npj Computational Materials最新文献_第7页

Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-03 DOI: 10.1038/s41524-024-01495-0

Aditya Venkatraman, Mark A. Wilson, David Montes de Oca Zapiain

{"title":"Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications","authors":"Aditya Venkatraman, Mark A. Wilson, David Montes de Oca Zapiain","doi":"10.1038/s41524-024-01495-0","DOIUrl":"https://doi.org/10.1038/s41524-024-01495-0","url":null,"abstract":"Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. Reactive force fields in classical MD enable accurate modeling of bond formation and breakage in the aqueous medium and at the metal-electrolyte interface, while also facilitating dynamic partial charge equilibration. However, MD simulations are computationally intensive and unsuitable for modeling the long time scales characteristic of corrosive phenomena. To address this, we develop reduced-order machine learning models that provide accurate and efficient predictions of charge density in corrosive environments. Specifically, we use Long Short-Term Memory (LSTM) networks to forecast charge density evolution based on atomic environments represented by Smooth Overlap of Atomic Positions (SOAP) descriptors. A physics-informed loss function enforces charge neutrality and electronegativity equivalence. The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. This demonstrates the excellent accuracy, computational efficiency, and validity of the developed model. Lastly, even though developed for corrosion, these protocols are formulated in a phenomenon-agnostic manner, allowing application to various variable-charge interatomic potentials and related fields.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"77 2 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077581","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

SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-28 DOI: 10.1038/s41524-024-01492-3

Shun Nanjo, Arifin, Hayato Maeda, Yoshihiro Hayashi, Kan Hatakeyama-Sato, Ryoji Himeno, Teruaki Hayakawa, Ryo Yoshida

{"title":"SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines","authors":"Shun Nanjo, Arifin, Hayato Maeda, Yoshihiro Hayashi, Kan Hatakeyama-Sato, Ryoji Himeno, Teruaki Hayakawa, Ryo Yoshida","doi":"10.1038/s41524-024-01492-3","DOIUrl":"https://doi.org/10.1038/s41524-024-01492-3","url":null,"abstract":"Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems. First-principles calculations and other computer experiments have been integrated into material design pipelines to address the lack of experimental data and the limitations of interpolative machine learning predictors. However, the enormous computational costs and technical challenges of automating computer experiments for polymeric materials have limited the availability of open-source automated polymer design systems that integrate molecular simulations and machine learning. We developed SPACIER, an open-source software program that incorporates RadonPy, a Python library for fully automated polymer physical property calculations based on all-atom classical molecular dynamics, into a Bayesian optimization-based polymer design system to overcome these challenges. As a proof-of-concept study, we synthesized optical polymers that surpass the Pareto boundary formed by the tradeoff between the refractive index and the Abbe number.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"25 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049792","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

Exploring parameter dependence of atomic minima with implicit differentiation

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-27 DOI: 10.1038/s41524-024-01506-0

Ivan Maliyov, Petr Grigorev, Thomas D. Swinburne

{"title":"Exploring parameter dependence of atomic minima with implicit differentiation","authors":"Ivan Maliyov, Petr Grigorev, Thomas D. Swinburne","doi":"10.1038/s41524-024-01506-0","DOIUrl":"https://doi.org/10.1038/s41524-024-01506-0","url":null,"abstract":"Interatomic potentials are essential to go beyond ab initio size limitations, but simulation results depend sensitively on potential parameters. Forward propagation of parameter variation is key for uncertainty quantification, whilst backpropagation has found application for emerging inverse problems such as fine-tuning or targeted design. Here, the implicit derivative of functions defined as a fixed point is used to Taylor-expand the energy and structure of atomic minima in potential parameters, evaluating terms via automatic differentiation, dense linear algebra or a sparse operator approach. The latter allows efficient forward and backpropagation through relaxed structures of arbitrarily large systems. The implicit expansion accurately predicts lattice distortion and defect formation energies and volumes with classical and machine-learning potentials, enabling high-dimensional uncertainty propagation without prohibitive overhead. We then show how the implicit derivative can be used to solve challenging inverse problems, minimizing an implicit loss to fine-tune potentials and stabilize solute-induced structural rearrangements at dislocations in tungsten.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"34 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143044123","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

Active oversight and quality control in standard Bayesian optimization for autonomous experiments

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-27 DOI: 10.1038/s41524-024-01485-2

Sumner B. Harris, Rama Vasudevan, Yongtao Liu

{"title":"Active oversight and quality control in standard Bayesian optimization for autonomous experiments","authors":"Sumner B. Harris, Rama Vasudevan, Yongtao Liu","doi":"10.1038/s41524-024-01485-2","DOIUrl":"https://doi.org/10.1038/s41524-024-01485-2","url":null,"abstract":"The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"58 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143044124","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

Feature engineering descriptors, transforms, and machine learning for grain boundaries and variable-sized atom clusters

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-26 DOI: 10.1038/s41524-024-01509-x

C. Braxton Owens, Nithin Mathew, Tyce W. Olaveson, Jacob P. Tavenner, Edward M. Kober, Garritt J. Tucker, Gus L. W. Hart, Eric R. Homer

{"title":"Feature engineering descriptors, transforms, and machine learning for grain boundaries and variable-sized atom clusters","authors":"C. Braxton Owens, Nithin Mathew, Tyce W. Olaveson, Jacob P. Tavenner, Edward M. Kober, Garritt J. Tucker, Gus L. W. Hart, Eric R. Homer","doi":"10.1038/s41524-024-01509-x","DOIUrl":"https://doi.org/10.1038/s41524-024-01509-x","url":null,"abstract":"Obtaining microscopic structure-property relationships for grain boundaries is challenging due to their complex atomic structures. Recent efforts use machine learning to derive these relationships, but the way the atomic grain boundary structure is represented can have a significant impact on the predictions. Key steps for property prediction common to grain boundaries and other variable-sized atom clustered structures include: (1) describing the atomic structure as a feature matrix, (2) transforming the variable-sized feature matrix to a fixed length common to all structures, and (3) applying a machine learning algorithm to predict properties from the transformed matrices. We examine how these steps and different combinations of engineered features impact the accuracy of grain boundary energy predictions using a database of over 7000 grain boundaries. Additionally, we assess how different engineered features support interpretability, offering insights into the physics of the structure-property relationships.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"20 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034981","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

Machine learning Hubbard parameters with equivariant neural networks

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-25 DOI: 10.1038/s41524-024-01501-5

Martin Uhrin, Austin Zadoks, Luca Binci, Nicola Marzari, Iurii Timrov

{"title":"Machine learning Hubbard parameters with equivariant neural networks","authors":"Martin Uhrin, Austin Zadoks, Luca Binci, Nicola Marzari, Iurii Timrov","doi":"10.1038/s41524-024-01501-5","DOIUrl":"https://doi.org/10.1038/s41524-024-01501-5","url":null,"abstract":"Density-functional theory with extended Hubbard functionals (DFT + U + V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations. Here, we present a machine learning model based on equivariant neural networks which uses atomic occupation matrices as descriptors, directly capturing the electronic structure, local chemical environment, and oxidation states of the system at hand. We target here the prediction of Hubbard parameters computed self-consistently with iterative linear-response calculations, as implemented in density-functional perturbation theory (DFPT), and structural relaxations. Remarkably, when trained on data from 12 materials spanning various crystal structures and compositions, our model achieves mean absolute relative errors of 3% and 5% for Hubbard U and V parameters, respectively. By circumventing computationally expensive DFT or DFPT self-consistent protocols, our model significantly expedites the prediction of Hubbard parameters with negligible computational overhead, while approaching the accuracy of DFPT. Moreover, owing to its robust transferability, the model facilitates accelerated materials discovery and design via high-throughput calculations, with relevance for various technological applications.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"77 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143031178","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

Building up accurate atomistic models of biofunctionalized magnetite nanoparticles from first-principles calculations

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-25 DOI: 10.1038/s41524-024-01476-3

Paulo Siani, Enrico Bianchetti, Cristiana Di Valentin

{"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":"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.","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}

引用次数: 0

Effect of Hubbard U-corrections on the electronic and magnetic properties of 2D materials: a high-throughput study

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-24 DOI: 10.1038/s41524-024-01503-3

Sahar Pakdel, Thomas Olsen, Kristian S. Thygesen

{"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":"We conduct a systematic investigation of the role of Hubbard U corrections in electronic structure calculations of two-dimensional (2D) materials containing 3d 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).","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}

引用次数: 0

Accelerating materials property prediction via a hybrid Transformer Graph framework that leverages four body interactions 通过利用四体相互作用的混合变压器图框架加速材料属性预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-18 DOI: 10.1038/s41524-024-01472-7

Mohammad Madani, Valentina Lacivita, Yongwoo Shin, Anna Tarakanova

{"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":"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.","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}

引用次数: 0

Excitons in nonlinear optical responses: shift current in MoS2 and GeS monolayers 非线性光学响应中的激子：MoS2和GeS单层中的移位电流

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-13 DOI: 10.1038/s41524-024-01504-2

J. J. Esteve-Paredes, M. A. García-Blázquez, A. J. Uría-Álvarez, M. Camarasa-Gómez, J. J. Palacios

{"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":"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 2p-like excitons, which are dark in the linear response regime, yield a contribution to the photocurrent comparable to that of 1s-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.","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}

引用次数: 0