Open-source machine learning in computational chemistry
A Hagg, KN Kirschner - Journal of Chemical Information and …, 2023 - ACS Publications
The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …
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 …
Ab initio electronic structure calculations based on numerical atomic orbitals: Basic fomalisms and recent progresses
The numerical atomic orbital (NAO) basis sets offer a computationally efficient option for
electronic structure calculations, as they require fewer basis functions compared with other …
electronic structure calculations, as they require fewer basis functions compared with other …
Universal interatomic potential for perovskite oxides
With their celebrated structural and chemical flexibility, perovskite oxides have served as a
highly adaptable material platform for exploring emergent phenomena arising from the …
highly adaptable material platform for exploring emergent phenomena arising from the …
Machine learning K-means clustering algorithm for interpolative separable density fitting to accelerate hybrid functional calculations with numerical atomic orbitals
The interpolative separable density fitting (ISDF) is an efficient and accurate low-rank
decomposition method to reduce the high computational cost and memory usage of the …
decomposition method to reduce the high computational cost and memory usage of the …
Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with ab initio accuracy
Simulating electronic behavior in materials and devices with realistic large system sizes
remains a formidable task within the ab initio framework due to its computational intensity …
remains a formidable task within the ab initio framework due to its computational intensity …
Subquadratic-scaling real-space random phase approximation correlation energy calculations for periodic systems with numerical atomic orbitals
The random phase approximation (RPA) as formulated as an orbital-dependent, fifth-rung
functional within the density functional theory framework offers a promising approach for …
functional within the density functional theory framework offers a promising approach for …
Intermetallic IrGa-IrOx core-shell electrocatalysts for oxygen evolution
LW Chen, F He, RY Shao, QQ Yan, P Yin, WJ Zeng… - Nano Research, 2022 - Springer
The development of high-performance Ir-based catalyst for electrocatalysis of oxygen
evolution reaction (OER) in acidic media plays a critical role in realizing the …
evolution reaction (OER) in acidic media plays a critical role in realizing the …
DPA-2: Towards a universal large atomic model for molecular and material simulation
D Zhang, X Liu, X Zhang, C Zhang, C Cai, H Bi… - arXiv preprint arXiv …, 2023 - arxiv.org
The rapid development of artificial intelligence (AI) is driving significant changes in the field
of atomic modeling, simulation, and design. AI-based potential energy models have been …
of atomic modeling, simulation, and design. AI-based potential energy models have been …
SPARC: Accurate and efficient finite-difference formulation and parallel implementation of density functional theory: Isolated clusters
S Ghosh, P Suryanarayana - Computer Physics Communications, 2017 - Elsevier
As the first component of SPARC (Simulation Package for Ab-initio Real-space
Calculations), we present an accurate and efficient finite-difference formulation and parallel …
Calculations), we present an accurate and efficient finite-difference formulation and parallel …