Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting
Author
Abstract
Suggested Citation
DOI: 10.1038/s41467-024-45566-8
Download full text from publisher
References listed on IDEAS
- Stefan Chmiela & Huziel E. Sauceda & Klaus-Robert Müller & Alexandre Tkatchenko, 2018. "Towards exact molecular dynamics simulations with machine-learned force fields," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
- Amil Merchant & Simon Batzner & Samuel S. Schoenholz & Muratahan Aykol & Gowoon Cheon & Ekin Dogus Cubuk, 2023. "Scaling deep learning for materials discovery," Nature, Nature, vol. 624(7990), pages 80-85, December.
- Justin S. Smith & Benjamin T. Nebgen & Roman Zubatyuk & Nicholas Lubbers & Christian Devereux & Kipton Barros & Sergei Tretiak & Olexandr Isayev & Adrian E. Roitberg, 2019. "Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Adil Kabylda & Valentin Vassilev-Galindo & Stefan Chmiela & Igor Poltavsky & Alexandre Tkatchenko, 2023. "Efficient interatomic descriptors for accurate machine learning force fields of extended molecules," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
- Gaétan de Rassenfosse & Adam B. Jaffe & Joel Waldfogel, 2024.
"Intellectual Property and Creative Machines,"
NBER Chapters, in: Entrepreneurship and Innovation Policy and the Economy, volume 4,
National Bureau of Economic Research, Inc.
- Gaétan de Rassenfosse & Adam Jaffe & Joal Waldfogel, 2024. "Intellectual Property and Creative Machines," Working Papers 27, Chair of Science, Technology, and Innovation Policy.
- Gaétan de Rassenfosse & Adam B. Jaffe & Joel Waldfogel, 2024. "Intellectual Property and Creative Machines," NBER Working Papers 32698, National Bureau of Economic Research, Inc.
- Peikun Zheng & Roman Zubatyuk & Wei Wu & Olexandr Isayev & Pavlo O. Dral, 2021. "Artificial intelligence-enhanced quantum chemical method with broad applicability," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
- Niklas W. A. Gebauer & Michael Gastegger & Stefaan S. P. Hessmann & Klaus-Robert Müller & Kristof T. Schütt, 2022. "Inverse design of 3d molecular structures with conditional generative neural networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
- Huziel E. Sauceda & Luis E. Gálvez-González & Stefan Chmiela & Lauro Oliver Paz-Borbón & Klaus-Robert Müller & Alexandre Tkatchenko, 2022. "BIGDML—Towards accurate quantum machine learning force fields for materials," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
- Zeyin Yan & Dacong Wei & Xin Li & Lung Wa Chung, 2024. "Accelerating reliable multiscale quantum refinement of protein–drug systems enabled by machine learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
- Stephan Thaler & Julija Zavadlav, 2021. "Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
- Yusong Wang & Tong Wang & Shaoning Li & Xinheng He & Mingyu Li & Zun Wang & Nanning Zheng & Bin Shao & Tie-Yan Liu, 2024. "Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
- Simon Batzner & Albert Musaelian & Lixin Sun & Mario Geiger & Jonathan P. Mailoa & Mordechai Kornbluth & Nicola Molinari & Tess E. Smidt & Boris Kozinsky, 2022. "E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
- Charlotte Loh & Thomas Christensen & Rumen Dangovski & Samuel Kim & Marin Soljačić, 2022. "Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
- Hanwen Zhang & Veronika Juraskova & Fernanda Duarte, 2024. "Modelling chemical processes in explicit solvents with machine learning potentials," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
- Tian Xie & Arthur France-Lanord & Yanming Wang & Jeffrey Lopez & Michael A. Stolberg & Megan Hill & Graham Michael Leverick & Rafael Gomez-Bombarelli & Jeremiah A. Johnson & Yang Shao-Horn & Jeffrey C, 2022. "Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
- Shuai Jiang & Yi-Rong Liu & Teng Huang & Ya-Juan Feng & Chun-Yu Wang & Zhong-Quan Wang & Bin-Jing Ge & Quan-Sheng Liu & Wei-Ran Guang & Wei Huang, 2022. "Towards fully ab initio simulation of atmospheric aerosol nucleation," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
- J. Thorben Frank & Oliver T. Unke & Klaus-Robert Müller & Stefan Chmiela, 2024. "A Euclidean transformer for fast and stable machine learned force fields," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
- Junjie Wang & Yong Wang & Haoting Zhang & Ziyang Yang & Zhixin Liang & Jiuyang Shi & Hui-Tian Wang & Dingyu Xing & Jian Sun, 2024. "E(n)-Equivariant cartesian tensor message passing interatomic potential," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
- Yuanming Bai & Leslie Vogt-Maranto & Mark E. Tuckerman & William J. Glover, 2022. "Machine learning the Hohenberg-Kohn map for molecular excited states," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
- Chang Jiang & Hongyuan He & Hongquan Guo & Xiaoxin Zhang & Qingyang Han & Yanhong Weng & Xianzhu Fu & Yinlong Zhu & Ning Yan & Xin Tu & Yifei Sun, 2024. "Transfer learning guided discovery of efficient perovskite oxide for alkaline water oxidation," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
- Albert Musaelian & Simon Batzner & Anders Johansson & Lixin Sun & Cameron J. Owen & Mordechai Kornbluth & Boris Kozinsky, 2023. "Learning local equivariant representations for large-scale atomistic dynamics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
- Pushkar G. Ghanekar & Siddharth Deshpande & Jeffrey Greeley, 2022. "Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45566-8. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.