| SchNet - A deep learning architecture for molecules and materials |
145 |
| A generally applicable atomic-charge dependent London dispersion correction |
71 |
| Less is more: Sampling chemical space with active learning |
70 |
| Communication: An improved linear scaling perturbative triples correction for the domain based local pair-natural orbital based singles and doubles coupled cluster method [DLPNO-CCSD(T)] |
59 |
| B97-3c: A revised low-cost variant of the B97-D density functional method |
58 |
| Alchemical and structural distribution based representation for universal quantum machine learning |
52 |
| Effects of ensembles, ligand, and strain on adsorbate binding to alloy surfaces |
49 |
| Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics |
40 |
| Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials |
38 |
| Implicit self-consistent electrolyte model in plane-wave density-functional theory |
36 |
| Reweighted autoencoded variational Bayes for enhanced sampling (RAVE) |
35 |
| wACSF-Weighted atom-centered symmetry functions as descriptors in machine learning potentials |
34 |
| Hierarchical modeling of molecular energies using a deep neural network |
32 |
| Advances in the experimental exploration of water's phase diagram |
32 |
| High-temperature superconductivity in alkaline and rare earth polyhydrides at high pressure: A theoretical perspective |
29 |
| Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm |
29 |
| Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions |
28 |
| Atom-density representations for machine learning |
28 |
| Grand canonical simulations of electrochemical interfaces in implicit solvation models |
26 |
| The electric double layer at metal-water interfaces revisited based on a charge polarization scheme |
26 |
| Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy |
25 |
| Perspective: Excess-entropy scaling |
25 |
| Survival of the most transferable at the top of Jacob's ladder: Defining and testing the omega B97M(2) double hybrid density functional |
25 |
| Fast semistochastic heat-bath configuration interaction |
25 |
| Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning |
24 |
| Automated design of collective variables using supervised machine learning |
24 |
| Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data |
24 |
| Constant size descriptors for accurate machine learning models of molecular properties |
23 |
| Perspective: Theory of quantum transport in molecular junctions |
23 |
| A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules |
23 |
| A force field of Li+, Na+, K+, Mg2+, Ca2+, Cl and SO42-in aqueous solution based on the TIP4P/2005 water model and scaled charges for the ions |
22 |
| DeePCG: Constructing coarse-grained models via deep neural networks |
21 |
| Grand-canonical approach to density functional theory of electrocatalytic systems: Thermodynamics of solid-liquid interfaces at constant ion and electrode potentials |
21 |
| Enhanced sampling in molecular dynamics |
21 |
| Operators in quantum machine learning: Response properties in chemical space |
20 |
| Perspective: Dynamics of confined liquids |
19 |
| Unsupervised machine learning in atomistic simulations, between predictions and understanding |
19 |
| Communication: Approaching exact quantum chemistry by cluster analysis of full configuration interaction quantum Monte Carlo wave functions |
19 |
| New aspects of operando Raman spectroscopy applied to electrochemical CO2 reduction on Cu foams |
19 |
| Perspective: Size selected clusters for catalysis and electrochemistry |
19 |
| Glassy dynamics in dense systems of active particles |
19 |
| Fantasy versus reality in fragment-based quantum chemistry |
19 |
| Exact parameterization of fermionic wave functions via unitary coupled cluster theory |
19 |
| Calcium ions in aqueous solutions: Accurate force field description aided by ab initio molecular dynamics and neutron scattering |
19 |
| Beyond Marcus theory and the Landauer-Buttiker approach in molecular junctions: A unified framework |
19 |
| Wave attenuation in glasses: Rayleigh and generalized-Rayleigh scattering scaling |
18 |
| Communication: Time-dependent optimized coupled-cluster method for multielectron dynamics |
18 |
| High-accuracy extrapolated ab initio thermochemistry. IV. A modified recipe for computational efficiency |
18 |
| Perspective: Multireference coupled cluster theories of dynamical electron correlation |
18 |
| Extending the accuracy of the SNAP interatomic potential form |
18 |
