TY - JOUR A1 - Ahmed, A. A. A. A1 - Alegret, N. A1 - Almeida, B. A1 - Alvarez-Puebla, R. A1 - Andrews, A. M. A1 - Ballerini, L. A1 - Barrios-Capuchino, J. J. A1 - Becker, C. A1 - Blick, R. H. A1 - Bonakdar, S. A1 - Chakraborty, I. A1 - Chen, X. A1 - Cheon, J. A1 - Chilla, G. A1 - Conceicao, A. L. C. A1 - Delehanty, J. A1 - Dulle, M. A1 - Efros, A. L. A1 - Epple, M. A1 - Fedyk, M. A1 - Feliu, N. A1 - Feng, M. A1 - Fernandez-Chacon, R. A1 - Fernandez-Cuesta, I. A1 - Fertig, N. A1 - Förster, S. A1 - Garrido, J. A. A1 - George, M. A1 - Guse, A. H. A1 - Hampp, N. A1 - Harberts, J. A1 - Han, J. A1 - Heekeren, H. R. A1 - Hofmann, U. G. A1 - Holzapfel, M. A1 - Hosseinkazemi, H. A1 - Huang, Y. A1 - Huber, P. A1 - Hyeon, T. A1 - Ingebrandt, S. A1 - Ienca, M. A1 - Iske, A. A1 - Kang, Y. A1 - Kasieczka, G. A1 - Kim, D.-H. A1 - Kostarelos, K. A1 - Lee, J.-H. A1 - Lin, K.-W. A1 - Liu, S. A1 - Liu, X. A1 - Liu, Y. A1 - Lohr, C. A1 - Mailänder, V. A1 - Maffongelli, L. A1 - Megahed, S. A1 - Mews, A. A1 - Mutas, M. A1 - Nack, L. A1 - Nakatsuka, N. A1 - Oertner, T. G. A1 - Offenhäusser, A. A1 - Oheim, M. A1 - Otange, B. A1 - Otto, F. A1 - Patrono, E. A1 - Peng, B. A1 - Picchiotti, A. A1 - Pierini, F. A1 - Pötter-Nerger, M. A1 - Pozzi, M. A1 - Pralle, A. A1 - Prato, M. A1 - Qi, B. A1 - Ramos-Cabrer, P. A1 - Resch-Genger, Ute A1 - Ritter, N. A1 - Rittner, M. A1 - Roy, S. A1 - Santoro, F. A1 - Schuck, N. W. A1 - Schulz, F. A1 - Seker, E. A1 - Skiba, M. A1 - Sosniok, M. A1 - Stephan, H. A1 - Wang, R. A1 - Wang, T. A1 - Wegner, Karl David A1 - Weiss, P. S. A1 - Xu, M. A1 - Yang, C. A1 - Zargarin, S. S. A1 - Zeng, Y. A1 - Zhou, Y. A1 - Zhu, D. A1 - Zierold, R. A1 - Parak, W. J. T1 - Interfacing with the Brain: How Nanotechnology Can Contribute N2 - Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain−machine interfaces have changed over time, along with the available technology. Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another. Nanotechnology, which encompasses engineered solid-state objects and integrated circuits, excels at small length scales of single to a few hundred nanometers and, thus, matches the sizes of biomolecules, biomolecular assemblies, and parts of cells. Consequently, we envision nanomaterials and nanotools as opportunities to interface with the brain in alternative ways. Here, we review the existing literature on the use of nanotechnology in brain−machine interfaces and look forward in discussing perspectives and limitations based on the authors’ expertise across a range of complementary disciplines from neuroscience, engineering, physics, and chemistry to biology and medicine, computer science and mathematics, and social science and jurisprudence. We focus on nanotechnology but also include information from related fields when useful and complementary. KW - Nanoneuro interface KW - Brain-on-a-chip KW - Nanostructured interface KW - Electrode arrays KW - Neuro-implants KW - Advanced nanomaterials KW - Quality assurance PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-634893 DO - https://doi.org/10.1021/acsnano.4c10525 SN - 1936-086X VL - 19 IS - 11 SP - 10630 EP - 10717 PB - ACS Publications AN - OPUS4-63489 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bernges, T. A1 - Hanus, R. A1 - Wankmiller, B. A1 - Imasato, K. A1 - Lin, S. A1 - Ghidiu, M. A1 - Gerlitz, M. A1 - Peterlechner, M. A1 - Graham, S. A1 - Hautier, G. A1 - Pei, Y. A1 - Hansen, M. R. A1 - Wilde, G. A1 - Snyder, G. J. A1 - George, Janine A1 - Agne, M T. A1 - Zeier, W. G. T1 - Considering the Role of Ion Transport in Diffuson-Dominated Thermal Conductivity N2 - Next-generation thermal management requires the development of low lattice thermal conductivity materials, as observed in ionic conductors. For example, thermoelectric efficiency is increased when thermal conductivity is decreased. Detrimentally, high ionic conductivity leads to thermoelectric device degradation. Battery safety and design also require an understanding of thermal transport in ionic conductors. Ion mobility, structural complexity, and anharmonicity have been used to explain the thermal transport properties of ionic conductors. However, thermal and ionic transport are rarely discussed in direct comparison. Herein, the ionic conductivity of Ag+ argyrodites is found to change by orders of magnitude without altering the thermal conductivity. Thermal conductivity measurements and two-channel lattice dynamics modeling reveal that the majority of Ag+ vibrations have a non-propagating diffuson-like character, similar to amorphous materials. It is found that high ionic mobility is not a requirement for diffuson-mediated transport. Instead, the same bonding and structural traits that can lead to fast ionic conduction also lead to diffuson-mediated transport. Bridging the fields of solid-state ionics and thermal transport, it is proposed that a vibrational perspective can lead to new design strategies for functional ionic conducting materials. As a first step, the authors relate the so-called Meyer-Neldel behavior in ionic conductors to phonon occupations. KW - Diffusons KW - DFT KW - Phonons KW - Thermoelectrics PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547140 DO - https://doi.org/10.1002/aenm.202200717 SN - 1614-6832 VL - 12 IS - 22 SP - 1 EP - 13 PB - Wiley VHC-Verlag AN - OPUS4-54714 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Albornoz, Ricardo Valencia A1 - Antypov, Dmytro A1 - Blanke, Gerd A1 - Borges, Itamar A1 - Marulanda Bran, Andres A1 - Cheung, Joshua A1 - Collins, Christopher M. A1 - David, Nicholas A1 - Day, Graeme M. A1 - Deringer, Volker L. A1 - Draxl, Claudia A1 - Eardley-Brunt, Annabel A1 - Evans, Matthew L. A1 - Fairlamb, Ian A1 - Fieseler, Kate A1 - Franklin, Barnabas A. A1 - George, Janine A1 - Grundy, Joanna A1 - Johal, Jay A1 - Kalikadien, Adarsh V. A1 - Kapil, Venkat A1 - Kotopanov, Lyubomir A1 - Kumar, Vishank A1 - Kuttner, Christian A1 - Lederbauer, Magdalena A1 - Ojeda-Porras, Andrea Carolina A1 - Pang, Jiayun A1 - Parkes, Michael A1 - Pemberton, Miles A1 - Ruscic, Branko A1 - Ryder, Matthew R. A1 - Sakaushi, Ken A1 - Saleh, Gabriele A1 - Savoie, Brett M. A1 - Schwaller, Philippe A1 - Skjelstad, Bastian Bjerkem A1 - Sun, Wenhao A1 - Taniguchi, Takuya A1 - Taylor, Christopher R. A1 - Torrisi, Steven A1 - Vishnoi, Shubham A1 - Walsh, Aron A1 - Wu, Ruiqi T1 - Discovering trends in big data: General discussion N2 - This article is a discussion of the paper "Specialising and analysing instruction-tuned and byte-level language models for organic reaction prediction" by Jiayun Pang and Ivan Vulić (Faraday discussions, 2025, 256, 413-433). KW - Automation KW - Big data KW - Machine learning KW - Materials design KW - Chemically complex materials PY - 2025 DO - https://doi.org/10.1039/D4FD90063D SN - 1359-6640 SN - 1364-5498 VL - 256 IS - Themed collection: Data-driven discovery in the chemical sciences SP - 520 EP - 550 PB - Royal Society of Chemistry (RSC) CY - Cambridge AN - OPUS4-62652 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Horton, Matthew K. A1 - Huck, Patrick A1 - Yang, Ruo Xi A1 - Munro, Jason M. A1 - Dwaraknath, Shyam A1 - Ganose, Alex M. A1 - Kingsbury, Ryan S. A1 - Wen, Mingjian A1 - Shen, Jimmy X. A1 - Mathis, Tyler S. A1 - Kaplan, Aaron D. A1 - Berket, Karlo A1 - Riebesell, Janosh A1 - George, Janine A1 - Rosen, Andrew S. A1 - Spotte-Smith, Evan W. C. A1 - McDermott, Matthew J. A1 - Cohen, Orion A. A1 - Dunn, Alex A1 - Kuner, Matthew C. A1 - Rignanese, Gian-Marco A1 - Petretto, Guido A1 - Waroquiers, David A1 - Griffin, Sinead M. A1 - Neaton, Jeffrey B. A1 - Chrzan, Daryl C. A1 - Asta, Mark A1 - Hautier, Geoffroy A1 - Cholia, Shreyas A1 - Ceder, Gerbrand A1 - Ong, Shyue Ping A1 - Jain, Anubhav A1 - Persson, Kristin A. T1 - Accelerated data-driven materials science with the Materials Project N2 - The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational Methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our eforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community. KW - Databases KW - Materials Informatics KW - Materials Design KW - Automation KW - Workflows KW - Accelerated Discovery PY - 2025 DO - https://doi.org/10.1038/s41563-025-02272-0 SN - 1476-1122 SP - 1 EP - 11 PB - Springer Science and Business Media LLC AN - OPUS4-63616 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hildebrandt, R. A1 - Seifert, M. A1 - George, Janine A1 - Blaurock, S. A1 - Botti, S. A1 - Krautscheid, H. A1 - Grundmann, M. A1 - Sturm, C. T1 - Determination of acoustic phonon anharmonicities via second-order Raman scattering in CuI N2 - We demonstrate the determination of anharmonic acoustic phonon properties via second-order Raman scattering exemplarily on copper iodide single crystals. The origin of multi-phonon features from the second-order Raman spectra was assigned by the support of the calculated 2-phonon density of states. In this way, the temperature dependence of acoustic phonons was determined down to 10 K. To determine independently the harmonic contributions of respective acoustic phonons, density functional theory in quasi-harmonic approximation was used. Finally, the anharmonic contributions were determined. The results are in agreement with earlier publications and extend CuI’s determined acoustic phonon properties to lower temperatures with higher accuracy. This approach demonstrates that it is possible to characterize the acoustic anharmonicities via Raman scattering down to zero-temperature renormalization constants of at least 0.1 cm⁻¹. KW - Thermoelectrics KW - Thermal transport KW - Phonon interactions KW - Density functional theory KW - Phonons PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-593164 DO - https://doi.org/10.1088/1367-2630/ad1141 SN - 1367-2630 VL - 25 IS - 12 SP - 1 EP - 12 PB - IOP Publishing CY - London AN - OPUS4-59316 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Hildebrandt, R. A1 - Seifert, M. A1 - George, Janine A1 - Blaurock, S. A1 - Botti, S. A1 - Krautscheid, H. A1 - Grundmann, M. A1 - Sturm, C. T1 - Determination of acoustic phonon anharmonicities via second-order Raman scattering in CuI N2 - We demonstrate the determination of anharmonic acoustic phonon properties via second-order Raman scattering exemplarily on copper iodide single crystals. The origin of multi-phonon features from the second-order Raman spectra was assigned by the support of the calculated 2-phonon density of states. In this way, the temperature dependence of acoustic phonons was determined down to 10 K. To determine independently the harmonic contributions of respective acoustic phonons, density functional theory (DFT) in quasi-harmonic approximation was used. Finally, the anharmonic contributions were determined. The results are in agreement with earlier publications and extend CuI's determined acoustic phonon properties to lower temperatures with higher accuracy. This approach demonstrates that it is possible to characterize the acoustic anharmonicities via Raman scattering down to zero-temperature renormalization constants of at least 0.1 cm⁻¹. KW - DFT KW - Quasi-harmonic approximation KW - Raman KW - Phonons KW - Anharmonicity PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-583699 DO - https://doi.org/10.48550/arXiv.2305.18931 SN - 2331-8422 SP - 1 EP - 14 PB - Cornell University CY - Ithaca, NY AN - OPUS4-58369 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Anker, Andy S. A1 - Aspuru-Guzik, Alán A1 - Bechtel, Tim A1 - Bigi, Filippo A1 - Briling, Ksenia R. A1 - Das, Basita A1 - David, Nicholas A1 - Day, Graeme M. A1 - Deringer, Volker L. A1 - Dyer, Matthew A1 - Eardley-Brunt, Annabel A1 - Evans, Matthew L. A1 - Evans, Rob A1 - Franklin, Barnabas A. A1 - Ganose, Alex M. A1 - George, Janine A1 - Goulding, Mark A1 - Hickey, Niamh A1 - James, Gillian A1 - Kalikadien, Adarsh V. A1 - Kapil, Venkat A1 - Kulik, Heather J. A1 - Kumar, Vishank A1 - Kuttner, Christian A1 - Lam, Erwin A1 - Lederbauer, Magdalena A1 - Lou, Yuchen A1 - Martin, Jennie A1 - Marulanda Bran, Andres A1 - Mathea, Miriam A1 - Pickard, Chris J. A1 - Ruscic, Branko A1 - Ryder, Matthew R. A1 - Sabanza Gil, Victor A1 - Schwaller, Philippe A1 - Segler, Marwin H. S. A1 - Sun, Wenhao A1 - Tanovic, Sara A1 - Treyde, Wojtek A1 - Walsh, Aron A1 - Wu, Ruiqi T1 - Discovering synthesis targets: General discussion N2 - This article is a discussion of the paper "Analysis of uncertainty of neural fingerprint-based models" by Christian W. Feldmann, Jochen Sieg and Miriam Mathea (Faraday discussions, 2025, DOI: 10.1039/D4FD00095A). KW - Automation KW - Materials acceleration platforms KW - Machine learning KW - Materials design KW - Materials discovery KW - Density functional theory KW - Ab initio PY - 2025 DO - https://doi.org/10.1039/D4FD90064B SN - 1359-6640 SN - 1364-5498 VL - 256 IS - Themed collection: Data-driven discovery in the chemical sciences SP - 639 EP - 663 PB - Royal Society of Chemistry (RSC) CY - Cambridge AN - OPUS4-62317 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Chan, George C.-Y. A1 - Hieftje, Gary M. A1 - Omenetto, Nicoló A1 - Axner, Ove A1 - Bengtson, Arne A1 - Bings, Nicolas H. A1 - Blades, Michael W. A1 - Bogaerts, Annemie A1 - Bolshov, Mikhail A. A1 - Broekaert, José A.C. A1 - Chan, WingTat A1 - Costa-Fernández, José M. A1 - Crouch, Stanley R. A1 - De Giacomo, Alessandro A1 - D’Ulivo, Alessandro A1 - Engelhard, Carsten A1 - Falk, Heinz A1 - Farnsworth, Paul B. A1 - Florek, Stefan A1 - Gamez, Gerardo A1 - Gornushkin, Igor B. A1 - Günther, Detlef A1 - Hahn, David W. A1 - Hang, Wei A1 - Hoffmann, Volker A1 - Jakubowski, Norbert A1 - Karanassios, Vassili A1 - Koppenaal, David W. A1 - Kenneth Marcus, R. A1 - Noll, Reinhard A1 - Olesik, John W. A1 - Palleschi, Vincenzo A1 - Panne, Ulrich A1 - Pisonero, Jorge A1 - Ray, Steven J. A1 - Resano, Martín A1 - Russo, Richard E. A1 - Scheeline, Alexander A1 - Smith, Benjamin W. A1 - Sturgeon, Ralph E. A1 - Todolí, José-Luis A1 - Tognoni, Elisabetta A1 - Vanhaecke, Frank A1 - Webb, Michael R. A1 - Winefordner, James D. A1 - Yang, Lu A1 - Yu, Jin A1 - Zhang, Zhanxia T1 - Landmark Publications in Analytical Atomic Spectrometry: Fundamentals and Instrumentation Development N2 - The almost-two-centuries history of spectrochemical analysis has generated a body of literature so vast that it has become nearly intractable for experts, much less for those wishing to enter the field. Authoritative, focused reviews help to address this problem but become so granular that the overall directions of the field are lost. This broader perspective can be provided partially by general overviews but then the thinking, experimental details, theoretical underpinnings and instrumental innovations of the original work must be sacrificed. In the present compilation, this dilemma is overcome by assembling the most impactful publications in the area of analytical atomic spectrometry. Each entry was proposed by at least one current expert in the field and supported by a narrative that justifies its inclusion. The entries were then assembled into a coherent sequence and returned to contributors for a round-robin review. KW - Analytical atomic spectrometry KW - Spectroscopy KW - Instrumental analysis PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-621588 DO - https://doi.org/10.1177/00037028241263567 SN - 1943-3530 VL - 78 SP - 1 EP - 456 PB - Sage CY - London AN - OPUS4-62158 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Batatia, Ilyes A1 - Benner, Philipp A1 - Chiang, Yuan A1 - Elena, Alin M. A1 - Kovács, Dávid P. A1 - Riebesell, Janosh A1 - Advincula, Xavier R. A1 - Asta, Mark A1 - Avaylon, Matthew A1 - Baldwin, William J. A1 - Berger, Fabian A1 - Bernstein, Noam A1 - Bhowmik, Arghya A1 - Bigi, Filippo A1 - Blau, Samuel M. A1 - Cărare, Vlad A1 - Ceriotti, Michele A1 - Chong, Sanggyu A1 - Darby, James P. A1 - De, Sandip A1 - Della Pia, Flaviano A1 - Deringer, Volker L. A1 - Elijošius, Rokas A1 - El-Machachi, Zakariya A1 - Fako, Edvin A1 - Falcioni, Fabio A1 - Ferrari, Andrea C. A1 - Gardner, John L. A. A1 - Gawkowski, Mikołaj J. A1 - Genreith-Schriever, Annalena A1 - George, Janine A1 - Goodall, Rhys E. A. A1 - Grandel, Jonas A1 - Grey, Clare P. A1 - Grigorev, Petr A1 - Han, Shuang A1 - Handley, Will A1 - Heenen, Hendrik H. A1 - Hermansson, Kersti A1 - Ho, Cheuk Hin A1 - Hofmann, Stephan A1 - Holm, Christian A1 - Jaafar, Jad A1 - Jakob, Konstantin S. A1 - Jung, Hyunwook A1 - Kapil, Venkat A1 - Kaplan, Aaron D. A1 - Karimitari, Nima A1 - Naik, Aakash A. A1 - Csányi, Gábor T1 - A foundation model for atomistic materials chemistry N2 - Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model—and its qualitative and at times quantitative accuracy—on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or “foundation” model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields. KW - Materials Design KW - Thermal Conducitivity KW - Nanoparticles KW - Batteries PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-647829 DO - https://doi.org/10.1063/5.0297006 SN - 0021-9606 VL - 163 IS - 18 SP - 1 EP - 89 PB - AIP Publishing AN - OPUS4-64782 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 - George, Janine A1 - Petretto, G. A1 - Naik, Aakash A1 - Esters, M. A1 - Jackson, A. J. A1 - Nelson, R. A1 - Dronskowski, R. A1 - Rignanese, G.-M. A1 - Hautier, G. T1 - Automated bonding analysis with crystal orbital Hamilton populuations N2 - Automated bonding analysis software has been developed based on Crystal Orbital Hamilton Populations to facilitate high-throughput bonding analysis and machine-learning of bonding features. This work presents the software and discusses its applications to simple and complex materials such as GaN, NaCl, the oxynitrides XTaO2N (X=Ca, Ba, Sr) and Yb14Mn1Sb11. KW - Chemical bonds KW - Automation KW - High-throughput PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-551641 DO - https://doi.org/10.1002/cplu.202200123 SN - 2192-6506 SP - 1 EP - 11 PB - Wiley-VCH CY - Weinheim AN - OPUS4-55164 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - George, Janine A1 - Petretto, G. A1 - Naik, Aakash A1 - Esters, M. A1 - Jackson, A. J. A1 - Nelson, R. A1 - Dronskowski, R. A1 - Rignanese, G.-M. A1 - Hautier, G. T1 - Automated bonding analysis with crystal orbital Hamilton populations (program code LobsterPy) N2 - This is the code for the program LobsterPy that can be used to automatically analyze and plot outputs of the program Lobster. KW - Automation KW - High-throughput computations KW - Bonding analysis PY - 2022 UR - https://doi.org/10.5281/zenodo.6320074 UR - https://doi.org/10.5281/zenodo.6415169 UR - https://doi.org/10.5281/zenodo.6415336 UR - https://doi.org/10.5281/zenodo.6581118 UR - https://doi.org/10.5281/zenodo.15034145 DO - https://doi.org/10.5281/zenodo.6320073 PB - Zenodo CY - Geneva AN - OPUS4-55174 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - George, Janine A1 - Petretto, G. A1 - Naik, Aakash A1 - Esters, M. A1 - Jackson, A. J. A1 - Nelson, R. A1 - Dronskowski, R. A1 - Rignanese, G.-M. A1 - Hautier, G. T1 - Cover profile for the article "Automated bonding analysis with crystal orbital Hamilton populuations" N2 - Invited for this month’s cover are researchers from Bundesanstalt für Materialforschung und -prüfung (Federal Institute for Materials Research and Testing) in Germany, Friedrich Schiller University Jena, Université catholique de Louvain, University of Oregon, Science & Technology Facilities Council, RWTH Aachen University, Hoffmann Institute of Advanced Materials, and Dartmouth College. The cover picture shows a workflow for automatic bonding analysis with Python tools (green python). The bonding analysis itself is performed with the program LOBSTER (red lobster). The starting point is a crystal structure, and the results are automatic assessments of the bonding situation based on Crystal Orbital Hamilton Populations (COHP), including automatic plots and text outputs. Coordination environments and charges are also assessed. More information can be found in the Research Article by J. George, G. Hautier, and co-workers. KW - Automation KW - Chemical bonds KW - High-throughput PY - 2022 DO - https://doi.org/10.1002/cplu.202200246 SN - 2192-6506 SP - 1 EP - 2 PB - Wiley-VCH CY - Weinheim AN - OPUS4-55557 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - RPRT A1 - Wiesler, F. A1 - Hund-Rinke, K. A1 - George, E. A1 - Greef, J.-M. A1 - Holz, F. A1 - Hölze, L.E. A1 - Hülsbergen, K.-J. A1 - Martin, S. A1 - Severin, K. A1 - Spielvogel, S. A1 - Geiger, P. A1 - Nawotke, C. A1 - Kehlenbeck, H. A1 - Schenkel, H. A1 - Adam, Christian A1 - Bloem, E. A1 - Frank, D. A1 - Goldbach, H.E. A1 - Heene, M. A1 - Kratz, S. A1 - Kruse, A. A1 - Möller, K. A1 - Pinnekamp, J. A1 - Schoumans, O.F. A1 - Vogel, Christian A1 - Wulf, S. T1 - Recyclingphosphate in der Düngung – Nutzen und Grenzen N2 - Weltweit werden etwa 80–85 % aller abgebauten Rohphosphate für die Produktion von Düngemitteln verwendet. Bei noch steigendem Bedarf an Phosphor (P) geht man derzeit davon aus, dass die weltweiten P-Reserven noch über 200 Jahre ausreichen. Um den P-Vorrat langfristig abzusichern, sollte der in Reststoffen enthaltene Phosphor möglichst umfänglich genutzt werden. Dieser Forderung trägt das von der Bundesregierung 2012 erstmals beschlossene Deutsche Ressourceneffizienzprogramm ProgRess Rechnung. Rohphosphat wird auch seitens der Europäischen Kommission aufgrundseiner ökonomischen Bedeutung und des unsicheren Angebots als „kritischer Rohstoff“ eingestuft. Vor diesem Hintergrund hat die Bundesregierung in der Verordnung zur Neuordnung der Klärschlammverwertung vom 27. September 2017 in Deutschland eine verpflichtende technische Rückgewinnung von Phosphor für Abwasserbehandlungsanlagen ab einer Ausbaugröße von 100.000 Einwohnerwerten (12 Jahre nach Inkrafttreten der Verordnung) bzw. ab einer Ausbaugröße von über 50.000 Einwohnerwerten (15 Jahre nach Inkrafttreten der Verordnung) vorgeschrieben. Die Verordnung enthält keine Vorgaben hinsichtlich der anzuwendenden Technologie bei der Phosphorrückgewinnung. Ausgenommen von der Rückgewinnungspflicht sind Klärschlämme mit niedrigen Phosphorgehalten (weniger als 20 Gramm Phosphor je Kilogramm Klärschlamm-Trockenmasse). Auch die im Jahre 2017 verabschiedeten neuen Rechtsvorschriften für die Düngung verlangen zukünftig einen nachhaltigen und ressourceneffizienten Umgang mit Nährstoffen bei der landwirtschaftlichen Erzeugung und schränken die P-Zufuhr insbesondere auf hoch versorgten Böden ein (Düngeverordnung vom 26. Mai 2017). Dies wird zu einem mehr am Pflanzenbedarf orientierten und ggf. vermehrt überbetrieblichen Einsatz von wirtschaftseigenen Düngern führen. Um den politischen und gesetzlichen Anforderungen gerecht zu werden, müssen einerseits wirksame und kosteneffiziente technische Verfahren zum P-Recycling (weiter)entwickelt werden. Andererseits müssen die Recyclingprodukte die Anforderungen für eine direkte Verwendung in der Landwirtschaft oder als Rohstoff für die Herstellung von Düngemitteln erfüllen. In dem vorliegenden Standpunkt wird zunächst der potenzielle Beitrag von Reststoffen zur Deckung des P-Bedarfs in der deutschen Pflanzenproduktion geschätzt. Für die wichtigsten Reststoffe werden technische Verfahren zur P-Rückgewinnung bzw. Aufarbeitung skizziert. Auf Basis der Anforderungen der Landwirtschaft bzw. der Düngemittelindustrie an die P-Recyclingprodukte werden deren Herstellungsverfahren bewertet. Schließlich wird der Regelungs- und Forschungsbedarf formuliert. KW - Phosphor KW - Düngemittel KW - Diffusive Gradients in thin films (DGT) PY - 2020 SP - 1 EP - 19 PB - Bundesministerium für Ernährung und Landwirtschaft (BMEL) CY - Berlin AN - OPUS4-51011 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Chan, George C.-Y. A1 - Engelhard, Carsten A1 - Wiley, Joshua S. A1 - Shoulds, Ayanna U. A1 - Cooks, R. Graham A1 - Hieftje, Gary M. A1 - Shelley, Jacob T. T1 - Characterization of a Low-Temperature Plasma (LTP) Ambient Ionization Source Using Temporally Resolved Monochromatic Imaging Spectrometry N2 - The low-temperature plasma (LTP) probe is a common plasma-based source used for ambient desorption–ionization mass spectrometry (MS). While the LTP probe has been characterized in detail with MS, relatively few studies have used optical spectroscopy. In this paper, two-dimensional (2D) imaging at selected wavelengths is used to visualize important species in the LTP plasma jet. First, 2D steady-state images of the LTP plume for N2+ (391.2 nm), He I (706.5 nm), and N2 (337.1 nm) emissions were recorded under selected plasma conditions. Second, time-resolved 2D emission maps of radiative species in the LTP plasma jet were recorded through the use of a 200 ns detection gate and varying gate delays with respect to the LTP trigger pulse. Emission from He I, N2+, and N2 in the plasma jet region was found to show a transient behavior (often referred to as plasma bullets) lasting only a few microseconds. The N2+ and He I maps were highly correlated in spatial and temporal structure. Further, emission from N2 showed two maxima in time, one before and one after the maximum emission for N2+ and He I, due to an initial electronic excitation wave and ion–electron recombination, respectively. Third, the interaction of the LTP probe with a sample substrate and an electrically grounded metallic needle was studied. Emission from a fluorophore on the sample substrate showed an initial photon-induced excitation from plasma-generated photons followed by electronic excitation by other plasma species. The presence of a grounded needle near the plasma jet significantly extended the plasma jet lifetime and also generated a long-lived corona discharge on the needle. The effect of LTP operating parameters on emission spectra was correlated with mass-spectral results including reagent-ion signals. Lastly, five movies provide a side-by-side comparison of the temporal behavior of emitting species and insights into the interactions of the emission clouds with a sample surface as well as an external needle. Temporally and spatially resolved imaging provided insights into important processes in the LTP plasma jet, which will help improve analyte ion sampling in LTP–MS. KW - Optical spectroscopy KW - Low-temperature plasma KW - Ambient ionization KW - Instrumentation PY - 2023 DO - https://doi.org/10.1177/00037028231184501 VL - 77 IS - 8 SP - 940 EP - 956 PB - SAGE Publications AN - OPUS4-61269 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Arnold, M. A1 - Katzmann, J. A1 - Naik, Aakash A1 - Görne, A. L. A1 - Härtling, Thomas A1 - George, Janine A1 - Schuster, C. T1 - Investigations on electron beam irradiated rare-earth doped SrF2 for application as low fading dosimeter material: Evidence for and DFT simulation of a radiation-induced phase N2 - A recent approach to measure electron radiation doses in the kGy range is the use of phosphors with an irradiation dose-dependent luminescence decay time. However, the applicability of the previously investigated material NaYF4:Yb3+,Er3+ is limited as it shows pronounced fading. Therefore, in this work, a modified SrF2 synthesis is presented that results in SrF2 nanoparticles codoped with Yb and either Er, Hm, or Tm. To assess their suitability as dosimeter material, dose response, as well as its degree of fading over 50 up to 140 days after irradiation were measured. Fading rates as small as 5% in SrF2:Er,Yb and 4% in SrF2:Ho,Yb were derived, which are comparable to established dosimeter materials. A combination of spectroscopy, diffraction and DFT calculations was used to elucidate the effect of irradiation, pointing towards the formation of a secondary phase of Yb2+ that we predict could be Yb2OF2. This irreversible formation of a secondary phase is considered to be the explanation for the low fading behavior in SrF2-based phosphors compared to NaYF4:Yb, Er, a highly attractive feature for electron beam dosimetry. KW - DFT KW - Structure prediction PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-554115 DO - https://doi.org/10.1039/D2TC01773C SN - 2050-7526 VL - 10 IS - 32 SP - 11579 EP - 11587 PB - RSC CY - London AN - OPUS4-55411 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Rosen, Andrew S. A1 - Gallant, Max A1 - George, Janine A1 - Riebesell, Janosh A1 - Sahasrabuddhe, Hrushikesh A1 - Shen, Jimmy-Xuan A1 - Wen, Mingjian A1 - Evans, Matthew L. A1 - Petretto, Guido A1 - Waroquiers, David A1 - Rignanese, Gian-Marco A1 - Persson, Kristin A. A1 - Jain, Anubhav A1 - Ganose, Alex M. T1 - Jobflow: Computational Workflows Made Simple N2 - We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of filesystem- and cloud-based databases without the data storage process being part of the underlying workflow logic itself. Jobflow has been intentionally designed to be fully independent of the choice of workflow manager used to dispatch the calculations on remote computing resources. At the time of writing, Jobflow workflows can be executed either locally or across distributed compute environments via an adapter to the FireWorks package, and Jobflow fully supports the integration of additional workflow execution adapters in the future. KW - Automation KW - Workflow KW - Computational Materials Science KW - Computations KW - Software PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-593104 DO - https://doi.org/10.21105/joss.05995 VL - 9 IS - 93 SP - 1 EP - 7 PB - The Open Journal AN - OPUS4-59310 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Cope, Elana J. A1 - Bustamante, Joana A1 - Johnson, Zöe M. A1 - Lancaster, Alicia A1 - Gurunathan, Ramya A1 - George, Janine A1 - Agne, Matthias T. T1 - Heat capacity estimation of complex materials for energy technologies N2 - The control of heat in energy materials is one of the greatest current engineering challenges. Accurate estimations of heat capacity are key in creating and using materials safely and efficiently. Current models for heat capacity are often limited due to crude estimations of the phonon density of states, which is a key component of the thermodynamic definition of heat capacity. Utilization of a more detailed phonon density of states, which can easily be obtained from machine-learned algorithms, combined with dilation and electronic contributions, yields heat capacity estimations that are 29% better than the widely utilized Debye model and are comparable to state-of-the-art quantum mechanical calculations. The framework and necessary tools for heat capacity estimations demonstrated herein can be built into more detailed models and analyses, such as high-throughput characterization, transport models, or other thermodynamic assessments. Consequently, the so-called vibrational + dilation + electronic (VDE) model of heat capacity developed in this work better enables the informed design of next-generation devices. KW - Materials Design KW - Heat Capacity KW - Machine learning potentials KW - Thermal Management PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-645711 DO - https://doi.org/10.1016/j.joule.2025.102054 SN - 2542-4351 VL - 9 IS - 8 SP - 1 EP - 12 PB - Elsevier Inc. AN - OPUS4-64571 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Janssen, Jan A1 - George, Janine A1 - Geiger, Julian A1 - Bercx, Marnik A1 - Wang, Xing A1 - Ertural, Christina A1 - Schaarschmidt, Joerg A1 - Ganose, Alex M. A1 - Pizzi, Giovanni A1 - Hickel, Tilmann A1 - Neugebauer, Joerg T1 - A Python workflow definition for computational materials design N2 - Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow exchange format to share workflows between Python-based WfMS, currently AiiDA, jobflow, and pyiron. This development is motivated by the similarity of these three Python-based WfMS, that represent the different workflow steps and data transferred between them as nodes and edges in a graph. With the PWD, we aim at fostering the interoperability and reproducibility between the different WfMS in the context of Findable, Accessible, Interoperable, Reusable (FAIR) workflows. To separate the scientific from the technical complexity, the PWD consists of three components: (1) a conda environment that specifies the software dependencies, (2) a Python module that contains the Python functions represented as nodes in the workflow graph, and (3) a workflow graph stored in the JavaScript Object Notation (JSON). The first version of the PWD supports directed acyclic graph (DAG)-based workflows. Thus, any DAG-based workflow defined in one of the three WfMS can be exported to the PWD and afterwards imported from the PWD to one of the other WfMS. After the import, the input parameters of the workflow can be adjusted and computing resources can be assigned to the workflow, before it is executed with the selected WfMS. This import from and export to the PWD is enabled by the PWD Python library that implements the PWD in AiiDA, jobflow, and pyiron. KW - Automation KW - Workflows KW - Materials Design KW - Multi-scale simulation KW - Digitalization PY - 2025 DO - https://doi.org/10.5281/zenodo.15516179 PB - Zenodo CY - Geneva AN - OPUS4-63233 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Janssen, Jan A1 - George, Janine A1 - Geiger, Julian A1 - Bercx, Marnik A1 - Wang, Xing A1 - Ertural, Christina A1 - Schaarschmidt, Joerg A1 - Ganose, Alex M. A1 - Pizzi, Giovanni A1 - Hickel, Tilmann A1 - Neugebauer, Joerg T1 - A Python workflow definition for computational materials design N2 - Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow exchange format to share workflows between Python-based WfMS, currently AiiDA, jobflow, and pyiron. This development is motivated by the similarity of these three Python-based WfMS, that represent the different workflow steps and data transferred between them as nodes and edges in a graph. With the PWD, we aim at fostering the interoperability and reproducibility between the different WfMS in the context of Findable, Accessible, Interoperable, Reusable (FAIR) workflows. To separate the scientific from the technical complexity, the PWD consists of three components: (1) a conda environment that specifies the software dependencies, (2) a Python module that contains the Python functions represented as nodes in the workflow graph, and (3) a workflow graph stored in the JavaScript Object Notation (JSON). The first version of the PWD supports directed acyclic graph (DAG)-based workflows. Thus, any DAG-based workflow defined in one of the three WfMS can be exported to the PWD and afterwards imported from the PWD to one of the other WfMS. After the import, the input parameters of the workflow can be adjusted and computing resources can be assigned to the workflow, before it is executed with the selected WfMS. This import from and export to the PWD is enabled by the PWD Python library that implements the PWD in AiiDA, jobflow, and pyiron. KW - Automation KW - Workflow KW - Materials Design KW - Multi-scale simulation KW - Digitalization PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-632328 DO - https://doi.org/10.48550/arXiv.2505.20366 SP - 1 EP - 12 AN - OPUS4-63232 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Rosen, Andrew S. A1 - Gallant, Max A1 - George, Janine A1 - Riebesell, Janosh A1 - Sahasrabuddhe, Hrushikesh A1 - Shen, Jimmy-Xuan A1 - Wen, Mingjian A1 - Evans, Matthew L. A1 - Petretto, Guido A1 - Waroquiers, David A1 - Rignanese, Gian-Marco A1 - Persson, Kristin A. A1 - Jain, Anubhav A1 - Ganose, Alex M. T1 - Jobflow: Computational Workflows Made Simple N2 - Jobflow is a free, open-source library for writing and executing workflows. Complex workflows can be defined using simple python functions and executed locally or on arbitrary computing resources using the FireWorks workflow manager. Some features that distinguish jobflow are dynamic workflows, easy compositing and connecting of workflows, and the ability to store workflow outputs across multiple databases. KW - Automation KW - Workflows KW - Computational Materials Science PY - 2024 DO - https://doi.org/10.5281/zenodo.10466868 PB - Zenodo CY - Geneva AN - OPUS4-59313 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Miliūtė, Aistė A1 - Bustamante, Joana A1 - Karafiludis, Stephanos A1 - Zöllner, Moritz A1 - Eddah, Mustapha A1 - Emmerling, Franziska A1 - Mieller, Björn A1 - George, Janine A1 - Stawski, Tomasz M. T1 - Synthesis and phase purity of the negative thermal expansion material ZrV2O7 N2 - Synthesis of pure, homogeneous, and reproducible materials is key for the comprehensive understanding, design, and tailoring of material properties. In this study, we focus on the synthesis of ZrV2O7, a material known for its negative thermal expansion properties. We investigate the influence of solid-state and wet chemistry synthesis methods on the purity and homogeneity of ZrV2O7 samples. Our findings indicate that different synthesis methods significantly impact the material's characteristics. The solid-state reaction provided high-purity material through extended milling time and repeated calcination cycles, while the sol-gel reaction enabled a “near-atomic” level of mixing and, therefore, homogenous phase-pure ZrV2O7. We confirmed purity via X-ray diffraction and Raman spectroscopy, highlighting differences between phase-pure and multiphase ceramics. These analytical techniques allowed us to distinguish subtle differences in the structure of the material. Based on ab initio simulated phonon data, we were able to interpret the Raman spectra and visualise Raman active atom vibrations. We show that phase purity enables the unbiased characterisation of material properties such as negative thermal expansion. KW - NTE KW - Sol-gel KW - Solid-state KW - Ab-initio KW - XRD KW - Raman PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-624013 DO - https://doi.org/10.1039/d4tc04095c SN - 2050-7534 SP - 1 EP - 13 PB - Royal Society of Chemistry (RSC) AN - OPUS4-62401 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hickel, Tilmann A1 - Waske, Anja A1 - Tehranchi, Ali A1 - Bhattacharya, Biswajit A1 - Stawski, Tomasz M. A1 - Fellinger, Tim-Patrick A1 - Mehmood, Asad A1 - Witt, Julia A1 - Ozcan, Ozlem A1 - Guilherme Buzanich, Ana A1 - Kumar, Sourabh A1 - Mishra, Rajesh Kumar A1 - Holzer, Marco A1 - Stucchi de Camargo, Andrea Simone A1 - Agudo Jácome, Leonardo A1 - Manzoni, Anna A1 - Fantin, Andrea A1 - John, Elisabeth A1 - Hodoroaba, Vasile-Dan A1 - Bührig, Sophia A1 - Murugan, Jegatheesan A1 - Marschall, Niklas A1 - George, Janine A1 - Darvishi Kamachali, Reza A1 - Maaß, Robert A1 - Emmerling, Franziska T1 - Chemically complex materials enable sustainable high-performance materials N2 - Chemically complex materials (CCMats) 􀀀 including high-entropy alloys, oxides, and related multi-principal element systems 􀀀 offer a paradigm shift in materials design by leveraging chemical diversity to simultaneously optimize functional, structural, and sustainability criteria. The vastness of the compositional and structural space in CCMats propels the field into an expanding exploratory state. To reconcile functional and structural performance across this immense parameter space remains an open challenge. This Perspective evaluates the opportunities and challenges associated with harnessing chemical complexity across a broad spectrum of applications, such as hydrogen storage, ionic conductors, catalysis, magnetics, dielectrics, semiconductors, optical materials, and multifunctional structural systems. It is delineated how three central design strategies: targeted substitution (SUB), defect engineering (DEF), and diversity management (DIV) enable the reconciliation of high functional performance with long-term structural stability and environmental responsibility. Advances in computational thermodynamics, microstructure simulations, machine learning, and multimodal characterization are accelerating the exploration and optimization of CCMats, while robust data infrastructures and automated synthesis workflows are emerging as essential tools for navigating their complex compositional space. By fostering cross-disciplinary knowledge transfer and embracing data-driven design, CCMats are poised to deliver next-generation materials solutions that address urgent technological, energy, and environmental demands. KW - Chemically complex materials KW - Structural stability KW - Functional performance KW - Design strategies KW - Sustainability PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-655598 UR - https://www.sciencedirect.com/science/article/pii/S1359028626000033?via%3Dihub DO - https://doi.org/10.1016/j.cossms.2026.101256 SN - 1359-0286 VL - 42 SP - 1 EP - 26 PB - Elsevier Ltd. CY - Amsterdam AN - OPUS4-65559 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -