Theoretical chemistry articles within Nature Communications

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  • Article
    | Open Access

    Bicyclo[1.1.1]pentanes (BCPs) are important motifs in contemporary drug design, however, approaches to BCPs featuring adjacent stereocenters are rather limited. Here, the authors report a photo- and organocatalyzed asymmetric addition of simple aldehydes to [1.1.1]propellane to generate enantioenriched α-chiral BCPs.

    • Marie L. J. Wong
    • , Alistair J. Sterling
    •  & Edward A. Anderson

  • Article
    | Open Access

    Spiroaromatic compounds are advantageous platforms for designing expanded aromatic systems. Herein, the authors present a tris‐spiro metalla‐aromatic Vanadium compound which forms a 40π Craig‐Möbius aromatic system.

    • Zhe Huang
    • , Yongliang Zhang
    •  & Zhenfeng Xi

  • Article
    | Open Access

    Vibrational strong coupling controls the ground-state reactivity of molecules in optical cavities, but the underlying theory is still elusive. The authors analyze a molecular system coupled to a cavity mode and find that the reaction rate is suppressed for a particular cavity frequency, related to the topology of the reaction barrier region, analogously to a solvent caging effect.

    • Xinyang Li
    • , Arkajit Mandal
    •  & Pengfei Huo

  • Article
    | Open Access

    Understanding ice re-crystallization is key to improve the current cryopreservation technologies. Here, the authors bring together experiments and simulations to unravel the atomistic details of the ice re-crystallization inhibition (IRI) activity of poly(vinyl)alcohol—the most potent biomimetic IRI agent.

    • Fabienne Bachtiger
    • , Thomas R. Congdon
    •  & Gabriele C. Sosso

  • Article
    | Open Access

    The accuracy of a machine-learned potential is limited by the quality and diversity of the training dataset. Here the authors propose an active learning approach to automatically construct general purpose machine-learning potentials here demonstrated for the aluminum case.

    • Justin S. Smith
    • , Benjamin Nebgen
    •  & Kipton Barros

  • Article
    | Open Access

    The diffusion of fluids in complex nanoporous geometries represents a challenge for modelling approaches. Here, the authors describe the macroscopic diffusivity of a simple fluid in disordered nanoporous materials by bridging microscopic and mesoscopic dynamics with parameters obtained from simple physical laws.

    • Colin Bousige
    • , Pierre Levitz
    •  & Benoit Coasne

  • Article
    | Open Access

    Colloidal CdSe nanocrystals hold great promise in applications due to their tunable optical spectrum. Using hybrid time-dependent density functional theory, the authors show that colloidal CdSe nanocrystals are inherently defective with a low energy spectrum dominated by dark, surface-associated excitations.

    • Tamar Goldzak
    • , Alexandra R. McIsaac
    •  & Troy Van Voorhis

  • Article
    | Open Access

    The nature of the bulk hydrated electron has been a challenge for both experiment and theory. Here the authors use a machine-learning model trained on MP2 data to achieve an accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron.

    • Jinggang Lan
    • , Venkat Kapil
    •  & Vladimir V. Rybkin

  • Article
    | Open Access

    Spin polarization is at the basis of quantum information and underlies some natural processes, but many aspects still need to be explored. Here, the authors, by quantum mechanical computations, show that even a weak spin-orbit coupling near a conical intersection can induce large spin selection, with consequences for spin manipulation in photochemical or electrochemical reactions.

    • Yanze Wu
    •  & Joseph E. Subotnik

  • Article
    | Open Access

    Complex interatomic interactions and diverse structures make computing the phase diagram of water very challenging. Here, a combination of machine learning and advanced free-energy methods at three levels of hybrid DFT enables the prediction of the phase diagram in close agreement with experiment.

    • Aleks Reinhardt
    •  & Bingqing Cheng

  • Article
    | Open Access

    The inclusion of nuclear quantum effects (NQE) in atomistic simulations of chemical systems is of key importance. Here the authors use machine learned force fields trained on coupled cluster reference data to show the dynamical strengthening of covalent and non-covalent molecular interactions induced by NQE.

    • Huziel E. Sauceda
    • , Valentin Vassilev-Galindo
    •  & Alexandre Tkatchenko

  • Article
    | Open Access

    Machine learning potentials do not account for long-range charge transfer. Here the authors introduce a fourth-generation high-dimensional neural network potential including non-local information of charge populations that is able to provide forces, charges and energies in excellent agreement with DFT data.

    • Tsz Wai Ko
    • , Jonas A. Finkler
    •  & Jörg Behler

  • Article
    | Open Access

    Semilocal density functionals, while computationally efficient, do not account for non-local correlation. Here, the authors propose a machine-learning approach to DFT that leads to non-local and transferable functionals applicable to non-covalent, ionic and covalent interactions across system of different sizes.

    • Johannes T. Margraf
    •  & Karsten Reuter

  • Article
    | Open Access

    High-level methods to describe van der Waals interactions are limited due to their computational cost. This work introduces a new theoretical approach, that extends the dipolar many-body dispersion formalism to higher-order contributions, demonstrated to be applicable to practically-relevant systems and nano-environments.

    • Martin Stöhr
    • , Mainak Sadhukhan
    •  & Alexandre Tkatchenko

  • Article
    | Open Access

    Fast-charging of lithium-ion batteries is hindered by the uncontrollable plating of metallic Li on the graphite anode during cycling. Here, the authors demonstrate the fast chargeability and long cycle lifetimes via surface engineering of graphite with a cooperative biphasic MoOx–MoPx promoter.

    • Sang-Min Lee
    • , Junyoung Kim
    •  & Min-Sik Park

  • Article
    | Open Access

    Computational catalysis would strongly benefit from general descriptors applicable for predicting adsorption energetics. Here the authors propose a machine-learning approach for adsorption energy predictions based on learning the relevant descriptors in a surface atom's density of states as part of the training.

    • Victor Fung
    • , Guoxiang Hu
    •  & Bobby G. Sumpter

  • Article
    | Open Access

    It is commonly accepted that electrolyte alkali metal cations modify the catalytic activity for oxygen evolution reaction. Here the authors challenge this assumption, showing that the activity is actually affected by a change in the electrolyte pH rather than a specific alkali cation.

    • Mikaela Görlin
    • , Joakim Halldin Stenlid
    •  & Oscar Diaz-Morales

  • Article
    | Open Access

    Developing a generalizable model to describe adsorption processes at metal surfaces can be extremely challenging due to complex phenomena involved. Here the authors introduce a Bayesian learning approach based on ab initio data and the d-band model to capture the essential physics of adsorbate–substrate interactions.

    • Siwen Wang
    • , Hemanth Somarajan Pillai
    •  & Hongliang Xin

  • Article
    | Open Access

    Porous materials acting as molecular sieves for propylene/propane separation are important for the petrochemical industry. Here the authors show an example of how specific guest-host interactions can result in structural changes in the porous host and shut down diffusion of one of the two similar guest molecules.

    • Dmytro Antypov
    • , Aleksander Shkurenko
    •  & Matthew S. Dyer

  • Article
    | Open Access

    Narrowing pores filled with an electrolyte usually slows down their charge-discharge dynamics. Here the authors demonstrate through molecular dynamics simulations and experiments with novolac-derived carbon electrodes how non-linear voltage sweeps can accelerate charging and discharging of subnanometer pores.

    • Konrad Breitsprecher
    • , Mathijs Janssen
    •  & Svyatoslav Kondrat

  • Article
    | Open Access

    Molecular understanding of water is challenging due to the structural complexity of liquid water and the large number of ice phases. Here the authors use a machine-learning potential trained on liquid water to demonstrate the structural similarity of liquid water and that of 54 real and hypothetical ice phases.

    • Bartomeu Monserrat
    • , Jan Gerit Brandenburg
    •  & Bingqing Cheng

  • Article
    | Open Access

    Accurate prediction of solubility represents a challenge for traditional computational approaches due to the complex nature of phenomena involved. Here the authors report a successful approach to solubility prediction in organic solvents and water using combination of machine learning and computational chemistry.

    • Samuel Boobier
    • , David R. J. Hose
    •  & Bao N. Nguyen

  • Article
    | Open Access

    Development of algorithms to predict reactant and reagents given a target molecule is key to accelerate retrosynthesis approaches. Here the authors demonstrate that applying augmentation techniques to the SMILE representation of target data significantly improves the quality of the reaction predictions.

    • Igor V. Tetko
    • , Pavel Karpov
    •  & Guillaume Godin

  • Article
    | Open Access

    Application of machine-learning approaches to exploring chemical reaction networks is challenging due to need of including open-shell reaction intermediates. Here the authors introduce a density functional theory database of closed and open-shell molecules for machine-learning predictions of reaction energies.

    • Sina Stocker
    • , Gábor Csányi
    •  & Johannes T. Margraf

  • Article
    | Open Access

    Spectroscopic studies of water clusters provide insight into the hydrogen bond structure of water and ice. The authors measure infrared spectra of neutral water octamers using a threshold photoionization technique based on a tunable vacuum-UV free electron laser, identifying two cubic isomers in addition to those previously observed.

    • Gang Li
    • , Yang-Yang Zhang
    •  & Ling Jiang

  • Article
    | Open Access

    Citrate-stabilized metallic colloids are key materials towards chemosensing and catalysis applications. Here the authors introduce a new theoretical model to estimate how the stoichiometry of citrate molecules absorbed onto spherical metallic nanoparticles influences their aggregation phenomena.

    • Sebastian Franco-Ulloa
    • , Giuseppina Tatulli
    •  & Marco De Vivo

  • Article
    | Open Access

    The abnormally low concentration of xenon compared to other noble gases in Earth’s atmosphere remains debated, as the identification of mantle minerals that can capture and stabilize xenon is challenging. Here, the authors propose that xenon iron oxides could be potential Xe hosts in Earth’s lower mantle.

    • Feng Peng
    • , Xianqi Song
    •  & Yanming Ma

  • Article
    | Open Access

    High-level ab initio quantum chemical methods carry a high computational burden, thus limiting their applicability. Here, the authors employ machine learning to generate coupled-cluster energies and forces at chemical accuracy for geometry optimization and molecular dynamics from DFT densities.

    • Mihail Bogojeski
    • , Leslie Vogt-Maranto
    •  & Kieron Burke

  • Article
    | Open Access

    Hydrogen has multiple molecular phases which are challenging to explore computationally. The authors develop a machine-learning approach, learning from reference ab initio molecular dynamics simulations, to derive a transferable hierarchical force model that provides insight into high pressure phases and the melting line of H2.

    • Hongxiang Zong
    • , Heather Wiebe
    •  & Graeme J. Ackland

  • Comment
    | Open Access

    Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order.

    • O. Anatole von Lilienfeld
    •  & Kieron Burke

  • Article
    | Open Access

    The origin of the covalent H–H bond is understood to be driven by kinetic energy lowering. Here the authors show this is not the case for bonds between heavier elements likely due to the presence of core electrons, and that constructive quantum interference instead drives bond formation.

    • Daniel S. Levine
    •  & Martin Head-Gordon

  • Article
    | Open Access

    Carbyne, a linear sp-hybridized carbon allotrope, is synthetically inaccessible and its properties are extrapolated from those of defined oligomers. Here the authors analyze weak optical bands in two series of oligoynes and reassess the optical and fundamental gap of carbyne to lower values than previously suggested.

    • Johannes Zirzlmeier
    • , Stephen Schrettl
    •  & Holger Frauenrath

  • Article
    | Open Access

    Heterogenous ice nucleation is a ubiquitous phenomenon, but predicting the ice nucleation ability of a substrate is challenging. Here the authors develop a machine-learning data-driven approach to predict the ice nucleation ability of substrates, which is based on four descriptors related to physical properties of the interface.

    • Martin Fitzner
    • , Philipp Pedevilla
    •  & Angelos Michaelides

  • Article
    | Open Access

    Though the goal of current organic solid-state laser research remains the realization of electrically pumped lasing, identifying organic semiconductors with ideal properties remains a challenge. Here, the authors report a computational strategy for screening electrical pumping lasing molecules.

    • Qi Ou
    • , Qian Peng
    •  & Zhigang Shuai

  • Article
    | Open Access

    Currently the cost of CO2 chemical fixation remains high because of harsh reaction conditions. Here, the authors report a covalent organic framework screened from 10994 candidates as the efficient CO2 fixation catalyst under ambient conditions based on the finding of a “pore enrichment effect”.

    • Wei Zhou
    • , Qi-Wen Deng
    •  & Wei-Qiao Deng

  • Article
    | Open Access

    Machine learning models insufficient for certain screening tasks can still provide valuable predictions in specific sub-domains of the considered materials. Here, the authors introduce a diagnostic tool to detect regions of low expected model error as demonstrated for the case of transparent conducting oxides.

    • Christopher Sutton
    • , Mario Boley
    •  & Matthias Scheffler

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