Sakib Matin

According to our database, Sakib Matin authored at least 6 papers between 2024 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials.

Austin Rodriguez, Justin S. Smith, Sakib Matin, Nicholas Lubbers, Kipton Barros, Jose L. Mendoza-Cortes

CoRR, March, 2026

2025

Sunny.jl: A Julia Package for Spin Dynamics.

David Dahlbom, Hao Zhang, Cole Miles, Sam Quinn, Alin Niraula, Bhushan Thipe, Matthew Wilson, Sakib Matin, Het Mankad, Steven Hahn, Daniel Pajerowski, Steve Johnston, Zhentao Wang, Harry Lane, Ying Wai Li, Xiaojian Bai, Martin Mourigal, Cristian D. Batista, Kipton Barros

J. Open Source Softw., December, 2025

Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials.

Sakib Matin, Emily Shinkle, Yulia Pimonova, Galen T. Craven, Aleksandra Pachalieva, Ying Wai Li, Kipton Barros, Nicholas Lubbers

CoRR, March, 2025

Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need^*.

Mitchell Messerly, Sakib Matin, Alice E. A. Allen, Benjamin T. Nebgen, Kipton Barros, Justin S. Smith, Nicholas Lubbers, Richard A. Messerly

Mach. Learn. Sci. Technol., 2025

Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation.

Luan G. F. dos Santos, Benjamin T. Nebgen, Alice E. A. Allen, Brenden W. Hamilton, Sakib Matin, Justin S. Smith, Richard A. Messerly

J. Chem. Inf. Model., 2025

2024

Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks.

Emily Shinkle, Aleksandra Pachalieva, Riti Bahl, Sakib Matin, Brendan Gifford, Galen T. Craven, Nicholas Lubbers

CoRR, 2024