147 citations · 261 across the 14 of their papers we have counts for
14 papers
Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models
Yu Xie, Ludwig Winkler, Lixin Sun +10
The rare-event sampling problem has long been the central limiting factor in molecular dynamics (MD), especially in biomolecular simulation. Recently, diffusion models such as BioE…
Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
Menghang Wang, Jingxuan Ding, Grace Xiong +8
In solid acid electrolytes CsHPO and CsHSO, the origin of fast proton conduction has long been debated, attributed either to local proton hopping or to polyanion rotati…
Incongruent Melting and Phase Diagram of SiC from Machine Learning Molecular Dynamics
Yu Xie, Menghang Wang, Senja Ramakers +2
Silicon carbide (SiC) is an important technological material, but its high-temperature phase diagram has remained unclear due to conflicting experimental results about congruent ve…
Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
Blake R. Duschatko, Xiang Fu, Cameron Owen +4
We present a differentiable formalism for learning free energies that is capable of capturing arbitrarily complex model dependencies on coarse-grained coordinates and finite-temper…
Stability, mechanisms and kinetics of emergence of Au surface reconstructions using Bayesian force fields
Cameron J. Owen, Yu Xie, Anders Johansson +2
Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformation…
Unraveling the Catalytic Effect of Hydrogen Adsorption on Pt Nanoparticle Shape-Change
Cameron J. Owen, Nicholas Marcella, Yu Xie +4
The activity of metal catalysts depends sensitively on dynamic structural changes that occur during operating conditions. The mechanistic understanding underlying such transformati…
Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set
Cameron J. Owen, Steven B. Torrisi, Yu Xie +6
This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we co…
Micron-scale heterogeneous catalysis with Bayesian force fields from first principles and active learning
Anders Johansson, Yu Xie, Cameron J. Owen +4
Quantum-mechanically accurate reactive molecular dynamics (MD) at the scale of billions of atoms has been achieved for the heterogeneous catalytic system of H/Pt(111) using the…
Uncertainty-aware molecular dynamics from Bayesian active learning for Phase Transformations and Thermal Transport in SiC
Yu Xie, Jonathan Vandermause, Senja Ramakers +3
Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.…
Active learning of reactive Bayesian force fields: Application to heterogeneous hydrogen-platinum catalysis dynamics
Jonathan Vandermause, Yu Xie, Jin Soo Lim +2
Accurate modeling of chemically reactive systems has traditionally relied on either expensive ab initio approaches or flexible bond-order force fields such as ReaxFF that require c…
Multitask machine learning of collective variables for enhanced sampling of rare events
Lixin Sun, Jonathan Vandermause, Simon Batzner +4
Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics.…
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene
Yu Xie, Jonathan Vandermause, Lixin Sun +2
We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions…
Spectral denoising for unsupervised analysis of correlated ionic transport
Nicola Molinari, Yu Xie, Ian Leifer +3
Computation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive as one cannot rely on the affordable mean square displacement…
On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomistic Rare Events
Jonathan Vandermause, Steven B. Torrisi, Simon Batzner +4
Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training effic…