Best practices in machine learning for chemistry

Machine Learning for Materials Scientists: An introductory guide towards best practices

(2020) Anthony Yu-Tung Wang et al.   CHEMISTRY OF MATERIALS

Charting a course for chemistry

(2019) Alán Aspuru-Guzik et al.   Nature Chemistry

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

(2019) Chi Chen et al.   CHEMISTRY OF MATERIALS

Three pitfalls to avoid in machine learning

(2019) Patrick Riley   NATURE

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

(2019) Justin S. Smith et al.   Nature Communications

Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis

(2019) Xiwen Jia et al.   NATURE

One neuron versus deep learning in aftershock prediction

(2019) Arnaud Mignan et al.   NATURE

Text-mined dataset of inorganic materials synthesis recipes

(2019) Olga Kononova et al.   Scientific Data

Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation

(2019) Zheng Xiong et al.   COMPUTATIONAL MATERIALS SCIENCE

Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm

(2018) Nongnuch Artrith et al.   JOURNAL OF CHEMICAL PHYSICS

Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics

(2018) Volker L. Deringer et al.   Journal of Physical Chemistry Letters

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Artificial intelligence faces reproducibility crisis

(2018) Matthew Hutson   SCIENCE

Predicting reaction performance in C–N cross-coupling using machine learning

(2018) Derek T. Ahneman et al.   SCIENCE

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Deep learning of aftershock patterns following large earthquakes

(2018) Phoebe M. R. DeVries et al.   NATURE

Inverse molecular design using machine learning: Generative models for matter engineering

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

Comment on “Predicting reaction performance in C–N cross-coupling using machine learning”

(2018) Kangway V. Chuang et al.   SCIENCE

Molecular de-novo design through deep reinforcement learning

(2017) Marcus Olivecrona et al.   Journal of Cheminformatics

Machine-learning-assisted materials discovery using failed experiments

(2016) Paul Raccuglia et al.   NATURE

Reproducibility in density functional theory calculations of solids

(2016) K. Lejaeghere et al.   SCIENCE

The FAIR Guiding Principles for scientific data management and stewardship

(2016) Mark D. Wilkinson et al.   Scientific Data

Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.

(2013) Robert P. Sheridan   Journal of Chemical Information and Modeling

QSAR Modeling is not “Push a Button and Find a Correlation”: A Case Study of Toxicity of (Benzo-)triazoles on Algae

(2012) Paola Gramatica et al.   Molecular Informatics

Best Practices for QSAR Model Development, Validation, and Exploitation

(2010) Alexander Tropsha   Molecular Informatics

Permutationally invariant potential energy surfaces in high dimensionality

(2009) Bastiaan J. Braams et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY

Are the Chemical Structures in Your QSAR Correct?

(2008) Douglas Young et al.   Quantitative structure-activity relationships & combinatorial science