Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.34948

Preparing State-Selected Molecular Beams with high Efficiency using Quantum Cascade Lasers

Ardra P. S.·Pranav R. Shirhatti
View original source ↗Read source document ↗Export metadata ↓

Abstract

We demonstrate the ro-vibrational state-selective preparation of CO molecules ($v$ = 0, J = 0 $\rightarrow$ $v$ = 1, J = 1) in an internally cold molecular beam using a continuous-wave, narrow-linewidth distributed feedback type quantum cascade laser, with an output wavelength of 4.65 $μ$m. Using a modulation-free wavelength stabilization scheme, we show that population transfer to near saturation levels can be obtained with sufficient long term stability, as needed for state-resolved scattering experiments. We show preliminary results of CO molecules prepared in the $v$ = 1 state, scattering from a clean Cu(100) surface. This study showcases the promise of QCL based excitation for state-selected molecular beam based scattering experiments, especially involving transitions in the mid-infrared domain ($λ$ > 4.5 $μ$m) which are otherwise difficult to access using conventional sources.

Explore related subjects

physics.chem-phphysics.atm-clusphysics.ins-det

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ardra P. S., Pranav R. Shirhatti. 2026-10-26. Preparing State-Selected Molecular Beams with high Efficiency using Quantum Cascade Lasers. https://arxiv.org/abs/2610.34948

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Integer electron-count cuts in variational second-order reduced-density-matrix theory. I. Molecular dissociation with fixed real-space and orbital partitions

Van Aggelen et al. [Phys. Chem. Chem. Phys. 11, 5558 (2009)] showed that variational second-order reduced-density-matrix (2-RDM) calculations with the standard two-index conditions dissociate diatomic molecules into fractionally charged atoms with energies far below the exact limit, a drastic violation of size consistency; the proposed subsystem constraints require auxiliary calculations with the physical fragment Hamiltonians. We show that two long-known ingredients, combined, remove most of this failure without such calculations: the requirement that the number of electrons in a part of the molecule be an integer, imposed as linear conditions on the 2-RDM, and a chemically motivated partition of the molecule into atomic regions bounded at the bond critical point of the Hartree-Fock density. In a minimal basis, compared with full configuration interaction, a single such condition reduces the energy errors of NO$^+$ and CN$^-$ at 6 Å from 108.9 and 80.7 m$E_\mathrm{h}$ to less than 0.02 m$E_\mathrm{h}$. In N$_2^+$, whose fragments have the correct mean population but too small a fluctuation, the region and three-index conditions act complementarily, reducing the error at 4 Å from 138.0 to 1.6 m$E_\mathrm{h}$. At the separated-atom limit, the region cuts make the $P$, $Q$, $G$ energy additive over the fragments to within 0.2 m$E_\mathrm{h}$ in 7 of 10 systems, 3 already nearly additive without cuts; adding the $T_1$ and $T_2$ conditions also makes CO nearly additive, while a nonadditive error remains for N$_2^+$ and CO$^+$. Effective partitions are those whose condition is nearly saturated by the exact state.

physics.chem-ph↗
arXiv2026

Machine Learning Hamiltonians with Polarizations for Heterogeneous Systems

Machine learning Hamiltonians use local atomic structures to determine local Hamiltonian matrix elements without carrying out self-consistent calculations. They have been used to study large systems and to carry out high-throughput screening for their electronic and optical properties. The prediction of the Hamiltonian is essentially the prediction of the electron electrostatic potential energy; however, for heterogeneous systems, this potential energy is often determined by faraway features, such as interface or surface dipoles, or even an applied voltage in a nanodevice. Thus, it cannot be determined by local atomic structures alone. In this sense, one needs to solve the electron electrostatic potential energy self-consistently using intrinsic atomic dipoles, the applied external potential energy, and polarizabilities. Fortunately, the intrinsic dipoles and polarizabilities are local properties, whereas the applied potential energy is a prescribed input. In this work, we propose DeepH-Polar, a physically informed machine learning Hamiltonian model that explicitly predicts intrinsic atomic dipoles and polarizability tensors and uses them to solve the electron electrostatic potential energy self-consistently. Benchmarks on polar GaN systems and crystalline-Si/amorphous-SiO$_2$ interfaces demonstrate improved Hamiltonian accuracy and reproduction of thickness-dependent electrostatic variations, as well as spatially nonuniform field-induced screening that models without polarization fail to capture. Our model also allows us to study nanoscale electronic devices. By iteratively solving the Hamiltonian eigenproblem and self-consistently updating free-carrier occupations and the electrostatic potential, we simulate an 8,194-atom gated semiconductor device.

physics.chem-ph↗
arXiv2026

Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields

Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-from-scratch and foundation fine-tuning workflows. Active learning can reduce this cost, but standard model-committee uncertainty is impractical for foundation MLFFs because each committee member requires a separate fine-tuning run. We present an active-learning workflow based on last-layer-projection regression (LLPR), a forward-pass-cheap per-configuration uncertainty estimator. Across molecular, condensed-phase, and electrolyte systems, LLPR identifies compact, high-value training sets that recover full-data accuracy using only a small fraction of electronic-structure labels. In foundation-model fine-tuning, LLPR-selected configurations approach the practical fine-tuning ceiling with substantially fewer labels than random selection. In iterative electrolyte fine-tuning, LLPR detects unphysical local coordination before new labelling, calibrates uncertainty in force-error units, and supports retrospective convergence monitoring. The resulting models reproduce reference density and Li--O coordination structure, providing a scalable uncertainty-quantification strategy across MLFF training regimes.

physics.chem-ph↗