My colleagues and I have developed a flexible, stretchable electronic device that runs machine-learning algorithms to continuously collect and analyze health data directly on the body. The skin-like sticker, developed in my lab at the University of Chicago's Pritzker School of Molecular Engineering, includes a soft, stretchable computing chip that mimics the human brain. To create this type of device, we turned to electrically conductive polymers that have been used to build semiconductors and transistors. These polymers are made to be stretchable, like a rubber band. Rather than working like a typical computer chip, though, the chip we're working with, called a neuromorphic computing chip, functions more like a human brain.

Prof. Sihong Wang shows a single neuromorphic device with three electrodes. Researchers at the University of Chicago's Pritzker School of Molecular Engineering (PME) have developed a flexible, stretchable computing chip that processes information by mimicking the human brain. The device, described in the journal Matter, aims to change the way health data is processed. "With this work we've bridged wearable technology with artificial intelligence and machine learning to create a powerful device which can analyze health data right on our own bodies," said Sihong Wang, a materials scientist and Assistant Professor of Molecular Engineering. Today, getting an in-depth profile about your health requires a visit to a hospital or clinic. In the future, Wang said, people's health could be tracked continuously by wearable electronics that can detect disease even before symptoms appear.

Researchers with the University of Chicago Pritzker School of Molecular Engineering have shown for the first time how to design the basic elements needed for logic operations using a kind of material called a liquid crystal--paving the way for a completely novel way of performing computations. The results, published Feb. 23 in Science Advances, are not likely to become transistors or computers right away, but the technique could point the way towards devices with new functions in sensing, computing and robotics. "We showed you can create the elementary building blocks of a circuit--gates, amplifiers, and conductors--which means you should be able to assemble them into arrangements capable of performing more complex operations," said Juan de Pablo, the Liew Family Professor in Molecular Engineering and senior scientist at Argonne National Laboratory, and the senior corresponding author on the paper. The research aimed to take a closer look at a type of material called a liquid crystal. The molecules in a liquid crystal tend to be elongated, and when packed together they adopt a structure that has some order, like the straight rows of atoms in a diamond crystal--but instead of being stuck in place as in a solid, this structure can also shift around as a liquid does.

Plastic pollution is one of the most pressing environmental issues, and the increase in the production of disposable plastics does not help at all. These plastics would often take many years before they degrade, which poisons the environment. This has prompted efforts from nations to create a global treaty to help reduce plastic pollution. A combination of machine learning and artificial intelligence has accelerated the design of making materials, including plastics, with properties that quickly degrade without harming the environment and super-strong lightweight plastics for aircraft and satellites that would one day replace the metals being used. The researchers from the Pritzker School of Molecular Engineering (PME) at the University of Chicago published their study in Science Advances on October 21, which shows a way toward designing polymers using a combination of modeling and machine learning.

A team lead by researchers in the Pritzker School of Molecular Engineering (PME) at the University of Chicago reports that it has developed an artificial intelligence-led process that uses big data to design new proteins that could have implications across the healthcare, agriculture, and energy sectors. By developing machine-learning models that can review protein information culled from genome databases, the scientists say they found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they discovered that they performed chemistries so well that they rivaled those found in nature. "We have all wondered how a simple process like evolution can lead to such a high-performance material as a protein," said Rama Ranganathan, PhD, Joseph Regenstein Professor in the Department of Biochemistry and Molecular Biology, Pritzker Molecular Engineering, and the College. "We found that genome data contains enormous amounts of information about the basic rules of protein structure and function, and now we've been able to bottle nature's rules to create proteins ourselves."

Proteins are fundamental molecules for life and are able to perform many functions, from creating structures to being catalysts for chemical reactions. Scientists and engineers, for years, have been looking for a way to create new proteins and make them perform new tasks. Thanks to machine learning the target seems close.Researchers from the Pritzker School of Molecular Engineering (PME) at the University of Chicago developed an artificial intelligence that uses machine learning algorithms (which you can learn more about here) to design new proteins.Taking advantage of the huge databases on proteins, created starting from the decoding of the genome of many living species, scientists have found a relatively simple rule for building new proteins. When they produced them in the laboratory, the researchers observed that they are able to rival those produced in nature."We "We have found that the genome contains a huge amount of data on the basic structures of proteins, we are now able to use the rules of nature to create artificial …

Proteins are essential to the life of cells, carrying out complex tasks and catalyzing chemical reactions. Scientists and engineers have long sought to harness this power by designing artificial proteins that can perform new tasks, like treat disease, capture carbon, or harvest energy, but many of the processes designed to create such proteins are slow and complex, with a high failure rate. In a breakthrough that could have implications across the healthcare, agriculture, and energy sectors, a team lead by researchers in the Pritzker School of Molecular Engineering (PME) at the University of Chicago has developed an artificial intelligence-led process that uses big data to design new proteins. By developing machine-learning models that can review protein information culled from genome databases, the researchers found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they found that they performed chemistries so well that they rivaled those found in nature.

A team lead by researchers in the Pritzker School of Molecular Engineering (PME) at the University of Chicago reports that it has developed an artificial intelligence-led process that uses big data to design new proteins that could have implications across the healthcare, agriculture, and energy sectors. By developing machine-learning models that can review protein information culled from genome databases, the scientists say they found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they discovered that they performed chemistries so well that they rivaled those found in nature. "We have all wondered how a simple process like evolution can lead to such a high-performance material as a protein," said Rama Ranganathan, PhD, Joseph Regenstein Professor in the Department of Biochemistry and Molecular Biology, Pritzker Molecular Engineering, and the College. "We found that genome data contains enormous amounts of information about the basic rules of protein structure and function, and now we've been able to bottle nature's rules to create proteins ourselves."

Proteins are essential to the life of cells, carrying out complex tasks and catalyzing chemical reactions. Scientists and engineers have long sought to harness this power by designing artificial proteins that can perform new tasks, like treat disease, capture carbon, or harvest energy, but many of the processes designed to create such proteins are slow and complex, with a high failure rate. In a breakthrough that could have implications across the healthcare, agriculture, and energy sectors, a team lead by researchers in the Pritzker School of Molecular Engineering (PME) at the University of Chicago has developed an artificial intelligence-led process that uses big data to design new proteins. By developing machine-learning models that can review protein information culled from genome databases, the researchers found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they found that they performed chemistries so well that they rivaled those found in nature.

Proteins are essential to cells, carrying out complex tasks and catalyzing chemical reactions. Scientists and engineers have long sought to harness this power by designing artificial proteins that can perform new tasks, like treat disease, capture carbon or harvest energy, but many of the processes designed to create such proteins are slow and complex, with a high failure rate. In a breakthrough that could have implications across the healthcare, agriculture, and energy sectors, a team lead by researchers in the Pritzker School of Molecular Engineering at the University of Chicago has developed an artificial intelligence-led process that uses big data to design new proteins. By developing machine-learning models that can review protein information culled from genome databases, the researchers found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they found that they performed chemical processes so well that they rivaled those found in nature.