1 citations · 1 across the 3 of their papers we have counts for
8 papers
Smooth Dynamic Cutoffs for Machine Learning Interatomic Potentials
Kevin Han, Haolin Cong, Bowen Deng +1
Machine learning interatomic potentials (MLIPs) have proven to be wildly useful for molecular dynamics simulations, powering countless drug and materials discovery applications. Ho…
Mechanisms of alkali ionic transport in amorphous oxyhalides solid state conductors
Luca Binci, KyuJung Jun, Bowen Deng +1
Amorphous oxyhalides have attracted significant attention due to their relatively high ionic conductivity (1 mS cm), excellent chemical stability, mechanical softness, an…
DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Kevin Han, Bowen Deng, Amir Barati Farimani +1
Large-scale atomistic simulations are essential to bridge computational materials and chemistry to realistic materials and drug discovery applications. In the past few years, rapid…
Cross-functional transferability in universal machine learning interatomic potentials
Xu Huang, Bowen Deng, Peichen Zhong +3
The rapid development of universal machine learning interatomic potentials (uMLIPs) has demonstrated the possibility for generalizable learning of the universal potential energy su…
Crystal structure prediction with host-guided inpainting generation and foundation potentials
Peichen Zhong, Xinzhe Dai, Bowen Deng +2
Unconditional crystal structure generation with diffusion models faces challenges in identifying symmetric crystals as the unit cell size increases. We present the Crystal Host-Gui…
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…