TY - JOUR A1 - Xie, Zhuocheng A1 - Atila, Achraf A1 - Guénolé, Julien A1 - Korte-Kerzel, Sandra A1 - Al-Samman, Talal A1 - Kerzel, Ulrich T1 - Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach N2 - Grain boundary (GB) segregation substantially influences the mechanical properties and performance of magnesium (Mg). Atomic-scale modeling, typically using ab-initio or semi-empirical approaches, has mainly focused on GB segregation at highly symmetric GBs in Mg alloys, often failing to capture the diversity of local atomic environments and segregation energies, resulting in inaccurate structure-property predictions. This study employs atomistic simulations and machine learning models to systematically investigate the segregation behavior of common solute elements in polycrystalline Mg at both 0 K and finite temperatures. The machine learning models accurately predict segregation thermodynamics by incorporating energetic and structural descriptors. We found that segregation energy and vibrational free energy follow skew-normal distributions, with hydrostatic stress, an indicator of excess free volume, emerging as an important factor influencing segregation tendency. The local atomic environment’s flexibility, quantified by flexibility volume, is also crucial in predicting GB segregation. Comparing the grain boundary solute concentrations calculated via the Langmuir-McLean isotherm with experimental data, we identified a pronounced segregation tendency for Nd, highlighting its potential for GB engineering in Mg alloys. This work demonstrates the powerful synergy of atomistic simulations and machine learning, paving the way for designing advanced lightweight Mg alloys with tailored properties. KW - Machine learning KW - Grain boundary segregation KW - Magnesium alloys KW - Atomistic simulation PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-638499 DO - https://doi.org/10.1016/j.jma.2025.03.021 SN - 2213-9567 VL - 13 IS - 6 SP - 2636 EP - 2650 PB - Elsevier B.V. AN - OPUS4-63849 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Atila, Achraf A1 - Bakhouch, Yasser A1 - Xie, Zhuocheng T1 - Revealing the void-size distribution of silica glass using persistent homology N2 - Oxide glasses have proven to be useful across a wide range of technological applications. Nevertheless, their medium-range structure has remained elusive. Previous studies focused on ring statistics as a metric of the medium-range structure, but this metric provides an incomplete picture of the glassy structure. Here, we use atomistic simulations and state-of-the-art topological analysis tools, namely persistent homology (PH), to analyze the medium-range structure of the archetypal oxide glass (Silica) at ambient temperatures and with varying pressures. PH presents an unbiased definition of loops and voids, providing an advantage over other methods for studying the structure and topology of complex materials, such as glasses, across multiple length scales. We captured subtle topological transitions in medium-range order and cavity distributions, providing new insights into glass structure. Our work provides a robust way for extracting the void distribution of oxide glasses based on PH. KW - Medium-range KW - Voids KW - Topology KW - Silica glass KW - Atomistic simulations KW - Persistent homology PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-655690 DO - https://doi.org/10.1016/j.mtla.2025.102613 VL - 44 SP - 1 EP - 11 PB - Elsevier Inc. AN - OPUS4-65569 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -