npj Computational Materials最新文献_第7页

A combined ML and DFT strategy for the prediction of dye candidates for indoor DSSCs

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-12 DOI: 10.1038/s41524-025-01521-9

Carmen Coppola, Anna Visibelli, Maria Laura Parisi, Annalisa Santucci, Lorenzo Zani, Ottavia Spiga, Adalgisa Sinicropi

引用次数: 0

Optimizing casting process using a combination of small data machine learning and phase-field simulations

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-11 DOI: 10.1038/s41524-025-01524-6

Xiaolong Pei, Jiaqi Pei, Hua Hou, Yuhong Zhao

{"title":"Optimizing casting process using a combination of small data machine learning and phase-field simulations","authors":"Xiaolong Pei, Jiaqi Pei, Hua Hou, Yuhong Zhao","doi":"10.1038/s41524-025-01524-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01524-6","url":null,"abstract":"It has been a challenge to employ machine learning (ML) to optimize casting processes due to the scarcity of data and difficulty in feature expansion. Here, we introduce a nearest neighbor search method to optimize the stratified random sampling in Latin hypercube sampling (LHS) and propose a new revised LHS coupled with Bayesian optimization (RLHS-BO). Using this method, we optimized the squeeze-casting process for mine fuel tank partition castings for the first time with an ultra-small dataset of 25 samples. Compared to traditional methods such as random sampling, interval sampling, orthogonal design (OD), and central composite design (CCD), our approach covers the process parameter space more, reduces the data volume by approximately 50%, and achieves process optimization beyond five factors-five levels with fewer data. Through RLHS and 6 iterations of experiments, the optimal process was identified, and the ultimate tensile strength (UTS) of partition casting under the optimal process reached 239.7 MPa, with an elongation (EL) of 12.2%, showing increases of 17.6% and 18.4% over the optimal values in the initial dataset. Finally, a combination of Shapley additive interpretation (SHAP) and phase-field method (PFM) of solidification dendrite growth was used to address the issue of weak physical interpretability in ML models.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"58 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143393095","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

ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-07 DOI: 10.1038/s41524-024-01497-y

Rebecca K. Lindsey, Sorin Bastea, Sebastien Hamel, Yanjun Lyu, Nir Goldman, Vincenzo Lordi

引用次数: 0

Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-02-06 DOI: 10.1038/s41524-024-01498-x

Thea Denell, Lauri Himanen, Markus Scheidgen, Claudia Draxl

{"title":"Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering","authors":"Thea Denell, Lauri Himanen, Markus Scheidgen, Claudia Draxl","doi":"10.1038/s41524-024-01498-x","DOIUrl":"https://doi.org/10.1038/s41524-024-01498-x","url":null,"abstract":"With the rapidly increasing amount of materials data being generated in a variety of projects, efficient and accurate classification of atomistic structures is essential. A current barrier to effective database queries lies in the often ambiguous, inconsistent, or completely missing classification of existing data, highlighting the need for standardized, automated, and verifiable classification methods. This work proposes a robust solution for identifying and classifying a wide spectrum of materials through an iterative technique, called symmetry-based clustering (SBC). Because SBC is not a machine learning-based method, it requires no prior training. Instead, it identifies clusters in atomistic systems by automatically recognizing common unit cells. We demonstrate the potential of SBC to provide automated, reliable classification and to reveal well-known symmetry properties of various materials. Even noisy systems are shown to be classifiable, showing the suitability of our algorithm for real-world data applications. The software implementation is provided in the open-source Python package, MatID, exploiting synergies with popular atomic-structure manipulation libraries and extending the accessibility of those libraries through the NOMAD platform.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"28 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191746","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 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