In this collection we highlight a selection of recent computational studies published in Nature Communications, featuring advances in computational chemistry methods and progress towards machine learning approaches.
Statistical tools based on machine learning are becoming integrated into chemistry research workflows. We discuss the elements necessary to train reliable, repeatable and reproducible models, and recommend a set of guidelines for machine learning reports. ...
Machine learning provides a set of new tools for the analysis, reduction and acceleration of combustion chemistry. The implementation of such tools is not new. However, with the emerging techniques of deep learning, renewed interest in implementing machine learning is fast growing. In this chapter,...
Machine learning tools for Chemistry. Contribute to CheML/CheML development by creating an account on GitHub.
Kernel ridge regression (KRR) is a powerful and popular tool for supervised machine learning in quantum chemistry.1, 2, 7, 13, 14, 15, 16, 17, 18, 19, 20 It is also very instructional and relatively easy to understand and implement.5, 7, 11 General principles of ML will be demonstr...
Learning from Data: From Classical Heuristics to Data Relationships Since the early days of chemistry, scientists have looked for patterns from often limited sets of data. This led to many of today’s widely used chemical heuristics such as the periodic system of elements, electronegativities, and...
John Hopfield and Geoffrey Hinton were awarded the Nobel Prize in physics Tuesday for discoveries and inventions that formed the building blocks of machine learning. "This year's two Nobel Laureates in physics have used ...
The access to clean and drinkable water is becoming one of the major health issues because most natural waters are now polluted in the context of rapid ind
Distributed Machine learning Tool Kit (DMTK) - A distributed machine learning (parameter server) framework by Microsoft. Enables training models on large data sets across multiple machines. Current tools bundled with it include: LightLDA and Distributed (Multisense) Word Embedding. DLib - A suite of...
I’ve long been working with generative models, mostly centered around SMILES-based deep learning models. However, I’ve been wanting to try out genetic algorithms for some time. Using Read More Non-conditional De Novo molecular Generation with Transformer Encoders ...