Electron-phonon coupling in Ni-based binary alloys with application to displacement cascade modeling

Tunable Chemical Disorder in Concentrated Alloys: Defect Physics and Radiation Performance

The development of advanced structural alloys with performance meeting the requirements of extreme environments in nuclear reactors has been long pursued. In the long history of alloy development, the search for metallic alloys with improved radiation tolerance or increased structural strength has relied on either incorporating alloying elements at low concentrations to synthesize so-called dilute alloys or incorporating nanoscale features to mitigate defects. In contrast to traditional approaches, recent success in synthesizing multicomponent concentrated solid-solution alloys (CSAs), including medium-entropy and high-entropy alloys, has vastly expanded the compositional space for new alloy discovery. Their wide variety of elemental diversity enables tunable chemical disorder and sets CSAs apart from traditional dilute alloys. The tunable electronic structure critically lowers the effectiveness of energy dissipation via the electronic subsystem. The tunable chemical complexity also modifies the scattering mechanisms in the atomic subsystem that control energy transport through phonons. The level of chemical disorder depends substantively on the specific alloying elements, rather than the number of alloying elements, as the disorder does not monotonically increase with a higher number of alloying elements. To go beyond our knowledge based on conventional alloys and take advantage of property enhancement by tuning chemical disorder, this review highlights synergistic effects involving valence electrons and atomic-level and nanoscale inhomogeneity in CSAs composed of multiple transition metals. Understanding of the energy dissipation pathways, deformation tolerance, and structural stability of CSAs can proceed by exploiting the equilibrium and non-equilibrium defect processes at the electronic and atomic levels, with or without microstructural inhomogeneities at multiple length scales. Knowledge of tunable chemical disorder in CSAs may advance the understanding of the substantial modifications in element-specific alloy properties that effectively mitigate radiation damage and control a material's response in extreme environments, as well as overcome strength-ductility trade-offs and provide overarching design strategies for structural alloys.

Learning grain boundary segregation energy spectra in polycrystals

The segregation of solute atoms at grain boundaries (GBs) can profoundly impact the structural properties of metallic alloys, and induce effects that range from strengthening to embrittlement. And, though known to be anisotropic, there is a limited understanding of the variation of solute segregation tendencies across the full, multidimensional GB space, which is critically important in polycrystals where much of that space is represented. Here we develop a machine learning framework that can accurately predict the segregation tendency—quantified by the segregation enthalpy spectrum—of solute atoms at GB sites in polycrystals, based solely on the undecorated (pre-segregation) local atomic environment of such sites. We proceed to use the learning framework to scan across the alloy space, and build an extensive database of segregation energy spectra for more than 250 metal-based binary alloys. The resulting machine learning models and segregation database are key to unlocking the full potential of GB segregation as an alloy design tool, and enable the design of microstructures that maximize the useful impacts of segregation.

Predicting segregation energies of alloy systems can be challenging even for a single grain boundary. Here the authors propose a machine-learning framework, which maps the local environments on a distribution of segregation energies, to predict segregation energies of alloy elements in polycrystalline materials.