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Raphaël J. Prentki

Publications and source records attributed to Raphaël J. Prentki.

3 recordsLinked to original sources

Constraints on Atomistic Disorder for Scalable Electron Spin Shuttling

Electron spin shuttling, the gate-controlled, coherent transport of electrons between qubit registers, increases qubit connectivity and enables efficient quantum-error-correction schemes. It is emerging as a key enabler of scalable silicon spin-qubit quantum computing. As an electron travels over micrometers, it encounters angstrom-scale disorder, causing fluctuations in its confinement potential, valley splitting, and valley phase. These lead to leakage into the valley-excited state, limiting high-fidelity shuttling speeds. Accurate predictions of shuttling fidelities thus require modeling tools that link atomistic and mesoscopic physics. We develop a multiscale simulation workflow to quantify these effects in the experimentally-realized Si/SiGe "QuBus" conveyor-belt architecture. First, we resolve the time-dependent, gate-controlled device electrostatics by solving the Poisson equation using the finite-element method. Second, we construct conveyor belt atomic structures with realistic atomistic disorder (random alloying and interface roughness); we resolve strain atomistically using the Keating valence force-field model. Third, we perform position-tracked atomistic tight-binding simulations of the shuttled electrons to obtain their time-dependent valley splittings and phases. Finally, these time traces parametrize a time-dependent Schrödinger equation, which we solve to predict valley dynamics. We find that interface roughness strongly suppresses shuttling fidelities, with a sharp anomaly near the atomic-layer scale. Overall, our predictions set practical, quantitative guidelines to realize scalable, high-fidelity shuttling in silicon spin-qubit architectures.

quant-ph↗

LatticeMind: A Validation-Gated Agentic Framework for Density Functional Theory Workflows

First-principles calculations are rarely single solver calls: they are multistage workflows in which structures, settings, and saved densities must pass correctly between dependent stages. Language models can write a valid single input, but reliability collapses once stages are chained: a dropped dependency, wrong file handoff, or hallucinated keyword can invalidate results even when the solver runs, and such errors surface only after costly execution. We introduce LatticeMind, a validation-gated agentic framework that converts natural-language requests into executable, checkable density functional theory (DFT) projects. A workflow contract specifies required stages, files, and dependencies; deterministic validators inspect the project before any solver launch; failed checks trigger bounded repair; and reports remain traceable to project files. On a 44-case RESCU benchmark spanning total-energy, density-of-states, band-structure, magnetic, phonon workflows, three non-flagship LLM backends reproduced every reference value; 119 of 132 runs passed on the first validated plan and the rest after one repair cycle. Ablations identify the workflow contract and pre-execution validation as the principal reliability mechanisms; a conventional trial-and-repair baseline reaches 71% at twice the token cost per success. The same architecture reproduces every reference value on a 19-case VASP benchmark, demonstrating transfer across solvers. Beyond these task families, LatticeMind generated and validated the 23 inputs of a multistage study of Rh-induced electronic responses at a Rh$_6$/In$_x$Ga$_{1-x}$N photocatalyst interface. These results establish workflow contracts, deterministic validation, bounded repair, and traceable records as a practical foundation for reliable AI-assisted scientific computing, where preventable errors are caught before execution and the solver is never the first validator.

cond-mat.mtrl-sci↗

First-principles predictions of band alignment in strained Si/Si1-xGex and Ge/Si1-xGex heterostructures

Accurate band offsets are essential for predictive continuum modeling of nanostructures such as quantum wells and quantum dots formed in strained Si/Si1-xGex and Ge/Si1-xGex heterostructures. Experimental offset data for these systems remain sparse away from endpoint compositions, making composition-dependent design difficult. We use atomistic first-principles density functional theory to compute valence- and conduction-band offsets across the full range 0 <= x <= 1. Random alloying is treated with special quasirandom structures, interface lineup terms are extracted from macroscopically averaged local Kohn-Sham potentials in thick periodic superlattices, valence-band spin-orbit coupling is included through species-resolved Mulliken weights, and conduction-band edges are refined using the screened hybrid Heyd-Scuseria-Ernzerhof functional. The resulting offsets show pronounced composition nonlinearity beyond the linear models explored in previous works, agree with experimental benchmarks, and reproduce the high-Ge slope change in the relaxed-alloy band gap. Analytic fitting expressions are provided for direct use in simulations, facilitating practical design of modern quantum technology devices.

cond-mat.mes-hall↗