53 citations · 59 across the 4 of their papers we have counts for
4 papers
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…
Learning Together: Towards foundational models for machine learning interatomic potentials with meta-learning
Alice E. A. Allen, Nicholas Lubbers, Sakib Matin +4
The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditio…
Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective
Alice E. A. Allen, Alexandre Tkatchenko
Learning from data has led to substantial advances in a multitude of disciplines, including text and multimedia search, speech recognition, and autonomous-vehicle navigation. Can m…
Atomic Permutationally Invariant Polynomials for Fitting Molecular Force Fields
Alice Allen, Gábor Csányi, Geneviève Dusson +1
We introduce and explore an approach for constructing force fields for small molecules, which combines intuitive low body order empirical force field terms with the concepts of dat…