首页|Reports on Machine Learning from National Autonomous University of Mexico (UNAM) Provide New Insights (Utilizing Wyckoff Sites To Construct Machine-learning-driven Interatomic Potentials for Crystalline Materials: a Case Study On A-alumina)
Reports on Machine Learning from National Autonomous University of Mexico (UNAM) Provide New Insights (Utilizing Wyckoff Sites To Construct Machine-learning-driven Interatomic Potentials for Crystalline Materials: a Case Study On A-alumina)
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NETL
NSTL
Amer Chemical Soc
Current study results on Machine Learning have been published. According to news originating from Ciudad de Mexico, Mexico, by NewsRx correspondents, research stated, “We present a methodology leveraging machine learning models to generate interatomic potentials for crystalline materials. This approach is rooted in the material’s crystallography in question.” Financial supporters for this research include LAREC Laboratory of the Institute of Physics, UNAM, LAREC Laboratory of the Institute of Physics, UNAM, Mexico. Our news journalists obtained a quote from the research from the National Autonomous University of Mexico (UNAM), “Specifically, we tap into the occupied Wyckoff sites, extracting the defining features that encapsulate the atomic local order in the material. Our choice for the target variable is the formation energy per atom, derived from the total energy of the structure’s representative cell. Our machine learning model’s architecture depends on the occupied Wyckoff sites. The diversity of these occupied sites conditions the layering scheme within the model. Atoms occupying a particular Wyckoff site were modeled with the architecture and learning parameters linked to the respective layer. To illustrate our method, we developed an interatomic potential for atomic interactions in alpha-alumina. For training purposes, we generated the samples through quantum mechanical computations. The evaluation of the learned interatomic potential involved conducting molecular dynamics calculations on a 2 x 2 x 2 supercell, yielding formation energies per atom deviating by less than 1.0 meV from the quantum mechanics results. The methodology described here paves the way for further innovations, potentially ushering in the creation of interatomic potentials that can be utilized for more than one material.”
Ciudad de MexicoMexicoNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningNational Autonomous University of Mexico (UNAM)
NETL
NSTL
Amer Chemical Soc
