Scientific discovery in the age of artificial intelligence
Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, hel** scientists to generate hypotheses, design experiments …
and accelerate research, hel** scientists to generate hypotheses, design experiments …
Machine learning for electrocatalyst and photocatalyst design and discovery
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …
reducing the impact of global warming, and providing solutions to environmental pollution …
Scaling deep learning for materials discovery
Novel functional materials enable fundamental breakthroughs across technological
applications from clean energy to information processing,,,,,,,,,–. From microchips to batteries …
applications from clean energy to information processing,,,,,,,,,–. From microchips to batteries …
A mobile robotic chemist
Technologies such as batteries, biomaterials and heterogeneous catalysts have functions
that are defined by mixtures of molecular and mesoscale components. As yet, this multi …
that are defined by mixtures of molecular and mesoscale components. As yet, this multi …
Machine learning for molecular and materials science
Here we summarize recent progress in machine learning for the chemical sciences. We
outline machine-learning techniques that are suitable for addressing research questions in …
outline machine-learning techniques that are suitable for addressing research questions in …
Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
Automatic chemical design using a data-driven continuous representation of molecules
We report a method to convert discrete representations of molecules to and from a
multidimensional continuous representation. This model allows us to generate new …
multidimensional continuous representation. This model allows us to generate new …
Inverse molecular design using machine learning: Generative models for matter engineering
The discovery of new materials can bring enormous societal and technological progress. In
this context, exploring completely the large space of potential materials is computationally …
this context, exploring completely the large space of potential materials is computationally …
ChemCrow: Augmenting large-language models with chemistry tools
Over the last decades, excellent computational chemistry tools have been developed.
Integrating them into a single platform with enhanced accessibility could help reaching their …
Integrating them into a single platform with enhanced accessibility could help reaching their …
On scientific understanding with artificial intelligence
An oracle that correctly predicts the outcome of every particle physics experiment, the
products of every possible chemical reaction or the function of every protein would …
products of every possible chemical reaction or the function of every protein would …