Gaussian process regression for materials and molecules
We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …
methods in computational materials science and chemistry. The focus of the present review …
Machine learning for electrocatalyst and photocatalyst design and discovery
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …
reducing the impact of global warming, and providing solutions to environmental pollution …
RETRACTED ARTICLE: Room-temperature superconductivity in a carbonaceous sulfur hydride
One of the long-standing challenges in experimental physics is the observation of room-
temperature superconductivity 1, 2. Recently, high-temperature conventional …
temperature superconductivity 1, 2. Recently, high-temperature conventional …
A universal graph deep learning interatomic potential for the periodic table
Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a
fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow …
fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow …
Machine learning for molecular and materials science
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 …
outline machine-learning techniques that are suitable for addressing research questions in …
The magnetic genome of two-dimensional van der Waals materials
Magnetism in two-dimensional (2D) van der Waals (vdW) materials has recently emerged as
one of the most promising areas in condensed matter research, with many exciting emerging …
one of the most promising areas in condensed matter research, with many exciting emerging …
Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
Polarons in materials
Polarons are quasiparticles that easily form in polarizable materials due to the coupling of
excess electrons or holes with ionic vibrations. These quasiparticles manifest themselves in …
excess electrons or holes with ionic vibrations. These quasiparticles manifest themselves in …
QuantumATK: an integrated platform of electronic and atomic-scale modelling tools
QuantumATK is an integrated set of atomic-scale modelling tools developed since 2003 by
professional software engineers in collaboration with academic researchers. While different …
professional software engineers in collaboration with academic researchers. While different …
Electrocatalytic reduction of CO2 to ethylene and ethanol through hydrogen-assisted C–C coupling over fluorine-modified copper
Electrocatalytic reduction of CO2 into multicarbon (C2+) products is a highly attractive route
for CO2 utilization; however, the yield of C2+ products remains low because of the limited …
for CO2 utilization; however, the yield of C2+ products remains low because of the limited …