Gaussian process regression for materials and molecules
VL Deringer, AP Bartók, N Bernstein… - Chemical …, 2021 - ACS Publications
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 …
Predicting lattice thermal conductivity via machine learning: a mini review
Y Luo, M Li, H Yuan, H Liu, Y Fang - NPJ Computational Materials, 2023 - nature.com
Over the past few decades, molecular dynamics simulations and first-principles calculations
have become two major approaches to predict the lattice thermal conductivity (κ L), which …
have become two major approaches to predict the lattice thermal conductivity (κ L), which …
Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport
We develop a neuroevolution-potential (NEP) framework for generating neural network-
based machine-learning potentials. They are trained using an evolutionary strategy for …
based machine-learning potentials. They are trained using an evolutionary strategy for …
Machine learning potentials for extended systems: a perspective
In the past two and a half decades machine learning potentials have evolved from a special
purpose solution to a broadly applicable tool for large-scale atomistic simulations. By …
purpose solution to a broadly applicable tool for large-scale atomistic simulations. By …
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 …
Recent progress on the effects of impurities and defects on the properties of Ga 2 O 3
Ga2O3 is attractive for power devices and solar-blind ultraviolet photodetectors due to its
ultra-wide bandgap, large breakdown field, and favorable stability. However, it is difficult to …
ultra-wide bandgap, large breakdown field, and favorable stability. However, it is difficult to …
Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent
IB Magdău, DJ Arismendi-Arrieta, HE Smith… - npj Computational …, 2023 - nature.com
Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for
studying molecular mechanisms in the condensed phase, however, they are too expensive …
studying molecular mechanisms in the condensed phase, however, they are too expensive …
Phonon thermal transport and its tunability in GaN for near-junction thermal management of electronics: A review
The heat dissipation issue has now become one of the most important bottlenecks for power
electronics due to the rapid increase in power density and working frequency. Towards the …
electronics due to the rapid increase in power density and working frequency. Towards the …
Unraveling thermal transport correlated with atomistic structures in amorphous gallium oxide via machine learning combined with experiments
Thermal transport properties of amorphous materials are crucial for their emerging
applications in energy and electronic devices. However, understanding and controlling …
applications in energy and electronic devices. However, understanding and controlling …
Bonding‐Enhanced Interfacial Thermal Transport: Mechanisms, Materials, and Applications
Rapid advancements in nanotechnologies for energy conversion and transport applications
urgently require a further understanding of interfacial thermal transport and enhancement of …
urgently require a further understanding of interfacial thermal transport and enhancement of …