While the ab initio molecular dynamics (AIMD) approach to gas− surface interaction has been instrumental in exploring important issues such as energy transfer and reactivity, it is only amenable to short-time events and a limited number of trajectories because of the on-the-fly nature of the density functional theory (DFT) calculations. Here, we report a high-dimensional global reactive potential energy surface (PES) constructed with high fidelity from judiciously placed DFT points, using a machine learning method; and it is ordersof-magnitude more efficient than AIMD in dynamical calculations and can be employed in various simulations without performing additional electronic structure calculations. Importantly, the surface atoms are included in such a PES, which provides a unique platform for studying energy transfer and scattering/ reaction of the impinging molecule on the solid surface on an equal footing.

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. With 60 degrees of freedom, this PES allows energy transfer between the energetic impinging molecule and thermal surface atoms. Classical trajectory calculations for the scattering of DCl from Au(111) on this PES are found to agree well with AIMD simulations, with ∼10-fold acceleration. Scattering of HCl from Au(111) is further investigated and compared with available experimental results.

Key messageQTL controlling flag leaf length, flag leaf width, flag leaf area and flag leaf angle were mapped in wheat.AbstractThis study aimed to advance our understanding of the genetic mechanisms underlying morphological traits of the flag leaves of wheat (Triticum aestivum L.). A recombinant inbred line (RIL) population derived from ND3331 and the Tibetan semi-wild wheat Zang1817 was used to identify quantitative trait loci (QTLs) controlling flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), and flag leaf angle (FLANG). Using an available simple sequence repeat genetic linkage map, 23 putative QTLs for FLL, FLW, FLA, and FLANG were detected on chromosomes 1B, 2B, 3A, 3D, 4B, 5A, 6B, 7B, and 7D. Individual QTL explained 4.3–68.52% of the phenotypic variance in different environments. Four QTLs for FLL, two for FLW, four for FLA, and five for FLANG were detected in at least two environments. Positive alleles of 17 QTLs for flag leaf-related traits originated from ND3331 and 6 originated from Zang1817. QTLs with pleiotropic effects or multiple linked QTL were also identified on chromosomes 1B, 4B, and 5A; these are potential target regions for fine-mapping and marker-assisted selection in wheat breeding programs.Electronic supplementary materialThe online version of this article (10.1007/s00122-017-3040-z) contains supplementary material, which is available to authorized users.

Direct dynamics simulations become increasingly popular in studying reaction dynamics for complex systems where analytical potential energy surfaces (PESs) are unavailable. Yet, the number and/or the propagation time of trajectories are often limited by high computational costs, and numerous energies and forces generated on-the-fly become wasted after simulations. We demonstrate here an example of reusing only a very small portion of existing direct dynamics data to reconstruct a 90-dimensional globally accurate reactive PES describing the interaction of CO 2 with a movable Ni(100) surface based on a machine learning approach. In addition to reproducing previous results with much better statistics, we predict scattering probabilities of CO 2 at the state-tostate level, which is extremely demanding for direct dynamics. We propose this unified way to investigate gaseous and gas−surface reactions of medium size, initiating with hundreds of preliminary direct dynamics trajectories, followed by low-cost and high-quality simulations on full-dimensional analytical PESs.

Schematic of the developed neural network potential energy surface enabling a unified and transferable description of dynamics of H₂ dissociative adsorption on multiple copper surfaces.