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 …
Emerging Xene‐Based Single‐Atom Catalysts: Theory, Synthesis, and Catalytic Applications
M Wang, Y Hu, J Pu, Y Zi, W Huang - Advanced Materials, 2024 - Wiley Online Library
In recent years, the emergence of novel 2D monoelemental materials (Xenes), eg,
graphdiyne, borophene, phosphorene, antimonene, bismuthene, and stanene, has …
graphdiyne, borophene, phosphorene, antimonene, bismuthene, and stanene, has …
[HTML][HTML] DeePMD-kit v2: A software package for deep potential models
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics
simulations using machine learning potentials known as Deep Potential (DP) models. This …
simulations using machine learning potentials known as Deep Potential (DP) models. This …
Liquid metal for high-entropy alloy nanoparticles synthesis
High-entropy alloy nanoparticles (HEA-NPs) show great potential as functional materials,–.
However, thus far, the realized high-entropy alloys have been restricted to palettes of similar …
However, thus far, the realized high-entropy alloys have been restricted to palettes of similar …
Reactant-induced dynamics of lithium imide surfaces during the ammonia decomposition process
Ammonia decomposition on lithium imide surfaces has been intensively investigated owing
to its potential role in a sustainable hydrogen-based economy. Here, through advanced …
to its potential role in a sustainable hydrogen-based economy. Here, through advanced …
[HTML][HTML] Deep potentials for materials science
To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic
simulations based on empirical interatomic potentials, a new class of descriptions of atomic …
simulations based on empirical interatomic potentials, a new class of descriptions of atomic …
Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning
For 35 years, ab initio molecular dynamics (AIMD) has been the method of choice for
modeling complex atomistic phenomena from first principles. However, most AIMD …
modeling complex atomistic phenomena from first principles. However, most AIMD …
[HTML][HTML] A deep potential model with long-range electrostatic interactions
Machine learning models for the potential energy of multi-atomic systems, such as the deep
potential (DP) model, make molecular simulations with the accuracy of quantum mechanical …
potential (DP) model, make molecular simulations with the accuracy of quantum mechanical …
[HTML][HTML] Temperature-pressure phase diagram of confined monolayer water/ice at first-principles accuracy with a machine-learning force field
Understanding the phase behaviour of nanoconfined water films is of fundamental
importance in broad fields of science and engineering. However, the phase behaviour of the …
importance in broad fields of science and engineering. However, the phase behaviour of the …
Critical review on the physical properties of gallium-based liquid metals and selected pathways for their alteration
S Handschuh-Wang, FJ Stadler… - The Journal of Physical …, 2021 - ACS Publications
Gallium-based liquid metals have gained plenty of attention in the scientific community due
to their extraordinary properties. The most intriguing properties of liquid metals are high …
to their extraordinary properties. The most intriguing properties of liquid metals are high …