Unveiling the predictive power of static structure in glassy systems
By Victor Bapst, T. Keck, Agnieszka Grabska-Barwinska, Craig Donner, Ekin Dogus Cubuk, Sam Schoenholz, A. Obika, A. Nelson, Trevor Back, Demis Hassabis and 1 more. 2020
349 citations recorded by the source corpus.
Subfields: Condensed Matter Physics, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/bf7c2861dd126fb80d68e86a639d0249cc1d49c2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Condensed Matter Physics
In Materials Chemistry
- Vijay Prakash Dwivedi
- Anh Tuan Luu
- Xavier Bresson
- Thomas Laurent
- Yoshua Bengio
- Pan Li
- Alvaro Sanchez-Gonzalez
- Peter W. Battaglia
Citations in the corpus
- Discovering Symbolic Models from Deep Learning with Inductive Biases (2020, background): “Recent work has leveraged the inductive biases of Interaction Networks (INs) [6] in their generalized form, the Graph Network, a type of Graph Neural Network [7, 8, 9], to learn models of particle systems in many physical domains [6, 10, 11, 12, 13, 14, 15, 16].”
- Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism (2022, methodology): “Introduction Graph learning models are widely used in science, such as physics (Bapst et al., 2020) and biochemistry (Jumper et al.”
- Recent advances in hot tearing during casting of aluminium alloys (2020, background): “Machine learning has been playing important role in different research fields like molecular dynamics calculation [156,157] and will also bring opportunities for multi-field simulations and hot tearing predictions.”
- Learning Mesh-Based Simulation with Graph Networks (2020, background): “Various works [22, 38, 33] use graph neural networks (GNNs) [34, 4] to model particle-based granular materials and fluids, as well as glassy dynamics [3].”
- Graph Neural Networks with Learnable Structural and Positional Representations (2021, background): “…2020; Gaudelet et al., 2020), social networks (Monti et al., 2019; Pal et al., 2020), traffic networks (Derrow-Pinion et al., 2021), physics (Cranmer et al., 2019; Bapst et al., 2020), combinatorial optimization (Bengio et al., 2021; Cappart et al., 2021) and medical diagnosis (Li et al., 2020c).”