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 - 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 -