147 citations · 231 across the 13 of their papers we have counts for
9 papers · 1 filter
Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
Menghang Wang, Jingxuan Ding, Grace Xiong +8
In solid acid solid electrolytes CsHPO and CsHSO, mechanisms of fast proton conduction have long been debated and attributed to either local proton hopping or polyanion…
Surface roughening in nanoparticle catalysts
Cameron J. Owen, Nicholas Marcella, Christopher R. O'Connor +7
Supported metal nanoparticle (NP) catalysts are vital for the sustainable production of chemicals, but their design and implementation are limited by the ability to identify and ch…
Atomistic evolution of active sites in multi-component heterogeneous catalysts
Cameron J. Owen, Lorenzo Russotto, Christopher R. O'Connor +4
Multi-component metal nanoparticles (NPs) are of paramount importance in the chemical industry, as most processes therein employ heterogeneous catalysts. While these multi-componen…
Unified Differentiable Learning of Electric Response
Stefano Falletta, Andrea Cepellotti, Anders Johansson +4
Predicting response of materials to external stimuli is a primary objective of computational materials science. However, current methods are limited to small-scale simulations due…
Unbiased Atomistic Predictions of Crystal Dislocation Dynamics using Bayesian Force Fields
Cameron J. Owen, Amirhossein D. Naghdi, Anders Johansson +3
Crystal dislocation dynamics, especially at high temperatures, represents a subject where experimental phenomenological input is commonly required, and parameter-free predictions,…
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…
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…