The Molpro quantum chemistry package
Journal of chemical physics online/The Journal of chemical physics/Journal of chemical physics,
04/2020
Journal Article
Fluoride ion batteries are potential "next-generation" electrochemical storage devices that offer high energy density. At present, such batteries are limited to operation at high temperatures because ...suitable fluoride ion-conducting electrolytes are known only in the solid state. We report a liquid fluoride ion-conducting electrolyte with high ionic conductivity, wide operating voltage, and robust chemical stability based on dry tetraalkylammonium fluoride salts in ether solvents. Pairing this liquid electrolyte with a copper-lanthanum trifluoride (Cu@LaF
) core-shell cathode, we demonstrate reversible fluorination and defluorination reactions in a fluoride ion electrochemical cell cycled at room temperature. Fluoride ion-mediated electrochemistry offers a pathway toward developing capacities beyond that of lithium ion technology.
Dynamics and dissipation in enzyme catalysis Boekelheide, Nicholas; Salomón-Ferrer, Romelia; Miller, Thomas F. III
Proceedings of the National Academy of Sciences - PNAS,
09/2011, Volume:
108, Issue:
39
Journal Article
Peer reviewed
Open access
We use quantized molecular dynamics simulations to characterize the role of enzyme vibrations in facilitating dihydrofolate reductase hydride transfer. By sampling the full ensemble of reactive ...trajectories, we are able to quantify and distinguish between statistical and dynamical correlations in the enzyme motion. We demonstrate the existence of nonequilibrium dynamical coupling between protein residues and the hydride tunneling reaction, and we characterize the spatial and temporal extent of these dynamical effects. Unlike statistical correlations, which give rise to nanometer-scale coupling between distal protein residues and the intrinsic reaction, dynamical correlations vanish at distances beyond 4–6 Å from the transferring hydride. This work finds a minimal role for nonlocal vibrational dynamics in enzyme catalysis, and it supports a model in which nanometer-scale protein fluctuations statistically modulate—or gate—the barrier for the intrinsic reaction.
Density functional theory (DFT) provides a formally exact framework for quantum embedding. The appearance of nonadditive kinetic energy contributions in this context poses significant challenges, but ...using optimized effective potential (OEP) methods, various groups have devised DFT-in-DFT methods that are equivalent to Kohn–Sham (KS) theory on the whole system. This being the case, we note that a very considerable simplification arises from doing KS theory instead. We then describe embedding schemes that enforce Pauli exclusion via a projection technique, completely avoiding numerically demanding OEP calculations. Illustrative applications are presented using DFT-in-DFT, wave-function-in-DFT, and wave-function-in-Hartree–Fock embedding, and using an embedded many-body expansion.
Viewing the atomic-scale motion and energy dissipation pathways involved in forming a covalent bond is a longstanding challenge for chemistry. We performed scattering experiments of H atoms from ...graphene and observed a bimodal translational energy loss distribution. Using accurate first-principles dynamics simulations, we show that the quasi-elastic channel involves scattering through the physisorption well where collision sites are near the centers of the six-membered C-rings. The second channel results from transient C-H bond formation, where H atoms lose 1 to 2 electron volts of energy within a 10-femtosecond interaction time. This remarkably rapid form of intramolecular vibrational relaxation results from the C atom's rehybridization during bond formation and is responsible for an unexpectedly high sticking probability of H on graphene.
Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. However, existing machine learning ...techniques are challenged by the scarcity of training data when exploring unknown chemical spaces. We overcome this barrier by systematically incorporating knowledge of molecular electronic structure into deep learning. By developing a physics-inspired equivariant neural network, we introduce a method to learn molecular representations based on the electronic interactions among atomic orbitals. Our method, OrbNet-Equi, leverages efficient tight-binding simulations and learned mappings to recover high-fidelity physical quantities. OrbNet-Equi accurately models a wide spectrum of target properties while being several orders of magnitude faster than density functional theory. Despite only using training samples collected from readily available small-molecule libraries, OrbNet-Equi outperforms traditional semiempirical and machine learning-based methods on comprehensive downstream benchmarks that encompass diverse main-group chemical processes. Our method also describes interactions in challenging charge-transfer complexes and open-shell systems. We anticipate that the strategy presented here will help to expand opportunities for studies in chemistry and materials science, where the acquisition of experimental or reference training data is costly.
Embedded Mean-Field Theory Fornace, Mark E; Lee, Joonho; Miyamoto, Kaito ...
Journal of chemical theory and computation,
02/2015, Volume:
11, Issue:
2
Journal Article
Peer reviewed
Open access
We introduce embedded mean-field theory (EMFT), an approach that flexibly allows for the embedding of one mean-field theory in another without the need to specify or fix the number of particles in ...each subsystem. EMFT is simple, is well-defined without recourse to parameters, and inherits the simple gradient theory of the parent mean-field theories. In this paper, we report extensive benchmarking of EMFT for the case where the subsystems are treated using different levels of Kohn–Sham theory, using PBE or B3LYP/6-31G* in the high-level subsystem and LDA/STO-3G in the low-level subsystem; we also investigate different levels of density fitting in the two subsystems. Over a wide range of chemical problems, we find EMFT to perform accurately and stably, smoothly converging to the high-level of theory as the active subsystem becomes larger. In most cases, the performance is at least as good as that of ONIOM, but the advantages of EMFT are highlighted by examples that involve partitions across multiple bonds or through aromatic systems and by examples that involve more complicated electronic structure. EMFT is simple and parameter free, and based on the tests provided here, it offers an appealing new approach to a multiscale electronic structure.
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