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 …
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 …
Cartesian atomic cluster expansion for machine learning interatomic potentials
B Cheng - npj Computational Materials, 2024 - nature.com
Abstract Machine learning interatomic potentials are revolutionizing large-scale, accurate
atomistic modeling in material science and chemistry. Many potentials use atomic cluster …
atomistic modeling in material science and chemistry. Many potentials use atomic cluster …
General-purpose machine-learned potential for 16 elemental metals and their alloys
Abstract Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the
lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their …
lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their …
Data generation for machine learning interatomic potentials and beyond
The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …
machine learning models for predicting molecular properties and behavior. Recent strides in …
Transferability and accuracy of ionic liquid simulations with equivariant machine learning interatomic potentials
Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from
energy storage to solvents, where they have been touted as “designer solvents” as they can …
energy storage to solvents, where they have been touted as “designer solvents” as they can …
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 …
DPA-2: a large atomic model as a multi-task learner
D Zhang, X Liu, X Zhang, C Zhang, C Cai… - npj Computational …, 2024 - nature.com
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes
in atomic modeling, simulation, and design. AI-driven potential energy models have …
in atomic modeling, simulation, and design. AI-driven potential energy models have …
Has generative artificial intelligence solved inverse materials design?
The directed design and discovery of compounds with pre-determined properties is a long-
standing challenge in materials research. We provide a perspective on progress toward …
standing challenge in materials research. We provide a perspective on progress toward …
Accurate Crystal Structure Prediction of New 2D Hybrid Organic–Inorganic Perovskites
Low-dimensional hybrid organic–inorganic perovskites (HOIPs) are promising electronically
active materials for light absorption and emission. The design space of HOIPs is extremely …
active materials for light absorption and emission. The design space of HOIPs is extremely …