Metatensor and metatomic: foundational libraries for interoperable atomistic machine learningIncorporation of machine learning (ML) techniques into atomic-scale modeling has proven to be an extremely effective strategy to improve the accuracy and reduce the computational cost of simulations.…Filippo Bigi, Joseph W. Abbott, Philip Loche et al.·Aug 21, 2025SaveLearn
Assessing the Reliability of Truncated Coupled Cluster Wavefunction: Estimating the Distance from the Exact SolutionA new approach is proposed to assess the reliability of the truncated wavefunction methods by estimating the deviation from the full configuration interaction (FCI) wavefunction. While typical…Ádám Ganyecz, Zsolt Benedek, Klára Petrov et al.·Aug 21, 2025SaveLearn
LFaB: Low fidelity as Bias for Active Learning in the chemical configuration spaceActive learning promises to provide an optimal training sample selection procedure in the construction of machine learning models. It often relies on minimizing the model's variance, which is assumed…Vivin Vinod, Peter Zaspel·Aug 21, 2025SaveLearn
GridFF: Efficient Simulation of Organic Molecules on Rigid SubstratesWe present GridFF, an efficient method for simulating molecules on rigid substrates, derived from techniques used in protein-ligand docking in biochemistry. By projecting molecule-substrate…Indranil Mal, Milan Kočí, Paolo Nicolini et al.·Aug 21, 2025SaveLearn
Reevaluating Anomalous Electric Fields at the Air-Water Interface: A Surface-Specific Spectroscopic SurveyThe notion that large electric fields at the air-water interface catalyze spontaneous chemical reactions has sparked significant debate, with far reaching implications for atmospheric chemistry and…Joseph C. Shirley, Zi Xuan Ng, Kuo-Yang Chiang et al.·Aug 21, 2025SaveLearn
Clay Edges Are Dynamic Proton-conducting Networks Modulated by Structure and pHMontmorillonite, a ubiquitous clay mineral, plays a vital role in geochemical and environmental processes due to its chemically complex edge surfaces. However, the molecular-scale acid-base…Yixuan Feng, Xavier R. Advincula, Hongwei Fang et al.·Aug 21, 2025SaveLearn
Re-Engineering Hematite: Synergistic Co-Doping Routes to Efficient Solar Water SplittingSolar-driven water electrolysis requires high-performance photoelectrodes that exhibit excellent photoabsorption, superior charge transport, and optimized thermal management. In this work, we…Abdul Ahad Mamun, Muhammad Anisuzzaman Talukder·Aug 21, 2025SaveLearn
Impact of ligand (OH) deformation on LuOH+ rovibrational spectraTriatomic cation 175LuOH+, featuring near-degenerate, opposite-parity l-doublets, offers enhanced sensitivity to P- and T-violating interactions. We present ab…Igor Kurchavov, Sergey Prosnyak, Leonid V. Skripnikov et al.·Aug 20, 2025SaveLearn
A novel framework for disinfection analisys in drinking water networksDisinfection in drinking water networks is performed to ensure water safety and potability. However, disinfectants can react with organic compounds present in the water networks. The reaction of…Daniele Laucelli, Lucia Vergine, Giuseppina Messa et al.·Aug 20, 2025SaveLearn
Ground and low-lying excited state potential energy surfaces of diiodomethane in four dimensionsWe report a set of adiabatic potential energy surfaces (PESs) for diiodomethane, including the ground electronic state and all excited states accessible via single-photon absorption near 260 nm.…Yijue Ding·Aug 20, 2025SaveLearn
Fundamental measure theory for predicting many-body correlation functionsWe study many-body correlation functions within various Fundamental Measure Theory (FMT) formulations and compare their predictions to Monte Carlo simulations of hard-sphere fluids. FMT accurately…Ilian Pihlajamaa, Teunike A. van de Pol, Liesbeth M. C. Janssen·Aug 20, 2025SaveLearn
A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine LearningMachine learning models are increasingly used to predict material properties and accelerate atomistic simulations, but the reliability of their predictions depends on the representativeness of the…Daniel Willimetz, Lukáš Grajciar·Aug 20, 2025SaveLearn
Numerically "exact" charge transport dynamics in a dissipative electron-phonon model rationalizing the success of the transient localization scenarioOptical conductivity in molecular semiconductors is suppressed in the terahertz region, featuring the displaced Drude peak that reflects carriers' transient localization (TL) by slow intermolecular…Veljko Janković·Aug 20, 2025SaveLearn
Nonadiabatic force matching for alchemical free-energy estimationWe propose a method to compute free-energy differences from nonadiabatic alchemical transformations using flow-based generative models. The method, nonadiabatic force matching, hinges on estimating…Jorge L. Rosa-Raíces, David T. Limmer·Aug 19, 2025SaveLearn
A particle view of many-body electronic structure with neural network wavefunctionIn the study of electronic structure, the wavefunction view dominates the current research landscape and forms the theoretical foundation of modern quantum mechanics. In contrast, Valence Bond (VB)…Zichen Wang, Weizhong Fu, Zhe Li et al.·Aug 19, 2025SaveLearn
Ion adsorption and zeta potential of hydrophobic interfacesHydrophobic interfaces have unique physicochemical properties and are used in various chemical products such as food, cosmetics, soap, and medicine and technologies such as pan coating and ski wax.…Yuki Uematsu·Aug 19, 2025SaveLearn
Statistical-Mechanical Theory on the Probability Distribution Function for the Net Charge of an Electrolyte DropletDroplets of electrolyte solutions in an insulating medium are ubiquitous in nature. The net charges of these droplets are normally nonzero, and they fluctuate. However, a theory on the probability…Yuki Uematsu, Keiju Suda·Aug 19, 2025SaveLearn
Diffuse-Layer Capacitance at the Potential of Zero Charge in Binary MixturesThe capacitance of the electric double layer has potential applications in supercapacitors, and theoretical investigations of the double-layer capacitance in binary mixtures are important. In this…Yuki Uematsu·Aug 19, 2025SaveLearn
Gold-Standard Chemical Database 137 (GSCDB137): A diverse set of accurate energy differences for assessing and developing density functionalsWe present GSCDB137, a rigorously curated benchmark library of 137 data sets (8377 entries) covering main-group and transition-metal reaction energies and barrier heights, (intramolecular)…Jiashu Liang, Martin Head-Gordon·Aug 19, 2025SaveLearn
Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid WaterSimulating liquid water to an accuracy that matches its wealth of available experimental data requires both precise electronic structure methods and reliable sampling of nuclear (quantum) motion.…Niamh O'Neill, Benjamin X. Shi, William Baldwin et al.·Aug 18, 2025SaveLearn
Enhanced Prediction of CO2 Solubility under Geological Conditions for CCUS via Improved Pitzer Parameters and Physics-Informed Machine LearningThe solubility of CO2 in formation brines plays a critical role in the efficiency of carbon capture and storage (CCS) operations. It is strongly influenced by pressure, temperature, and brine…Abdeldjalil Latrach, Lily Jackson, Minou Rabiei·Aug 18, 2025SaveLearn
Quantum Many-Body Simulations of Catalytic Metal SurfacesQuantum simulations of metal surfaces are critical for catalytic innovation. Yet existing methods face a cost-accuracy dilemma: density functional theory is efficient but system-dependent in…Changsu Cao, Hung Q. Pham, Zhen Guo et al.·Aug 18, 2025SaveLearn
How reactive is water at the nanoscale and how to control it?Nanoconfined water plays a key role in nanofluidics, electrochemistry, and catalysis, yet its reactivity remains a matter of debate. Prior studies have reported both enhanced and suppressed water…Xavier R. Advincula, Yair Litman, Kara D. Fong et al.·Aug 18, 2025SaveLearn
Computing Exchange Coupling constants in Transition metal complexes with Tensor Product Selected Configuration InteractionTransition metal complexes present significant challenges for electronic structure theory due to strong electron correlation arising from partially filled d-orbitals. We compare our recently…Arnab Bachhar, Nicholas J. Mayhall·Aug 18, 2025SaveLearn
aims-PAX: Parallel Active eXploration for the automated construction of Machine Learning Force FieldsRecent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets,…Tobias Henkes, Shubham Sharma, Alexandre Tkatchenko et al.·Aug 18, 2025SaveLearn