标题
Best practices in machine learning for chemistry
作者
关键词
-
出版物
Nature Chemistry
Volume 13, Issue 6, Pages 505-508
出版商
Springer Science and Business Media LLC
发表日期
2021-06-01
DOI
10.1038/s41557-021-00716-z
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- 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
Become a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get StartedAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started