研飞ivySCI (青藤学术）

npj Computational Materials

短名	npj Comput Mater
Journal Impact	9.57
国际分区	MATERIALS SCIENCE, MULTIDISCIPLINARY(Q1)
期刊索引	SCI Q1中科院 1 区

ISSN	2057-3960
h-index	78
国内分区	材料科学(1区)材料科学材料科学综合(1区)材料科学物理化学(2区)

NPJ COMPUTATIONAL MATERIALS 发表高质量的研究论文，将计算方法应用于新材料的设计，并增强我们对现有材料的理解。促进这些目标的新计算技术以及对当前方法的改进也受到欢迎，补充计算结果的实验论文也是受欢迎的。代表性期刊范围包括：现有和新兴理论和模拟方法在材料研究中的应用和发展它们的全部（有机和无机）。新材料的结构和性能预测，例如通过“设计材料”范式。对材料基础、行为和性能的重大新理解。用于验证、支持和支持的综合/补充实验工作扩展计算结果。用于大数据集生成和材料数据挖掘的高通量技术——计算和实验。

期刊主页投稿网址

涉及主题	物理量子力学材料科学化学工程类计算机科学有机化学数学凝聚态物理复合材料热力学生物人工智能电气工程冶金光电子学纳米技术机器学习
出版信息	出版商: Nature Publishing Group，出版周期: ，期刊类型: journal
基本数据	创刊年份: 2015，原创研究文献占比: 97.22%，自引率:4.30%， Gold OA占比: 99.71%
平均审稿周期	网友分享经验：6 Weeks

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Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

2024-9-14

Hidden magnetism and split off flat bands in the insulator metal transition in VO2

2024-9-13

Emergence and transformation of polar skyrmion lattices via flexoelectricity

2024-9-13

Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks

2024-9-13

A unified moment tensor potential for silicon, oxygen, and silica

2024-9-13

Accelerating multiscale electronic stopping power predictions with time-dependent density functional theory and machine learning

2024-9-12

Construction frontier molecular orbital prediction model with transfer learning for organic materials

2024-9-11

Machine learning driven performance for hole transport layer free carbon-based perovskite solar cells

2024-9-10

Deep learning for symmetry classification using sparse 3D electron density data for inorganic compounds

2024-9-9

Bayesian optimization acquisition functions for accelerated search of cluster expansion convex hull of multi-component alloys

2024-9-8

Graph neural network coarse-grain force field for the molecular crystal RDX

2024-9-7

Towards end-to-end structure determination from x-ray diffraction data using deep learning

2024-9-7

Efficient simulations of charge density waves in the transition metal Dichalcogenide TiSe2

2024-9-7

Impact of heteroatoms and chemical functionalisation on crystal structure and carrier mobility of organic semiconductors

2024-9-4

Accelerated discovery of eutectic compositionally complex alloys by generative machine learning

2024-9-3

High temperature ferrimagnetic semiconductors by spin-dependent doping in high temperature antiferromagnets

2024-9-3

Co-training machine learning enables interpretable discovery of near-infrared phosphors with high performance

2024-9-3

Obtaining parallax-free X-ray powder diffraction computed tomography data with a self-supervised neural network

2024-9-2

Enhanced high harmonic efficiency through phonon-assisted photodoping effect

2024-9-2

Unraveling the electronic properties in SiO2 under ultrafast laser irradiation

2024-8-31

Machine learning surrogate for 3D phase-field modeling of ferroelectric tip-induced electrical switching

2024-8-30

A deep generative modeling architecture for designing lattice-constrained perovskite materials

2024-8-30

CELL: a Python package for cluster expansion with a focus on complex alloys

2024-8-30

Competing nucleation pathways in nanocrystal formation

2024-8-30

Distinct amorphization resistance in high-entropy MAX-phases (Ti, M)2AlC (M=Nb, Ta, V, Zr) under in situ irradiation

2024-8-30

Developing new electrocatalysts for oxygen evolution reaction via high throughput experiments and artificial intelligence

2024-8-28

Learning dislocation dynamics mobility laws from large-scale MD simulations

2024-8-27

Stretched non-negative matrix factorization

2024-8-27

The interlayer twist effectively regulates interlayer excitons in InSe/Sb van der Waals heterostructure

2024-8-26

Dynamical phase-field model of cavity electromagnonic systems

2024-8-26

High-throughput screening to identify two-dimensional layered phase-change chalcogenides for embedded memory applications

2024-8-25

Progress in computational understanding of ferroelectric mechanisms in HfO2

2024-8-23

Intrinsic multiferroicity in molybdenum oxytrihalides nanowires

2024-8-20

Self-supervised probabilistic models for exploring shape memory alloys

2024-8-15

Low-symmetry vacancy-related spin qubit in hexagonal boron nitride

2024-8-15

Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning

2024-8-15

Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders

2024-8-14

Accelerating the discovery of acceptor materials for organic solar cells by deep learning

2024-8-14

Internal consistency of multi-tier GW+EDMFT

2024-8-14

Intrinsic single-layer multiferroics in transition-metal-decorated chromium trihalides

2024-8-13

Advancing first-principles dielectric property prediction of complex microwave materials: an elemental-unit decomposition approach

2024-8-13

A momentum-resolved view of polaron formation in materials

2024-8-13

Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning

2024-8-12

Enhancing the thermal conductivity of semiconductor thin films via phonon funneling

2024-8-12

Machine learning assisted prediction of organic salt structure properties

2024-8-12

Berry curvature in the photoelectron emission delay

2024-8-9

Superconductivity of metastable dihydrides at ambient pressure

2024-8-9

A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks

2024-8-9

Unsupervised learning-aided extrapolation for accelerated design of superalloys

2024-8-4

Magnetic order in the computational 2D materials database (C2DB) from high throughput spin spiral calculations

2024-8-4

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npj Computational Materials

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最新文章

Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

Hidden magnetism and split off flat bands in the insulator metal transition in VO2

Emergence and transformation of polar skyrmion lattices via flexoelectricity

Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks

A unified moment tensor potential for silicon, oxygen, and silica

Accelerating multiscale electronic stopping power predictions with time-dependent density functional theory and machine learning

Construction frontier molecular orbital prediction model with transfer learning for organic materials

Machine learning driven performance for hole transport layer free carbon-based perovskite solar cells

Deep learning for symmetry classification using sparse 3D electron density data for inorganic compounds

Bayesian optimization acquisition functions for accelerated search of cluster expansion convex hull of multi-component alloys

Graph neural network coarse-grain force field for the molecular crystal RDX

Towards end-to-end structure determination from x-ray diffraction data using deep learning

Efficient simulations of charge density waves in the transition metal Dichalcogenide TiSe2

Impact of heteroatoms and chemical functionalisation on crystal structure and carrier mobility of organic semiconductors

Accelerated discovery of eutectic compositionally complex alloys by generative machine learning

High temperature ferrimagnetic semiconductors by spin-dependent doping in high temperature antiferromagnets

Co-training machine learning enables interpretable discovery of near-infrared phosphors with high performance

Obtaining parallax-free X-ray powder diffraction computed tomography data with a self-supervised neural network

Enhanced high harmonic efficiency through phonon-assisted photodoping effect

Unraveling the electronic properties in SiO2 under ultrafast laser irradiation

Machine learning surrogate for 3D phase-field modeling of ferroelectric tip-induced electrical switching

A deep generative modeling architecture for designing lattice-constrained perovskite materials

CELL: a Python package for cluster expansion with a focus on complex alloys

Competing nucleation pathways in nanocrystal formation

Distinct amorphization resistance in high-entropy MAX-phases (Ti, M)2AlC (M=Nb, Ta, V, Zr) under in situ irradiation

Developing new electrocatalysts for oxygen evolution reaction via high throughput experiments and artificial intelligence

Learning dislocation dynamics mobility laws from large-scale MD simulations

Stretched non-negative matrix factorization

The interlayer twist effectively regulates interlayer excitons in InSe/Sb van der Waals heterostructure

Dynamical phase-field model of cavity electromagnonic systems

High-throughput screening to identify two-dimensional layered phase-change chalcogenides for embedded memory applications

Progress in computational understanding of ferroelectric mechanisms in HfO2

Intrinsic multiferroicity in molybdenum oxytrihalides nanowires

Self-supervised probabilistic models for exploring shape memory alloys

Low-symmetry vacancy-related spin qubit in hexagonal boron nitride

Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning

Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders

Accelerating the discovery of acceptor materials for organic solar cells by deep learning

Internal consistency of multi-tier GW+EDMFT

Intrinsic single-layer multiferroics in transition-metal-decorated chromium trihalides

Advancing first-principles dielectric property prediction of complex microwave materials: an elemental-unit decomposition approach

A momentum-resolved view of polaron formation in materials

Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning

Enhancing the thermal conductivity of semiconductor thin films via phonon funneling

Machine learning assisted prediction of organic salt structure properties

Berry curvature in the photoelectron emission delay

Superconductivity of metastable dihydrides at ambient pressure

A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks

Unsupervised learning-aided extrapolation for accelerated design of superalloys

Magnetic order in the computational 2D materials database (C2DB) from high throughput spin spiral calculations

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