463 citations · 701 across the 5 of their papers we have counts for
5 papers
-Girsanov: A Generalized Method to Construct Markov State Models from Non-Equilibrium and Multiensemble Biased Simulations
Mingyuan Zhang, Yong Wang, Bettina G. Keller +1
We introduce -Girsanov, a new method for constructing Markov state models from biased enhanced-sampling molecular dynamics simulations based on Girsanov reweighting. The key ide…
E(n)-Equivariant Cartesian Tensor Passing Potential
Junjie Wang, Yong Wang, Haoting Zhang +6
Machine learning potential (MLP) has been a popular topic in recent years for its potential to replace expensive first-principles calculations in some large systems. Meanwhile, mes…
General-purpose machine-learned potential for 16 elemental metals and their alloys
Keke Song, Rui Zhao, Jiahui Liu +25
Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicabil…
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
Zheyong Fan, Yanzhou Wang, Penghua Ying +17
We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2…
Correlations in the chain melting of host-guest Calcium
Jiahao Dong, Yao Luo, Yong Wang +1
In this paper, we study chain melting state in the commensurate host-guest structure of calcium. We trained a NN (neural network) force field with DeepMD-kit, and by implementing c…