{"ok":true,"nota":{"id":"6537c3825f53596f","slug":"disenar-aleaciones-nuevas-ya-no-depende-solo-de-probar-a-ciegas-se-com-6537c3","titulo":"Diseñar aleaciones nuevas ya no depende solo de probar a ciegas: se combinan computadoras e IA","resumen":"Un trabajo de revisión describe cómo se diseñan aleaciones metálicas formadas por varios elementos en proporciones similares, combinando simulaciones por computadora, procesos de fabricación e inteligencia artificial para predecir sus propiedades antes de fabricarlas.","fuente":"phys","fuenteNombre":"Phys.org","color":"#2b5c8a","autor":"","url":"https://phys.org/news/2026-10-trial-error-ai-high-entropy.html","imagen":"https://scx1.b-cdn.net/csz/news/tmb/2026/beyond-trial-and-error.jpg","tema":"ciencia","subtemas":["aleaciones","materiales","inteligencia artificial","simulacion computacional","ingenieria de materiales"],"entidades":["aleaciones de alta entropia","inteligencia artificial"],"formato":"dato","entretenimiento":false,"pubTs":"2026-10-26T21:20:01.000Z","indexTs":"2026-10-26T21:23:10.549Z","hash":"09fe9c840dd84ea63ead98c266834e317e29ce1b51dd93cfbfd11b83efd2d400","tsa":{"time":"Oct 26 21:23:38 2026 GMT","provider":"freetsa.org","sobre":"sello"},"stats":{"views":1,"likes":0,"clicks":0,"dwell":0},"tipo":"normal","registro":"hallazgo","porQueImporta":"Estos metales de mezcla múltiple pueden ser más resistentes que los tradicionales, y de ellos depende buena parte de autos, aviones y maquinaria industrial. Cuando el diseño se hace con modelos que predicen el resultado, se gastan menos materiales y menos tiempo en ensayos fallidos, lo que baja costos para la industria. El límite es que estas predicciones todavía necesitan validarse con experimentos reales antes de llevarlas a una planta.","quienFirma":"Universidad no especificada en la fuente","confianza":"revisado","consulta":null},"sello":{"hash":"09fe9c840dd84ea63ead98c266834e317e29ce1b51dd93cfbfd11b83efd2d400","sello":"c1dc7ef4ad4b5750b8a9bc8600ae0692a777225de16285f4047f674626c6186e","beacon":{"drand":{"cadena":"8990e7a9aaed2ffed73dbd7092123d6f289930540d7651336225dc172e51b2ce","ronda":6587292,"aleatorio":"149b813611385cb11dcf120b885d95cd690a84911a3a1d8634afc07a58304cf9","tiempo":"2026-10-26T21:23:00.000Z"},"nist":{"cadena":2,"pulso":2000840,"salida":"BB9721B8A1727AD8CDE7C1295DD625056A92BF581C746552660A03EE4B6AD78F1313BE614665FA1E5DC5960A511918C4466E4A0CC70562EA131AFA9659A5EA51","tiempo":"2026-10-26T21:23:00.000Z"},"noAntes":"2026-10-26T21:23:00.000Z"},"verificacion":{"drand":true,"nist":true},"tsa":{"time":"Oct 26 21:23:38 2026 GMT","provider":"freetsa.org","sobre":"sello"},"original":{"titulo":"Beyond trial and error: Integrating computation, processing, and AI to design high-entropy alloys","descripcion":"Designing structural high-entropy alloys (SHEAs) means solving a problem with an enormous number of moving parts: which combinations of composition and processing conditions will yield the phases, microstructures, and mechanical properties a given application demands? Because these alloys mix multiple principal elements in comparable proportions, that question spans length and time scales that no single experiment or model can cover on its own.","pubDate":"Mon, 26 Oct 2026 17:20:01 EDT","url":"https://phys.org/news/2026-10-trial-error-ai-high-entropy.html"},"indexado":"2026-10-26T21:23:10.549Z"}}