Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
[HTML][HTML] A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning
Understanding and controlling the complex and dynamic processes at battery interfaces
holds the key to develo** more durable and ultra high performance secondary batteries …
holds the key to develo** more durable and ultra high performance secondary batteries …
[HTML][HTML] Perspective on integrating machine learning into computational chemistry and materials science
Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …
electronic structure theory and molecular simulation. In particular, ML has become firmly …
Representation of compounds for machine-learning prediction of physical properties
The representations of a compound, called “descriptors” or “features”, play an essential role
in constructing a machine-learning model of its physical properties. In this study, we adopt a …
in constructing a machine-learning model of its physical properties. In this study, we adopt a …
Accurate interatomic force fields via machine learning with covariant kernels
We present a novel scheme to accurately predict atomic forces as vector quantities, rather
than sets of scalar components, by Gaussian process (GP) regression. This is based on …
than sets of scalar components, by Gaussian process (GP) regression. This is based on …
Bayesian machine learning for quantum molecular dynamics
RV Krems - Physical Chemistry Chemical Physics, 2019 - pubs.rsc.org
This article discusses applications of Bayesian machine learning for quantum molecular
dynamics. One particular formulation of quantum dynamics advocated here is in the form of …
dynamics. One particular formulation of quantum dynamics advocated here is in the form of …
Learning physical descriptors for materials science by compressed sensing
The availability of big data in materials science offers new routes for analyzing materials
properties and functions and achieving scientific understanding. Finding structure in these …
properties and functions and achieving scientific understanding. Finding structure in these …
Employing artificial intelligence to steer exascale workflows with colmena
Computational workflows are a common class of application on supercomputers, yet the
loosely coupled and heterogeneous nature of workflows often fails to take full advantage of …
loosely coupled and heterogeneous nature of workflows often fails to take full advantage of …
Efficient non-parametric fitting of potential energy surfaces for polyatomic molecules with Gaussian processes
J Cui, RV Krems - Journal of Physics B: Atomic, Molecular and …, 2016 - iopscience.iop.org
We explore the efficiency of a statistical learning technique based on Gaussian process (GP)
regression as an efficient non-parametric method for constructing multi-dimensional …
regression as an efficient non-parametric method for constructing multi-dimensional …
Machine learning, quantum chemistry, and chemical space
R Ramakrishnan… - Reviews in computational …, 2017 - Wiley Online Library
A number of machine learning (ML) studies have appeared with the commonality that
quantum mechanical properties are being predicted based on regression models defined in …
quantum mechanical properties are being predicted based on regression models defined in …