npj Computational Materials最新文献

Strain and ligand effects in the 1-D limit: reactivity of steps

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-25 DOI: 10.1038/s41524-025-01616-3

Onyinyechukwu Goodness Njoku, Paige Fronczak, Kara Smeltz, Ian T. McCrum

引用次数: 0

Photoinduced ferroelectric phase transition triggering photocatalytic water splitting

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-25 DOI: 10.1038/s41524-025-01601-w

Jun Wen, Zhi-rui Luo, Lin-can Fang, Wen-xian Chen, Gui-lin Zhuang

{"title":"Photoinduced ferroelectric phase transition triggering photocatalytic water splitting","authors":"Jun Wen, Zhi-rui Luo, Lin-can Fang, Wen-xian Chen, Gui-lin Zhuang","doi":"10.1038/s41524-025-01601-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01601-w","url":null,"abstract":"Utilizing two-dimensional (2D) ferroelectric semiconductors for photocatalytic water splitting (PWS) to produce clean hydrogen fuel shows promise but faces performance regulation challenges. This study employs real-time time-dependent density functional theory (rt-TDDFT) and first-principle calculations to propose a “one stone, two birds” strategy: light induces ferroelectric phase transitions and triggers PWS on monolayer Hf2Ge2S6. Electronically, monolayer Hf2Ge2S6 exhibits excellent stability, mechanical properties, an appropriate band gap, optimal band edge positions, and broad light absorption. Its ferroelectric (FE) phase promotes oxygen evolution reaction(OER), while the paraelectric (PE) phase enhances hydrogen evolution reaction(HER). Specifically, applying 10% compressive strain effectively suppresses OER on the FE phase, while a mere 2% tensile strain can induce complete spontaneity in HER on the PE phase. Finally, rt-TDDFT simulation results demonstrate that laser pulses can drive effective ion displacements of Ge atoms in monolayer Hf2Ge2S6 and thereby generate the transition from FE to PE, which is attributed to the maintenance of charge distribution asymmetry through internal atomic electron transfers. More importantly, this recyclable ferroelectric photocatalyst, activated by light and electric fields, effectively prevents performance drawbacks from pure electric fields, demonstrating that a photoelectric alternating field can regulate PWS performance. These findings demonstrate that a photoelectric alternating field is an effective strategy to regulate photocatalytic performance for PWS.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"44 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A machine learning model with minimize feature parameters for multi-type hydrogen evolution catalyst prediction

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-24 DOI: 10.1038/s41524-025-01607-4

Chao Wang, Bing Wang, Changhao Wang, Aojian Li, Zhipeng Chang, Ruzhi Wang

引用次数: 0

Uncertainty quantification for neural network potential foundation models

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-24 DOI: 10.1038/s41524-025-01572-y

Jenna A. Bilbrey, Jesun S. Firoz, Mal-Soon Lee, Sutanay Choudhury

引用次数: 0

Advancing organic photovoltaic materials by machine learning-driven design with polymer-unit fingerprints

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-24 DOI: 10.1038/s41524-025-01608-3

Xiumin Liu, Xinyue Zhang, Ye Sheng, Zihe Zhang, Pan Xiong, Xuehai Ju, Junwu Zhu, Caichao Ye

{"title":"Advancing organic photovoltaic materials by machine learning-driven design with polymer-unit fingerprints","authors":"Xiumin Liu, Xinyue Zhang, Ye Sheng, Zihe Zhang, Pan Xiong, Xuehai Ju, Junwu Zhu, Caichao Ye","doi":"10.1038/s41524-025-01608-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01608-3","url":null,"abstract":"To enhance the power conversion efficiency (PCE) of organic photovoltaic (OPV) cells, the identification of high-performance polymer/macromolecule materials and understanding their relationship with photovoltaic performance before synthesis are critical objectives. In this study, we developed five algorithms using a dataset of 1343 experimentally validated OPV NFA acceptor materials. The random forest (RF) algorithm exhibited the best predictive performance for material design and screening. Additionally, we explored a newly developed polymer/macromolecule structure expression, polymer-unit fingerprint (PUFp), which outperformed the molecular access system (MACCS) across diverse machine learning (ML) algorithms. PUFp facilitated the interpretability of structure-property relationships, enabling PCE predictions of conjugated polymers/macromolecules formed by the combination of donor (D) and acceptor (A) units. Our PUFp-ML model efficiently pre-evaluated and classified numerous acceptor materials, identifying and screening the two most promising NFA candidates. The proposed framework demonstrates the ability to design novel materials based on PUFp-ML-established feature/substructure-property relationships, providing rational design guidelines for developing high-performance OPV acceptors. These methodologies are transferable to donor materials, thereby supporting accelerated material discovery and offering insights for designing innovative OPV materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"13 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ELEQTRONeX: A GPU-accelerated exascale framework for non-equilibrium quantum transport in nanomaterials

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-24 DOI: 10.1038/s41524-025-01604-7

Saurabh S. Sawant, François Léonard, Zhi Yao, Andrew Nonaka

{"title":"ELEQTRONeX: A GPU-accelerated exascale framework for non-equilibrium quantum transport in nanomaterials","authors":"Saurabh S. Sawant, François Léonard, Zhi Yao, Andrew Nonaka","doi":"10.1038/s41524-025-01604-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01604-7","url":null,"abstract":"Non-equilibrium electronic quantum transport is crucial for existing and envisioned electronic, optoelectronic, and spintronic devices. Encompassing atomistic to mesoscopic length scales in the same nonequilibrium device simulations has been challenging due to the computational cost of high-fidelity coupled multiphysics and multiscale requirements. In this work, we present ELEQTRONeX (ELEctrostatic Quantum TRansport modeling Of Nanomaterials at eXascale), a massively parallel GPU-accelerated framework for self-consistently solving the nonequilibrium Green’s function formalism and electrostatics in complex device geometries. By customizing algorithms for GPU multithreading, we achieve significant improvement in computational time, and excellent scaling on up to 512 GPUs and billions of spatial grid cells. We validate our code by computing band structures, current-voltage characteristics, conductance, and drain-induced barrier lowering for various 3D configurations of carbon nanotube field-effect transistors, and demonstrate its suitability for complex device/material geometries where periodic approaches are not feasible, such as arrays of misaligned carbon nanotubes requiring fully 3D simulations.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"34 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143872933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Insights into oxygen diffusion in rare earth disilicate environmental barrier coatings

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-24 DOI: 10.1038/s41524-024-01508-y

Shiqiang Hao, Richard P. Oleksak, Ömer N. Doğan, Michael C. Gao

{"title":"Insights into oxygen diffusion in rare earth disilicate environmental barrier coatings","authors":"Shiqiang Hao, Richard P. Oleksak, Ömer N. Doğan, Michael C. Gao","doi":"10.1038/s41524-024-01508-y","DOIUrl":"https://doi.org/10.1038/s41524-024-01508-y","url":null,"abstract":"Environmental barrier coatings (EBC) are crucial for the use of SiC-based ceramic matrix composites in high-temperature combustion environments, yet knowledge of oxygen diffusion in these coatings is limited. This study investigates oxygen diffusion dynamics in the β-RE2Si2O7 system to minimize oxygen penetration in rare earth disilicates. We analyze defect formation energy under varying oxygen conditions, identifying key diffusion mechanisms. In oxygen-rich environments, the most favorable neutral interstitial oxygen diffuses along the [110] direction. In oxygen-poor conditions, neutral oxygen vacancies rotate around Y and Si atoms, exhibiting a diffusivity of 6.59×10−22 m2/s at 1500 K for β-Yb2Si2O7. Under intermediate oxygen levels, charged interstitial oxygen diffuses via concerted interstitialcy along the [001] direction with a diffusivity of 6.21×10−17 m2/s. Additionally, alloying rare earth Y with Er and Yb increases diffusion barriers, contributing to improved EBC performance in extreme environments. The insights gained provides valuable guidance for designing robust coatings tailored to withstand extreme operational environments.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"14 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchy-boosted funnel learning for identifying semiconductors with ultralow lattice thermal conductivity

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-22 DOI: 10.1038/s41524-025-01583-9

Mengfan Wu, Shenshen Yan, Jie Ren

{"title":"Hierarchy-boosted funnel learning for identifying semiconductors with ultralow lattice thermal conductivity","authors":"Mengfan Wu, Shenshen Yan, Jie Ren","doi":"10.1038/s41524-025-01583-9","DOIUrl":"https://doi.org/10.1038/s41524-025-01583-9","url":null,"abstract":"Data-driven machine learning (ML) has demonstrated tremendous potential in material property predictions. However, the scarcity of materials data with costly property labels in the vast chemical space presents a significant challenge for ML in efficiently predicting properties and uncovering structure-property relationships. Here, we propose a novel hierarchy-boosted funnel learning (HiBoFL) framework, which is successfully applied to identify semiconductors with ultralow lattice thermal conductivity (κL). By training on only a few hundred materials targeted by unsupervised learning from a pool of hundreds of thousands, we achieve efficient and interpretable supervised predictions of ultralow κL, thereby circumventing large-scale brute-force ab initio calculations without clear objectives. As a result, we provide a list of candidates with ultralow κL for potential thermoelectric applications and discover a new factor that significantly influences structural anharmonicity. This HiBoFL framework offers a novel practical pathway for accelerating the discovery of functional materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"37 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of BCC/FCC dual-solid solution refractory high-entropy alloys through CALPHAD, machine learning and experimental methods 通过 CALPHAD、机器学习和实验方法设计 BCC/FCC 双固溶耐火高熵合金

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-20 DOI: 10.1038/s41524-025-01597-3

Longjun He, Chaoyue Wang, Mina Zhang, Jinghao Li, Tianlun Chen, Xianglin Zhou

{"title":"Design of BCC/FCC dual-solid solution refractory high-entropy alloys through CALPHAD, machine learning and experimental methods","authors":"Longjun He, Chaoyue Wang, Mina Zhang, Jinghao Li, Tianlun Chen, Xianglin Zhou","doi":"10.1038/s41524-025-01597-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01597-3","url":null,"abstract":"Refractory high-entropy alloys (RHEAs) typically exhibit a body-centered cubic (BCC) structure with excellent strength but poor ductility, which limits their practical applications. In this study, we designed BCC/FCC dual-phase RHEAs through phase diagram calculations and neural network modeling. The analysis of the binary phase formation relationships among alloying elements enabled the preliminary screening and inclusion of 13 liquid-phase-separated BCC/FCC dual-phase RHEAs in the training dataset for the machine learning model. Two strategic binary classifications of this dataset were conducted on HEAs to identify their “multiphase” and “solid solution” structures. Consequently, two neural network models were trained, achieving accuracies of 89.52% and 89.83%, respectively. These models predicted 51 BCC/FCC dual-phase RHEAs among 504 novel RHEAs, representing the first successful compositional design of metastable BCC/FCC dual-phase RHEAs. The arc-melted alloys exhibited refined dendritic structure. This study provides valuable insights for the tailored design of novel multi-phase RHEAs to achieve specific targeted properties.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"24 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143853576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

P-type dopability in Half-Heusler thermoelectric semiconductors

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-19 DOI: 10.1038/s41524-025-01595-5

Lirong Hu, Shen Han, Tiejun Zhu, Tianqi Deng, Chenguang Fu

引用次数: 0