2 citations · 6 across the 3 of their papers we have counts for
3 papers
physics.chem-ph2025★ 2 cited
A Simple Iterative Approach for Constant Chemical Potential Simulations at Interfaces
Ademola Soyemi, Khagendra Baral, Tibor Szilvasi
Chemical potential of species in solution is essential for understanding various chemical processes at interfaces. Molecular dynamics (MD) simulations, constrained by fixed composi…
physics.chem-ph2024★ 2 cited
Transferable Water Potentials Using Equivariant Neural Networks
Tristan Maxson, Tibor Szilvasi
Machine learning interatomic potentials (MLIPs) are an emerging modeling technique that promises to provide electronic structure theory accuracy for a fraction of its cost, however…
physics.chem-ph2024★ 2 cited
Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices
Tristan Maxson, Ademola Soyemi, Benjamin W. J. Chen +1
Recent developments in machine learning interatomic potentials (MLIPs) have empowered even non-experts in machine learning to train MLIPs for accelerating materials simulations. Ho…