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 …
Social physics
Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …
phenomena. This development has been due to physicists venturing outside of their …
Combustion machine learning: Principles, progress and prospects
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
Physics-informed machine learning
Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …
to wonder what lessons can be learned from other fields undergoing similar developments …
Machine learning for alloys
Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …
data-science-inspired work. The dawn of computational databases has made the integration …
Deep learning for geophysics: Current and future trends
Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …
approaches, has attracted increasing attention in geophysical community, resulting in many …
Hamiltonian neural networks
S Greydanus, M Dzamba… - Advances in neural …, 2019 - proceedings.neurips.cc
Even though neural networks enjoy widespread use, they still struggle to learn the basic
laws of physics. How might we endow them with better inductive biases? In this paper, we …
laws of physics. How might we endow them with better inductive biases? In this paper, we …
Explainable machine learning for scientific insights and discoveries
Machine learning methods have been remarkably successful for a wide range of application
areas in the extraction of essential information from data. An exciting and relatively recent …
areas in the extraction of essential information from data. An exciting and relatively recent …
The 2021 room-temperature superconductivity roadmap
Designing materials with advanced functionalities is the main focus of contemporary solid-
state physics and chemistry. Research efforts worldwide are funneled into a few high-end …
state physics and chemistry. Research efforts worldwide are funneled into a few high-end …