136 papers
This paper reveals that interatomic and intermolecular Coulombic decay (ICD), previously thought to be limited to weakly bound systems, can also occur efficiently in atomic and molecular gases despite large inter-unit distances, operating through a distinct mechanism that significantly expands the phenomenon's scope and potential applications.
Using DMRG calculations on the Pariser-Parr-Pople model for polyene chains, this study quantifies the triplet-pair population of the $2^1A_g$ dark state, predicting a finite-size scaling value of approximately 75% that confirms its predominantly triplet-pair character and supports its role in singlet fission mechanisms.
This paper demonstrates that machine learning models trained to predict the two-electron reduced density matrix (2-RDM) can accurately surrogate correlated wavefunction methods, enabling coupled-cluster-quality electronic structure calculations for large solvated systems at a fraction of the conventional computational cost.
This paper introduces Compositional Probe Decomposition (CPD) to demonstrate that linear disentanglement of geometric and compositional information in atomistic foundation models is primarily driven by task alignment rather than architecture, revealing a significant performance gradient where models trained on specific properties like HOMO-LUMO gaps outperform energy-trained models and exhibit symmetry-dependent information routing.
This paper introduces Mixture-of-Experts (MoE) and Mixture-of-Linear-Experts (MoLE) architectures for Machine Learning Interatomic Potentials, demonstrating that element-wise routing with shared nonlinear experts achieves state-of-the-art accuracy across multiple benchmarks while revealing chemically interpretable specialization aligned with periodic-table trends.
This paper demonstrates that combining machine learning with gold-standard CCSD(T) electronic structure theory enables the first fully converged, quantitative prediction of the ion pairing free energy for CaCO in water, resolving long-standing challenges in accurately capturing enthalpic and entropic effects for complex aqueous systems.
This study employs first-principles calculations to reveal that halide segregation in mixed halide perovskites is driven by the thermodynamic preference of bromide ions for surface sites and photo-induced iodide vacancy formation, with the extent of segregation significantly modulated by the A-site cation composition.
This paper demonstrates that circularly polarized light can induce spin polarization in an achiral metalloporphyrin complex by selectively exciting ring currents to break spin degeneracy, with the resulting transient polarization depending on spin-orbit coupling and Jahn-Teller dephasing rates.
This paper introduces Quantum Krylov using Unitary Decomposition (QKUD), a time-evolution-free method that maps Hamiltonian powers to implementable unitaries via a tunable Hermitian transform to overcome basis collapse and ill-conditioning, thereby enabling robust and accurate quantum simulation of many-body systems.
This review surveys theoretical and experimental advances in the physics of trions within two-dimensional monolayers, highlighting their enhanced binding energies due to reduced screening and confinement, the impact of environmental and external field factors, and their connections to broader many-body phenomena.
This paper presents highly accurate *ab initio* adiabatic potential energy surfaces and non-adiabatic couplings for the four low-lying singlet states of ozone, constructed using advanced multi-reference methods to reproduce experimental dissociation energies and frequencies, locate conical intersections, and provide a four-state diabatic Hamiltonian free of "reef" features.
This study employs a numerically exact hybrid MPS-HEOM approach to demonstrate that in disordered molecular polariton systems, phonon timescales and dynamic disorder govern the activation of dark states and determine the critical system size () required to reach the thermodynamic limit by suppressing collective light-matter dynamics.
This paper introduces a symmetry-based multi-reference perturbation theory (SBPT) that leverages enhanced symmetries in a reference Hamiltonian to significantly reduce computational costs in both classical configuration interaction and quantum computing applications, while offering scalable solutions and improved robustness for various molecular systems.
This paper introduces a classically driven hybrid quantum algorithm that reduces measurement overhead in electronic-structure simulations by iteratively transforming the Hamiltonian toward a diagonal form using sequential Givens rotations determined via classical low-dimensional block analysis, thereby minimizing quantum circuit depth and measurement requirements.
This paper introduces "paces," a GPU-optimized parallel algorithm that computes quantum dynamics by constructing and evolving a restricted subspace via repeated Hamiltonian applications, offering an efficient alternative to matrix-product states for sparse Hamiltonians.
This paper introduces a general framework featuring a task-specific resource impact functional and associated bounds to operationally quantify and isolate the maximal influence of quantum resources on specific chemical dynamics and observables.
NMIRacle is a novel two-stage generative framework that accurately elucidates molecular structures from IR and NMR spectra by first learning count-aware fragment representations and then fine-tuning a generator to map spectral embeddings directly to molecular structures, outperforming existing baselines across varying levels of complexity.
This paper establishes a rigorous mathematical framework for hybrid classical-quantum systems by deriving their microcanonical ensemble via a maximum entropy principle, demonstrating its well-defined nature for continuous energy values and its consistency with the canonical ensemble, while validating the theory through a toy model.
This paper introduces a Cholesky-based compression (CBC) algorithm that efficiently applies tree tensor network operators to tree tensor network states, demonstrating runtime performance superior to most established methods while maintaining accuracy comparable to state-of-the-art techniques in both random benchmarks and realistic circuit simulations.
The paper introduces a new "Pairs and Squares" rendering of the Periodic Table that leverages the fact that the number of orbitals at each atomic energy level forms a perfect square to create a more regular and intuitive structure compared to existing presentations.