Zobrazeno 1 - 10
of 7 189
pro vyhledávání: '"Thaler, P."'
Neural network potentials (NNPs) offer a fast and accurate alternative to ab-initio methods for molecular dynamics (MD) simulations but are hindered by the high cost of training data from high-fidelity Quantum Mechanics (QM) methods. Our work introdu
Externí odkaz:
http://arxiv.org/abs/2412.06064
While photodissociation of molecular systems has been extensively studied, the photoinduced formation of chemical bonds remains largely unexplored. Especially for larger aggregates, the electronic and nuclear dynamics involved in the cluster formatio
Externí odkaz:
http://arxiv.org/abs/2412.01458
Autor:
Gabellini, Cristian, Shenoy, Nikhil, Thaler, Stephan, Canturk, Semih, McNeela, Daniel, Beaini, Dominique, Bronstein, Michael, Tossou, Prudencio
Machine Learning Interatomic Potentials (MLIPs) are a highly promising alternative to force-fields for molecular dynamics (MD) simulations, offering precise and rapid energy and force calculations. However, Quantum-Mechanical (QM) datasets, crucial f
Externí odkaz:
http://arxiv.org/abs/2411.19629
As LLMs are increasingly integrated into user-facing applications, addressing biases that perpetuate societal inequalities is crucial. While much work has gone into measuring or mitigating biases in these models, fewer studies have investigated their
Externí odkaz:
http://arxiv.org/abs/2411.19240
Autor:
Ortiz, Brenden R., Meier, William R., Pokharel, Ganesh, Chamorro, Juan, Yang, Fazhi, Mozaffari, Shirin, Thaler, Alex, Alvarado, Steven J. Gomez, Zhang, Heda, Parker, David S., Samolyuk, German D., Paddison, Joseph A. M., Yan, Jiaqiang, Ye, Feng, Sarker, Suchismita, Wilson, Stephen D., Miao, Hu, Mandrus, David, McGuire, Michael A.
The kagome motif is a versatile platform for condensed matter physics, hosting rich interactions between magnetic, electronic, and structural degrees of freedom. In recent years, the discovery of a charge density wave (CDW) in the AV$_3$Sb$_5$ superc
Externí odkaz:
http://arxiv.org/abs/2411.10635
Autor:
Viti, Bruno, Thaler, Franz, Kapper, Kathrin Lisa, Urschler, Martin, Holler, Martin, Karabelas, Elias
Segmentation of cardiac magnetic resonance images (MRI) is crucial for the analysis and assessment of cardiac function, helping to diagnose and treat various cardiovascular diseases. Most recent techniques rely on deep learning and usually require an
Externí odkaz:
http://arxiv.org/abs/2411.06911
Autor:
Mondal, Parmita, Nagesh, Swetadri Vasan Setlur, Sommers-Thaler, Sam, Shields, Allison, Bhurwani, Mohammad Mahdi Shiraz, Williams, Kyle A, Baig, Ammad, Snyder, Kenneth, Siddiqui, Adnan H, Levy, Elad, Ionita, Ciprian N
Intraoperative 2D quantitative angiography (QA) for intracranial aneurysms (IAs) has accuracy challenges due to the variability of hand injections. Despite the success of singular value decomposition (SVD) algorithms in reducing biases in computed to
Externí odkaz:
http://arxiv.org/abs/2411.03655
Autor:
Brehmer, Johann, Bresó, Víctor, de Haan, Pim, Plehn, Tilman, Qu, Huilin, Spinner, Jonas, Thaler, Jesse
We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Collider. L-GATr represents data in a geometric algebra over space-time and
Externí odkaz:
http://arxiv.org/abs/2411.00446
Autor:
Thaler, Jesse, Trifinopoulos, Sokratis
The Cabibbo-Kobayashi-Maskawa (CKM) matrix, which controls flavor mixing between the three generations of quark fermions, is a key input to the Standard Model of particle physics. In this paper, we identify a surprising connection between quantum ent
Externí odkaz:
http://arxiv.org/abs/2410.23343
Autor:
Hassan, Majdi, Shenoy, Nikhil, Lee, Jungyoon, Stark, Hannes, Thaler, Stephan, Beaini, Dominique
Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery. Existing state-of-the-art approaches either resort to large scale transformer-based models that diffuse over c
Externí odkaz:
http://arxiv.org/abs/2410.22388