Abstract Machine learning (ML) promises to tackle the grand challenges in chemistry and
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …

Best practices in machine learning for chemistry | Nature Chemistry Skip to main content
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Abstract Machine learning (ML) algorithms are currently emerging as powerful tools in all
areas of science. Conventionally, ML is understood as a fundamentally data‐driven …

The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …

Research in molecular sciences witnessed the rise and fall of Artificial Intelligence
(AI)/Machine Learning (ML) methods, especially artificial neural networks, few decades ago …

Machine learning (ML) has emerged as a general, problem-solving paradigm with many
applications in computer vision, natural language processing, digital safety, or medicine. By …

Machine learning (ML) plays a growing role in the design and discovery of chemicals,
aiming to reduce the need to perform expensive experiments and simulations. ML for such …

Standfirst Over the last decade, we have witnessed the emergence of ever more machine
learning applications in all aspects of the chemical sciences. Here, we highlight specific …

There has been an upsurge of interest in applying machine-learning (ML) techniques to
chemistry, and a number of these applications have achieved impressive predictive …

Conspectus In the domain of reaction development, one aims to obtain higher efficacies as
measured in terms of yield and/or selectivities. During the empirical cycles, an admixture of …