most citedMachine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS2

2 citations · 2 across the 1 of their papers we have counts for

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2 papers

cond-mat.mtrl-sci2026★ 2 cited

Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS2

Abrar Faiyad, Ashlie Martini

Dopants can tune the performance of MoS2 in various applications, but use of molecular dynamics simulations for doped MoS2 materials discovery is limited by the lack of multi-dopan…

cs.LG2025

Computational Design of Low-Volatility Lubricants for Space Using Interpretable Machine Learning

Daniel Miliate, Ashlie Martini

The function and lifetime of moving mechanical assemblies (MMAs) in space depend on the properties of lubricants. MMAs that experience high speeds or high cycles require liquid bas…