Nicholas Lubbers

According to our database, Nicholas Lubbers authored at least 32 papers between 2010 and 2026.

Collaborative distances:

• Dijkstra number of four.
• 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

Statistical Insight into Meta-Learning via Predictor Subspace Characterization and Quantification of Task Diversity.

Saptati Datta, Nicolas W. Hengartner, Yulia Pimonova, Natalie E. Klein, Nicholas Lubbers

CoRR, September, 2025

Meta-Learning Linear Models for Molecular Property Prediction.

Yulia Pimonova, Michael G. Taylor, Alice Allen, Ping Yang, Nicholas Lubbers

CoRR, September, 2025

Optimal Invariant Bases for Atomistic Machine Learning.

Alice E. A. Allen, Emily Shinkle, Roxana Bujack, Nicholas Lubbers

CoRR, March, 2025

Flexible Moment-Invariant Bases from Irreducible Tensors.

Roxana Bujack, Emily Shinkle, Alice Allen, Tomás Suk, Nicholas Lubbers

CoRR, March, 2025

GPU-Accelerated Charge-Equilibration for Shadow Molecular Dynamics in Python.

Mehmet Cagri Kaymak, Nicholas Lubbers, Christian F. A. Negre, Michael E. Wall, Anders M. N. Niklasson

CoRR, March, 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

Toward machine learning interatomic potentials for modeling uranium mononitride.

Lorena Alzate-Vargas, Kashi N. Subedi, Nicholas Lubbers, Michael W. D. Cooper, Roxanne M. Tutchton, Tammie Gibson, Richard A. Messerly

Mach. Learn. Sci. Technol., 2025

Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry.

Shuhao Zhang, Michael Chigaev, Olexandr Isayev, Richard A. Messerly, Nicholas Lubbers

J. Chem. Inf. Model., 2025

Discrete Spatial Diffusion: Intensity-Preserving Diffusion Modeling.

Javier E. Santos, Agnese Marcato, Roman Colman, Nicholas Lubbers, Yen Ting Lin

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Model-Agnostic Knowledge Guided Correction for Improved Neural Surrogate Rollout.

Bharat Srikishan, Daniel O'Malley, Mohamed Mehana, Nicholas Lubbers, Nikhil Muralidhar

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Learning a general model of single phase flow in complex 3D porous media.

Javier E. Santos, Agnese Marcato, Qinjun Kang, Mohamed Mehana, Daniel O'Malley, Hari S. Viswanathan, Nicholas Lubbers

Mach. Learn. Sci. Technol., 2024

Journey over destination: dynamic sensor placement enhances generalization.

Agnese Marcato, Eric Guiltinan, Hari S. Viswanathan, Daniel O'Malley, Nicholas Lubbers, Javier E. Santos

Mach. Learn. Sci. Technol., 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

2023

Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information.

Cullen Roth, Vrinda Venu, Vanessa Job, Nicholas Lubbers, Karissa Y. Sanbonmatsu, Christina R. Steadman, Shawn R. Starkenburg

BMC Bioinform., December, 2023

Development of the Senseiver for efficient field reconstruction from sparse observations.

Javier E. Santos, Zachary R. Fox, Arvind T. Mohan, Daniel O'Malley, Hari S. Viswanathan, Nicholas Lubbers

Nat. Mac. Intell., October, 2023

FitSNAP: Atomistic machine learning with LAMMPS.

Andrew Rohskopf, C. Sievers, Nicholas Lubbers, Mary Alice Cusentnio, James Goff, Jan Janssen, Megan J. McCarthy, David Montes Oca de Zapiain, Svetoslav Nikolov, Khachik Sargsyan, Dionysios Sema, Ember Sikorski, Logan Williams, Aidan P. Thompson, Mitchell A. Wood

J. Open Source Softw., March, 2023

Latent Dirichlet Allocation modeling of environmental microbiomes.

Anastasiia Kim, Sanna Sevanto, Eric R. Moore, Nicholas Lubbers

PLoS Comput. Biol., 2023

Uncertainty-driven dynamics for active learning of interatomic potentials.

Maksim Kulichenko, Kipton Barros, Nicholas Lubbers, Ying Wai Li, Richard A. Messerly, Sergei Tretiak, Justin S. Smith, Benjamin T. Nebgen

Nat. Comput. Sci., 2023

Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces.

Javier E. Santos, Zachary R. Fox, Nicholas Lubbers, Yen Ting Lin

Proceedings of the International Conference on Machine Learning, 2023

2022

GLUE Code: A framework handling communication and interfaces between scales.

Aleksandra Pachalieva, Robert S. Pavel, Javier E. Santos, Abdourahmane Diaw, Nicholas Lubbers, Mohammed Mehana, Jeffrey R. Haack, Hari S. Viswanathan, Daniel Livescu, Timothy C. Germann, Christoph Junghans

J. Open Source Softw., December, 2022

Predictive Scale-Bridging Simulations through Active Learning.

Satish Karra, Mohammed Mehana, Nicholas Lubbers, Yu Chen, Abdourahmane Diaw, Javier E. Santos, Aleksandra Pachalieva, Robert S. Pavel, Jeffrey R. Haack, Michael M. McKerns, Christoph Junghans, Qinjun Kang, Daniel Livescu, Timothy C. Germann, Hari S. Viswanathan

CoRR, 2022

2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media.

Kun Wang, Yu Chen, Mohammed Mehana, Nicholas Lubbers, Kane C. Bennett, Qinjun Kang, Hari S. Viswanathan, Timothy C. Germann

J. Comput. Phys., 2021

Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search.

Michael Tynes, Wenhao Gao, Daniel J. Burrill, Enrique R. Batista, Danny Perez, Ping Yang, Nicholas Lubbers

J. Chem. Inf. Model., 2021

Multi-Scale Neural Networks for to Fluid Flow in 3D Porous Media.

Javier E. Santos, Ying Yin, Honggeun Jo, Wen Pan, Qinjun Kang, Hari S. Viswanathan, Masa Prodanovic, Michael J. Pyrcz, Nicholas Lubbers

CoRR, 2021

2020

Rapid Exploration of Optimization Strategies on Advanced Architectures using TestSNAP and LAMMPS.

Rahulkumar Gayatri, Stan G. Moore, Evan Weinberg, Nicholas Lubbers, Sarah Anderson, Jack Deslippe, Danny Perez, Aidan P. Thompson

CoRR, 2020

Modeling nanoconfinement effects using active learning.

Javier E. Santos, Mohammed Mehana, Hao Wu, Masa Prodanovic, Michael J. Pyrcz, Qinjun Kang, Nicholas Lubbers, Hari S. Viswanathan

CoRR, 2020

Automated discovery of a robust interatomic potential for aluminum.

Justin S. Smith, Benjamin T. Nebgen, Nithin Mathew, Jie Chen, Nicholas Lubbers, Leonid Burakovsky, Sergei Tretiak, Hai Ah Nam, Timothy C. Germann, Saryu Fensin, Kipton Barros

CoRR, 2020

2018

Less is more: sampling chemical space with active learning.

Justin S. Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, Adrian E. Roitberg

CoRR, 2018

2016

Inferring low-dimensional microstructure representations using convolutional neural networks.

Nicholas Lubbers, Turab Lookman, Kipton Barros

CoRR, 2016

2010

On generalized harmonic number sums.

Mark W. Coffey, Nicholas Lubbers

Appl. Math. Comput., 2010