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The kinetics of the ice–water interface from ab initio machine learning simulations
The kinetics of the ice–water interface from ab initio machine learning simulations
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The kinetics of the ice–water interface from ab initio machine learning simulations

Montero de Hijes, P. ; Romano, S. ; Gorfer, A. ; Dellago, C.

The Journal of chemical physics, 2023-05, Vol.158 (20) [Periódico revisado por pares]

United States: American Institute of Physics

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2
Characterizing Protein–Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV‑1 Protease
Characterizing Protein–Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV‑1 Protease
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Characterizing Protein–Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV‑1 Protease

Whitfield, Troy W ; Ragland, Debra A ; Zeldovich, Konstantin B ; Schiffer, Celia A

Journal of chemical theory and computation, 2020-02, Vol.16 (2), p.1284-1299 [Periódico revisado por pares]

United States: American Chemical Society

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3
PiNNwall: Heterogeneous Electrode Models from Integrating Machine Learning and Atomistic Simulation
PiNNwall: Heterogeneous Electrode Models from Integrating Machine Learning and Atomistic Simulation
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PiNNwall: Heterogeneous Electrode Models from Integrating Machine Learning and Atomistic Simulation

Dufils, Thomas ; Knijff, Lisanne ; Shao, Yunqi ; Zhang, Chao

Journal of chemical theory and computation, 2023-08, Vol.19 (15), p.5199-5209 [Periódico revisado por pares]

United States: American Chemical Society

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4
Stable All-Solid-State Lithium Metal Batteries Enabled by Machine Learning Simulation Designed Halide Electrolytes
Stable All-Solid-State Lithium Metal Batteries Enabled by Machine Learning Simulation Designed Halide Electrolytes
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Stable All-Solid-State Lithium Metal Batteries Enabled by Machine Learning Simulation Designed Halide Electrolytes

Li, Feng ; Cheng, Xiaobin ; Lu, Lei-Lei ; Yin, Yi-Chen ; Luo, Jin-Da ; Lu, Gongxun ; Meng, Yu-Feng ; Mo, Hongsheng ; Tian, Te ; Yang, Jing-Tian ; Wen, Wen ; Liu, Zhi-Pan ; Zhang, Guozhen ; Shang, Cheng ; Yao, Hong-Bin

Nano letters, 2022-03, Vol.22 (6), p.2461-2469 [Periódico revisado por pares]

United States: American Chemical Society

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5
Deep learning simulations of the microwave sky
Deep learning simulations of the microwave sky
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Deep learning simulations of the microwave sky

Han, Dongwon ; Sehgal, Neelima ; Villaescusa-Navarro, Francisco

Physical review. D, 2021-12, Vol.104 (12), Article 123521 [Periódico revisado por pares]

United States: American Physical Society (APS)

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6
Insights into the structure and dynamics of K+ ions at the muscovite–water interface from machine learning potential simulations
Insights into the structure and dynamics of K+ ions at the muscovite–water interface from machine learning potential simulations
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Insights into the structure and dynamics of K+ ions at the muscovite–water interface from machine learning potential simulations

Raman, Abhinav S. ; Selloni, Annabella

The Journal of chemical physics, 2024-06, Vol.160 (24) [Periódico revisado por pares]

Melville: American Institute of Physics

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7
Effects of learning support in simulation-based physics learning
Effects of learning support in simulation-based physics learning
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Effects of learning support in simulation-based physics learning

Chang, Kuo-En ; Chen, Yu-Lung ; Lin, He-Yan ; Sung, Yao-Ting

Computers and education, 2008-12, Vol.51 (4), p.1486-1498 [Periódico revisado por pares]

Elsevier Ltd

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8
Elucidating Morphology‐Mobility Relationships of Organic Thin Films Through Transfer Learning‐Assisted Multiscale Simulation
Elucidating Morphology‐Mobility Relationships of Organic Thin Films Through Transfer Learning‐Assisted Multiscale Simulation
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Elucidating Morphology‐Mobility Relationships of Organic Thin Films Through Transfer Learning‐Assisted Multiscale Simulation

Tan, Tianhao ; Duan, Lian ; Wang, Dong

Advanced functional materials, 2024-05, Vol.34 (22), p.n/a [Periódico revisado por pares]

Hoboken: Wiley Subscription Services, Inc

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9
SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network
SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network
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SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network

Luu, Huan Minh ; Park, Sung-Hong

NeuroImage (Orlando, Fla.), 2023-12, Vol.284, p.120449-120449, Article 120449 [Periódico revisado por pares]

Amsterdam: Elsevier Inc

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10
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
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Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

Sun, Luning ; Gao, Han ; Pan, Shaowu ; Wang, Jian-Xun

Computer methods in applied mechanics and engineering, 2020-04, Vol.361, p.112732, Article 112732 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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