Unsupervised machine learning for supercooled liquids
Fuente:
arXiv
Saved in:
| Main Authors: | Qiu, Yunrui, Jang, Inhyuk, Huang, Xuhui, Yethiraj, Arun |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Relationship of structural disorder and stability of supercooled liquid state with glass-forming ability of metallic glasses
by: Cui, J. B., et al.
Published: (2025)
by: Cui, J. B., et al.
Published: (2025)
Universal mechanism of shear thinning in supercooled liquids
by: Mizuno, Hideyuki, et al.
Published: (2024)
by: Mizuno, Hideyuki, et al.
Published: (2024)
Hydrogen liquid-liquid transition from first principles and machine learning
by: Tenti, Giacomo, et al.
Published: (2025)
by: Tenti, Giacomo, et al.
Published: (2025)
Viscosity, breakdown of Stokes-Einstein relation and dynamical heterogeneity in supercooled liquid Ge$_2$Sb$_2$Te$_5$ from simulations with a neural network potential
by: Marcorini, Simone, et al.
Published: (2025)
by: Marcorini, Simone, et al.
Published: (2025)
Liquid and solid layers in a thermal deep learning machine
by: Huang, Gang, et al.
Published: (2025)
by: Huang, Gang, et al.
Published: (2025)
Unsupervised machine learning for detecting mutual independence among eigenstate regimes in interacting quasiperiodic chains
by: Beveridge, Colin, et al.
Published: (2024)
by: Beveridge, Colin, et al.
Published: (2024)
Normalizing flows as an enhanced sampling method for atomistic supercooled liquids
by: Jung, Gerhard, et al.
Published: (2024)
by: Jung, Gerhard, et al.
Published: (2024)
Influence of anisotropy on the study of critical behavior of spin models by machine learning methods
by: Sukhoverkhova, Diana, et al.
Published: (2024)
by: Sukhoverkhova, Diana, et al.
Published: (2024)
Shear-induced diffusivity in supercooled liquids
by: Bhendale, Mangesh, et al.
Published: (2024)
by: Bhendale, Mangesh, et al.
Published: (2024)
A cost-effective strategy of enhancing machine learning potentials by transfer learning from a multicomponent dataset on ænet-PyTorch
by: Aisnadaa, An Niza El, et al.
Published: (2024)
by: Aisnadaa, An Niza El, et al.
Published: (2024)
Revisiting the machine-learning density functional for the one-dimensional Hubbard model with random external potential
by: Salmon, Octavio D. R., et al.
Published: (2026)
by: Salmon, Octavio D. R., et al.
Published: (2026)
Transferable potential for molecular dynamics simulations of borosilicate glasses and structural comparison of machine learning optimized parameters
by: Yang, Kai, et al.
Published: (2025)
by: Yang, Kai, et al.
Published: (2025)
Trade-off relations between quantum coherence and measure of many-body localization
by: Garg, Arti, et al.
Published: (2024)
by: Garg, Arti, et al.
Published: (2024)
Diffusion in liquid metals is directed by competing collective modes
by: Demmel, Franz, et al.
Published: (2024)
by: Demmel, Franz, et al.
Published: (2024)
Graph-Dynamics correspondence in metallic glass-forming liquids
by: Zhou, Xin-Jia, et al.
Published: (2025)
by: Zhou, Xin-Jia, et al.
Published: (2025)
Energy landscapes of combinatorial optimization in Ising machines
by: Dobrynin, Dmitrii, et al.
Published: (2024)
by: Dobrynin, Dmitrii, et al.
Published: (2024)
Estimating predictability of depinning dynamics by machine learning
by: Haavisto, Valtteri, et al.
Published: (2023)
by: Haavisto, Valtteri, et al.
Published: (2023)
Thermodynamic potentials of metallic alloys in the undercooled liquid and solid glassy states
by: Makarov, A. S., et al.
Published: (2025)
by: Makarov, A. S., et al.
Published: (2025)
Quantum and classical processing with photonic quantum machine learning
by: Carreño, J. C. López, et al.
Published: (2026)
by: Carreño, J. C. López, et al.
Published: (2026)
Hydrodynamic fields in fluctuating environment: a model for isochoric heat capacity of simple liquids
by: de Freitas, I. P., et al.
Published: (2025)
by: de Freitas, I. P., et al.
Published: (2025)
Phase probabilities in first-order transitions using machine learning
by: Sukhoverkhova, Diana, et al.
Published: (2024)
by: Sukhoverkhova, Diana, et al.
Published: (2024)
Thermal transport of glasses via machine learning driven simulations
by: Pegolo, Paolo, et al.
Published: (2024)
by: Pegolo, Paolo, et al.
Published: (2024)
Unsupervised and probabilistic learning with Contrastive Local Learning Networks: The Restricted Kirchhoff Machine
by: Guzman, Marcelo, et al.
Published: (2025)
by: Guzman, Marcelo, et al.
Published: (2025)
A facilitation-induced fluidization transition in supercooled water triggered by a few active molecules
by: Truong, Quoc Tuan, et al.
Published: (2025)
by: Truong, Quoc Tuan, et al.
Published: (2025)
Putting machine learning to the test in a quantum many-body system
by: Gao, Yilun, et al.
Published: (2026)
by: Gao, Yilun, et al.
Published: (2026)
Comparing the effects of Boltzmann machines as associative memory in Generative Adversarial Networks between classical and quantum sampling
by: Urushibata, Mitsuru, et al.
Published: (2022)
by: Urushibata, Mitsuru, et al.
Published: (2022)
Accelerated characterization of two-level systems in superconducting qubits via machine learning
by: Pathapati, Avinash, et al.
Published: (2025)
by: Pathapati, Avinash, et al.
Published: (2025)
Constructing and evaluating machine-learned interatomic potentials for Li-based disordered rocksalts
by: Choyal, Vijay, et al.
Published: (2023)
by: Choyal, Vijay, et al.
Published: (2023)
Critical feature learning in deep neural networks
by: Fischer, Kirsten, et al.
Published: (2024)
by: Fischer, Kirsten, et al.
Published: (2024)
Efficient training of machine learning potentials for metallic glasses: CuZrAl validation
by: Wadowski, Antoni, et al.
Published: (2024)
by: Wadowski, Antoni, et al.
Published: (2024)
A machine learning based approach to the identification of spectral densities in quantum open systems
by: Barr, Jessica, et al.
Published: (2025)
by: Barr, Jessica, et al.
Published: (2025)
Machine learning of phases and structures for model systems in physics
by: Bayo, Djenabou, et al.
Published: (2024)
by: Bayo, Djenabou, et al.
Published: (2024)
Lecture notes: From Gaussian processes to feature learning
by: Helias, Moritz, et al.
Published: (2026)
by: Helias, Moritz, et al.
Published: (2026)
The impact of physicochemical features of carbon electrodes on the capacitive performance of supercapacitors: A machine learning approach
by: Mishra, Sachit, et al.
Published: (2022)
by: Mishra, Sachit, et al.
Published: (2022)
Renormalization group for deep neural networks: Universality of learning and scaling laws
by: Coppola, Gorka Peraza, et al.
Published: (2025)
by: Coppola, Gorka Peraza, et al.
Published: (2025)
Topological mechanical neural networks as classifiers through in situ backpropagation learning
by: Li, Shuaifeng, et al.
Published: (2025)
by: Li, Shuaifeng, et al.
Published: (2025)
Structural properties of amorphous Na$_3$OCl electrolyte by first-principles and machine learning molecular dynamics
by: Pham, T. -L., et al.
Published: (2024)
by: Pham, T. -L., et al.
Published: (2024)
Automatic virtual voltage extraction of a 2x2 array of quantum dots with machine learning
by: Oakes, Giovanni A., et al.
Published: (2020)
by: Oakes, Giovanni A., et al.
Published: (2020)
Expanding the search space of high entropy oxides and predicting synthesizability using machine learning interatomic potentials
by: Dicks, Oliver A., et al.
Published: (2025)
by: Dicks, Oliver A., et al.
Published: (2025)
Planted vertex cover problem on regular random graphs and nonmonotonic temperature-dependence in the supercooled region
by: Fan, Xin-Yi, et al.
Published: (2023)
by: Fan, Xin-Yi, et al.
Published: (2023)
Similar Items
-
Relationship of structural disorder and stability of supercooled liquid state with glass-forming ability of metallic glasses
by: Cui, J. B., et al.
Published: (2025) -
Universal mechanism of shear thinning in supercooled liquids
by: Mizuno, Hideyuki, et al.
Published: (2024) -
Hydrogen liquid-liquid transition from first principles and machine learning
by: Tenti, Giacomo, et al.
Published: (2025) -
Viscosity, breakdown of Stokes-Einstein relation and dynamical heterogeneity in supercooled liquid Ge$_2$Sb$_2$Te$_5$ from simulations with a neural network potential
by: Marcorini, Simone, et al.
Published: (2025) -
Liquid and solid layers in a thermal deep learning machine
by: Huang, Gang, et al.
Published: (2025)