Recent advances and outstanding challenges for machine learning interatomic potentials
Machine learning interatomic potentials (MLIPs) enable materials simulations at extended
length and time scales with near-ab initio accuracy. They have broad applications in the …
length and time scales with near-ab initio accuracy. They have broad applications in the …
Exploring the Structural, Dynamic, and Functional Properties of Metal‐Organic Frameworks through Molecular Modeling
This review spotlights the role of atomic‐level modeling in research on metal‐organic
frameworks (MOFs), especially the key methodologies of density functional theory (DFT) …
frameworks (MOFs), especially the key methodologies of density functional theory (DFT) …
[HTML][HTML] A reactive neural network framework for water-loaded acidic zeolites
Under operating conditions, the dynamics of water and ions confined within protonic
aluminosilicate zeolite micropores are responsible for many of their properties, including …
aluminosilicate zeolite micropores are responsible for many of their properties, including …
Diffusion mechanisms of fast lithium-ion conductors
The quest for next-generation energy-storage technologies has pivoted towards all-solid-
state batteries, primarily owing to their potential for enhanced safety and energy density. At …
state batteries, primarily owing to their potential for enhanced safety and energy density. At …
Performance assessment of universal machine learning interatomic potentials: Challenges and directions for materials' surfaces
B Focassio, LP M. Freitas… - ACS Applied Materials & …, 2024 - ACS Publications
Machine learning interatomic potentials (MLIPs) are one of the main techniques in the
materials science toolbox, able to bridge ab initio accuracy with the computational efficiency …
materials science toolbox, able to bridge ab initio accuracy with the computational efficiency …
[HTML][HTML] Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling
Abstract Machine learning interatomic potentials (MLIPs) enable accurate simulations of
materials at scales beyond that accessible by ab initio methods and play an increasingly …
materials at scales beyond that accessible by ab initio methods and play an increasingly …
Decoding Electrochemical Processes of Lithium‐Ion Batteries by Classical Molecular Dynamics Simulations
Lithium‐ion batteries (LIBs) have played an essential role in the energy storage industry and
dominated the power sources for consumer electronics and electric vehicles. Understanding …
dominated the power sources for consumer electronics and electric vehicles. Understanding …
[HTML][HTML] Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation
Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry
for the creation and optimization of electrocatalysts, which enhance key electrochemical …
for the creation and optimization of electrocatalysts, which enhance key electrochemical …
[HTML][HTML] A human-machine interface for automatic exploration of chemical reaction networks
Autonomous reaction network exploration algorithms offer a systematic approach to explore
mechanisms of complex chemical processes. However, the resulting reaction networks are …
mechanisms of complex chemical processes. However, the resulting reaction networks are …
[HTML][HTML] Nanoscale chemical reaction exploration with a quantum magnifying glass
Nanoscopic systems exhibit diverse molecular substructures by which they facilitate specific
functions. Theoretical models of them, which aim at describing, understanding, and …
functions. Theoretical models of them, which aim at describing, understanding, and …