MIT scientists have developed tiny, soft-bodied robots that can be controlled with a weak magnet. The robots, formed from rubbery magnetic spirals, can be programmed to walk, crawl, swim -- all in response to a simple, easy-to-apply magnetic field. "This is the first time this has been done, to be able to control three-dimensional locomotion of robots with a one-dimensional magnetic field," says Professor Polina Anikeeva, whose team published an open-access paper on the magnetic robots in the journal Advanced Materials. "And because they are predominantly composed of polymer and polymers are soft, you don't need a very large magnetic field to activate them. It's actually a really tiny magnetic field that drives these robots," adds Anikeeva, who is a professor of materials science and engineering and brain and cognitive sciences at MIT, a McGovern Institute for Brain Research associate investigator, as well as the associate director of MIT's Research Laboratory of Electronics and director of MIT's K. Lisa Yang Brain-Body Center.

The School of Engineering has announced that MIT has granted tenure to 14 members of its faculty in the departments of Biological Engineering, Civil and Environmental Engineering, Electrical Engineering and Computer Science (which reports jointly to the School of Engineering and MIT Schwarzman College of Computing), Materials Science and Engineering, and Mechanical Engineering. "I am truly amazed by our newest cohort of tenured faculty," says Anantha Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. "They are a diverse group of educators and scholars whose research and commitment to teaching has had a tremendous impact on our community, in the classroom, as well as in the lab." This year's newly tenured associate professors are: Guy Bresler, an associate professor of electrical engineering and computer science, conducts research at the interface of information theory, statistics, theoretical computer science, and probability. His work aims to understand the fundamental interplay between information properties, computational complexity, and combinatorial structure in modern statistical inference problems.

Using a method that models drug and target protein interactions using natural language processing techniques, the researchers achieved up to 97% accuracy in identifying promising drug candidates. The results were published recently in the journal Briefings in Bioinformatics. The technique represents drug-protein interactions through words for each protein binding site and uses deep learning to extract the features that govern the complex interactions between the two. "With AI becoming more available, this has become something that AI can tackle," says study co-author Ozlem Garibay, an assistant professor in UCF's Department of Industrial Engineering and Management Systems. "You can try out so many variations of proteins and drug interactions and find out which are more likely to bind or not."

Predicting mechanical properties in metal additive manufacturing (MAM) is vital to ensure the printed parts' performance, reliability, and whether they can fulfill requirements for a specific application. Conducting experiments to estimate mechanical properties in MAM processes, however, is a laborious and expensive task. Also, they can solely be designed for a particular material in a certain MAM process. Nonetheless, Machine learning (ML) methods, which are more flexible and cost-effective solutions, can be utilized to predict mechanical properties based on the processing parameters and material properties. To this end, in this work, a comprehensive framework for benchmarking ML for mechanical properties is introduced. An extensive experimental dataset is collected from more than 90 MAM articles and 140 MAM companies' data sheets containing MAM processing conditions, machines, materials, and resultant mechanical properties, including yield strength, ultimate tensile strength, elastic modulus, elongation, hardness as well as surface roughness. Physics-aware MAM featurization, adjustable ML models, and evaluation metrics are proposed to construct a comprehensive learning framework for mechanical properties prediction. Additionally, the Explainable AI method, i.e., SHAP analysis was studied to explain and interpret the ML models' predicted values for mechanical properties. Moreover, data-driven explicit models have been identified to estimate mechanical properties based on the processing parameters and material properties with more interpretability as compared to the employed ML models.

As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage. Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analog artificial "neurons" and "synapses" that execute computations just like a digital neural network. This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.

Digital information is everywhere in the era of smart technology, where data is continuously generated by and communicated among cell phones, smart watches, cameras, smart speakers and other devices. Securing digital data on handheld devices requires massive amounts of energy, according to an interdisciplinary group of Penn State researchers, who warn that securing these devices from bad actors is becoming a greater concern than ever before. Led by Saptarshi Das, Penn State associate professor of engineering science and mechanics, researchers developed a smart hardware platform, or chip, to mitigate energy consumption while adding a layer of security. The researchers published their results on June 23 in Nature Communications. "Information from our devices is currently stored in one location, the cloud, which is shared and stored in large servers," said Das, who also is affiliated with the Penn State School of Electrical Engineering and Computer Science, the Materials Research Institute and the College of Earth and Mineral Sciences' Department of Materials Science and Engineering.

Researchers at the University of Central Florida have created AI technology that mimics the human eye. The technology might result in highly developed artificial intelligence that can instantaneously understand what it sees and has uses in robotics and self-driving cars. Researchers at the University of Central Florida (UCF) have built a device for artificial intelligence that replicates the retina of the eye. The research might result in cutting-edge AI that can identify what it sees right away, such as automated descriptions of photos captured with a camera or a phone. The technology could also be used in robots and self-driving vehicles.

Members of the SARA team are pictured in Duffield Hall. From left: Duncan Sutherland, Ph.D. student in materials science and engineering; Carla Gomes, professor of computer science; Mike Thompson, professor of materials science and engineering; and Sebastian Ament, Ph.D. student in computer science. When a master chef develops a new cake recipe, she doesn't try every conceivable combination of ingredients to see which one works best. The chef uses prior baking knowledge and basic principles to more efficiently search for that winning formula. Materials scientists use a similar method in searching for novel materials with unique properties in fields such as renewable energy and microelectronics.

The person staring back from the computer screen may not actually exist, thanks to artificial intelligence (AI) capable of generating convincing but ultimately fake images of human faces. Now this same technology may power the next wave of innovations in materials design, according to Penn State scientists. "We hear a lot about deepfakes in the news today -- AI that can generate realistic images of human faces that don't correspond to real people," said Wesley Reinhart, assistant professor of materials science and engineering and Institute for Computational and Data Sciences faculty co-hire, at Penn State. "That's exactly the same technology we used in our research. The scientists trained a generative adversarial network (GAN) to create novel refractory high-entropy alloys, materials that can withstand ultra-high temperatures while maintaining their strength and that are used in technology from turbine blades to rockets. "There are a lot of rules about what makes an image of a human face or what makes an alloy, and it would be really difficult for you to know what all those rules are or to write them down by hand," Reinhart said. "The whole principle of this GAN is you have two neural networks that basically compete in order to learn what those rules are, and then generate examples that follow the rules." The team combed through hundreds of published examples of alloys to create a training dataset. The network features a generator that creates new compositions and a critic that tries to discern whether they look realistic compared to the training dataset. If the generator is successful, it is able to make alloys that the critic believes are real, and as this adversarial game continues over many iterations, the model improves, the scientists said. After this training, the scientists asked the model to focus on creating alloy compositions with specific properties that would be ideal for use in turbine blades. "Our preliminary results show that generative models can learn complex relationships in order to generate novelty on demand," said Zi-Kui Liu, Dorothy Pate Enright Professor of Materials Science and Engineering at Penn State. It's really what we are missing in our computational community in materials science in general."

Understanding structure-property relations is a key goal of materials research, according to Joshua Agar, a faculty member in Lehigh University's Department of Materials Science and Engineering. And yet currently no metric exists to understand the structure of materials because of the complexity and multidimensional nature of structure. Artificial neural networks, a type of machine learning, can be trained to identify similarities―and even correlate parameters such as structure and properties―but there are two major challenges, says Agar. One is that the majority of vast amounts of data generated by materials experiments are never analyzed. This is largely because such images, produced by scientists in laboratories all over the world, are rarely stored in a usable manner and not usually shared with other research teams.