arXiv subjects

Mary Alice Cusentino

Publications and source records attributed to Mary Alice Cusentino.

3 recordsLinked to original sources

arXiv2026

Thermal Transport in SiC with Intrinsic Defects and Mg Transmutation Products

Silicon carbide is a leading candidate material for advanced nuclear energy systems, but irradiation-induced defects and transmutation products can severely degrade its thermal conductivity. In fusion environments, Mg is predicted to be a major solid transmutant in SiC, yet it is not well understood how different Mg-related defects affect phonon transport. Here, we develop a machine-learning interatomic potential, MLIP4SiC-Mg, for 3C-SiC containing intrinsic point defects, Mg-related defects, and Mg-defect complexes. The potential is trained on a large DFT dataset and reproduces DFT energies, forces, equation-of-state behavior, phonon dispersions, and lattice thermal conductivities with near-DFT accuracy. Combined with Green-Kubo molecular dynamics, force-error correction, and a resistance-based treatment for dilute defective systems, MLIP4SiC-Mg enables quantitative thermal-conductivity calculations in large defective supercells. The corrected thermal conductivity of pristine 3C-SiC is 421 W/(mK) at 300 K, in good agreement with available experimental data. All defects considered strongly reduce thermal conductivity, but their scattering strengths are highly configuration dependent. V_C and Mg_TC act as strong phonon scatterers, whereas isolated Mg_Si is comparatively weak. Residual thermal resistivity analysis shows that defect-induced thermal resistance is not strictly linear with concentration and should be treated as an effective temperature- and concentration-dependent scattering metric. Mg_Si-V_C clustering enhances scattering relative to isolated Mg_Si, but reduces the total excess resistance relative to spatially separated Mg_Si and V_C defects. These results clarify the configuration-dependent role of Mg transmutation in irradiation-degraded SiC and provide an atomistic framework for quantifying defect-controlled heat transport in nuclear ceramics.

cond-mat.mtrl-sci↗

arXiv2020

Explicit Multi-element Extension of the Spectral Neighbor Analysis Potential for Chemically Complex Systems

A natural extension of the descriptors used in the Spectral Neighbor Analysis Potential (SNAP) method is derived to treat atomic interactions in chemically complex systems. Atomic environment descriptors within SNAP are obtained from a basis function expansion of the weighted density of neighboring atoms. This new formulation instead partitions the neighbor density into partial densities for each chemical element, thus leading to explicit multi-element descriptors. For $N_{elem}$ chemical elements, the number of descriptors increases as $\mathcal{O}(N_{elem}^3)$, while the computational cost of the force calculation as implemented in LAMMPS is limited to $\mathcal{O}(N_{elem}^2)$ and the favorable linear scaling in the number of atoms is retained. We demonstrate these chemically aware descriptors by producing an interatomic potential for indium phosphide capable of capturing high-energy defects that result from radiation damage cascades. This new explicit multi-element SNAP method reproduces the relaxed defect formation energies with substantially greater accuracy than weighted-density SNAP, while retaining accurate representation of the bulk indium phosphide properties.

physics.chem-ph↗

arXiv2019

Data-driven Material Models for Atomistic Simulation

The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and ingenuity is required to construct viable functional forms and find accurate parameterizations for potentials using traditional approaches. Machine-learning has emerged as an effective alternative approach to develop accurate and robust interatomic potentials. Starting with a very general model form, the potential is learned directly from a database of electronic structure calculations and therefore can be viewed as a multiscale link between quantum and classical atomistic simulations. Risk of inaccurate extrapolation exists outside the narrow range of time- and length-scales where the two methods can be directly compared. In this work, we use the Spectral Neighbor Analysis Potential (SNAP) and show how a fit can be produced with minimal interpolation errors which is also robust in extrapolating beyond training. To demonstrate the method, we have developed a new tungsten-beryllium potential suitable for the full range of binary compositions. Subsequently, large-scale molecular dynamics simulations were performed of high energy Be atom implantation onto the (001) surface of solid tungsten. The new machine learned W-Be potential generates a population of implantation structures consistent with quantum calculations of defect formation energies. A very shallow (<2nm) average Be implantation depth is predicted which may explain ITER diverter degradation in the presence of beryllium.

physics.comp-ph↗