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If every US home and personal vehicle goes electric, power outages could spike unless key measures are taken

A future where all homes and vehicles in the U.S. are fully electrified could overwhelm power supply and risk outages unless key upgrades are made, says a new study conducted by Purdue University engineers. But a few strategies could cut two-thirds of the potential costs of reinforcing the nation’s distribution grid to handle this demand.

Electrifying would mean switching a home’s heating system from a boiler to a and transitioning from gas-or diesel-fueled vehicles to electric vehicles.

“If we install a whole bunch of new electric heating systems for homes and use more electric vehicles and electric water heaters, then we’re going to increase electricity demand a lot. And that’s basically going to require putting in thicker wires, bigger transformers and other infrastructure into the ,” said Kevin Kircher, a Purdue assistant professor of mechanical engineering and faculty member in the university’s Ray W. Herrick Laboratories. “And if that happens, utilities will pass the cost of those upgrades to us, the customers.”

Researchers trace genetic code’s origins to early protein structures

Genes are the building blocks of life, and the genetic code provides the instructions for the complex processes that make organisms function. But how and why did it come to be the way it is?

A recent study from the University of Illinois Urbana-Champaign sheds new light on the origin and evolution of the , providing valuable insights for genetic engineering and bioinformatics. The study is published in the Journal of Molecular Biology.

“We find the origin of the genetic code mysteriously linked to the dipeptide composition of a proteome, the collective of proteins in an organism,” said corresponding author Gustavo Caetano-Anollés, professor in the Department of Crop Sciences, the Carl R. Woese Institute for Genomic Biology, and Biomedical and Translation Sciences of Carle Illinois College of Medicine at U. of I.

‘Virtual clinical trials’ may predict success of heart failure drugs

Mayo Clinic researchers have developed a new way to predict whether existing drugs could be repurposed to treat heart failure, one of the world’s most pressing health challenges. By combining advanced computer modeling with real-world patient data, the team has created “virtual clinical trials” that may facilitate the discovery of effective therapies while reducing the time, cost, and risk of failed studies.

“We’ve shown that with our framework, we can predict the clinical effect of a drug without a . We can say with high confidence if a drug is likely to succeed or not,” says Nansu Zong, Ph.D., a biomedical informatician at Mayo Clinic and lead author of the study, which was published in npj Digital Medicine.

Machine learning unravels quantum atomic vibrations in materials

Caltech scientists have developed an artificial intelligence (AI)–based method that dramatically speeds up calculations of the quantum interactions that take place in materials. In new work, the group focuses on interactions among atomic vibrations, or phonons—interactions that govern a wide range of material properties, including heat transport, thermal expansion, and phase transitions. The new machine learning approach could be extended to compute all quantum interactions, potentially enabling encyclopedic knowledge about how particles and excitations behave in materials.

Scientists like Marco Bernardi, professor of applied physics, physics, and at Caltech, and his graduate student Yao Luo (MS ‘24) have been trying to find ways to speed up the gargantuan calculations required to understand such particle interactions from first principles in real materials—that is, beginning with only a material’s atomic structure and the laws of quantum mechanics.

Last year, Bernardi and Luo developed a data-driven method based on a technique called singular value decomposition (SVD) to simplify the enormous mathematical matrices scientists use to represent the interactions between electrons and phonons in a material.

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