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Computational chemistry methods with an optimal balance between predictive accuracy and computational cost hold major promise for accelerating the discovery of new molecules and materials. We at Nature Communications are eager to continue our engagement in this exciting and rapidly evolving field.
Theoretical description of light-matter coupling in the strong-coupling regime is challenging. Here the authors introduce a fully consistent ab-initio method of molecular orbital theory applicable to material systems in quantum electrodynamics environments.
The authors devise an efficient quantum approach to address the van der Waals interactions due to photoexcitations by approximating the Bethe-Salpeter equation. Both attractive/repulsive forces can arise, that could couple to collective protein dynamics.
Theoretical estimations of solvation free energy by continuum solvation models are generally not accurate. Here the authors report a polarizable force field fitted entirely to first-principles calculations for the estimation of free energy of solvation of arbitrary molecules.
Current machine-learned force fields typically ignore electronic degrees of freedom. SpookyNet is a deep neural network that explicitly treats electronic degrees of freedom, closing an important remaining gap for models in quantum chemistry.
No existing density functional correctly describes the properties of water across the entire phase diagram. The authors report a data-driven many-body potential energy function based on density-corrected SCAN functional that quantitatively reproduces the energetics of gas-phase water clusters, and correctly predicts the properties of liquid water.
The concept of delocalization, resonance and aromaticity are commonly discussed within electronic structure frameworks relying on specific wave function expansions. Here the authors propose a redefinition of these concepts from first-principles by investigating saddle points of the all-electron probability density.
Accurate computational prediction of atomistic structure with traditional methods is challenging. The authors report a kernel-based machine learning model capable of reconstructing 3D atomic coordinates from predicted interatomic distances across a variety of system classes.
Quantum-mechanical methods of benchmark quality are widely used for describing molecular interactions. The present work shows that interaction energies by CCSD(T) and DMC are not in consistent agreement for a set of polarizable supramolecules calling for cooperative efforts solving this conundrum.
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.
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.
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.
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.
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.
Accurate interpretation of molecular vibrational spectroscopic signals is key to understand chemical processes. Here the authors introduce a new computational approach to represent vibrational modes in terms of nuclear densities that captures anharmonic effects in protonated glycine.
Protein-ligand unbinding processes are out of reach for atomistic simulations due to time-scale involved. Here the authors demonstrate an approach relying on dissipation-corrected targeted molecular dynamics that enables to provide binding and unbinding rates with a speed-up of several orders of magnitude.
The unexpectedly long-ranged interface stress observed in recent delamination experiments is yet to be clarified. Here, the authors develop an analytical approach to show the wavelike atomic deformation as the origin for the observed ultra long-range stress in delamination of graphene from various substrates.
The electronic structure of benzene has been a test bed for competing theories along the years. Here the authors show via quantum chemistry calculations that the wavefunction of benzene can be partitioned into tiles which show that the two electron spins exhibit staggered Kekulé structures.
Despite the fact that layered materials are often employed as lubricants, many of the underlying mechanisms are still controversial. Here the authors present a fundamental model for describing friction on atomically thin sheets that reveals the dynamics of strengthening and layer-number dependence of the friction.
The determination of the speciation of ions and molecules in supercritical aqueous fluids under pressure is key to understanding their mass transport in the Earth’s interior. Here the authors present a strategy based on ab-initio molecular dynamics to determine the speciation of carbonates in aqueous fluids.
The inverse DFT problem of mapping the ground-state density to its exchange correlation potential has been numerically challenging so far. Here, the authors propose an approach for an accurate solution to the inverse DFT problem, enabling the evaluation of exact exchange and correlation potential from an ab initio density.
Quantum algorithms for simulating chemical systems are limited because of the a priori assumption about the form of the target wavefunction. Here the authors present a new variational hybrid quantum-classical algorithm which allows the system being simulated to determine its own optimal state.
Rational design of metal organic frameworks (MOFs) with shape-memory nanopores is a formidable challenge. Here the authors use an accurate theoretical approach to design thermo-responsive MOFs based on a balance of van der Waals and entropy contributions.
The description of van der Waals interactions should often account for coupling with pervasive electric fields, but this effect has been omitted in atomistic simulations. Here, the authors develop a model to study the effects of external charge on long-range van der Waals interactions.
Despite being essential to organic chemistry, the curly arrow notation of reaction mechanisms has been treated with suspicion due to its unclear connection with quantum mechanics. Here, the authors show that analysis of wavefunction 'tiles' along a reaction coordinate reveals the electron motion depicted by curly arrows.
The electron affinity of liquid water is a fundamental property which has not yet been accurately measured. Here, the authors predict this property by coupling path-integral molecular dynamics with ab initio potentials and electronic structure calculations, revisiting several estimates used in the literature.