npj Computational Materials最新文献_第7页

Towards end-to-end structure determination from x-ray diffraction data using deep learning 利用深度学习从 X 射线衍射数据中实现端到端结构确定

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-09-07 DOI: 10.1038/s41524-024-01401-8

Gabe Guo, Judah Goldfeder, Ling Lan, Aniv Ray, Albert Hanming Yang, Boyuan Chen, Simon J. L. Billinge, Hod Lipson

{"title":"Towards end-to-end structure determination from x-ray diffraction data using deep learning","authors":"Gabe Guo, Judah Goldfeder, Ling Lan, Aniv Ray, Albert Hanming Yang, Boyuan Chen, Simon J. L. Billinge, Hod Lipson","doi":"10.1038/s41524-024-01401-8","DOIUrl":"https://doi.org/10.1038/s41524-024-01401-8","url":null,"abstract":"Powder crystallography is the experimental science of determining the structure of molecules provided in crystalline-powder form, by analyzing their x-ray diffraction (XRD) patterns. Since many materials are readily available as crystalline powder, powder crystallography is of growing usefulness to many fields. However, powder crystallography does not have an analytically known solution, and therefore the structural inference typically involves a laborious process of iterative design, structural refinement, and domain knowledge of skilled experts. A key obstacle to fully automating the inference process computationally has been formulating the problem in an end-to-end quantitative form that is suitable for machine learning, while capturing the ambiguities around molecule orientation, symmetries, and reconstruction resolution. Here we present an ML approach for structure determination from powder diffraction data. It works by estimating the electron density in a unit cell using a variational coordinate-based deep neural network. We demonstrate the approach on computed powder x-ray diffraction (PXRD), along with partial chemical composition information, as input. When evaluated on theoretically simulated data for the cubic and trigonal crystal systems, the system achieves up to 93.4% average similarity (as measured by structural similarity index) with the ground truth on unseen materials, both with known and partially-known chemical composition information, showing great promise for successful structure solution even from degraded and incomplete input data. The approach does not presuppose a crystalline structure and the approach are readily extended to other situations such as nanomaterials and textured samples, paving the way to reconstruction of yet unresolved nanostructures.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"67 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144116","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

Graph neural network coarse-grain force field for the molecular crystal RDX 分子晶体 RDX 的图神经网络粗粒力场

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-09-07 DOI: 10.1038/s41524-024-01407-2

Brian H. Lee, James P. Larentzos, John K. Brennan, Alejandro Strachan

{"title":"Graph neural network coarse-grain force field for the molecular crystal RDX","authors":"Brian H. Lee, James P. Larentzos, John K. Brennan, Alejandro Strachan","doi":"10.1038/s41524-024-01407-2","DOIUrl":"https://doi.org/10.1038/s41524-024-01407-2","url":null,"abstract":"Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks (GNN) and data from an all-atom simulations and apply it to the high-energy density material RDX. We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder. The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces. The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"46 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144118","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

Impact of heteroatoms and chemical functionalisation on crystal structure and carrier mobility of organic semiconductors 杂原子和化学功能化对有机半导体晶体结构和载流子迁移率的影响

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-09-04 DOI: 10.1038/s41524-024-01397-1

S. Hutsch, F. Ortmann

{"title":"Impact of heteroatoms and chemical functionalisation on crystal structure and carrier mobility of organic semiconductors","authors":"S. Hutsch, F. Ortmann","doi":"10.1038/s41524-024-01397-1","DOIUrl":"https://doi.org/10.1038/s41524-024-01397-1","url":null,"abstract":"The substitution of heteroatoms and the functionalisation of molecules are established strategies in chemical synthesis. They target the precise tuning of the electronic properties of hydrocarbon molecules to improve their performance in various applications and increase their versatility. Modifications to the molecular structure often lead to simultaneous changes in the morphology such as different crystal structures. These changes can have a stronger and unpredictable impact on the targeted property. The complex relationships between substitution/functionalization in chemical synthesis and the resulting modifications of properties in thin films or crystals are difficult to predict and remain elusive. Here we address these effects for charge carrier transport in organic crystals by combining simulations of carrier mobilities with crystal structure prediction based on density functional theory and density functional tight binding theory. This enables the prediction of carrier mobilities based solely on the molecular structure and allows for the investigation of chemical modifications prior to synthesis and characterisation. Studying nine specific molecules with tetracene and rubrene as reference compounds along with their combined modifications of the molecular cores and additional functionalisations, we unveil systematic trends for the carrier mobilities of their polymorphs. The positive effect of phenyl groups that is responsible for the marked differences between tetracene and rubrene can be transferred to other small molecules such as NDT and NBT leading to a mobility increase by large factors of about five.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"23 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142138000","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

Co-training machine learning enables interpretable discovery of near-infrared phosphors with high performance 联合训练机器学习可实现高性能近红外荧光粉的可解释性发现

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-09-03 DOI: 10.1038/s41524-024-01395-3

Wei Xu, Rui Wang, Chunhai Hu, Guilin Wen, Junqi Cui, Longjiang Zheng, Zhen Sun, Yungang Zhang, Zhiguo Zhang

{"title":"Co-training machine learning enables interpretable discovery of near-infrared phosphors with high performance","authors":"Wei Xu, Rui Wang, Chunhai Hu, Guilin Wen, Junqi Cui, Longjiang Zheng, Zhen Sun, Yungang Zhang, Zhiguo Zhang","doi":"10.1038/s41524-024-01395-3","DOIUrl":"https://doi.org/10.1038/s41524-024-01395-3","url":null,"abstract":"Near-infrared (NIR) phosphors based on Cr3+ doped garnets present great potential in the next generation of NIR light sources. Nevertheless, the huge searching space for the garnet composition makes the rapid discovery of NIR phosphors with high performance remain a great challenge for the scientific community. Herein, a generalizable machine learning (ML) strategy is designed to accelerate the exploration of innovative NIR phosphors via establishing the relationship between key parameters and emission peak wavelength (EPW). We propose a semi-supervised co-training model based on kernel ridge regression (KRR) and support vector regression (SVR), which successfully establishes an expanded dataset with unlabeled dataset (previously unidentified garnets), addressing the overfitting issue resulted from a small dataset and greatly improving the model generalization capability. The model is then interpreted to extract valuable insights into the contribution originated from different features. And a new type NIR luminescent material of Lu3Y2Ga3O12: Cr3+ (EPW~750 nm) is efficiently screened, which demonstrates a high internal (external) quantum efficiency of 97.1% (38.8%) and good thermal stability, particularly exhibiting promising application in the NIR phosphor-converted LEDs (pc-LED). These results suggest the strategy proposed in this work could provide new viewpoint and direction for developing NIR luminescence materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"6 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123607","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

High temperature ferrimagnetic semiconductors by spin-dependent doping in high temperature antiferromagnets 在高温反铁磁体中通过自旋掺杂实现高温铁磁半导体

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-09-03 DOI: 10.1038/s41524-024-01362-y

Jia-Wen Li, Gang Su, Bo Gu

{"title":"High temperature ferrimagnetic semiconductors by spin-dependent doping in high temperature antiferromagnets","authors":"Jia-Wen Li, Gang Su, Bo Gu","doi":"10.1038/s41524-024-01362-y","DOIUrl":"https://doi.org/10.1038/s41524-024-01362-y","url":null,"abstract":"To realize room temperature ferromagnetic (FM) semiconductors is still a challenge in spintronics. Many antiferromagnetic (AFM) insulators and semiconductors with high Neel temperature TN are obtained in experiments, such as LaFeO3, BiFeO3, etc. High concentrations of magnetic impurities can be doped into these AFM materials, but AFM state with very tiny net magnetic moments was obtained in experiments because the magnetic impurities were equally doped into the spin up and down sublattices of the AFM materials. Here, we propose that the effective magnetic field provided by a FM substrate could guarantee the spin-dependent doping in AFM materials, where the doped magnetic impurities prefer one sublattice of spins, and the ferrimagnetic (FIM) materials are obtained. To demonstrate this proposal, we study the Mn-doped AFM insulator LaFeO3 with FM substrate of Fe metal by the density functional theory (DFT) calculations. It is shown that the doped magnetic Mn impurities prefer to occupy one sublattice of the AFM insulator and introduce large magnetic moments in La(Fe, Mn)O3. For the AFM insulator LaFeO3 with high TN = 740 K, several FIM semiconductors with high Curie temperature TC > 300 K and the band gap less than 2 eV are obtained by DFT calculations when 1/8 or 1/4 Fe atoms in LaFeO3 are replaced by the other 3d, 4d transition metal elements. The large magneto-optical Kerr effect (MOKE) is obtained in these LaFeO3-based FIM semiconductors. In addition, the FIM semiconductors with high TC are also obtained by spin-dependent doping in some other AFM materials with high TN, including BiFeO3, SrTcO3, CaTcO3, etc. Our theoretical results propose a way to obtain high TC FIM semiconductors by spin-dependent doping in high TN AFM insulators and semiconductors.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"42 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130758","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

Accelerated discovery of eutectic compositionally complex alloys by generative machine learning 通过生成式机器学习加速发现共晶成分复杂的合金

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-09-03 DOI: 10.1038/s41524-024-01385-5

Z. Q. Chen, Y. H. Shang, X. D. Liu, Y. Yang

{"title":"Accelerated discovery of eutectic compositionally complex alloys by generative machine learning","authors":"Z. Q. Chen, Y. H. Shang, X. D. Liu, Y. Yang","doi":"10.1038/s41524-024-01385-5","DOIUrl":"https://doi.org/10.1038/s41524-024-01385-5","url":null,"abstract":"Eutectic alloys have garnered significant attention due to their promising mechanical and physical properties, as well as their technological relevance. However, the discovery of eutectic compositionally complex alloys (ECCAs) (e.g. high entropy eutectic alloys) remains a formidable challenge in the vast and intricate compositional space, primarily due to the absence of readily available phase diagrams. To address this issue, we have developed an explainable machine learning (ML) framework that integrates conditional variational autoencoder (CVAE) and artificial neutral network (ANN) models, enabling direct generation of ECCAs. To overcome the prevalent problem of data imbalance encountered in data-driven ECCA design, we have incorporated thermodynamics-derived data descriptors and employed K-means clustering methods for effective data pre-processing. Leveraging our ML framework, we have successfully discovered dual- or even tri-phased ECCAs, spanning from quaternary to senary alloy systems, which have not been previously reported in the literature. These findings hold great promise and indicate that our ML framework can play a pivotal role in accelerating the discovery of technologically significant ECCAs.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"7 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123606","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

Obtaining parallax-free X-ray powder diffraction computed tomography data with a self-supervised neural network 利用自监督神经网络获取无视差 X 射线粉末衍射计算机断层扫描数据

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-09-02 DOI: 10.1038/s41524-024-01389-1

H. Dong, S. D. M. Jacques, K. T. Butler, O. Gutowski, A.-C. Dippel, M. von Zimmerman, A. M. Beale, A. Vamvakeros

引用次数: 0

Enhanced high harmonic efficiency through phonon-assisted photodoping effect 通过声子辅助光掺杂效应提高高次谐波效率

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-09-02 DOI: 10.1038/s41524-024-01399-z

Jin Zhang, Ofer Neufeld, Nicolas Tancogne-Dejean, I-Te Lu, Hannes Hübener, Umberto De Giovannini, Angel Rubio

{"title":"Enhanced high harmonic efficiency through phonon-assisted photodoping effect","authors":"Jin Zhang, Ofer Neufeld, Nicolas Tancogne-Dejean, I-Te Lu, Hannes Hübener, Umberto De Giovannini, Angel Rubio","doi":"10.1038/s41524-024-01399-z","DOIUrl":"https://doi.org/10.1038/s41524-024-01399-z","url":null,"abstract":"High-harmonic generation (HHG) has emerged as a central technique in attosecond science and strong-field physics, providing a tool for investigating ultrafast dynamics. However, the microscopic mechanism of HHG in solids is still under debate, and it is unclear how it is modified in the ubiquitous presence of phonons. Here we theoretically investigate the role of collectively coherent vibrations in HHG in a wide range of solids (e.g., hBN, graphite, 2H-MoS2, and diamond). We predict that phonon-assisted high harmonic yields can be significantly enhanced, compared to the phonon-free case – up to a factor of ~20 for a transverse optical phonon in bulk hBN. We also show that the emitted harmonics strongly depend on the character of the pumped vibrational modes. Through state-of-the-art ab initio calculations, we elucidate the physical origin of the HHG yield enhancement – phonon-assisted photoinduced carrier doping, which plays a paramount role in both intraband and interband electron dynamics. Our research illuminates a clear pathway toward comprehending phonon-mediated nonlinear optical processes within materials, offering a powerful tool to deliberately engineer and govern solid-state high harmonics.<figure></figure>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"51 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142118185","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

Unraveling the electronic properties in SiO2 under ultrafast laser irradiation 揭示超快激光照射下二氧化硅的电子特性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-31 DOI: 10.1038/s41524-024-01350-2

Arshak Tsaturyan, Elena Kachan, Razvan Stoian, Jean-Philippe Colombier

{"title":"Unraveling the electronic properties in SiO2 under ultrafast laser irradiation","authors":"Arshak Tsaturyan, Elena Kachan, Razvan Stoian, Jean-Philippe Colombier","doi":"10.1038/s41524-024-01350-2","DOIUrl":"https://doi.org/10.1038/s41524-024-01350-2","url":null,"abstract":"First-principles simulations were conducted to explore various electronic properties of crystalline SiO2 (α-quartz) under ultrafast laser irradiation. Employing Density Functional Perturbation Theory and the many-body (GW) approximation, we calculated the impact of thermally excited electrons on the electronic specific heat, electron pressure, effective mass, deformation potential, electron-phonon coupling and electron relaxation time of quartz, covering a wide range of electron temperatures, up to 100,000 K. We show that the electron-phonon relaxation time of highly-excited quartz becomes twice faster compared to low-excited states. The deformation potential, which dictates atomic displacement, has a non-monotonic behavior with a well-pronounced minimum at around 16,000 K (2.7 × 1021 cm−3 of excited electrons) where the bond ionicity of the Si-O starts decreasing followed by a cohesion loss at 35,000 K due to the pressure exerted by the excited electrons on the lattice. Consequently, our calculated data, illustrating the evolution of physical parameters, can facilitate simulations of laser-matter interactions and provide predictive insights into the behavior of quartz under experimental conditions.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"66 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101646","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

Competing nucleation pathways in nanocrystal formation 纳米晶体形成过程中的竞争成核途径

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-08-30 DOI: 10.1038/s41524-024-01371-x

Carlos R. Salazar, Akshay Krishna Ammothum Kandy, Jean Furstoss, Quentin Gromoff, Jacek Goniakowski, Julien Lam

引用次数: 0