{"id":62414,"date":"2025-04-10T20:49:43","date_gmt":"2025-04-10T20:49:43","guid":{"rendered":""},"modified":"2025-04-10T20:49:43","modified_gmt":"2025-04-10T20:49:43","slug":"%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-62414","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-62414\/","title":{"rendered":"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u062a\u0633\u0631\u06cc\u0639 \u0645\u062d\u0627\u0633\u0628\u0627\u062a \u0648\u06cc\u0698\u06af\u06cc \u0630\u0648\u0628 \u0627\u0648\u0644\u06cc\u0647 \u0628\u0627 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646: \u06a9\u0627\u0631\u0628\u0631\u062f \u062f\u0631 \u0622\u0644\u06cc\u0627\u0698 \u0622\u0646\u062a\u0631\u0648\u067e\u06cc \u0628\u0627\u0644\u0627 TaVCrW"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nAccelerating ab initio melting property calculations with machine learning: Application to the high entropy alloy TaVCrW<\/td>\n<\/tr>\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u062a\u0633\u0631\u06cc\u0639 \u0645\u062d\u0627\u0633\u0628\u0627\u062a \u0648\u06cc\u0698\u06af\u06cc \u0630\u0648\u0628 \u0627\u0648\u0644\u06cc\u0647 \u0628\u0627 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646: \u06a9\u0627\u0631\u0628\u0631\u062f \u062f\u0631 \u0622\u0644\u06cc\u0627\u0698 \u0622\u0646\u062a\u0631\u0648\u067e\u06cc \u0628\u0627\u0644\u0627 TaVCrW<\/td>\n<\/tr>\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\nLi-Fang Zhu, Fritz Koermann, Qing Chen, Malin Selleby, Joerg Neugebauer, and Blazej Grabowski<\/td>\n<\/tr>\n
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\u062a\u0648\u0636\u06cc\u062d\u0627\u062a <\/td>\nSubmitted 16 August, 2024; originally announced August 2024. , Comments: 14 pages, 6 figures<\/td>\n<\/tr>\n
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\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n

Melting properties are critical for designing novel materials, especially for discovering high-performance, high-melting refractory materials. Experimental measurements of these properties are extremely challenging due to their high melting temperatures. Complementary theoretical predictions are, therefore, indispensable. The conventional free energy approach using density functional theory (DFT) has been a gold standard for such purposes because of its high accuracy. However,it generally involves expensive thermodynamic integration using ab initio molecular dynamic simulations. The high computational cost makes high-throughput calculations infeasible. Here, we propose a highly efficient DFT-based method aided by a specially designed machine learning potential. As the machine learning potential can closely reproduce the ab initio phase space, even for multi-component alloys, the costly thermodynamic integration can be fully substituted with more efficient free energy perturbation calculations. The method achieves overall savings of computational resources by 80% compared to current alternatives. We apply the method to the high-entropy alloy TaVCrW and calculate its melting properties, including melting temperature, entropy and enthalpy of fusion, and volume change at the melting point. Additionally, the heat capacities of solid and liquid TaVCrW are calculated. The results agree reasonably with the calphad extrapolated values.<\/p>\n

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