npj Computational Materials

Titel Veröffentlichungsdatum Sprache Zitate
A universal strategy for the creation of machine learning-based atomistic force fields2017/09/18English176
Atomistic Line Graph Neural Network for improved materials property predictions2021/11/15English172
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks2019/05/17English167
Machine learning guided appraisal and exploration of phase design for high entropy alloys2019/12/20English166
Theoretical prediction of high melting temperature for a Mo–Ru–Ta–W HCP multiprincipal element alloy2021/01/04English165
Exchange-correlation functionals for band gaps of solids: benchmark, reparametrization and machine learning2020/07/10English157
Machine learning hydrogen adsorption on nanoclusters through structural descriptors2018/07/19English156
Efficient first-principles prediction of solid stability: Towards chemical accuracy2018/03/09English151
Inverse-designed spinodoid metamaterials2020/06/05English149
Effective mass and Fermi surface complexity factor from ab initio band structure calculations2017/02/23English143
Discovery of high-entropy ceramics via machine learning2020/05/01English132
De novo exploration and self-guided learning of potential-energy surfaces2019/10/11English131
Genetic algorithms for computational materials discovery accelerated by machine learning2019/04/10English128
Virtual screening of inorganic materials synthesis parameters with deep learning2017/12/01English128
Physics and applications of charged domain walls2018/11/30English128
Identifying Pb-free perovskites for solar cells by machine learning2019/03/26English128
Ab initio theory of the negatively charged boron vacancy qubit in hexagonal boron nitride2020/04/24English121
Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures2020/02/04English120
Completing density functional theory by machine learning hidden messages from molecules2020/05/05English119
Complex strengthening mechanisms in the NbMoTaW multi-principal element alloy2020/06/02English118
First-principles calculations of lattice dynamics and thermal properties of polar solids2016/05/13English118
A critical examination of compound stability predictions from machine-learned formation energies2020/07/10English116
Coarse-graining auto-encoders for molecular dynamics2019/12/18English113
Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials2018/07/16English112
A property-oriented design strategy for high performance copper alloys via machine learning2019/08/27English111
Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials2019/05/01English109
Insights into the design of thermoelectric Mg3Sb2 and its analogs by combining theory and experiment2019/07/17English109
Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS22019/02/01English109
Nanotwinned and hierarchical nanotwinned metals: a review of experimental, computational and theoretical efforts2018/02/05English109
Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials2020/06/26English107