Zobrazeno 1 - 10
of 7 313
pro vyhledávání: '"Kitchin, A"'
Access to the potential energy Hessian enables determination of the Gibbs free energy, and certain approaches to transition state search and optimization. Here, we demonstrate that off-the-shelf pretrained Open Catalyst Project (OCP) machine learned
Externí odkaz:
http://arxiv.org/abs/2410.01650
Graph neural networks (GNNs) have been shown to be astonishingly capable models for molecular property prediction, particularly as surrogates for expensive density functional theory calculations of relaxed energy for novel material discovery. However
Externí odkaz:
http://arxiv.org/abs/2407.10844
Autor:
Kolluru, Adeesh, Kitchin, John R
Determining the optimal configuration of adsorbates on a slab (adslab) is pivotal in the exploration of novel catalysts across diverse applications. Traditionally, the quest for the lowest energy adslab configuration involves placing the adsorbate on
Externí odkaz:
http://arxiv.org/abs/2405.03962
CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural network potential trained on the OC20 dataset, a related but different task,
Externí odkaz:
http://arxiv.org/abs/2405.02078
Autor:
Wang, Xiaoxiao, Musielewicz, Joseph, Tran, Richard, Ethirajan, Sudheesh Kumar, Fu, Xiaoyan, Mera, Hilda, Kitchin, John R., Kurchin, Rachel C., Ulissi, Zachary W.
Although density functional theory (DFT) has aided in accelerating the discovery of new materials, such calculations are computationally expensive, especially for high-throughput efforts. This has prompted an explosion in exploration of machine learn
Externí odkaz:
http://arxiv.org/abs/2311.01987
Autor:
Shoghi, Nima, Kolluru, Adeesh, Kitchin, John R., Ulissi, Zachary W., Zitnick, C. Lawrence, Wood, Brandon M.
Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across
Externí odkaz:
http://arxiv.org/abs/2310.16802
According to density functional theory, any chemical property can be inferred from the electron density, making it the most informative attribute of an atomic structure. In this work, we demonstrate the use of established physical methods to obtain i
Externí odkaz:
http://arxiv.org/abs/2309.04811
Autor:
Charissa C. Naidoo, Rouxjeane Venter, Francesc Codony, Gemma Agustí, Natasha Kitchin, Selisha Naidoo, Hilary Monaco, Hridesh Mishra, Yonghua Li, Jose C. Clemente, Robin M. Warren, Leopoldo N. Segal, Grant Theron
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract DNA characterisation in people with tuberculosis (TB) is critical for diagnostic and microbiome evaluations. However, extracellular DNA, more frequent in people on chemotherapy, confounds results. We evaluated whether nucleic acid dyes [prop
Externí odkaz:
https://doaj.org/article/74ec4169bc7344518d316f9672c00713
Publikováno v:
BMC Public Health, Vol 24, Iss 1, Pp 1-13 (2024)
Abstract Background Gambling marketing communications create a public health risk by increasing the normalisation of gambling in sports. In a context where broad level studies report significant underage gambling, currently no evidence exists on how
Externí odkaz:
https://doaj.org/article/9e40f7366d7b4d4790edba52a1184027
The practical applications of determining the relative difference in adsorption energies are extensive, such as identifying optimal catalysts, calculating reaction energies, and determining the lowest adsorption energy on a catalytic surface. Althoug
Externí odkaz:
http://arxiv.org/abs/2303.10797