Zobrazeno 1 - 10
of 283
pro vyhledávání: '"Kitchin, John R"'
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
This paper introduces WhereWulff, a semi-autonomous workflow for modeling the reactivity of catalyst surfaces. The workflow begins with a bulk optimization task that takes an initial bulk structure, and returns the optimized bulk geometry and magneti
Externí odkaz:
http://arxiv.org/abs/2302.14103
Autor:
Kolluru, Adeesh, Shuaibi, Muhammed, Palizhati, Aini, Shoghi, Nima, Das, Abhishek, Wood, Brandon, Zitnick, C. Lawrence, Kitchin, John R, Ulissi, Zachary W
The development of machine learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While effective in interpolating between available materials, these approaches struggle to gen
Externí odkaz:
http://arxiv.org/abs/2206.02005
Publikováno v:
In Journal of Catalysis December 2024 440
