Quantum machine learning corrects classical forcefields: Stretching DNA base pairs in explicit solvent

in Journal of Chemical Physics (2022), 157(6),

In order to improve the accuracy of molecular dynamics simulations, classical forcefields are supplemented with a kernel-based machine learning method trained on quantum-mechanical fragment energies. As ... [more ▼]

In order to improve the accuracy of molecular dynamics simulations, classical forcefields are supplemented with a kernel-based machine learning method trained on quantum-mechanical fragment energies. As an example application, a potential-energy surface is generalized for a small DNA duplex, taking into account explicit solvation and long-range electron exchange–correlation effects. A long-standing problem in molecular science is that experimental studies of the structural and thermodynamic behavior of DNA under tension are not well confirmed by simulation; study of the potential energy vs extension taking into account a novel correction shows that leading classical DNA models have excessive stiffness with respect to stretching. This discrepancy is found to be common across multiple forcefields. The quantum correction is in qualitative agreement with the experimental thermodynamics for larger DNA double helices, providing a candidate explanation for the general and long-standing discrepancy between single molecule stretching experiments and classical calculations of DNA stretching. The new dataset of quantum calculations should facilitate multiple types of nucleic acid simulation, and the associated Kernel Modified Molecular Dynamics method (KMMD) is applicable to biomolecular simulations in general. KMMD is made available as part of the AMBER22 simulation software. [less ▲]

BIGDML—Towards accurate quantum machine learning force fields for materials

in Nature Communications (2022)

Four-Dimensional Scaling of Dipole Polarizability in Quantum Systems

in Physical Review Letters (2022)

Quantum framework for describing retarded and nonretarded molecular interactions in external electric fields

in Physical Review Research (2022)

Correlated Wave Functions for Electron–Positron Interactions in Atoms and Molecules

in Journal of Chemical Theory and Computation (2022), 18(4), 22672280

The positron, as the antiparticle of the electron, can form metastable states with atoms and molecules before its annihilation with an electron. Such metastable matter–positron complexes are stabilized by ... [more ▼]

The positron, as the antiparticle of the electron, can form metastable states with atoms and molecules before its annihilation with an electron. Such metastable matter–positron complexes are stabilized by a variety of mechanisms, which can have both covalent and noncovalent character. Specifically, electron–positron binding often involves strong many-body correlation effects, posing a substantial challenge for quantum-chemical methods based on atomic orbitals. Here we propose an accurate, efficient, and transferable variational ansatz based on a combination of electron–positron geminal orbitals and a Jastrow factor that explicitly includes the electron–positron correlations in the field of the nuclei, which are optimized at the level of variational Monte Carlo (VMC). We apply this approach in combination with diffusion Monte Carlo (DMC) to calculate binding energies for a positron e+ and a positronium Ps (the pseudoatomic electron–positron pair), bound to a set of atomic systems (H–, Li+, Li, Li–, Be+, Be, B–, C–, O– and F–). For PsB, PsC, PsO, and PsF, our VMC and DMC total energies are lower than that from previous calculations; hence, we redefine the state of the art for these systems. To assess our approach for molecules, we study the potential-energy surfaces (PES) of two hydrogen anions H– mediated by a positron (e+H22–), for which we calculate accurate spectroscopic properties by using a dense interpolation of the PES. We demonstrate the reliability and transferability of our correlated wave functions for electron–positron interactions with respect to state-of-the-art calculations reported in the literature. [less ▲]

Anisotropic Interlayer Force Field for Transition Metal Dichalcogenides: The Case of Molybdenum Disulfide

in Journal of Chemical Theory and Computation (2021)

Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package

in Journal of Chemical Physics (2021)

Machine Learning Force Fields: Recent Advances and Remaining Challenges

in Journal of Physical Chemistry Letters (2021)

Methyl-Induced Polarization Destabilizes the Noncovalent Interactions of N-Methylated Lysines

in Chemistry: A European Journal (2021)

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning

in Journal of Chemical Physics (2021)