Nicholas Lubbers

Los Alamos National Laboratory — United States

Materials Science · Physical Sciences

33h-index4.8kcitations157works6.52yr avg
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Research Topics

Machine Learning in Materials Science(79), Computational Drug Discovery Methods(34), Protein Structure and Dynamics(27), Enhanced Oil Recovery Techniques(14), Various Chemistry Research Topics(11)

Publications157 total

Science-guided transfer learning for molecular dynamics of confined fluids in shale nanopores
Fluid Phase Equilibria·2025
GPU-Accelerated Graph-Based Semiempirical Quantum Chemistry
Journal of Chemical Theory and Computation·2025
Effects of Structural Allocation of Geometric Task Diversity in Linear Meta-Learning Models
ArXiv.org·2025Open Access
Meta-Learning Linear Models for Molecular Property Prediction
arXiv (Cornell University)·2025Open Access
Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
arXiv (Cornell University)·2025· 1 citedOpen Access
Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks
UNC Libraries·2025Open Access
Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need<sup>*</sup>
Machine Learning Science and Technology·2025· 3 citedOpen Access
Toward machine learning interatomic potentials for modeling uranium mononitride
Machine Learning Science and Technology·2025· 2 citedOpen Access
Discovering a Transferable Charge Assignment Model Using Machine Learning
UNC Libraries·2025Open Access
Discrete Spatial Diffusion: Intensity-Preserving Diffusion Modeling
arXiv (Cornell University)·2025Open Access
Multi-fidelity learning for interatomic potentials: Low-level forces and high-level energies are all you need
arXiv (Cornell University)·2025· 1 citedOpen Access
Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry
Journal of Chemical Information and Modeling·2025· 4 citedOpen Access
Including physics-informed atomization constraints in neural networks for reactive chemistry
2025Open Access
Optimal Invariant Bases for Atomistic Machine Learning
arXiv (Cornell University)·2025Open Access
GPU-Accelerated Charge-Equilibration for Shadow Molecular Dynamics in Python
arXiv (Cornell University)·2025Open Access
Flexible Moment-Invariant Bases from Irreducible Tensors
arXiv (Cornell University)·2025Open Access
Challenges and Opportunities for Machine Learning Potentials in Transition Path Sampling: Alanine Dipeptide and Azobenzene Studies
2025Open Access
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
arXiv (Cornell University)·2025Open Access
Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential
Journal of Chemical Theory and Computation·2025· 2 cited
Model-Agnostic Knowledge Guided Correction for Improved Neural Surrogate Rollout
arXiv (Cornell University)·2025Open Access
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Frequent Co-authors

Kipton Barros(28), Benjamin Nebgen(23), Sergei Tretiak(23), Justin S. Smith(18), Olexandr Isayev(14), Adrián E. Roitberg(9), Richard A. Messerly(9), R.I. Zubatyuk(8), Ying Wai Li(7), Hari Viswanathan(6), Maksim Kulichenko(5), Arvind Mohan(5), Daniel Livescu(5), Timothy C. Germann(5), Nikita Fedik(4), Andrew E. Sifain(4), Michael Chertkov(4), Qinjun Kang(4), Christian Devereux(3), Andrey Y. Lokhov(3)