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1 0 A D V A N C E D M A T E R I A L S & P R O C E S S E S | J A N U A R Y / F E B R U A R Y 2 0 2 2 as zirconium dioxide or zirconia. They demonstrated that as the microstruc- ture of the material is reduced in size, it behaves similarly to ferroelectrics, a much better understood materials class. The researchers note the findings could have implications in many other areas besides memory, as antiferro- electrics have a range of unique proper- ties that make them useful in different devices including high-energy-density capacitors, transducers, and electro- optics circuits. The defining feature of an anti- ferroelectric material is the peculiar way it responds to an external electric field. This response combines features of non-ferroelectric and ferroelectric materials. The behavior of a ferroelec- tric material also depends on its size. As a sample of material is made thin- ner, a stronger electric field is required to create a permanent polarization, in accordance with the predictable Janovec–Kay–Dunn (JKD) law. In the new work, researchers discovered that zirconia antiferroelectrics also obey something like a JKD law. However, unlike for ferroelectrics, the microstruc- ture of the material plays a key role. For a smaller crystallite size, it takes a stronger critical field to switch an anti- ferroelectric material into its ferroelec- tric phase, even if the thinness of the sample remains the same. The researchers say the next step is to figure out exactly how to control the crystallite size, thereby tailoring the properties of the material for its use in circuits. gatech.edu . TESTING | CHARACTERIZATION MACHINE LEARNING PREDICTS STRESS For the first time, scientists used machine learning to predict stress in copper at the atomic scale. Until now, predicting grain boundary stresses responsible for fracture and fatigue properties of metal was limited to molecular dynamics simulation mod- els. The new method, created by aero- space engineers at the University of Illinois Urbana-Champaign, uses data- driven approaches based on machine learning to quantify grain boundary stresses in actual metal specimens imaged by electron microscopy. According to the researchers, they used molecular dynamics simulations of copper grain boundaries to train their machine learning algorithm to recognize the arrangements of atoms along boundaries and identify patterns in the stress distributions within differ- ent grain boundary structures. Even- tually, the algorithm was able to very accurately predict the grain boundary stresses from both simulation and experimental image data with atom- ic-level resolution. Measuring these grain boundary stresses is the first step toward designing aerospace materials for extreme environment applications. The team emphasizes that the algo- rithm they developed is very general and can be used to quantify the atom- ic-scale stresses governing fracture and failure processes in many other mate- rial systems. illinois.edu . IMPROVING DEVICE MEMORY The miniaturization of circuits has played a key role in improving memory performance over the last fifty years. Understanding how prop- erties of antiferroelectric materials change with shrinking size can enable more efficient component design. Now, a collaborative research team led by Georgia Tech, Atlanta, discov- ered unexpectedly familiar behavior in the antiferroelectric material known Triangular holes make this material more likely to crack from left to right. Courtesy of N.R. Brodnik et al./ Phys. Rev. Lett. Applied Technical Services (ATS), Marietta, Ga., added an electromagnetic interference/compatibility testing chamber and is now offering this service to aerospace and military clientele. Standards including MIL-STD-461, MIL- STD-704, and RTCA-DO 160 are supported, among others. atslab.com/environmental-testing/emi-emc-testing. BRIEF Left: Machine learning based on artificial neural networks as constitutive laws for atomic stress predictions. Right: Quantifying the local stress state of grain boundaries from atomic coordinate information. Courtesy of University of Illinois Urbana-Champaign. This EMI/EMC fully anechoic chamber was newly in talled at the ATS Marietta, Ga., facility.