Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to
find the low-dimensional structure of data. Dimension reduction for large, high-dimensional …

Machine learning methods have been remarkably successful for a wide range of application
areas in the extraction of essential information from data. An exciting and relatively recent …

We present an optimized implementation of the recently proposed symmetric gradient
domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce …

We present the construction of molecular force fields for small molecules (less than 25
atoms) using the recently developed symmetrized gradient-domain machine learning …

Complex spatiotemporal dynamics of physicochemical processes are often modeled at a
microscopic level (through, eg, atomistic, agentbased, or lattice models) based on first …

Modern machine learning force fields (ML-FF) are able to yield energy and force predictions
at the accuracy of high-level ab initio methods, but at a much lower computational cost. On …

We introduce a data-driven approach to building reduced dynamical models through
manifold learning; the reduced latent space is discovered using Diffusion Maps (a manifold …

We present a data-driven approach to characterizing nonidentifiability of a model's
parameters and illustrate it through dynamic as well as steady kinetic models. By employing …

Systems of coupled dynamical units (eg, oscillators or neurons) are known to exhibit
complex, emergent behaviors that may be simplified through coarse-graining: a process in …