On generalized harmonic number sums. - Mark W. Coffey; Nicholas Lubbers (Appl. Math. Comput., 2010)

Inferring low-dimensional microstructure representations using convolutional neural networks. - Nicholas Lubbers; Turab Lookman; Kipton Barros (CoRR, 2016)

Less is more: sampling chemical space with active learning. - Justin S. Smith; Ben Nebgen; Nicholas Lubbers; Olexandr Isayev; Adrian E. Roitberg (CoRR, 2018)

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)

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)

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)

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)

Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search. - Michael Tynes; Wenhao Gao 001; Daniel J. Burrill; Enrique R. Batista; Danny Perez; Ping Yang; Nicholas Lubbers (J. Chem. Inf. Model., 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)

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; et al. (CoRR, 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., 2022)

Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces. - Javier E. Santos; Zachary R. Fox; Nicholas Lubbers; Yen Ting Lin (ICML, 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)

Latent Dirichlet Allocation modeling of environmental microbiomes. - Anastasiia Kim; Sanna Sevanto; Eric R. Moore; Nicholas Lubbers (PLoS Comput. Biol., 2023)

FitSNAP: Atomistic machine learning with LAMMPS. - Andrew Rohskopf; C. Sievers; Nicholas Lubbers; Mary Alice Cusentnio; James Goff; Jan Janssen; Megan McCarthy; David Montes Oca de Zapiain; Svetoslav Nikolov; Khachik Sargsyan; Dionysios Sema; Ember Sikorski; et al. (J. Open Source Softw., 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., 2023)

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)

Model-Agnostic Knowledge Guided Correction for Improved Neural Surrogate Rollout. - Bharat Srikishan; Daniel O'Malley; Mohamed Mehana; Nicholas Lubbers; Nikhil Muralidhar (ICLR, 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)

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, 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, 2025)

Flexible Moment-Invariant Bases from Irreducible Tensors. - Roxana Bujack; Emily Shinkle; Alice Allen; Tomás Suk; Nicholas Lubbers (CoRR, 2025)

Optimal Invariant Bases for Atomistic Machine Learning. - Alice E. A. Allen; Emily Shinkle; Roxana Bujack; Nicholas Lubbers (CoRR, 2025)

Discrete Spatial Diffusion: Intensity-Preserving Diffusion Modeling. - Javier E. Santos; Agnese Marcato; Roman Colman; Nicholas Lubbers; Yen Ting Lin (CoRR, 2025)