Self-driving laboratories for chemistry and materials science
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …
Through the automation of experimental workflows, along with autonomous experimental …
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 …
Mattergen: a generative model for inorganic materials design
The design of functional materials with desired properties is essential in driving
technological advances in areas like energy storage, catalysis, and carbon capture …
technological advances in areas like energy storage, catalysis, and carbon capture …
Emerging materials intelligence ecosystems propelled by machine learning
The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …
successes and promises, several AI ecosystems are blossoming, many of them within the …
Data‐Driven Materials Innovation and Applications
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …
experimental and computational investigative methodologies, the massive amounts of data …
Wide band gap chalcogenide semiconductors
Wide band gap semiconductors are essential for today's electronic devices and energy
applications because of their high optical transparency, controllable carrier concentration …
applications because of their high optical transparency, controllable carrier concentration …
Crystal structure generation with autoregressive large language modeling
The generation of plausible crystal structures is often the first step in predicting the structure
and properties of a material from its chemical composition. However, most current methods …
and properties of a material from its chemical composition. However, most current methods …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Machine learning: a new paradigm in computational electrocatalysis
Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms
at an atomic level, and uncovering scientific insights lie at the center of the development of …
at an atomic level, and uncovering scientific insights lie at the center of the development of …
How to validate machine-learned interatomic potentials
Machine learning (ML) approaches enable large-scale atomistic simulations with near-
quantum-mechanical accuracy. With the growing availability of these methods, there arises …
quantum-mechanical accuracy. With the growing availability of these methods, there arises …