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 …
Mechanism of charge transport in lithium thiophosphate
Lithium ortho-thiophosphate (Li3PS4) has emerged as a promising candidate for solid-state
electrolyte batteries, thanks to its highly conductive phases, cheap components, and large …
electrolyte batteries, thanks to its highly conductive phases, cheap components, and large …
Transferability and accuracy of ionic liquid simulations with equivariant machine learning interatomic potentials
Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from
energy storage to solvents, where they have been touted as “designer solvents” as they can …
energy storage to solvents, where they have been touted as “designer solvents” as they can …
Introduction to machine learning potentials for atomistic simulations
Abstract Machine learning potentials have revolutionised the field of atomistic simulations in
recent years and are becoming a mainstay in the toolbox of computational scientists. This …
recent years and are becoming a mainstay in the toolbox of computational scientists. This …
Understanding Defects in Amorphous Silicon with Million‐Atom Simulations and Machine Learning
The structure of amorphous silicon (a‐Si) is widely thought of as a fourfold‐connected
random network, and yet it is defective atoms, with fewer or more than four bonds, that make …
random network, and yet it is defective atoms, with fewer or more than four bonds, that make …
Thermal transport of glasses via machine learning driven simulations
Accessing the thermal transport properties of glasses is a major issue for the design of
production strategies of glass industry, as well as for the plethora of applications and …
production strategies of glass industry, as well as for the plethora of applications and …
Thermal transport of LiPS solid electrolytes with ab initio accuracy
The vast amount of computational studies on electrical conduction in solid state electrolytes
is not mirrored by comparable efforts addressing thermal conduction, which has been …
is not mirrored by comparable efforts addressing thermal conduction, which has been …
Crash testing machine learning force fields for molecules, materials, and interfaces: Model analysis in the tea challenge 2023
Atomistic simulations are routinely employed in academia and industry to study the behavior
of molecules, materials, and their interfaces. Central to these simulations are force fields …
of molecules, materials, and their interfaces. Central to these simulations are force fields …
Prediction rigidities for data-driven chemistry
The widespread application of machine learning (ML) to the chemical sciences is making it
very important to understand how the ML models learn to correlate chemical structures with …
very important to understand how the ML models learn to correlate chemical structures with …
Uncertainty quantification by direct propagation of shallow ensembles
M Kellner, M Ceriotti - Machine Learning: Science and …, 2024 - iopscience.iop.org
Statistical learning algorithms provide a generally-applicable framework to sidestep time-
consuming experiments, or accurate physics-based modeling, but they introduce a further …
consuming experiments, or accurate physics-based modeling, but they introduce a further …