TY - JOUR A1 - Ganose, Alex M. A1 - Sahasrabuddhe, Hrushikesh A1 - Asta, Mark A1 - Beck, Kevin A1 - Biswas, Tathagata A1 - Bonkowski, Alexander A1 - Bustamante, Joana A1 - Chen, Xin A1 - Chiang, Yuan A1 - Chrzan, Daryl C. A1 - Clary, Jacob A1 - Cohen, Orion A. A1 - Ertural, Christina A1 - Gallant, Max C. A1 - George, Janine A1 - Gerits, Sophie A1 - Goodall, Rhys E. A. A1 - Guha, Rishabh D. A1 - Hautier, Geoffroy A1 - Horton, Matthew A1 - Inizan, T. J. A1 - Kaplan, Aaron D. A1 - Kingsbury, Ryan S. A1 - Kuner, Matthew C. A1 - Li, Bryant A1 - Linn, Xavier A1 - McDermott, Matthew J. A1 - Mohanakrishnan, Rohith Srinivaas A1 - Naik, Aakash A. A1 - Neaton, Jeffrey B. A1 - Parmar, Shehan M. A1 - Persson, Kristin A. A1 - Petretto, Guido A1 - Purcell, Thomas A. R. A1 - Ricci, Francesco A1 - Rich, Benjamin A1 - Riebesell, Janosh A1 - Rignanese, Gian-Marco A1 - Rosen, Andrew S. A1 - Scheffler, Matthias A1 - Schmidt, Jonathan A1 - Shen, Jimmy-Xuan A1 - Sobolev, Andrei A1 - Sundararaman, Ravishankar A1 - Tezak, Cooper A1 - Trinquet, Victor A1 - Varley, Joel B. A1 - Vigil-Fowler, Derek A1 - Wang, Duo A1 - Waroquiers, David A1 - Wen, Mingjian A1 - Yang, Han A1 - Zheng, Hui A1 - Zheng, Jiongzhi A1 - Zhu, Zhuoying A1 - Jain, Anubhav T1 - Atomate2: Modular workflows for materials science N2 - High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science. KW - Automation KW - Materials design KW - DFT workflows KW - Phonons KW - Thermal conductivity KW - Bonding analysis PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-635759 DO - https://doi.org/10.1039/d5dd00019j SN - 2635-098X SP - 1 EP - 30 PB - Royal Society of Chemistry (RSC) CY - Cambridge AN - OPUS4-63575 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Ganose, Alex M. A1 - Sahasrabuddhe, Hrushikesh A1 - Asta, Mark A1 - Beck, Kevin A1 - Biswas, Tathagata A1 - Bonkowski, Alexander A1 - Bustamante, Joana A1 - Chen, Xin A1 - Chiang, Yuan A1 - Chrzan, Daryl C. A1 - Clary, Jacob A1 - Cohen, Orion A. A1 - Ertural, Christina A1 - Gallant, Max C. A1 - George, Janine A1 - Gerits, Sophie A1 - Goodall, Rhys E. A. A1 - Guha, Rishabh D. A1 - Hautier, Geoffroy A1 - Horton, Matthew A1 - Inizan, T. J. A1 - Kaplan, Aaron D. A1 - Kingsbury, Ryan S. A1 - Kuner, Matthew C. A1 - Li, Bryant A1 - Linn, Xavier A1 - McDermott, Matthew J. A1 - Mohanakrishnan, Rohith Srinivaas A1 - Naik, Aakash A. A1 - Neaton, Jeffrey B. A1 - Parmar, Shehan M. A1 - Persson, Kristin A. A1 - Petretto, Guido A1 - Purcell, Thomas A. R. A1 - Ricci, Francesco A1 - Rich, Benjamin A1 - Riebesell, Janosh A1 - Rignanese, Gian-Marco A1 - Rosen, Andrew S. A1 - Scheffler, Matthias A1 - Schmidt, Jonathan A1 - Shen, Jimmy-Xuan A1 - Sobolev, Andrei A1 - Sundararaman, Ravishankar A1 - Tezak, Cooper A1 - Trinquet, Victor A1 - Varley, Joel B. A1 - Vigil-Fowler, Derek A1 - Wang, Duo A1 - Waroquiers, David A1 - Wen, Mingjian A1 - Yang, Han A1 - Zheng, Hui A1 - Zheng, Jiongzhi A1 - Zhu, Zhuoying A1 - Jain, Anubhav T1 - Correction: Atomate2: Modular workflows for materials science N2 - Correction for “Atomate2: modular workflows for materials science” by Alex M. Ganose et al., Digital Discovery, 2025, 4, 1944–1973, https://doi.org/10.1039/D5DD00019J. PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-640297 DO - https://doi.org/10.1039/d5dd90036k SN - 2635-098X SP - 1 EP - 2 PB - Royal Society of Chemistry (RSC) CY - Cambridge AN - OPUS4-64029 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Goryacheva, O. A. A1 - Wegner, Karl David A1 - Sobolev, A. M. A1 - Häusler, I. A1 - Gaponik, N. A1 - Gorycheva, I. Y. A1 - Resch-Genger, Ute T1 - Influence of particle architecture on the photoluminescence properties of silica‑coated CdSe core/shell quantum dots N2 - Light-emitting nanoparticles like semiconductor nanocrystals (termed quantum dots, QDs) are promising candidates for biosensing and bioimaging applications based on their bright and stable photoluminescent properties. As high-quality QDs are often synthesized in organic solvents, strategies needed to be developed to render them water-dispersible without affecting their optical properties and prevent changes in postmodification steps like the biofunctionalization with antibodies or DNA. Despite a large number of studies on suitable surface modification procedures, the preparation of water-soluble QDs for nanobiotechnology applications still presents a challenge. To highlight the advantages of surface silanization, we systematically explored the influence of the core/multishell architecture of CdSe/CdS/ZnS QDs and the silanization conditions on the optical properties of the resulting silanized QDs. Our results show that the optical properties of silica-coated CdSe/CdS/ZnS QDs are best preserved in the presence of a thick CdS (6 monolayers (ML)) intermediate shell, providing a high photoluminescence quantum yield (PL QY), and a relatively thick ZnS (4.5 ML) external shell, effectively shielding the QDs from the chemical changes during silica coating. In addition to the QD core/shell architecture, other critical parameters of the silica-coating process, that can have an influence on the optical properties of the QD, include the choice of the surfactant and its concentration used for silica coating. The highest PL QY of about 46% was obtained by a microemulsion silica-coating procedure with the surfactant Brij L4, making these water-dispersible QDs to wellsuited optical reporters in future applications like fluorescence immunoassays, biomedicine, and bioimaging. KW - Nano KW - Nanomaterial KW - Particle KW - Semiconductor KW - Quantum do KW - Photoluminescence KW - Photophysics KW - Lifetime KW - Sensor KW - Mechanism KW - Surface KW - Shell KW - Silica KW - Silanization KW - Synthesis PY - 2022 DO - https://doi.org/10.1007/s00216-022-04005-7 SP - 1 EP - 13 PB - Springer AN - OPUS4-54546 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -