Mapping materials and molecules - Cheng, B; Griffiths, R-R; Wengert, S; Kunkel, C; Stenczel, T; Zhu, B; Deringer, VL; Bernstein, N; Margraf, JT; Reuter, K; Csanyi, G (American Chemical Society, 2020)
Conspectus The visualization of data is indispensable in scientific research, from the early stages when human insight forms to the final step of communicating results. In computational physics, chemistry and materials science, it can be as simple as making a scatter plot or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the “big data” revolution, these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bioactivities.