872 citations · 1.1k across the 13 of their papers we have counts for
16 papers
Bridging Molecular Scales with Implicit Score Matching for Bottom-Up Coarse Graining
Patrick G. Sahrmann, Nicholas Lubbers, Benjamin T. Nebgen +4
Molecular dynamics simulations provide a computational microscope for atomic scale processes but remain restricted to relatively small spatial and temporal scales. Coarse-grained m…
Learning Thermal Response Forces: A Method for Extending the Thermodynamic Transferability of Coarse-Grained Models via Machine-Learning
Patrick G. Sahrmann, Benjamin T. Nebgen, Kipton Barros +1
Machine-learned (ML) coarse-grained (CG) models are a promising tool for significantly enhancing the efficiency of molecular simulations by systematically removing degrees of freed…
Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
Alice E. A. Allen, Rui Li, Sakib Matin +8
Accurately modeling chemical reactions at the atomistic level requires high-level electronic structure theory due to the presence of unpaired electrons and the need to properly des…
Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
Shriya Gumber, Lorena Alzate-Vargas, Benjamin T. Nebgen +4
Machine learning interatomic potentials (MLIPs) are inherently limited by the accuracy of the training data, usually consisting of energies and forces obtained from quantum mechani…
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 +5
The promise of machine learning interatomic potentials (MLIPs) has led to an abundance of public quantum mechanical (QM) training datasets. The quality of an MLIP is directly limit…
Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
Sakib Matin, Alice E. A. Allen, Emily Shinkle +9
Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures tr…
Machine learning potentials with Iterative Boltzmann Inversion: training to experiment
Sakib Matin, Alice Allen, Justin S. Smith +7
Methodologies for training machine learning potentials (MLPs) to quantum-mechanical simulation data have recently seen tremendous progress. Experimental data has a very different c…
Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles
Adela Habib, Nicholas Lubbers, Sergei Tretiak +1
Highly energetic electron-hole pairs (hot carriers) formed from plasmon decay in metallic nanostructures promise sustainable pathways for energy-harvesting devices. However, effici…
Semi-Empirical Shadow Molecular Dynamics: A PyTorch implementation
Maksim Kulichenko, Kipton Barros, Nicholas Lubbers +5
Extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) in its most recent shadow potential energy version has been implemented in the semiempirical PyTorch-based softwar…
Lightweight and Effective Tensor Sensitivity for Atomistic Neural Networks
Michael Chigaev, Justin S. Smith, Steven Anaya +4
Atomistic machine learning focuses on the creation of models which obey fundamental symmetries of atomistic configurations, such as permutation, translation, and rotation invarianc…
Using Machine Learning Hamiltonians To Compute Molecular Motor Barrier Heights
Aaron Philip, Guoqing Zhou, Benjamin Nebgen
Machine Learning Inter-atomic Potentials (MLIPs) have become a common tool in use by computational chemists due to their combination of accuracy and speed. Yet, it is still not cle…
A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization
Benjamin T. Nebgen, Raviteja Vangara, Miguel A. Hombrados-Herrera +2
Non-negative Matrix Factorization (NMF) has proven to be a powerful unsupervised learning method for uncovering hidden features in complex and noisy data sets with applications in…
Automated discovery of a robust interatomic potential for aluminum
Justin S. Smith, Benjamin Nebgen, Nithin Mathew +8
Accuracy of molecular dynamics simulations depends crucially on the interatomic potential used to generate forces. The gold standard would be first-principles quantum mechanics (QM…
Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks
Tetiana Zubatyuk, Ben Nebgen, Nicholas Lubbers +7
The Hückel Hamiltonian is an incredibly simple tight-binding model famed for its ability to capture qualitative physics phenomena arising from electron interactions in molecules an…
Transferable Molecular Charge Assignment Using Deep Neural Networks
Ben Nebgen, Nick Lubbers, Justin S. Smith +6
We use HIP-NN, a neural network architecture that excels at predicting molecular energies, to predict atomic charges. The charge predictions are accurate over a wide range of molec…
Less is more: sampling chemical space with active learning
Justin S. Smith, Ben Nebgen, Nicholas Lubbers +2
The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train suc…