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A fast and reliable geometry optimization algorithm is presented that optimizes atomic positions and lattice vectors simultaneously. Using a series of benchmarks, it is shown that the method presented in this paper outperforms in most cases the standard optimization methods implemented in popular codes such as QUANTUM ESPRESSO and VASP. To motivate the variable cell shape optimization method presented in here, the eigenvalues of the lattice Hessian matrix are investigated thoroughly. It is shown that they change depending on the shape of the cell and the number of particles inside the cell. For certain cell shapes the resulting condition number of the lattice matrix can grow quadratically with respect to the number of particles. By a coordinate transformation which can be applied to all variable cell shape optimization methods, the undesirable conditioning of the lattice Hessian matrix is eliminated.

Since late 2018, young people around the world have united to demand greater action on climate change. Aside from their stated concerns and demands, however, very little is known about why young people have been joining this growing movement. Using a large sample (N = 4057) of people in Switzerland aged between 14 and 25, we show that social identity is most strongly associated with participation, followed by beliefs about the effectiveness of youth strikes, level of education, and worry about climate change. Our findings affirm the relevance of both climate change risk perceptions and social identity-related processes for collective climate change action, and pave the way for promising opportunities in theory development and integration. The study also provides lessons for those who seek to maintain and increase collective action on climate change: concern about climate change is an important motivating factor, but social identity processes are at least as relevant for young people&rsquo, s participation.

The understanding of intra-urban temperature variations is crucial for cities’ adaptation to impacts of present and future anthropogenic climate change. Depending on extensive measurement networks, high-resolution air temperature measurements in urban environments are challenging due to high instrumentation and maintenance costs. Here, we present a low-cost measurement device (LCD) consisting of a temperature logger and a custom made, naturally ventilated radiation shield. Besides inter-comparisons with automated weather stations (AWS) at three reference sites during record-dry summer 2018, we tested the potential using a network of 76 LCD to map the intra-urban variability of urban heat island (UHI) patterns throughout an intense heat wave based in the city of Bern, Switzerland. We found positive mean measurement biases between LCD and AWS of 0.61 to 0.93 K (RMSE: 0.78 to 1.17 K) during daytime, of which up to 82.8% could be explained by radiative heating of solar irradiance and insufficient ventilation by prevailing winds. During night, average measurement biases were markedly lower and eventually negative with -0.12 to 0.23 K (RMSE: 0.19 to 0.34 K). Our results further highlight the importance of sensor inter-comparisons being conducted at multiple locations with differing urban forms given that biases varied considerably between the reference sites. Data retrieved by the city-wide measurement network showed that the LCD approach is well suited for the analysis of spatiotemporal UHI-patterns during night with maximum UHI intensities of up to 5.5 K. In conclusion, the here presented LCD measurement approach represents a valuable option for cost-effective analyses of urban air temperature variability across multiple scales, which may be of particular value for the development, appliance, and monitoring of adaptation strategies to climate change in cities with restricted financial resources.