Chemical Physics Papers of the Week

One emerging approach is to variationally optimize the orbitals of a single user-selected Configuration State Function (CSF), i.e., a linear combination of Slater determinants that is constructed as an eigenstate of the total spin operator. In this work we explore the use of these orbital-optimized single CSFs as trial wavefunctions in phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC), a stochastic method that recovers electron correlation by evolving an initial state in imaginary time, subject to a constraint imposed by the trial state.

We investigate the effects of basis-set superposition error (BSSE) and an explicit Axilrod-Teller-Muto (ATM) three-body dispersion contribution on molecular-liquid predictions.

Samples of the marginal CV distribution must be generated by molecular dynamics (MD), whose trajectories can remain trapped in metastable states and rarely cross the free-energy barriers governing conformational changes or chemical reactions. We introduce VESPER, which couples the generation of these samples to neural-network-driven free-energy learning: MD supplies configurations for learning, while the evolving model biases the MD to accelerate exploration and generate further training data.

Machine-learned force fields (MLFFs) aim to create a unified model that describes all major classes of (bio)molecules at quantum-mechanical accuracy. We present SO3LR Tauri, a family of MLFFs developed toward this goal, coupling a fast SO3krates network to explicit, physics-based interaction modules: pair-specific short-range repulsion, environment-dependent dispersion, and electrostatics, each improved beyond its original formulation.

We introduce spectral symmetry fingerprints (SymFP), which enable discrete point-group identification through rank-template matching and continuous symmetry analysis by quantifying spectral energy beyond target rank constraints.

A general density functional theory based relativistic theory for paramagnetic NMR (pNMR) shifts, EPR g-tensors and hyperfine coupling tensors as well as magnetizabilities is derived.

We present pair_style uma, a LAMMPS pair style that evaluates the Universal Model for Atoms (UMA) through LibTorch on NVIDIA and Intel GPUs from a single source tree.

We introduce ROSE, a deep-learning exchange--correlation energy functional built from the occupied-state density matrix.

We introduce Coupled-Cluster Coupled Lindblad (C$^3$L), a deterministic approach that combines the recently developed coupled-Lindblad compact bath representation with coupled-cluster theory to compute steady states and Green's functions directly in imaginary and real frequency.

We propose a minimum reporting standard for the preparation and associated speed measurements of catalytic Janus particles.

Overall, this study provides key design insights into vibrational polariton condensation as a novel approach for controlling liquid-phase chemistry.

We introduce FlowBatt, a use-inspired conditional generative framework that adapts flow matching with a diffusion transformer (DiT) backbone to battery degradation trajectory modeling.

We introduce the Complete Active-Space Wave Function Reconstruction (CAS-WR) model, which learns the geometry-dependent electronic structure from CASSCF data and reconstructs the CAS wave function without repeated orbital optimization.

In this work, a quantum algorithm is presented for ground state energy estimation of weakly correlated molecules using Trotterized time evolution, with an end-to-end quantum operations cost that scales as $\tilde{O}(n^{4.8}/\varepsilon^2)$ in the limit of high-order product formulae when run as a low-depth, shot-noise limited algorithm, or $\tilde{O}(n^{4.8} + n^{3.8}/\varepsilon)$ when scaling the simulation time $T\propto 1/\varepsilon$.

Here we introduce graph symbolic distillation (GSD), a framework for decoding graph data into interpretable symbolic formulas.

We show that microwave shielding, which enables collisionally stable ultracold polar molecules, also leads to genuine nonadditive three-body interactions comparable to the shielding interaction itself.

Here, we demonstrate that existing strategies based purely on features derived from an external electric field fail to distinguish certain environments, ultimately limiting their accuracy. As a solution, we propose an extension to the equivariant TensorNet architecture that mitigates this shortcoming by incorporating classical atoms close to the quantum region into the message-passing scheme, combined with features derived from the electric field for describing the environment farther away.

However, currently available quantum embedding methods place restrictive demands on near-future quantum computers, such as the need to measure reduced density matrices or the need for repeated evaluations. Here, we introduce the new orbital embedding method eCAS that steers clear of these pitfalls by constructing a multi-CAS problem, the active spaces of which are localized and can be solved independently of each other with any quantum algorithm.

The biotin-streptavidin interaction is a key component of nanobiotechnology, yet the nature of its cooperativity remains a debated question. Here, we introduce a label-free spectroscopy approach based on confocal UV autofluorescence microscopy to probe the binding cooperativity of this high-affinity system.

Here we introduce FACTO, an atom-centered equivariant architecture that confines evolving learned state and dense channel mixing to atoms, expressing neighbor interactions as fully fusible reductions.