Matter Sciences

The work introduces nonorthogonal variational quantum simulation (NOVQS), which applies linear combinations of parameterized quantum states to real- and imaginary-time evolutions, designs a shallow hardware-friendly ansatz tailored to second-quantized electronic-structure Hamiltonians together with resource-efficient protocols for measuring the matrices and vectors in the parameter equations of motion, and provides error analysis and resource estimation; numerical simulations of hydrogen chains and the nitrogen molecule show that a collection of shallow, or even single-layer, parameterized quantum circuits can match or outperform a much deeper circuit in variational quantum simulation, revealing a trade-off between circuit number and depth.

The authors propose AIDEN, an Atomic-Interaction Density Equivariant Network for real-space charge density, which separates the element-dependent one-center density from environment-induced density redistribution, represents the latter through complementary atom- and edge-centered tensor correlations, and reconstructs density at arbitrary spatial coordinates with a continuous low-rank Gaussian decoder; it achieves state-of-the-art accuracy on periodic crystal benchmarks, remains competitive for molecular systems, shows zero-shot transferability across several structurally distinct out-of-distribution case studies, and offers substantially faster inference than both baseline models and full SCF calculations.

This work proposes that solid-state synthesis through interfacial-melt-mediated routes requires, beyond the target phase being thermodynamically stable on the formation energy convex hull, that the interfacial melt at the target composition itself remain locally stable against spinodal decomposition; using melt-quench molecular dynamics driven by a fine-tuned machine-learning interatomic potential in the classical Fe-B system, the authors find that at ambient pressure the B-rich interfacial melt near the FeB4 composition develops a concave free-energy landscape signaling a demixing instability, corroborated by the concentration-concentration structure factor and correlated with low-energy icosahedral and pentagonal-pyramidal boron motifs; in contrast to FeB4, metastable Fe3B and Fe23B6 rem

For SEM micrographs of 316L stainless steel, this work proposes a Leave-One-Region-Out region-held-out cross-validation protocol over 14 spatial regions (8 AR, 6 H2; 31 images) and compares six feature-classifier combinations built on LBP, GLCM, self-supervised convolutional embeddings, and a CNN; the simplest approach, LBP+SVM, performed best with balanced accuracy 0.79, H2 recall 0.69, and H2 precision 0.82, outperforming every deep-learning and combined-feature model, while a group-level permutation test (500 permutations sampled from the 3,003 possible region-to-label assignments) yielded p = 0.008 and Grad-CAM maps from a CNN tended to concentrate on localized surface and grain-boundary features.

The work presents and validates a hybrid pipeline that first uses a volumetric convolutional neural network (a D3M-based VNet taking six channels of displacement and velocity) to classify each simulation particle as a halo or non-halo member, then applies a highly optimised and parallelised Friends-of-Friends algorithm to group the predicted halo members into distinct dark matter haloes; trained on GADGET-4 simulations labelled by ROCKSTAR, it reaches over 98% across all primary metrics for particle classification at the highest-resolution L100-N1283 configuration, yields catalogues with purity generally above 95% and completeness stable at about 93% above 5×10^11 M⊙, reproduces the halo mass function to within 5% of the reference while faithfully reconstructing internal density profiles,

The authors built synthetic datasets of roughly 135–155 images each for spherical, ellipsoidal, cuboid, and rod-like colloidal particles, manually annotated into five classes (isolated particles, dimers, chains, loops, clusters), trained four YOLOv8-seg models with polygonal instance segmentation, obtained about 97–99% accuracy on synthetic test sets, and found that transfer to 40 experimental micrographs (ten per shape) degraded sharply, with an average relative error of 43.1% against the synthetic benchmark: 20% for spheres, 44.7% for ellipsoids, 49.2% for rods, and 58.5% for cuboids; the datasets and trained models are openly available and integrated into the isanm.space information system.

The authors develop a hybrid matrix product state–hierarchical equations of motion (MPS–HEOM) approach for numerically exact simulations of molecular polariton dynamics under static and dynamic disorder, introduce a convergence scale NT (the number of molecules needed for photonic dynamics to reach the thermodynamic limit), and find that dynamic disorder demands larger NT than static disorder while NT shows a turnover as the bath becomes more Markovian, rooted microscopically in phonon timescales regulating bright-to-dark energy transfer and the suppression of collective behavior.