139 papers
This paper presents a theoretical investigation identifying promising vibrational transitions in helical osmocene with significant parity-violating shifts, proposing a pathway for the first experimental detection of parity violation in a chiral molecule using ultra-precise mid-IR spectroscopy.
This paper introduces a scalable quantum-nuclei-corrected NMR crystallography approach (QNC-NMR) that leverages the machine-learning potential PET-MOLS to generate quantum ensembles, thereby significantly improving the accuracy of chemical shielding predictions for hydrogen-bonded protons and enabling applications to amorphous materials without empirical corrections.
This paper investigates how the characteristics of laser pulses (long versus ultrashort) shape the initial excited molecular state, demonstrating that the exact factorization framework challenges standard Born-Huang concepts like population transfer and vertical excitation by revealing a more complex dependence on the light source.
This paper presents a novel Bayesian inference framework that integrates molecular dynamics simulations and polarisation analysis to overcome the limitations of conventional fitting methods, successfully resolving the previously ambiguous anisotropic rotational motion of liquid benzene and establishing a new paradigm for understanding molecular dynamics in catalysis and energy materials.
This study introduces a novel RT-TDDFTB+LQBE simulation framework to investigate real-time electron-electron scattering in plasmonic nanoclusters, revealing that quasiparticle lifetimes and relaxation dynamics are highly energy-dependent, exhibit quantum fluctuations in sub-2 nm clusters, and are significantly modulated by interband transitions and Auger scattering in gold.
This paper develops a reaction-coordinate-resolved information-theoretic framework using Partial Information Decomposition to analyze chemical reactivity, demonstrating how mutual information between electronic readouts and geometric progress variables reveals distinct redundant, unique, and synergistic signatures of bonding evolution in prototypical S2 reactions.
This paper proposes a constructive, physics-based approach to designing interatomic potentials by leveraging density functional theory theorems and analytic constraints to build interpretable latent space embeddings that formally couple electronic and atomic scales, thereby addressing the limitations of current machine learning models regarding combinatorial complexity and unseen bonding motifs.
This paper introduces Drifting Models for molecular conformation generation, leveraging a novel Drifting Force Identity to incorporate physical force labels into one-step sampling, thereby achieving million-fold acceleration over traditional molecular dynamics while maintaining perfect structural validity and Boltzmann distribution accuracy.
This paper introduces IR-GeoDiff, a latent diffusion model that recovers three-dimensional molecular geometries from infrared spectra by integrating spectral information into molecular representations, thereby addressing the limitations of existing 2D approaches in capturing the relationship between spectral features and 3D structure.
The paper introduces FragFM, a hierarchical framework utilizing fragment-level discrete flow matching and a stochastic fragment bag strategy to achieve efficient, scalable, and property-controllable molecular generation, validated through a new Natural Product Generation (NPGen) benchmark where it outperforms existing atom-based methods.
This study evaluates the Boltz-2 AI model for drug discovery and finds that while it offers rapid structural predictions, it lacks the energetic accuracy and resolution required for reliable lead identification, necessitating the continued use of physics-based methods for refinement.
This paper provides a guide for NLP researchers and interdisciplinary scientists on popular digital molecular representations inspired by natural language processing and their applications in AI-driven chemistry and materials science.
This paper presents an open-source dynamical box model to simulate global abiotic sulfur cycling on Earth-like planets, revealing that the absence of life would result in marine sediment sulfate concentrations two orders of magnitude higher and sulfide concentrations four orders of magnitude lower than on present-day Earth.
This paper introduces the Tensor Atomic Cluster Expansion (TACE), a unified atomistic machine learning framework that employs irreducible Cartesian tensors to efficiently model both scalar and tensorial observables without complex angular-momentum coupling, demonstrating robust accuracy and scalability across diverse chemical systems and tasks.
This paper proposes a machine learning approach that learns data-driven, structure-preserving (symplectic and time-reversible) maps equivalent to the mechanical action of a system, enabling accurate long-time-step molecular dynamics simulations that eliminate the energy conservation and equipartition artifacts typical of non-structure-preserving ML predictors.
This paper introduces AllScAIP, a scalable, attention-based machine-learning interatomic potential that leverages all-to-all node attention to effectively capture long-range interactions and achieve state-of-the-art accuracy across diverse molecular and material systems without relying on explicit physics-based terms.
This paper advocates for the integration of agentic AI, machine learning workflows, and self-driving laboratories with high-throughput experimentation to create a virtuous, data-driven cycle for accelerating catalyst design, mechanistic understanding, and scale-up across diverse catalytic systems in chemical reaction engineering.
This study demonstrates that the iterative qubit coupled-cluster (iQCC) algorithm, simulated at unprecedented scale on classical hardware, achieves superior accuracy over leading classical methods for predicting the excited states of complex organometallic compounds, thereby establishing a threshold of approximately 200 logical qubits where quantum advantage in computational chemistry may emerge.
This paper introduces a semidefinite machine learning framework that combines input convex neural networks with semidefinite programming to learn a data-driven, vertex-based approximation of the -representable two-electron reduced density matrix (2-RDM) boundary, enabling direct variational calculations with accuracy comparable to higher-order positivity constraints but at the computational cost of two-positivity methods.
This paper introduces Simulated Bifurcation-based Configuration Interaction (SBCI), a classical mechanics-inspired algorithm that significantly reduces the computational cost of high-accuracy electronic structure calculations while maintaining reliability comparable to standard methods.