Machine learning for a sustainable energy future
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …
demands advances—at the materials, devices and systems levels—for the efficient …
Everything is connected: Graph neural networks
P Veličković - Current Opinion in Structural Biology, 2023 - Elsevier
In many ways, graphs are the main modality of data we receive from nature. This is due to
the fact that most of the patterns we see, both in natural and artificial systems, are elegantly …
the fact that most of the patterns we see, both in natural and artificial systems, are elegantly …
[HTML][HTML] Discovering faster matrix multiplication algorithms with reinforcement learning
Improving the efficiency of algorithms for fundamental computations can have a widespread
impact, as it can affect the overall speed of a large amount of computations. Matrix …
impact, as it can affect the overall speed of a large amount of computations. Matrix …
Learning skillful medium-range global weather forecasting
Global medium-range weather forecasting is critical to decision-making across many social
and economic domains. Traditional numerical weather prediction uses increased compute …
and economic domains. Traditional numerical weather prediction uses increased compute …
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 …
MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
Creating fast and accurate force fields is a long-standing challenge in computational
chemistry and materials science. Recently, Equivariant Message Passing Neural Networks …
chemistry and materials science. Recently, Equivariant Message Passing Neural Networks …
Illuminating protein space with a programmable generative model
Three billion years of evolution has produced a tremendous diversity of protein molecules,
but the full potential of proteins is likely to be much greater. Accessing this potential has …
but the full potential of proteins is likely to be much greater. Accessing this potential has …
Revisiting heterophily for graph neural networks
Abstract Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using
graph structures based on the relational inductive bias (homophily assumption). While …
graph structures based on the relational inductive bias (homophily assumption). While …
GraphCast: Learning skillful medium-range global weather forecasting
R Lam, A Sanchez-Gonzalez, M Willson… - arXiv preprint arXiv …, 2022 - arxiv.org
Global medium-range weather forecasting is critical to decision-making across many social
and economic domains. Traditional numerical weather prediction uses increased compute …
and economic domains. Traditional numerical weather prediction uses increased compute …
A survey of uncertainty in deep neural networks
Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …
become a crucial part of various real world applications. Due to the increasing spread …