Zobrazeno 1 - 10
of 538
pro vyhledávání: '"Kresse, Georg"'
Autor:
Unzog, Martin, Tal, Alexey, Melo, Pedro, Senga, Ryosuke, Suenaga, Kazu, Pichler, Thomas, Kresse, Georg
We investigate the relationship between the K-edge fine structure of isolated single-wall carbon nanotubes (SWCNTs) and the Van Hove singularities (VHSs) in the conduction band density of states. To this end, we model X-ray absorption spectra of SWCN
Externí odkaz:
http://arxiv.org/abs/2409.17619
Constructing a self-consistent first-principles framework that accurately predicts the properties of electron transfer reactions through finite-temperature molecular dynamics simulations is a dream of theoretical electrochemists and physical chemists
Externí odkaz:
http://arxiv.org/abs/2409.11000
Autor:
Romano, Salvatore, de Hijes, Pablo Montero, Meier, Matthias, Kresse, Georg, Franchini, Cesare, Dellago, Christoph
The magnetite/water interface is commonly found in nature and plays a crucial role in various technological applications. However, our understanding of its structural and dynamical properties at the molecular scale remains still limited. In this stud
Externí odkaz:
http://arxiv.org/abs/2408.11538
This study presents a long-range descriptor for machine learning force fields (MLFFs) that maintains translational and rotational symmetry, similar to short-range descriptors while being able to incorporate long-range electrostatic interactions. The
Externí odkaz:
http://arxiv.org/abs/2406.17595
Autor:
Hütner, Johanna I., Conti, Andrea, Kugler, David, Mittendorfer, Florian, Kresse, Georg, Schmid, Michael, Diebold, Ulrike, Balajka, Jan
Publikováno v:
Science 385, 6714, 1241-1244 (2024)
Macroscopic properties of materials stem from fundamental atomic-scale details, yet for insulators, resolving surface structures remains a challenge. The basal (0001) plane of ${\alpha}$-Al$_{2}$O$_{3}$ was imaged with noncontact atomic force microsc
Externí odkaz:
http://arxiv.org/abs/2405.19263
Autor:
Schmiedmayer, Bernhard, Kresse, Georg
We develop a strategy that integrates machine learning and first-principles calculations to achieve technical accurate predictions of infrared spectra. Specifically, the methodology allows to predict infrared spectra for complex systems at finite tem
Externí odkaz:
http://arxiv.org/abs/2404.19674
We report modifications of the ph-AFQMC algorithm that allow the use of large time steps and reliable time step extrapolation. Our modified algorithm eliminates size-consistency errors present in the standard algorithm when large time steps are emplo
Externí odkaz:
http://arxiv.org/abs/2403.02542
Autor:
de Hijes, Pablo Montero, Dellago, Christoph, Jinnouchi, Ryosuke, Schmiedmayer, Bernhard, Kresse, Georg
In this paper we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE+D3. Specifically, we scrutinize kernel-based regression and high-dimensional neural networks t
Externí odkaz:
http://arxiv.org/abs/2312.15213
Autor:
Liu, Mingfeng, Wang, Jiantao, Hu, Junwei, Liu, Peitao, Niu, Haiyang, Yan, Xuexi, Li, Jiangxu, Yan, Haile, Yang, Bo, Sun, Yan, Chen, Chunlin, Kresse, Georg, Zuo, Liang, Chen, Xing-Qiu
Publikováno v:
Nat Commun 15, 3079 (2024)
Reconstructive phase transitions involving breaking and reconstruction of primary chemical bonds are ubiquitous and important for many technological applications. In contrast to displacive phase transitions, the dynamics of reconstructive phase trans
Externí odkaz:
http://arxiv.org/abs/2310.05683
Redox potentials of electron transfer reactions are of fundamental importance for the performance and description of electrochemical devices. Despite decades of research, accurate computational predictions for the redox potential of even simple metal
Externí odkaz:
http://arxiv.org/abs/2309.13217