Han Wang
Materials Chemistry · Computer Physics Communications · 2017–2023
A researcher in the Science corpus: 7 papers; trusts 20 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 133 citations into it; #2,989 of 11,513 by trust here, #4,389 by citations
- Atomic and Molecular Physics, and Optics — 66 citations into it; #3,397 of 7,135 by trust here, #1,971 by citations
- Computational Theory and Mathematics — 58 citations into it
- Computational Chemistry & Drug Discovery — 42 citations into it
- Molecular Biology — 39 citations into it; #17,327 of 17,773 by trust here, #12,227 by citations
- Artificial Intelligence — 28 citations into it
- AI for Physical Sciences — 27 citations into it
- Physical and Theoretical Chemistry — 26 citations into it
Whom Han Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- J. Behler
- S. Plimpton
- Linfeng Zhang
- A. P. Bart'ok
- Kristof T. Schütt
- Michele Parrinello
- Jimmy Ba
- Berk Hess
- Roberto Car
Who trusts Han Wang overall
- Linfeng Zhang
- T. Wen
- Rohit Batra
- Zheyong Fan
- Jiequn Han
- Risi Kondor
- Le Song
- Simon L. Batzner
- E. Weinan
- Xianglin Liu
Papers from Han Wang's perspective
Ordered by how strongly the researchers Han Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics (2017) — 1,554 citations
- DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics (2017) — 1,831 citations
- Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation (2018) — 422 citations
- End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems (2018) — 460 citations
- DeePMD-kit v2: A software package for deep potential models (2023) — 540 citations
- Deep Potentials for Materials Science (2022) — 191 citations
- DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models (2019) — 831 citations