Search Results - "Allen, Alice E. A."

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics by Horton, Joshua T, Allen, Alice E. A, Dodda, Leela S, Cole, Daniel J

“…Modern molecular mechanics force fields are widely used for modeling the dynamics and interactions of small organic molecules using libraries of transferable…”
Get full text

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE by Kovács, Dávid Péter, Oord, Cas van der, Kucera, Jiri, Allen, Alice E. A, Cole, Daniel J, Ortner, Christoph, Csányi, Gábor

“…We demonstrate that fast and accurate linear force fields can be built for molecules using the atomic cluster expansion (ACE) framework. The ACE models…”
Get full text

Development and Validation of the Quantum Mechanical Bespoke Protein Force Field by Allen, Alice E. A, Robertson, Michael J, Payne, Michael C, Cole, Daniel J

“…Molecular mechanics force field parameters for macromolecules, such as proteins, are traditionally fit to reproduce experimental properties of small molecules,…”
Get full text

Harmonic Force Constants for Molecular Mechanics Force Fields via Hessian Matrix Projection by Allen, Alice E. A, Payne, Michael C, Cole, Daniel J

“…A modification to the Seminario method [ Int. J. Quantum Chem. 1996, 60, 1271−1277 ] is proposed, which derives accurate harmonic bond and angle molecular…”
Get full text

Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning by Allen, Alice E. A., Lubbers, Nicholas, Matin, Sakib, Smith, Justin, Messerly, Richard, Tretiak, Sergei, Barros, Kipton

“…The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material…”
Get full text

Machine learning of material properties: Predictive and interpretable multilinear models by Allen, Alice E A, Tkatchenko, Alexandre

“…Machine learning models can provide fast and accurate predictions of material properties but often lack transparency. Interpretability techniques can be used…”
Get full text

Data Generation for Machine Learning Interatomic Potentials and Beyond by Kulichenko, Maksim, Nebgen, Benjamin, Lubbers, Nicholas, Smith, Justin S., Barros, Kipton, Allen, Alice E. A., Habib, Adela, Shinkle, Emily, Fedik, Nikita, Li, Ying Wai, Messerly, Richard A., Tretiak, Sergei

“…The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and…”
Get full text

Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment by Matin, Sakib, Allen, Alice E. A., Smith, Justin, Lubbers, Nicholas, Jadrich, Ryan B., Messerly, Richard, Nebgen, Benjamin, Li, Ying Wai, Tretiak, Sergei, Barros, Kipton

“…Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data…”
Get full text

Atomic permutationally invariant polynomials for fitting molecular force fields by Allen, Alice E A, Dusson, Geneviève, Ortner, Christoph, Csányi, Gábor

“…We introduce and explore an approach for constructing force fields for small molecules, which combines intuitive low body order empirical force field terms…”
Get full text

Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective by Allen, Alice E. A, Tkatchenko, Alexandre

“…Learning from data has led to substantial advances in a multitude of disciplines, including text and multimedia search, speech recognition, and…”
Get full text

Learning Together: Towards foundational models for machine learning interatomic potentials with meta-learning by Allen, Alice E. A, Lubbers, Nicholas, Matin, Sakib, Smith, Justin, Messerly, Richard, Tretiak, Sergei, Barros, Kipton

“…The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material…”
Get full text