Scientific discovery in the age of artificial intelligence

H Wang, T Fu, Y Du, W Gao, K Huang, Z Liu… - Nature, 2023 - nature.com
Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, hel** scientists to generate hypotheses, design experiments …

Machine learning for electrocatalyst and photocatalyst design and discovery

H Mai, TC Le, D Chen, DA Winkler… - Chemical …, 2022 - ACS Publications
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

Scaling deep learning for materials discovery

A Merchant, S Batzner, SS Schoenholz, M Aykol… - Nature, 2023 - nature.com
Novel functional materials enable fundamental breakthroughs across technological
applications from clean energy to information processing,,,,,,,,,–. From microchips to batteries …

A mobile robotic chemist

B Burger, PM Maffettone, VV Gusev, CM Aitchison… - Nature, 2020 - nature.com
Technologies such as batteries, biomaterials and heterogeneous catalysts have functions
that are defined by mixtures of molecular and mesoscale components. As yet, this multi …

Machine learning for molecular and materials science

KT Butler, DW Davies, H Cartwright, O Isayev, A Walsh - Nature, 2018 - nature.com
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 …

Combining machine learning and computational chemistry for predictive insights into chemical systems

JA Keith, V Vassilev-Galindo, B Cheng… - Chemical …, 2021 - ACS Publications
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Automatic chemical design using a data-driven continuous representation of molecules

R Gómez-Bombarelli, JN Wei, D Duvenaud… - ACS central …, 2018 - ACS Publications
We report a method to convert discrete representations of molecules to and from a
multidimensional continuous representation. This model allows us to generate new …

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

B Sanchez-Lengeling, A Aspuru-Guzik - Science, 2018 - science.org
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 …

ChemCrow: Augmenting large-language models with chemistry tools

AM Bran, S Cox, O Schilter, C Baldassari… - arxiv preprint arxiv …, 2023 - arxiv.org
Over the last decades, excellent computational chemistry tools have been developed.
Integrating them into a single platform with enhanced accessibility could help reaching their …

On scientific understanding with artificial intelligence

M Krenn, R Pollice, SY Guo, M Aldeghi… - Nature Reviews …, 2022 - nature.com
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 …