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 …
Recent progress and future prospects on all-organic polymer dielectrics for energy storage capacitors
QK Feng, SL Zhong, JY Pei, Y Zhao, DL Zhang… - Chemical …, 2021 - ACS Publications
With the development of advanced electronic devices and electric power systems, polymer-
based dielectric film capacitors with high energy storage capability have become particularly …
based dielectric film capacitors with high energy storage capability have become particularly …
LAMMPS-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
Since the classical molecular dynamics simulator LAMMPS was released as an open source
code in 2004, it has become a widely-used tool for particle-based modeling of materials at …
code in 2004, it has become a widely-used tool for particle-based modeling of materials at …
Recent progress of amorphous nanomaterials
J Kang, X Yang, Q Hu, Z Cai, LM Liu, L Guo - Chemical Reviews, 2023 - ACS Publications
Amorphous materials are metastable solids with only short-range order at the atomic scale,
which results from local intermolecular chemical bonding. The lack of long-range order …
which results from local intermolecular chemical bonding. The lack of long-range order …
Machine-learned potentials for next-generation matter simulations
The choice of simulation methods in computational materials science is driven by a
fundamental trade-off: bridging large time-and length-scales with highly accurate …
fundamental trade-off: bridging large time-and length-scales with highly accurate …
Machine learning in materials informatics: recent applications and prospects
Propelled partly by the Materials Genome Initiative, and partly by the algorithmic
developments and the resounding successes of data-driven efforts in other domains …
developments and the resounding successes of data-driven efforts in other domains …
Concepts of artificial intelligence for computer-assisted drug discovery
X Yang, Y Wang, R Byrne, G Schneider… - Chemical …, 2019 - ACS Publications
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …
opportunities for the discovery and development of innovative drugs. Various machine …
The MLIP package: moment tensor potentials with MPI and active learning
The subject of this paper is the technology (the'how') of constructing machine-learning
interatomic potentials, rather than science (the'what'and'why') of atomistic simulations using …
interatomic potentials, rather than science (the'what'and'why') of atomistic simulations using …
From DFT to machine learning: recent approaches to materials science–a review
Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …
and complexity of generated data. This massive amount of raw data needs to be stored and …
Machine learning for electronically excited states of molecules
Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …
as well as photobiology and also play a role in material science. Their theoretical description …