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Our large-scale simulations provide direct evidence of the incongruent melting of SiC, confirming its decomposition into silicon-rich and carbon phases at high temperature and pressure. https://arxiv.org/abs/2502.17804

Incongruent Melting and Phase Diagram of SiC from Machine Learning Molecular Dynamics Silicon carbide (SiC) is an important technological material, but its high-temperature phase diagram has remained unclear due to conflicting experimental results about congruent versus incongruent melting. Here, we employ large-scale machine learning molecular dynamics (MLMD) simulations to gain ins...

Have you imagined a simple machine learning solution able to prevent revenue leakage and monitor your distributed application? https://www.slideshare.net/slideshow/preventing-revenue-leakage-and-monitoring-distributed-systems-with-machine-learning-eiti-kimura-and-flavio-clsio/81441857

Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Learning Eiti Kimura and Flavio Clésio Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Learning Eiti Kimura and Flavio Clésio - Download as a PDF or view online for free

Bio-Hybrid Films from Chirich Tuber Starch: A Sustainable Approach with Machine Learning-Driven Optimization. https://www.mdpi.com/2071-1050/17/5/1935

Bio-Hybrid Films from Chirich Tuber Starch: A Sustainable Approach with Machine Learning-Driven Optimization This study investigates the potential of Chirich (Asphodelus aestivus) tuber, one of Turkey’s natural resources, for sustainable bio-hybrid film production. Bio-hybrid films developed from Chirich tuber starch in composite form with polyvinyl alcohol (PVOH) were thoroughly examined for their physi...

"Connecticut residents—and all Americans—deserve to have transparency into these systems and rights if they are harmed by companies using AI. https://www.acluct.org/en/press-releases/coalition-consumer-technology-racial-justice-organizations-testify-connecticut

Coalition of Consumer, Technology, & Racial Justice Organizations Testify on Connecticut Artificial Intelligence Bill; Send Letter Urging Stronger Protections Against Bias Groups Represent Racial Justice, Consumer Protection, Labor, Democracy, and Technology Advocates

\u201d\n\nNot enough domain-specific knowledge\n\nEffective use of machine learning frequently requires extensive knowledge of the issue or field being addressed, Sun says. https://www.infoworld.com/article/3812589/10-machine-learning-mistakes-and-how-to-avoid-them.html

10 machine learning mistakes and how to avoid them Machine learning is a multibillion-dollar business with seemingly endless potential, but it poses some risks. Here's how to avoid the most common machine learning mistakes.