Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods.

Modern ab initio methods have rapidly increased our understanding of solid state materials properties, chemical reactions, and the quantum interactions between atoms. However, poor scaling often renders direct ab initio calculations intractable for large or complex systems. There are two obvious avenues through which to remedy this problem: (i) develop new, less expensive methods to calculate system properties, or (ii) make existing methods faster.

Representing Global Reactive Potential Energy Surfaces Using Gaussian Processes.

Representation of multidimensional global potential energy surfaces suitable for spectral and dynamical calculations from high-level ab initio calculations remains a challenge. Here, we present a detailed study on constructing potential energy surfaces using a machine learning method, namely, Gaussian process regression. Tests for the A″ state of SH, which facilitates the SH + H ↔ S(P) + H abstraction reaction and the SH + H' ↔ SH' + H exchange reaction, suggest that the Gaussian process is capable of providing a reasonable potential energy surface with a small number (∼1 × 10) of ab initio points, but it needs substantially more points (∼1 × 10) to converge reaction probabilities.

High-Dimensional Atomistic Neural Network Potentials for Molecule-Surface Interactions: HCl Scattering from Au(111).

Ab initio molecular dynamics (AIMD) simulations of molecule-surface scattering allow first-principles characterization of the dynamics. However, the large number of density functional theory calculations along the trajectories is very costly, limiting simulations of long-time events and giving rise to poor statistics. To avoid this computational bottleneck, we report here the development of a high-dimensional molecule-surface interaction potential energy surface (PES) with movable surface atoms, using a machine learning approach.

Communication: Energy transfer and reaction dynamics for DCl scattering on Au(111): An ab initio molecular dynamics study.

Scattering and dissociative chemisorption of DCl on Au(111) are investigated using ab initio molecular dynamics with a slab model, in which the top two layers of Au are mobile. Substantial kinetic energy loss in the scattered DCl is found, but the amount of energy transfer is notably smaller than that observed in the experiment. On the other hand, the dissociative chemisorption probability reproduces the experimental trend with respect to the initial kinetic energy, but is about one order of magnitude larger than the reported initial sticking probability.

Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks.

The applicability and accuracy of the Behler-Parrinello atomistic neural network method for fitting reactive potential energy surfaces is critically examined in three systems, H + H2 → H2 + H, H + H2O → H2 + OH, and H + CH4 → H2 + CH3. A pragmatic Monte Carlo method is proposed to make efficient choice of the atom-centered mapping functions. The accuracy of the potential energy surfaces is not only tested by fitting errors but also validated by direct comparison in dynamically important regions and by quantum scattering calculations.

Control of valence and conduction band energies in layered transition metal phosphates via surface functionalization.

Layered transition metal phosphates and phosphites (TMPs) are a class of materials composed of layers of 2D sheets bound together via van der Waals interactions and/or hydrogen bonds. Explored primarily for use in proton transfer, their unique chemical tunability also makes TMPs of interest for forming large-scale hybrid materials. Further, unlike many layered materials, TMPs can readily be solution exfoliated to form single 2D sheets or bilayers, making them exciting candidates for a variety of applications.

Binding interactions in dimers of phenalenyl and closed-shell analogues.

Phenalenyl, an open-shell neutral radical that can form both π-stacked dimers and conducting molecular crystals, has gained attention for its interesting and potentially useful electrical and magnetic properties. The properties of this complex physical system are fairly well understood, making it an ideal testing ground for the newly developed van der Waals density functional (vdW-DF). We invoke a simple approximation, allowing the vdW-DF to be used within spin-polarized density functional theory and test this approximation on the π-stacked phenalenyl dimer.

Structural evolution of amino acid crystals under stress from a non-empirical density functional.

Use of the non-local correlation functional vdW-DF (from 'van der Waals density functional'; Dion M et al 2004 Phys. Rev. Lett.