](<https://devfeed.tech/tags/mit-schwarzman-college-of-computing.md>), [national-science-foundation-nsf](<https://devfeed.tech/tags/national-science-foundation-nsf.md>), [navigation](<https://devfeed.tech/tags/navigation.md>), [peter-zhi-xuan-li](<https://devfeed.tech/tags/peter-zhi-xuan-li.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [research](<https://devfeed.tech/tags/research.md>), [research-laboratory-of-electronics](<https://devfeed.tech/tags/research-laboratory-of-electronics.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [school-of-engineering](<https://devfeed.tech/tags/school-of-engineering.md>), [sertac-karaman](<https://devfeed.tech/tags/sertac-karaman.md>), [system-on-a-chip](<https://devfeed.tech/tags/system-on-a-chip.md>), [trajectory-planning](<https://devfeed.tech/tags/trajectory-planning.md>), [vivienne-sze](<https://devfeed.tech/tags/vivienne-sze.md>), [zih-sing-fu](<https://devfeed.tech/tags/zih-sing-fu.md>)

### AI overview

MIT researchers developed a low-power chip that combines an efficient mapping algorithm with dedicated hardware to generate detailed 3D maps for robot navigation in real time. The system-on-a-chip uses about 6 milliwatts and is intended for tiny autonomous robots and other battery-limited devices.

### Source excerpt

Researchers combined an efficient algorithm with dedicated hardware to rapidly generate 3D maps for navigation using minimal memory and power.