Работы, цитирующие эту работу

Работ: 3

Работа: Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures

1. <scp>LAX</scp> phases: A family of novel stable layered materials, informatics‐based discovery

   Ehsan Alibagheri, Mohammad Khazaei, Mehdi Estili +5

   СтатьяMXene and MAX Phase MaterialsInfoMat2025Цитирований: 2

   ABIABI:AkademIndex/openalex/2025.article.000581

2. Synthesizable Two-Dimensional Haeckelites with Auxetic Behavior and Multifunctional Properties: A Computational Study Integrating Machine Learning Interatomic Potentials

   Ehsan Alibagheri, Mohammad Khazaei, Bassam Rabihavi +3

   СтатьяMachine Learning in Materials ScienceACS Materials Letters2025Цитирований: 0

   ABIABI:AkademIndex/openalex/2025.article.001839

3. Data-driven discovery of novel synthesizable MAX phase materials

   Ehsan Alibagheri, Mohammad Khazaei, Mehdi Estili +3

   СтатьяMXene and MAX Phase MaterialsApplied Materials Today2025Цитирований: 0

   ABIABI:AkademIndex/openalex/2025.article.002746