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Bridging the gap between design and production for optical equipment.

Artificial Intelligence, Biological engineering, Computer science and technology, Electronics, Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT.nano, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 13, 2024191Views0Likes0Comments
Researchers from MIT and the Chinese University of Hong Kong are using machine learning to close the gap between design and manufacturing processes in photolithography - a method used in the creation of computer chips and optical devices. Photolithography involves using light to etch features onto a surface. However, tiny variations during production often lead…

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The computational model successfully records the difficult-to-detect transition phases of chemical reactions.

Artificial Intelligence, Chemical engineering, Chemistry, Machine learning, National Science Foundation (NSF), Research, School of Engineering, School of Science, UncategorizedApril 13, 2024174Views0Likes0Comments
Chemical reactions reach a 'transition state' when molecules gain enough energy for the reaction to proceed. This state is brief and hard to observe experimentally. The arrangement of these transition states can be calculated through quantum chemistry, but it is highly time-consuming. Scientists at MIT have developed a faster method using machine learning which computes…

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A technology scholar is expanding the horizons of geometric study.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Faculty, Machine learning, MIT Schwarzman College of Computing, Profile, School of Engineering, UncategorizedApril 12, 2024177Views0Likes0Comments
Justin Solomon, based at MIT's Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory, is using modern geometric techniques to solve a wide range of mathematical and AI problems. Drawing on the principles of ancient geometry, Solomon's work has applications from autonomous vehicles identifying pedestrians using…

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Bridging the gap between design and production in the field of optical devices.

Artificial Intelligence, Biological engineering, Computer science and technology, Electronics, Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT.nano, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 12, 2024200Views0Likes0Comments
Photolithography, the process of manipulating light to etch features on to a surface, is crucial in making computer chips and optical devices. However, the performance of devices made using this process often falls short of their original designs due to minute deviations during manufacturing. To address this design-to-manufacturing gap, researchers from MIT and the Chinese…

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Bridging the gap between design and production for optical instruments.

Artificial Intelligence, Biological engineering, Computer science and technology, Electronics, Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT.nano, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 12, 2024212Views0Likes0Comments
Photolithography, a process of etching detailed patterns onto surfaces using light, is a crucial technique in the design and production of computer chips and other optical devices, such as lenses. However, minute deviations during manufacturing can cause a discrepancy between the designer's intentions and the actual produced device. To help bridge this gap between design…

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Computational model successfully identifies the hard-to-catch transitional stages of chemical reactions.

Artificial Intelligence, Chemical engineering, Chemistry, Machine learning, National Science Foundation (NSF), Research, School of Engineering, School of Science, UncategorizedApril 12, 2024175Views0Likes0Comments
A team of researchers from the Massachusetts Institute of Technology (MIT) has developed a machine learning model that can quickly calculate the structures of transition states in chemical reactions. These fleeting moments occur when molecules have gained enough energy to proceed with a reaction, but are notoriously difficult to study due to their ephemeral nature.…

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A computer science expert is expanding the frontiers of geometry.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Faculty, Machine learning, MIT Schwarzman College of Computing, Profile, School of Engineering, UncategorizedApril 12, 2024173Views0Likes0Comments
Over 2,000 years ago, Greek mathematician Euclid, often called the father of geometry, revolutionized the understanding of shapes. In today's technological era, a 21st-century geometer, Justin Solomon, uses sophisticated techniques to solve complex problems related to shapes but often unrelated to them. Solomon applies geometry to study datasets for comparing their effectiveness in machine learning…

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Bridging the gap between design and production for optical equipment.

Artificial Intelligence, Biological engineering, Computer science and technology, Electronics, Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT.nano, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 12, 2024186Views0Likes0Comments
Researchers at MIT and the Chinese University of Hong Kong have developed a machine learning model to close the gap between design and manufacturing in the field of photolithography. The technique, which involves manipulating light to etch onto surfaces, sees use in the creation of computer chips and optical devices but often falls short of…

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The computational model successfully encapsulates the hard-to-catch transition stages of chemical reactions.

Artificial Intelligence, Chemical engineering, Chemistry, Machine learning, National Science Foundation (NSF), Research, School of Engineering, School of Science, UncategorizedApril 12, 2024181Views0Likes0Comments
During a chemical reaction, molecules move towards a transition state, a high-energy state that dictates how the reaction will proceed. However, this transition state is difficult to predict and observe due to its fleeting nature. Traditionally, scientists use quantum chemistry methods like density functional theory to evaluate these transition states, though these calculations tend to…

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Computational model successfully identifies the elusive transitional phases of chemical processes.

Artificial Intelligence, Chemical engineering, Chemistry, Machine learning, National Science Foundation (NSF), Research, School of Engineering, School of Science, UncategorizedApril 12, 2024143Views0Likes0Comments
MIT researchers have developed an approach based on machine learning that can calculate transition states of chemical reactions within seconds. The structures of these transition states, a temporary condition in the middle of a chemical reaction, can typically only be calculated using techniques based on quantum chemistry – a process that can be extremely time-consuming.…

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A novel approach to AI successfully encapsulates ambiguity present in medical imagery.

Artificial Intelligence, Broad Institute, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Health care, Imaging, Machine learning, MIT Schwarzman College of Computing, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 12, 2024175Views0Likes0Comments
In the field of biomedicine, segmentation refers to the process of highlighting important structures in a medical image, from organs to cells. Artificial intelligence (AI) models are starting to play a pivotal role in this task, but there are limitations with most existing models, mainly due to the fact that they are unable to factor…

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