Continuous perception and adaptive metrics for scalable structure refinementWe present a geometry-refinement workflow that adapts to the cost of an energy--gradient evaluation and the reliability of molecular topology. An initial Cartesian calculation and continuous bonding…Vincenzo Barone·Sep 22, 2026SaveLearn
MolExplain: An Interactive Tool for Explainable Molecular Property PredictionThe application of machine learning to molecular property prediction has become increasingly prevalent in drug discovery, yet most models operate as black boxes, returning a prediction without…Pirm Dhararag, Dylan Cashman·Sep 21, 2026SaveLearn
Near Chemical Accuracy From Size-Consistent Padé Resummed Møller-Plesset Perturbation TheoryPadé resummation of the correlation energy from Møller-Plesset (MP) perturbation theory is not size-consistent and is therefore seldom used. We present an orbital-based Padé resummation of the MP2…Kyle Bystrom, Timothy C. Berkelbach, Diptarka Hait·Sep 21, 2026SaveLearn
Reply to Comment on "Efficient implementation of the superposition of atomic potentials initial guess for electronic structure calculations in Gaussian basis sets"We reply to the Comment by Surjuse, Deng, Asadchev, and Valeev [arXiv:2603.16989] on our article "Efficient implementation of the superposition of atomic potentials initial guess for electronic…Susi Lehtola, Lucas Visscher, Eberhard Engel·Sep 21, 2026SaveLearn
Benchmarking higher-ranking multipoles and polarizability tensors for small molecular systemsMolecular properties govern how molecules interact with one another or external fields. Accurate molecular properties are essential for constructing intermolecular interaction models, and to achieve…Bruno V. von Bruening, Alston J. Misquitta·Sep 21, 2026SaveLearn
Composition-Dependent Self-Diffusion Coefficients in Liquid Mixtures from Hybrid Machine LearningSelf-diffusion coefficients are key descriptors of molecular mobility, yet experimental data remain scarce, highlighting the need for reliable prediction methods. In previous work, we introduced the…Jens Wagner, Thomas Specht, Hans Hasse et al.·Sep 21, 2026SaveLearn
From Heuristics to Machine Learning: The Performance Ceiling for Single-Ion Magnets and Its Electronic OriginMachine learning (ML) is expected to speed up the discovery of single-ion magnets (SIMs), but does the structural information available before synthesis allow such predictions? For 1215 lanthanide…Federico Zahariev, Regina Pereyra, Vassiliki-Alexandra Glezakou et al.·Sep 21, 2026SaveLearn
Rachel: A general-purpose language model directs and revises retrosynthetic routesRetrosynthetic planning advances through decisions that reshape the remaining chemical problem: a locally plausible disconnection can leave precursors whose chemoselectivity constraints complicate…Qisheng Li, Shunchao Jiang, Chen Qi et al.·Sep 20, 2026SaveLearn
AI-assisted analytical theory in soft matter and multiphysics: new results and lessons from diffusiophoresis and bipolar membranesWe present a framework to advance analytical theory in the area of soft matter and multiphysics using assistance from AI. The framework emphasizes (i) setting "honesty rules", (ii) focusing…Ankur Gupta·Sep 20, 2026SaveLearn
Attosecond charge migration timescales are dominated by transition dipoles, not correlationsAttosecond charge migration (CM) is an ultrafast process occurring when a molecule is irradiated by ultrashort laser pulses, creating a localized hole. The hole propagates rapidly through the…Km Akanksha Dubey, Ofer Neufeld·Sep 20, 2026SaveLearn
Quantitative Classical Relaxation Dynamics of Penning-Ionized He/Ne--H2+/HD+ ComplexesPenning ionization allows in situ generation of ionic complexes to launch vibrational relaxation and dissociation dynamics for rare gas--H2+/HD+ complexes. The ensuing translational…Meenu Upadhyay, Baruch Margulis, Octavio Roncero et al.·Sep 20, 2026SaveLearn
Solvent Inertia Changes Product Probability by Relocating the Phase-Space Reactivity BoundaryChanging solvent inertia changes the kinetic part of a molecular Hamiltonian and hence its flow in phase space, without requiring a change in the potential energy surface (PES). We study how this…Stephen Wiggins·Sep 20, 2026SaveLearn
ECENet: An Edge Cluster Expansion Line-Graph Neural NetworkMachine-learned interatomic potentials (MLIPs) have emerged as a promising alternative to classical force fields and first-principles theory for predicting the properties of chemical and material…R. Allen LaCour, Teresa Head-Gordon·Sep 19, 2026SaveLearn
Can Chemically Inspired Parameter Initialization Mitigate Barren Plateaus in Variational Quantum Eigensolvers?Recent advances in the variational quantum eigensolver (VQE) have strengthened its promise for molecular electronic-structure calculations on near-term noisy intermediate-scale quantum (NISQ)…Zhangyu Yang, Jinzhao Sun, Jianpeng Chen et al.·Sep 19, 2026SaveLearn
SPIBER: Reconstructing Free Energy Landscapes from Short, Unconverged Trajectories with Generative Flow NetworksMolecular systems have many degrees of freedom, but their metastable behavior can often be described by a few collective variables. Identifying these variables and estimating free energies along them…Venkata Sai Sreyas Adury, Pratyush Tiwary·Sep 19, 2026SaveLearn
Mass-Scaling of Quantum Tunnelling in Hydrogen Bonds: Analytical Model and Comparison with Multidimensional PotentialsQuantum tunnelling plays a central role in the structure and spectroscopy of hydrogen-bonded systems, and its sensitivity to isotopic substitution provides a stringent probe of the underlying…Krishna Kingkar Pathak·Sep 18, 2026SaveLearn
XC100: A Wavefunction-Derived Exchange-Correlation Energy Dataset for Atomic and Molecular SpeciesA workflow is introduced for constructing accurate, wavefunction-derived Kohn-Sham (KS) exchange-correlation (XC) energies across atomic and molecular species. Correlated total energies and densities…Vaibhav Khanna, Paul M. Zimmerman·Sep 18, 2026SaveLearn
cboamd: A Machine Learning Molecular Dynamics Framework for Vibrational Strong CouplingUnder vibrational strong coupling (VSC), molecular vibrations hybridize with an optical cavity mode to form polaritons, offering a route to modify chemical and material properties without external…Yifan Li, Roberto Car, Johannes Flick·Sep 18, 2026SaveLearn
ReaxKit: A Modular Python Toolkit for Preparing, Parsing, and Analyzing ReaxFF Molecular Dynamics SimulationsEmpirical reactive force field (RFF) molecular dynamics enables atomistic simulation of bond breaking, bond formation, charge redistribution, and structural evolution in chemically complex systems.…Ali Mohammadi Dinani, Alireza Sepehrinezhad, Anirban Phukan et al.·Sep 18, 2026SaveLearn
Development of a Non-Empirical Exchange-Hole Dipole Moment Dispersion ModelThe inclusion of dispersion effects is important in density-functional theory (DFT) to model non-covalent interactions correctly, a task that is essential in many applications of the theory. Many…Alastair J. A. Price, Alberto Otero-de-la-Roza·Sep 18, 2026SaveLearn
fix uvt and fix pimd/uvt: A Unified LAMMPS Framework for Constant-Potential Constant-Temperature Molecular DynamicsAccurate simulations of electrochemical interfaces require the simultaneous treatment of constant-potential conditions, nuclear quantum effects, and sufficient configurational sampling. Integrating…Li Fu, Yifan Li, Shenzhen Xu·Sep 18, 2026SaveLearn
Orbital-Free Surrogate Functionals Yield Transferable Interatomic Potentials and Electron DensitiesOrbital-free density functional theory seeks to compute the energy of an electronic system directly from its electron density, avoiding one-electron wave functions and thereby offering a route to…Simon Wagner, Marc K. Ickler, Manuel V. Klockow et al.·Sep 18, 2026SaveLearn
Enriching molecular Raman spectroscopy with vibrational strong couplingRaman spectroscopy is widely used for molecular identification in biological samples, but spectral congestion often obscures key molecular fingerprints. Strategies to enrich vibrational spectra with…Matteo Castagnola, Anne Todsen Hansen, Morten Hanefeld Dziegiel et al.·Sep 18, 2026SaveLearn
From sparse quantum-computing data to atomistic simulation with universal machine-learning interatomic potentialsWe propose a framework for incorporating quantum-computing-based electronic-structure calculations into universal machine-learning interatomic potentials (uMLIPs). Rather than constructing an…Tuan Minh Do, Yuichiro Yoshida, Kenji Ishihara et al.·Sep 18, 2026SaveLearn
A fast physics-based matrix model for the impedance of a PEM fuel cell: Incorporating functionally graded catalyst layer and channel impedancesWe extend a recent physics-based matrix model for calculating PEM fuel cell impedance (doi:10.1149/2754-2734/ad6ce8) to cases of low air flow stoichiometry and functionally graded cathode catalyst…Andrei Kulikovsky·Sep 18, 2026SaveLearn