npj Computational Materials最新文献

{"title":"Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints","authors":"Ryong-Gyu Lee, Yong-Hoon Kim","doi":"10.1038/s41524-024-01433-0","DOIUrl":"https://doi.org/10.1038/s41524-024-01433-0","url":null,"abstract":"<p>The self-consistent field (SCF) generation of the three-dimensional (3D) electron density distribution (<i>ρ</i>) represents a fundamental aspect of density functional theory (DFT) and related first-principles calculations, and how one can shorten or bypass the SCF loop represents a critical question in electronic structure theory from both practical and fundamental standpoints. Herein, a machine learning strategy, DeepSCF, is presented in which the map between the SCF <i>ρ</i> and the initial guess density (<i>ρ</i>₀) constructed by the summation of neutral atomic densities is learned using 3D convolutional neural networks (CNNs). High accuracy and transferability of DeepSCF are achieved by first encoding <i>ρ</i>₀ on a 3D grid and then expanding the input features to include atomic fingerprints beyond <i>ρ</i>₀. The prediction of the residual density (δ<i>ρ</i>) rather than <i>ρ</i> itself is targeted, and given that δ<i>ρ</i> is indicative of chemical bonding information, a dataset of small-sized organic molecules featuring diverse bonding characters is adopted. The fidelity of DeepSCF is finally enhanced by subjecting the atomic geometries of the dataset to random rotations and strains. The effectiveness of DeepSCF is demonstrated using a complex carbon nanotube-based DNA sequencer model. This work evidences that the nearsightedness in electronic structure can be optimally represented via the spatial locality in CNNs, offering insight into the success of various machine learning-based atomistic materials simulations.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"30 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488773","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}

{"title":"Enhanced spin Hall ratio in two-dimensional semiconductors","authors":"Jiaqi Zhou, Samuel Poncé, Jean-Christophe Charlier","doi":"10.1038/s41524-024-01434-z","DOIUrl":"https://doi.org/10.1038/s41524-024-01434-z","url":null,"abstract":"<p>The conversion efficiency from charge current to spin current via the spin Hall effect is evaluated by the spin Hall ratio (SHR). Through state-of-the-art ab initio calculations involving both charge conductivity and spin Hall conductivity, we report the SHRs of the III-V monolayer family, revealing an ultrahigh ratio of 0.58 in the hole-doped GaAs monolayer. In order to find more promising 2D materials, a descriptor for high SHR is proposed and applied to a high-throughput database, which provides the fully relativistic band structures and Wannier Hamiltonians of 216 exfoliable monolayer semiconductors and has been released to the community. Among potential candidates for high SHR, the MXene monolayer Sc₂CCl₂ is identified with the proposed descriptor and confirmed by computation, demonstrating the descriptor validity for high SHR materials discovery.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"67 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142487422","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}

{"title":"Active learning accelerated exploration of single-atom local environments in multimetallic systems for oxygen electrocatalysis","authors":"Hoje Chun, Jaclyn R. Lunger, Jeung Ku Kang, Rafael Gómez-Bombarelli, Byungchan Han","doi":"10.1038/s41524-024-01432-1","DOIUrl":"https://doi.org/10.1038/s41524-024-01432-1","url":null,"abstract":"<p>Single-atom catalysts (SACs) with multiple active sites exhibit high activity for a wide range of sluggish reactions, but identifying optimal multimetallic SAC is challenging due to the vast design space. Here, we present a self-driving computational strategy that combines first-principles calculations and equivariant graph neural network (GNN) to explore over 30,000 binary metallic sites with varying combinations of 3<i>d</i> transition metals and different ligand environments for oxygen reduction and evolution reactions (ORR/OER). Active learning facilitates the investigation of the search space by balancing the exploration of unseen atomic structures with the exploitation of the active ones. The GNN learns the chemical environments to capture composition-structure-property relationships for ORR/OER activity and selectivity. The computational predictions of promising Co-Fe, Co-Co, and Co-Zn metal pairs are consistent with the state-of-the-art results of experimental measurements reported in the literature. This approach can be extended to a broader class of multi-element high entropic materials systems.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"8 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451432","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}

{"title":"MD-HIT: Machine learning for material property prediction with dataset redundancy control","authors":"Qin Li, Nihang Fu, Sadman Sadeed Omee, Jianjun Hu","doi":"10.1038/s41524-024-01426-z","DOIUrl":"https://doi.org/10.1038/s41524-024-01426-z","url":null,"abstract":"<p>Materials datasets usually contain many redundant (highly similar) materials due to the tinkering approach historically used in material design. This redundancy skews the performance evaluation of machine learning (ML) models when using random splitting, leading to overestimated predictive performance and poor performance on out-of-distribution samples. This issue is well-known in bioinformatics for protein function prediction, where tools like CD-HIT are used to reduce redundancy by ensuring sequence similarity among samples greater than a given threshold. In this paper, we survey the overestimated ML performance in materials science for material property prediction and propose MD-HIT, a redundancy reduction algorithm for material datasets. Applying MD-HIT to composition- and structure-based formation energy and band gap prediction problems, we demonstrate that with redundancy control, the prediction performances of the ML models on test sets tend to have relatively lower performance compared to the model with high redundancy, but better reflect models’ true prediction capability.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"25 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448117","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}

{"title":"Accurate formation enthalpies of solids using reaction networks","authors":"Rasmus Fromsejer, Bjørn Maribo-Mogensen, Georgios M. Kontogeorgis, Xiaodong Liang","doi":"10.1038/s41524-024-01404-5","DOIUrl":"https://doi.org/10.1038/s41524-024-01404-5","url":null,"abstract":"<p>Crystalline solids play a fundamental role in a host of materials and technologies, ranging from pharmaceuticals to renewable energy. The thermodynamic properties of these solids are crucial determinants of their stability and therefore their behavior. The advent of large density functional theory databases with properties of solids has stimulated research on predictive methods for their thermodynamic properties, especially for the enthalpy of formation Δ_f<i>H</i>. Increasingly sophisticated artificial intelligence and machine learning (ML) models have primarily driven development in this field in recent years. However, these models can suffer from lack of generalizability and poor interpretability. In this work, we explore a different route and develop and evaluate a framework for the application of reaction network (RN) theory to the prediction of Δ_f<i>H</i> of crystalline solids. For an experimental dataset of 1550 compounds we are able to obtain a mean absolute error w.r.t Δ_f<i>H</i> of 29.6 meV atom⁻¹ using the RN approach. This performance is better than existing ML-based predictive methods and close to the experimental uncertainty. Moreover, we show that the RN framework allows for straightforward estimation of the uncertainty of the predictions.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"9 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431461","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}

{"title":"Unsupervised deep denoising for four-dimensional scanning transmission electron microscopy","authors":"Alireza Sadri, Timothy C. Petersen, Emmanuel W. C. Terzoudis-Lumsden, Bryan D. Esser, Joanne Etheridge, Scott D. Findlay","doi":"10.1038/s41524-024-01428-x","DOIUrl":"https://doi.org/10.1038/s41524-024-01428-x","url":null,"abstract":"<p>By simultaneously achieving high spatial and angular sampling resolution, four dimensional scanning transmission electron microscopy (4D STEM) is enabling analysis techniques that provide great insight into the atomic structure of materials. Applying these techniques to scientifically and technologically significant beam-sensitive materials remains challenging because the low doses needed to minimise beam damage lead to noisy data. We demonstrate an unsupervised deep learning model that leverages the continuity and coupling between the probe position and the electron scattering distribution to denoise 4D STEM data. By restricting the network complexity it can learn the geometric flow present but not the noise. Through experimental and simulated case studies, we demonstrate that denoising as a preprocessing step enables 4D STEM analysis techniques to succeed at lower doses, broadening the range of materials that can be studied using these powerful structure characterization techniques.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"10 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431459","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}

{"title":"Phonon-limited mobility for electrons and holes in highly-strained silicon","authors":"Nicolas Roisin, Guillaume Brunin, Gian-Marco Rignanese, Denis Flandre, Jean-Pierre Raskin, Samuel Poncé","doi":"10.1038/s41524-024-01425-0","DOIUrl":"https://doi.org/10.1038/s41524-024-01425-0","url":null,"abstract":"<p>Strain engineering is a widely used technique for enhancing the mobility of charge carriers in semiconductors, but its effect is not fully understood. In this work, we perform first-principles calculations to explore the variations of the mobility of electrons and holes in silicon upon deformation by uniaxial strain up to 2% in the [100] crystal direction. We compute the <i>π</i>₁₁ and <i>π</i>₁₂ electron piezoresistances based on the low-strain change of resistivity with temperature in the range 200 K to 400 K, in excellent agreement with experiment. We also predict them for holes which were only measured at room temperature. Remarkably, for electrons in the transverse direction, we predict a minimum room-temperature mobility about 1200 cm²V⁻¹s⁻¹ at 0.3% uniaxial tensile strain while we observe a monotonous increase of the longitudinal transport, reaching a value of 2200 cm²V⁻¹s⁻¹ at high strain. We confirm these findings experimentally using four-point bending measurements, establishing the reliability of our first-principles calculations. For holes, we find that the transport is almost unaffected by strain up to 0.3% uniaxial tensile strain and then rises significantly, more than doubling at 2% strain. Our findings open new perspectives to boost the mobility by applying a stress in the [100] direction. This is particularly interesting for holes for which shear strain was thought for a long time to be the only way to enhance the mobility.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"9 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142415501","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}

{"title":"Fast prediction of anharmonic vibrational spectra for complex organic molecules","authors":"Mattia Miotto, Lorenzo Monacelli","doi":"10.1038/s41524-024-01400-9","DOIUrl":"https://doi.org/10.1038/s41524-024-01400-9","url":null,"abstract":"<p>Interpreting Raman and IR vibrational spectra in complex organic molecules lacking symmetries poses a formidable challenge. In this study, we propose an innovative approach for simulating vibrational spectra and attributing observed peaks to molecular motions, even when highly anharmonic, without the need for computationally expensive ab initio calculations. Our approach stems from the time-dependent stochastic self-consistent harmonic approximation to capture quantum nuclear fluctuations in atom dynamics while describing interatomic interaction through state-of-the-art reactive machine-learning force fields. Finally, we employ an isotropic charge model and a bond capacitor model trained on ab initio data to predict the intensity of IR and Raman signals.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"62 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398219","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}

{"title":"Deuteration removes quantum dipolar defects from KDP crystals","authors":"Bingjia Yang, Pinchen Xie, Roberto Car","doi":"10.1038/s41524-024-01431-2","DOIUrl":"https://doi.org/10.1038/s41524-024-01431-2","url":null,"abstract":"<p>Dielectric properties of the hydrogen-bonded ferroelectric crystal KH₂PO₄ (KDP) differ significantly from those of KD₂PO₄ (DKDP). It is well established that deuteration affects the interplay of hydrogen-bond switches and heavy ion displacements that underlie the emergence of macroscopic polarization, but a detailed microscopic model is missing. We show that all-atom path integral molecular dynamics simulations can predict the isotope effects, revealing the microscopic mechanism that differentiates KDP and DKDP. Proton tunneling generates phosphate configurations that do not contribute to the polarization. At low temperatures, these quantum dipolar defects are substantial in KDP but negligible in DKDP. These intrinsic defects explain why KDP has lower spontaneous polarization and transition entropy than DKDP. The prominent role of quantum fluctuations in KDP is related to the unusual strength of the hydrogen bonds and should be equally important in other crystals of the KDP family, which exhibit similar isotope effects.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"80 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142405450","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}

{"title":"Tunable Schottky barriers and magnetoelectric coupling driven by ferroelectric polarization reversal of MnI3/In2Se3 multiferroic heterostructures","authors":"Tao Zhang, Hao Guo, Jiao Shen, Ying Liang, Haidong Fan, Wentao Jiang, Qingyuan Wang, Xiaobao Tian","doi":"10.1038/s41524-024-01429-w","DOIUrl":"https://doi.org/10.1038/s41524-024-01429-w","url":null,"abstract":"<p>Two-dimensional (2D) multiferroic materials are recognized as promising candidates for next-generation nanodevices due to their tunable magnetoelectric coupling and distinctive physical phenomena. In this study, we proposed a novel 2D multiferroic van der Waals heterostructure (vdWH) by stacking atomic layers of ferroelectric In₂Se₃ and ferromagnetic MnI₃. Using first-principles calculations, we found that the MnI₃/In₂Se₃ vdWH exhibit robust metallic conductivity across various spin and polarization states, preserving the distinctive band characteristics of isolated In₂Se₃ and MnI₃. However, the alignment of Fermi levels causes the conduction band minimum (CBM) and valence band maximum (VBM) of In₂Se₃ and MnI₃ to shift relative to their original band structures. Remarkably, the MnI₃/In₂Se₃ with the upward polarization state of In₂Se₃ exhibits an Ohmic contact. Switching the polarization direction of In₂Se₃ from upward to downward can transform the MnI₃/In₂Se₃ vdWH from an Ohmic contact to a p-type Schottky contact, while also modifying its dipole moment, magnetic strength and direction. Based on these properties of MnI₃/In₂Se₃ vdWH, we designed the field-effect transistors (FETs) with high on/off rates and nonvolatile data storage device. Furthermore, the Schottky barrier heights (SBHs), magnetic moment, and dipole moment of MnI₃/In₂Se₃ vdWH can also be effectively regulated by reducing the interlayer distance. With the continuous reduction of the interlayer distance of MnI₃/In₂Se₃ vdWH, its easy magnetization axis is expected to shift from in-plane to out-of-plane. These findings offer new insights for the design and development of the next-generation spintronic and nonvolatile memory nanodevices.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"71 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142385187","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}