BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery

Autor: John, Peter St., Lin, Dejun, Binder, Polina, Greaves, Malcolm, Shah, Vega, John, John St., Lange, Adrian, Hsu, Patrick, Illango, Rajesh, Ramanathan, Arvind, Anandkumar, Anima, Brookes, David H, Busia, Akosua, Mahajan, Abhishaike, Malina, Stephen, Prasad, Neha, Sinai, Sam, Edwards, Lindsay, Gaudelet, Thomas, Regep, Cristian, Steinegger, Martin, Rost, Burkhard, Brace, Alexander, Hippe, Kyle, Naef, Luca, Kamata, Keisuke, Armstrong, George, Boyd, Kevin, Cao, Zhonglin, Chou, Han-Yi, Chu, Simon, Costa, Allan dos Santos, Darabi, Sajad, Dawson, Eric, Didi, Kieran, Fu, Cong, Geiger, Mario, Gill, Michelle, Hsu, Darren, Kaushik, Gagan, Korshunova, Maria, Kothen-Hill, Steven, Lee, Youhan, Liu, Meng, Livne, Micha, McClure, Zachary, Mitchell, Jonathan, Moradzadeh, Alireza, Mosafi, Ohad, Nashed, Youssef, Paliwal, Saee, Peng, Yuxing, Rabhi, Sara, Ramezanghorbani, Farhad, Reidenbach, Danny, Ricketts, Camir, Roland, Brian, Shah, Kushal, Shimko, Tyler, Sirelkhatim, Hassan, Srinivasan, Savitha, Stern, Abraham C, Toczydlowska, Dorota, Veccham, Srimukh Prasad, Venanzi, Niccolò Alberto Elia, Vorontsov, Anton, Wilber, Jared, Wilkinson, Isabel, Wong, Wei Jing, Xue, Eva, Ye, Cory, Yu, Xin, Zhang, Yang, Zhou, Guoqing, Zandstein, Becca, Dallago, Christian, Trentini, Bruno, Kucukbenli, Emine, Rvachov, Timur, Calleja, Eddie, Israeli, Johnny, Clifford, Harry, Haukioja, Risto, Haemel, Nicholas, Tretina, Kyle, Tadimeti, Neha, Costa, Anthony B

Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increasingly relies on computational scale, with recent protein language mode

Externí odkaz: http://arxiv.org/abs/2411.10548

Direct measurement of 2DEG states in shallow Si:Sb $\delta$-layers

Autor: Strand, Frode S., Cooil, Simon P., Campbell, Quinn T., Flounders, John J., Røst, Håkon I., Åsland, Anna Cecilie, Skarpeid, Alv Johan, Stalsberg, Marte P., Hu, Jinbang, Bakkelund, Johannes, Bjelland, Victoria, Preobrajenski, Alexei B., Li, Zheshen, Bianchi, Marco, Miwa, Jill A., Wells, Justin W.

We investigate the electronic structure of high-density layers of Sb dopants in a silicon host, so-called Si:Sb $\delta$-layers. We show that, in spite of the known challenges in producing highly confined Sb $\delta$-layers, sufficient confinement is

Externí odkaz: http://arxiv.org/abs/2410.17387

5G as Enabler for Industrie 4.0 Use Cases: Challenges and Concepts

Autor: Gundall, M., Schneider, J., Schotten, H. D., Aleksy, M., Schulz, D., Franchi, N., Schwarzenberg, N., Markwart, C., Halfmann, R., Rost, P., Wübben, D., Neumann, A., Düngen, M., Neugebauer, T., Blunk, R., Kus, M., Grießbach, J.

The increasing demand for highly customized products, as well as flexible production lines, can be seen as trigger for the "fourth industrial revolution", referred to as "Industrie 4.0". Current systems usually rely on wire-line technologies to conne

Externí odkaz: http://arxiv.org/abs/2410.08726

Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential

Autor: Sivak, Jacob T., Almishal, Saeed S. I., Caucci, Mary K., Tan, Yueze, Srikanth, Dhiya, Furst, Matthew, Chen, Long-Quin, Rost, Christina M., Maria, Jon-Paul, Sinnott, Susan B.

High-entropy materials shift the traditional materials discovery paradigm to one that leverages disorder, enabling access to unique chemistries unreachable through enthalpy alone. We present a self-consistent approach integrating computation and expe

Externí odkaz: http://arxiv.org/abs/2408.06322

Zero-energy photoelectric effect

Autor: Azizi, Sajad, Saalmann, Ulf, Rost, Jan M.

We predict a near-threshold ("zero energy") peak in multi-photon ionization for a dynamical regime where the photon frequency is large compared to the binding energy of the electron. The peak position does not depend on the laser frequency, but on th

Externí odkaz: http://arxiv.org/abs/2407.16270