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SEPTEMBER 2023 I SMT007 MAGAZINE 63 correct. Why don't you take some time and see if you can understand it?" "We'll give you a hint," Sue said, teasing. "Look at the labor and the rent." Paul looked at the spreadsheet and then it hit him: "e labor and rent are constant, even though the Pinnacle line produced more boards," he exclaimed. Andy tried to draw out more of Paul's con- clusions, and asked, "And what else?" "e labor and rent cost per board is less on the Pinnacle line because it produces so many more boards," Paul said. "I'm surprised how much difference it makes in total profit—over $442,000!" "So, 'profit potential' is much more impor- tant than 'cost of ownership,'" Paul opined. A meeting is scheduled soon to explain the results that so favor Pinnacle CPMs. Will Paul be able to explain it? What will Hal Lindsay say? Stay tuned to find out. SMT007 Ronald C. Lasky is an instruc- tional professor of engineer- ing for the Thayer School of Engineering at Dartmouth Col- lege, and senior technologist at Indium Corporation. To read past columns, click here. Researchers at Georgia Tech and Hanoi Univer- sity have capitalized on a powerful supercomputer to build a database that could identify new super- conducting materials that work at room tempera- ture. The team has identified two possible candidates using new machine learning models they devel- oped and deployed with the capabilities of the San Diego Supercomputer Center at the Univer- sity of California, San Diego. They published their progress recently in the journal Physical Review Materials. Superconductors allow electricity to pass with no resistance, but conventional materials require temperatures near absolute zero (nearly -460 degrees Fahren- heit). For more than a century, scientists have been searching for materials able to accomplish the feat at room temperature and ambient pres- sure. " T h e m a i n c h a l - lenge of the [artificial intelligence/machine learning] method is that we need, but never have, the desired database of superconduc- tors," said Huan Tran, senior research scientist in the Georgia Tech School of Materials Science and Engineering. "All previous works relied on data- bases that are sometimes large enough, but com- pletely lacking in atomic-level information—which is absolutely crucial for accurate predictions." Tran and Tuoc Vu from Hanoi University have been building a database with that atomic-level information, filling in a critical gap in available data so they can train machine learning models to accurately predict promising superconductive materials. (Source: Georgia Tech) Supercomputing for Superconductors

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