npj Computational Materials最新文献

{"title":"Building up accurate atomistic models of biofunctionalized magnetite nanoparticles from first-principles calculations","authors":"Paulo Siani, Enrico Bianchetti, Cristiana Di Valentin","doi":"10.1038/s41524-024-01476-3","DOIUrl":"https://doi.org/10.1038/s41524-024-01476-3","url":null,"abstract":"<p>Biofunctionalized magnetite nanoparticles offer unique multifunctional capabilities that can drive nanomedical innovations. Designing synthetic bioorganic coatings and controlling their molecular behavior is crucial for achieving superior performance. However, accurately describing the interactions between bio-inorganic nanosystem components requires reliable computational tools, with empirical force fields at their core. In this work, we integrate first-principles calculations with mainstream force fields to construct and simulate atomistic models of pristine and biofunctionalized magnetite nanoparticles with quantum mechanical accuracy. The practical implications of this approach are demonstrated through a case study of PEG (polyethylene glycol)-coated magnetite nanoparticles in physiological conditions, where we investigate how polymer chain length, in both heterogeneous and homogeneous coatings, impacts key functional properties in advanced nanosystem design. Our findings reveal that coating morphology controls polymer ordering, conformation, and polymer corona hydrogen bonding, highlighting the potential of this computational toolbox to advance next-generation magnetite-based nanosystems with enhanced performance in nanomedicine.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"35 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034982","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":"Effect of Hubbard U-corrections on the electronic and magnetic properties of 2D materials: a high-throughput study","authors":"Sahar Pakdel, Thomas Olsen, Kristian S. Thygesen","doi":"10.1038/s41524-024-01503-3","DOIUrl":"https://doi.org/10.1038/s41524-024-01503-3","url":null,"abstract":"<p>We conduct a systematic investigation of the role of Hubbard U corrections in electronic structure calculations of two-dimensional (2D) materials containing 3<i>d</i> transition metals. Specifically, we use density functional theory (DFT) with the PBE and PBE+U approximations to calculate the crystal structure, band gaps, and magnetic parameters of 638 monolayers. Based on a comprehensive comparison to experiments we first establish that the inclusion of the U correction worsens the accuracy for the lattice constants. Consequently, PBE structures are used for subsequent property evaluations. The band gaps show a significant dependence on U. In particular, for 134 (21%) of the materials the U parameter induces a metal-to-insulator transition. For the magnetic materials we calculate the magnetic moment, magnetic exchange coupling, and magnetic anisotropy parameters. In contrast to the band gaps, the size of the magnetic moments shows only weak dependence on U. Both the exchange energies and magnetic anisotropy parameters are systematically reduced by the U correction. On this basis we conclude that the Hubbard U correction will lead to lower predicted Curie temperatures in 2D materials. All the calculated properties are available in the Computational 2D Materials Database (C2DB).</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"13 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026830","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":"Accelerating materials property prediction via a hybrid Transformer Graph framework that leverages four body interactions","authors":"Mohammad Madani, Valentina Lacivita, Yongwoo Shin, Anna Tarakanova","doi":"10.1038/s41524-024-01472-7","DOIUrl":"https://doi.org/10.1038/s41524-024-01472-7","url":null,"abstract":"<p>Machine learning has advanced the rapid prediction of inorganic materials properties, yet data scarcity for specific properties and capturing thermodynamic stability remains challenging. We propose a framework utilizing a Graph Neural Network with composition-based and crystal structure-based architectures, combined with a transfer learning scheme. This approach accurately predicts energy-related properties (e.g., total energy, energy above the convex hull, energy band gap) and data-scarce mechanical properties (e.g., bulk and shear modulus). Our model incorporates four-body interactions, capturing periodicity and structural characteristics. It outperforms state-of-the-art models in 8 materials property regression tasks. Also, this model predicts local atomic environments and global structural features better than several models. Transfer learning addresses mechanical property data scarcity, while separate architecture analysis allows application to materials lacking crystal structure information. Our framework’s interpretability aids in understanding elemental contributions, enhancing material design and discovery. Continuous advancements promise further performance improvements, driving efficient and accurate materials property prediction.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"7 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988820","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":"Excitons in nonlinear optical responses: shift current in MoS2 and GeS monolayers","authors":"J. J. Esteve-Paredes, M. A. García-Blázquez, A. J. Uría-Álvarez, M. Camarasa-Gómez, J. J. Palacios","doi":"10.1038/s41524-024-01504-2","DOIUrl":"https://doi.org/10.1038/s41524-024-01504-2","url":null,"abstract":"<p>It is well-known that exciton effects are determinant to understanding the optical absorption spectrum of low-dimensional materials. However, the role of excitons in nonlinear optical responses has been much less investigated at the experimental level. Additionally, computational methods to calculate nonlinear conductivities in real materials are still not widespread, particularly taking into account excitonic interactions. We present a methodology to calculate the excitonic second-order optical responses in 2D materials relying on: (i) ab initio tight-binding Hamiltonians obtained by Wannier interpolation and (ii) solving the Bethe-Salpeter equation with effective electron-hole interactions. Here, in particular, we explore the role of excitons in the shift current of monolayer materials. Focusing on MoS₂ and GeS monolayer systems, our results show that 2<i>p</i>-like excitons, which are dark in the linear response regime, yield a contribution to the photocurrent comparable to that of 1<i>s</i>-like excitons. Under radiation with intensity ~10⁴W/cm², the excitonic theory predicts in-gap photogalvanic currents of almost ~10 nA in sufficiently clean samples, which is typically one order of magnitude higher than the value predicted by independent-particle theory near the band edge.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"11 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968282","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":"Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone","authors":"Christophe Bonneville, Nathan Bieberdorf, Arun Hegde, Mark Asta, Habib N. Najm, Laurent Capolungo, Cosmin Safta","doi":"10.1038/s41524-024-01488-z","DOIUrl":"https://doi.org/10.1038/s41524-024-01488-z","url":null,"abstract":"<p>Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have been developed to understand the mechanisms leading to complex morphologies. However, the LMD governing equations in these models often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, numerical stiffness in the PDEs requires an extremely refined time step size (on the order of 10⁻¹²<i>s</i> or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose a U-shaped adaptive Fourier neural operator (U-AFNO), a machine learning (ML) based model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics of mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g., the deformation of the liquid-metal interface, lost metal, etc.) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in spite of the chaotic nature of LMD. Most notably, our model reproduces the key microstructure statistics and QoIs with a level of accuracy on par with the high-fidelity numerical solver, while achieving a significant 11, 200 × speed-up on a high-resolution grid when comparing the computational expense per time step. Finally, we also investigate the opportunity of using hybrid simulations, in which we alternate forward leaps in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"29 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968283","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":"Sub-bandgap charge harvesting and energy up-conversion in metal halide perovskites: ab initio quantum dynamics","authors":"Bipeng Wang, Weibin Chu, Yifan Wu, Wissam A. Saidi, Oleg V. Prezhdo","doi":"10.1038/s41524-024-01467-4","DOIUrl":"https://doi.org/10.1038/s41524-024-01467-4","url":null,"abstract":"<p>Metal halide perovskites (MHPs) exhibit unusual properties and complex dynamics. By combining ab initio time-dependent density functional theory, nonadiabatic molecular dynamics and machine learning, we advance quantum dynamics simulation to nanosecond timescale and demonstrate that large fluctuations of MHP defect energy levels extend light absorption to longer wavelengths and enable trapped charges to escape into bands. This allows low energy photons to contribute to photocurrent through energy up-conversion. Deep defect levels can become shallow transiently and vice versa, altering the traditional defect classification into shallow and deep. While defect levels fluctuate more in MHPs than traditional semiconductors, some levels, e.g., Pb interstitials, remain far from band edges, acting as charge recombination centers. Still, many defects deemed detrimental based on static structures, are in fact benign and can contribute to energy up-conversion. The extended light harvesting and energy up-conversion provide strategies for design of novel solar, optoelectronic, and quantum information devices.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"21 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961531","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":"Magnetic anisotropy of 4f atoms on a WSe2 monolayer: a DFT + U study","authors":"Johanna P. Carbone, Gustav Bihlmayer, Stefan Blügel","doi":"10.1038/s41524-024-01502-4","DOIUrl":"https://doi.org/10.1038/s41524-024-01502-4","url":null,"abstract":"<p>Inspired by recent advancements in the field of single-atom magnets, particularly those involving rare-earth (RE) elements, we present a theoretical exploration employing DFT+<i>U</i> calculations to investigate the magnetic properties of selected 4<i>f</i> atoms, specifically Eu, Gd, and Ho, on a monolayer of the transition-metal dichalcogenide WSe₂ in the 1H-phase. This study comparatively examines RE with diverse 4<i>f</i> orbital fillings and valence chemistry, aiming to understand how different coverage densities atop WSe₂ affect magnetocrystalline anisotropy. We observe that RE lacking 5<i>d</i> occupation exhibit larger magnetic anisotropy energies at high densities, while those with outer 5<i>d</i> electrons show larger anisotropies in dilute configurations. Additionally, even half-filled 4<i>f</i> shell atoms with small orbital magnetic moments can generate substantial energy barriers for magnetization rotation due to prominent orbital hybridizations with WSe₂. Open 4<i>f</i> shell atoms further enhance anisotropy barriers through spin-orbit coupling effects. These aspects are crucial for realizing stable magnetic information units experimentally.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"7 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967797","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":"Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty","authors":"Qinghua Wei, Yuanhao Wang, Guo Yang, Tianyuan Li, Shuting Yu, Ziqiang Dong, Tong-Yi Zhang","doi":"10.1038/s41524-024-01480-7","DOIUrl":"https://doi.org/10.1038/s41524-024-01480-7","url":null,"abstract":"<p>We present a multi-objective Bayesian active learning strategy, which greatly accelerates the discovery of super high-strength and high-ductility lead-free solder alloys. The active learning strategy demonstrates that a machine learning model will have high generalizability if experimental data uncertainty is included, which greatly improves the model prediction or the material design accuracy. The feature-point-start forward method in multi-objective optimization adopts two Gaussian process regression (GPR) models, one for strength and one for elongation, and their outputs build up the acquisition-function-modified objective space of strength and elongation. Then, Bayesian sampling is applied to design the next experiments by balancing exploitation and exploration. Seven multi-objective active learning iterations discovered two novel super high-strength and high-ductility lead-free solder alloys. After that, various material characterizations were conducted on the two novel solder alloys, and the results exhibited their high performances in melting properties, wettability, electrical conductivity, and shear strength of the solder joint and explored the mechanism of high strength and high ductility of the alloys. The present work systematically analyzes the important role of experimental uncertainty in machine learning, especially in the global optimization for material design, which demands high generalizability of predictions.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"29 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961533","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":"Systematic softening in universal machine learning interatomic potentials","authors":"Bowen Deng, Yunyeong Choi, Peichen Zhong, Janosh Riebesell, Shashwat Anand, Zhuohan Li, KyuJung Jun, Kristin A. Persson, Gerbrand Ceder","doi":"10.1038/s41524-024-01500-6","DOIUrl":"https://doi.org/10.1038/s41524-024-01500-6","url":null,"abstract":"<p>Machine learning interatomic potentials (MLIPs) have introduced a new paradigm for atomic simulations. Recent advancements have led to universal MLIPs (uMLIPs) that are pre-trained on diverse datasets, providing opportunities for universal force fields and foundational machine learning models. However, their performance in extrapolating to out-of-distribution complex atomic environments remains unclear. In this study, we highlight a consistent potential energy surface (PES) softening effect in three uMLIPs: M3GNet, CHGNet, and MACE-MP-0, which is characterized by energy and force underprediction in atomic-modeling benchmarks including surfaces, defects, solid-solution energetics, ion migration barriers, phonon vibration modes, and general high-energy states. The PES softening behavior originates primarily from the systematically underpredicted PES curvature, which derives from the biased sampling of near-equilibrium atomic arrangements in uMLIP pre-training datasets. Our findings suggest that a considerable fraction of uMLIP errors are highly systematic, and can therefore be efficiently corrected. We argue for the importance of a comprehensive materials dataset with improved PES sampling for next-generation foundational MLIPs.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"36 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961532","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":"Inverse catalysts: tuning the composition and structure of oxide clusters through the metal support","authors":"Luuk H. E. Kempen, Mie Andersen","doi":"10.1038/s41524-024-01507-z","DOIUrl":"https://doi.org/10.1038/s41524-024-01507-z","url":null,"abstract":"<p>Computational modeling of metal–oxide interfaces is challenging due to the large search space of compositions and structures and the complexity of catalyst materials under operating conditions in general. In this work, we develop an efficient structure search workflow to discover chemically unique and relevant nanocluster geometries of inverse catalysts and apply it to Zn_<i>y</i>O_<i>x</i> and In_<i>y</i>O_<i>x</i> on Cu(111), Pd(111), and Au(111). We show that the workflow is successful in obtaining a large range of chemically distinct structures. Structural geometry trends are identified, including stable motifs such as tripod, rhombus, and pyramidal motifs. Using ab initio thermodynamics, we explore the in situ stability of the structures, including single-atom alloys, at a range of oxygen availabilities. This approach allows us to find trends such as the susceptibility to oxidation of the different systems and the range of stability of different cluster motifs. Our analysis highlights the importance of taking the diversity of sites exposed by metal–oxide interfaces into account in catalyst design studies.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"204 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939614","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}