2024
Effective subgrouping enhances machine learning prediction in complex materials science phenomena: Inoue's subgrouping in discovering bulk metallic glasses
Liu G, Sohn S, O'Hern C, Gilbert A, Schroers J. Effective subgrouping enhances machine learning prediction in complex materials science phenomena: Inoue's subgrouping in discovering bulk metallic glasses. Acta Materialia 2024, 265: 119590. DOI: 10.1016/j.actamat.2023.119590.Peer-Reviewed Original ResearchMaterials science problemsScience problemsPhysical insightStatistical methodsMetallic glass formationMaterials discoveryGlass formationMachine learningML modelsHigh prediction accuracyProblem spacePrediction accuracyProblemScience phenomenaML strategiesMetallic glassesMaterials science phenomenaGlass-forming abilityComposition-property relationshipsModelSpaceWide rangePhenomenonEntire datasetRepresentation
2013
Computational studies of the glass-forming ability of model bulk metallic glasses
Zhang K, Wang M, Papanikolaou S, Liu Y, Schroers J, Shattuck MD, O'Hern CS. Computational studies of the glass-forming ability of model bulk metallic glasses. The Journal Of Chemical Physics 2013, 139: 124503. PMID: 24089782, DOI: 10.1063/1.4821637.Peer-Reviewed Original ResearchBulk metallic glassGlass-forming abilityMetallic glassesTypical bulk metallic glassesLiquid metal alloysCritical cooling rateBMGs increasesGlass transition temperatureParticle size differencesAtomic size ratioMetal alloysCooling rateNegative heatKey parametersBinary LJ mixturesGlass formationTransition temperatureOrders of magnitudeSize ratioHeatGlass-forming mixturesGlassSimulationsDynamics simulationsMolecular dynamics simulations