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 …
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 …
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 …
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 …
Emerging atomistic modeling methods for heterogeneous electrocatalysis
Heterogeneous electrocatalysis lies at the center of various technologies that could help
enable a sustainable future. However, its complexity makes it challenging to accurately and …
enable a sustainable future. However, its complexity makes it challenging to accurately and …
DFT exchange: sharing perspectives on the workhorse of quantum chemistry and materials science
In this paper, the history, present status, and future of density-functional theory (DFT) is
informally reviewed and discussed by 70 workers in the field, including molecular scientists …
informally reviewed and discussed by 70 workers in the field, including molecular scientists …
Machine learning for high-entropy alloys: Progress, challenges and opportunities
High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …
mechanical properties and the vast compositional space for new HEAs. However …
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 …
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 …
Best practices in machine learning for chemistry
Best practices in machine learning for chemistry | Nature Chemistry Skip to main content
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