Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry.

@article{Rupp2018GuestES,
  title={Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry.},
  author={Matthias Rupp and O. Anatole von Lilienfeld and Kieron Burke},
  journal={The Journal of chemical physics},
  year={2018},
  volume={148 24},
  pages={
          241401
        }
}
A survey of the contributions to the Special Topic on Data-enabled Theoretical Chemistry is given, including a glossary of relevant machine learning terms. 

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References

SHOWING 1-10 OF 78 REFERENCES

The drug-maker's guide to the galaxy

How machine learning and big data are helping chemists search the vast chemical universe for better medicines.

Learning with kernels

TLDR
This book is intended to be a guide to the art of self-consistency and should not be used as a substitute for a comprehensive guide to self-confidence.

The Nature of Statistical Learning Theory

  • V. Vapnik
  • Computer Science
    Statistics for Engineering and Information Science
  • 2000
Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing

Compositional descriptor-based recommender system for the materials discovery.

TLDR
A descriptor-based recommender-system approach to estimate the relevance of chemical compositions where crystals can be formed and the phase stability for compounds at expected CRCs using density functional theory calculations.

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning.

TLDR
A combination of physics-based potentials with machine learning (ML) is proposed, coined IPML, which is transferable across small neutral organic and biologically relevant molecules and able to handle new molecules and conformations without explicit prior parametrization.

Less is more: sampling chemical space with active learning

TLDR
This work presents a fully automated approach for the generation of datasets with the intent of training universal ML potentials based on the concept of active learning (AL) via Query by Committee (QBC), which uses the disagreement between an ensemble ofML potentials to infer the reliability of the ensemble's prediction.

Quantum Machine Learning in Chemical Compound Space

TLDR
The case is made for quantum machine learning: An inductive molecular modeling approach which can be applied to quantum chemistry problems.

Predicting molecular properties with covariant compositional networks.

TLDR
A neural network based machine learning algorithm which, assuming a sufficiently large training sample of actual DFT results, can instead learn to predict certain properties of molecules purely from their molecular graphs.

Machine learning of molecular properties: Locality and active learning.

TLDR
A new machine learning algorithm for predicting molecular properties that is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers is proposed.

A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information.

TLDR
A data-driven method to construct a potential energy surface based on neural networks is presented, which is accurate across chemical and configurational space and demonstrated by applying it to datasets from nonreactive and reactive molecular dynamics simulations and a diverse database of equilibrium structures.
...