A Doubly Nudged Elastic Band Method for Finding Transition States

A modification of the nudged elastic band (NEB) method is presented that enables stable optimisations to be run using both the limited-memory quasi-Newton (L-BFGS) and slow-response quenched velocity Verlet (SQVV) minimisers. The performance of this new `doubly nudged' DNEB method is analysed in conjunction with both minimisers and compared with previous NEB formulations. We find that the fastest DNEB approach (DNEB/L-BFGS) can be quicker by up to two orders of magnitude. Applications to permutational rearrangements of the seven-atom Lennard-Jones cluster (LJ7) and highly cooperative rearrangements of LJ38 and LJ75 are presented. We also outline an updated algorithm for constructing complicated multi-step pathways using successive DNEB runs.Comment: 13 pages, 8 figures, 2 table

A comparative study of the water budgets of lawns under three management scenarios

The fate of irrigation in urban ecosystems is highly uncertain, due to uncertainties in urban ecohydrology. We compared irrigation rates, soil moisture, evapotranspiration (ET), stomatal conductance, and water budgets of landscape ecosystems managed with different turfgrass species and irrigation technologies. The “Typical” landscape had a cool-season fescue and was irrigated by an automatic timer. The “Alternative1” landscape had a warm-season paspalum and a “smart” soil moisture sensor-based irrigation system. The “Alternative2” landscape had a cool-season native sedge and a “smart” weather station-based drip irrigation system. ET was measured with a portable closed chamber and modeled using a Penman-Monteith approach, and the two methods agreed well. The water applied to the Alternative1 was 54 % less than the water applied to the Typical landscape, and the water applied to the Alternative2 was 24 % less. Soil moisture was similar in the Typical and Alternative2, while Alternative1 was drier in spring. The stomatal conductance of sedge was lower than the other two species, but its ET was not lower due to higher leaf area. Irrigation efficiencies (ET/applied irrigation) were 57 - 58 %, 86 – 97 %, and 78 - 80 % for the Typical, Alternative1, and Alternative2 landscapes, respectively. Runoff was less than 2 % in each landscape, and excess irrigation primarily drained below the root zone. Differences in irrigation efficiency between landscapes were due mainly to irrigation application, which varied more than species water use. Smart irrigation systems provided substantial water savings relative to a timer-based system, and prevented significant drainage losses. The utilization of smart sensors was more important than the choice of turfgrass species for irrigation efficiency