npj Computational Materials最新文献

{"title":"A Ring2Vec description method enables accurate predictions of molecular properties in organic solar cells","authors":"Ting Zhang, Kangzhong Wang, Kunlei Jing, Gang Li, Qing Li, Chen Zhang, He Yan","doi":"10.1038/s41524-024-01372-w","DOIUrl":"https://doi.org/10.1038/s41524-024-01372-w","url":null,"abstract":"<p>Predicting the properties of non-fullerene acceptors (NFAs), complex organic molecules used in organic solar cells (OSCs), poses a significant challenge. Some existing approaches primarily focus on atom-level information and may overlook high-level molecular features, including the subunits of NFAs. While other methods that effectively represent subunit information show improved prediction performance, they require labor-intensive data labeling. In this paper, we introduce an efficient molecular description method that automatically extracts molecular information at both the atom and subunit levels without any labor-intensive data labeling. Inspired by the Word2Vec method, our Ring2Vec method treats the “rings” in organic molecules as analogous to “words” in sentences. We achieve fast and accurate predictions of the energy levels of NFA molecules, with a minimal prediction error of merely 0.06 eV. Furthermore, our method can potentially have broad applicability across various domains of molecular description and property prediction, owing to the efficiency of the Ring2Vec model.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"129 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691056","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":"Dielectric tensor prediction for inorganic materials using latent information from preferred potential","authors":"Zetian Mao, WenWen Li, Jethro Tan","doi":"10.1038/s41524-024-01450-z","DOIUrl":"https://doi.org/10.1038/s41524-024-01450-z","url":null,"abstract":"<p>Dielectrics are crucial for technologies like flash memory, CPUs, photovoltaics, and capacitors, but public data on these materials are scarce, restricting research and development. Existing machine learning models have focused on predicting scalar polycrystalline dielectric constants, neglecting the directional nature of dielectric tensors essential for material design. This study leverages multi-rank equivariant structural embeddings from a universal neural network potential to enhance predictions of dielectric tensors. We develop an equivariant readout decoder to predict total, electronic, and ionic dielectric tensors while preserving O(3) equivariance, and benchmark its performance against state-of-the-art algorithms. Virtual screening of thermodynamically stable materials from Materials Project for two discovery tasks, high-dielectric and highly anisotropic materials, identifies promising candidates including Cs₂Ti(WO₄)₃ (band gap <i>E</i>_<i>g</i> = 2.93eV, dielectric constant <i>ε</i> = 180.90) and CsZrCuSe₃ (anisotropic ratio <i>α</i>_<i>r</i> = 121.89). The results demonstrate our model’s accuracy in predicting dielectric tensors and its potential for discovering novel dielectric materials.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"61 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684207","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":"Automated optimization and uncertainty quantification of convergence parameters in plane wave density functional theory calculations","authors":"Jan Janssen, Edgar Makarov, Tilmann Hickel, Alexander V. Shapeev, Jörg Neugebauer","doi":"10.1038/s41524-024-01388-2","DOIUrl":"https://doi.org/10.1038/s41524-024-01388-2","url":null,"abstract":"<p>First principles approaches have revolutionized our ability in using computers to predict, explore, and design materials. A major advantage commonly associated with these approaches is that they are fully parameter-free. However, numerically solving the underlying equations requires to choose a set of convergence parameters. With the advent of high-throughput calculations, it becomes exceedingly important to achieve a truly parameter-free approach. Utilizing uncertainty quantification (UQ) and linear decomposition we derive a numerically highly efficient representation of the statistical and systematic error in the multidimensional space of the convergence parameters for plane wave density functional theory (DFT) calculations. Based on this formalism we implement a fully automated approach that requires as input the target precision rather than convergence parameters. The performance and robustness of the approach are shown by applying it to a large set of elements crystallizing in a cubic fcc lattice.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670991","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":"Understanding chiral charge-density wave by frozen chiral phonon","authors":"Shuai Zhang, Kaifa Luo, Tiantian Zhang","doi":"10.1038/s41524-024-01440-1","DOIUrl":"https://doi.org/10.1038/s41524-024-01440-1","url":null,"abstract":"<p>Charge density wave (CDW) is discovered within a wide interval in solids, however, its microscopic nature is still not transparent in most realistic materials, and the recently studied chiral ones with chiral structural distortion remain unclear. In this paper, we try to understand the driving forces of chiral CDW transition by chiral phonons from the electron-phonon coupling scenario. We use the prototypal monolayer 1T-TiSe₂ as a case study to unveil the absence of chirality in the CDW transition and propose a general approach, i.e., symmetry-breaking stimuli, to engineer the chirality of CDW in experiments. Inelastic scattering patterns are also studied as a benchmark of chiral CDW (CCDW, which breaks the mirror/inversion symmetry in 2D/3D systems). We notice that the anisotropy changing of Bragg peak profiles, which is contributed by the soft chiral phonons, can show a remarkable signature for CCDW. Our findings pave a path to understanding the CCDW from the chiral phonon perspective, especially in van der Waals materials, and provides a powerful way to manipulate the chirality of CDW.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"6 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673871","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":"Large language models design sequence-defined macromolecules via evolutionary optimization","authors":"Wesley F. Reinhart, Antonia Statt","doi":"10.1038/s41524-024-01449-6","DOIUrl":"https://doi.org/10.1038/s41524-024-01449-6","url":null,"abstract":"<p>We demonstrate the ability of a large language model to perform evolutionary optimization for materials discovery. Anthropic’s Claude 3.5 model outperforms an active learning scheme with handcrafted surrogate models and an evolutionary algorithm in selecting monomer sequences to produce targeted morphologies in macromolecular self-assembly. Utilizing pre-trained language models can potentially reduce the need for hyperparameter tuning while offering new capabilities such as self-reflection. The model performs this task effectively with or without context about the task itself, but domain-specific context sometimes results in faster convergence to good solutions. Furthermore, when this context is withheld, the model infers an approximate notion of the task (e.g., calling it a protein folding problem). This work provides evidence of Claude 3.5’s ability to act as an evolutionary optimizer, a recently discovered emergent behavior of large language models, and demonstrates a practical use case in the study and design of soft materials.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"250 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670992","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":"From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows","authors":"Sarath Menon, Yury Lysogorskiy, Alexander L. M. Knoll, Niklas Leimeroth, Marvin Poul, Minaam Qamar, Jan Janssen, Matous Mrovec, Jochen Rohrer, Karsten Albe, Jörg Behler, Ralf Drautz, Jörg Neugebauer","doi":"10.1038/s41524-024-01441-0","DOIUrl":"https://doi.org/10.1038/s41524-024-01441-0","url":null,"abstract":"<p>We present a comprehensive and user-friendly framework built upon the <span>pyiron</span> integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high-dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"248 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645912","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":"Exploring electron-beam induced modifications of materials with machine-learning assisted high temporal resolution electron microscopy","authors":"Matthew G. Boebinger, Ayana Ghosh, Kevin M. Roccapriore, Sudhajit Misra, Kai Xiao, Stephen Jesse, Maxim Ziatdinov, Sergei V. Kalinin, Raymond R. Unocic","doi":"10.1038/s41524-024-01448-7","DOIUrl":"https://doi.org/10.1038/s41524-024-01448-7","url":null,"abstract":"<p>Directed atomic fabrication using an aberration-corrected scanning transmission electron microscope (STEM) opens new pathways for atomic engineering of functional materials. In this approach, the electron beam is used to actively alter the atomic structure through electron beam induced irradiation processes. One of the impediments that has limited widespread use thus far has been the ability to understand the fundamental mechanisms of atomic transformation pathways at high spatiotemporal resolution. Here, we develop a workflow for obtaining and analyzing high-speed spiral scan STEM data, up to 100 fps, to track the atomic fabrication process during nanopore milling in monolayer MoS₂. An automated feedback-controlled electron beam positioning system combined with deep convolution neural network (DCNN) was used to decipher fast but low signal-to-noise datasets and classify time-resolved atom positions and nature of their evolving atomic defect configurations. Through this automated decoding, the initial atomic disordering and reordering processes leading to nanopore formation was able to be studied across various timescales. Using these experimental workflows a greater degree of speed and information can be extracted from small datasets without compromising spatial resolution. This approach can be adapted to other 2D materials systems to gain further insights into the defect formation necessary to inform future automated fabrication techniques utilizing the STEM electron beam.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"43 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142642874","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":"A high-throughput framework for lattice dynamics","authors":"Zhuoying Zhu, Junsoo Park, Hrushikesh Sahasrabuddhe, Alex M. Ganose, Rees Chang, John W. Lawson, Anubhav Jain","doi":"10.1038/s41524-024-01437-w","DOIUrl":"https://doi.org/10.1038/s41524-024-01437-w","url":null,"abstract":"<p>We develop an automated high-throughput workflow for calculating lattice dynamical properties from first principles including those dictated by anharmonicity. The pipeline automatically computes interatomic force constants (IFCs) up to 4th order from perturbed training supercells, and uses the IFCs to calculate lattice thermal conductivity, coefficient of thermal expansion, and vibrational free energy and entropy. It performs phonon renormalization for dynamically unstable compounds to obtain real effective phonon spectra at finite temperatures and calculates the associated free energy corrections. The methods and parameters are chosen to balance computational efficiency and result accuracy, assessed through convergence testing and comparisons with experimental measurements. Deployment of this workflow at a large scale would facilitate materials discovery efforts toward functionalities including thermoelectrics, contact materials, ferroelectrics, aerospace components, as well as general phase diagram construction.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"246 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637125","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":"Facilitated the discovery of new γ/γ′ Co-based superalloys by combining first-principles and machine learning","authors":"ZhaoJing Han, ShengBao Xia, ZeYu Chen, Yihui Guo, ZhaoXuan Li, Qinglian Huang, Xing-Jun Liu, Wei-Wei Xu","doi":"10.1038/s41524-024-01455-8","DOIUrl":"https://doi.org/10.1038/s41524-024-01455-8","url":null,"abstract":"<p>Superalloys are indispensable materials for the fabrication of high-temperature components in aircraft engines. The discovery of a novel class of γ/γ′ Co-Al-W alloys has ignited a surge of interest in Co-based superalloys, with the aspiration to transcend the inherent constraints of their Ni-based counterparts. However, the conventional methodologies utilized in the design and advancement of new γ/γ′ Co-based superalloys are frequently characterized by their laborious and resource-intensive nature. In this study, we employed a coupled Density Functional Theory (DFT) and machine learning (ML) approach to predict and analyze the stability of the crucial γ′ phase, which is instrumental in expediting the discovery of γ/γ′ Co-based alloys. A dataset comprised of thousands of reliable formation (<i>H</i>_f) and decomposition (<i>H</i>_d) energies was obtained through high-throughput DFT calculations. Through regression model selection and feature engineering, our trained Random Forest (RF) model achieved prediction accuracies of 98.07% for <i>H</i>_f and 97.05% for <i>H</i>_d. Utilizing the well-trained RF model, we predicted the energies of over 150,000 ternary and quaternary γ′ phases within the Co-Ni-Fe-Cr-Al-W-Ti-Ta-V-Mo-Nb system. The energy analyses revealed that the presence of Ni, Nb, Ta, Ti, and V significantly reduced the <i>H</i>_f and the <i>H</i>_d of γ′, while Mo and W deteriorate the stability by increasing both energy values. Interestingly, although Al reduces the <i>H</i>_f, it increases <i>H</i>_d, thereby adversely affecting the stability of γ′. Applying domain-specific screening based on our knowledge, we identified 1049 out of &gt;150,000 compositions likely to form stable γ′ phases, predominantly distributed across 11 Al-containing systems and 25 Al-free systems. Combining the analysis of CALPHAD method, we experimentally synthesized two new Co-based alloys with γ/γ′ dual-phase microstructures, corroborating the reliability of our theoretical prediction model.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"20 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637126","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":"Data-driven design of novel lightweight refractory high-entropy alloys with superb hardness and corrosion resistance","authors":"Tianchuang Gao, Jianbao Gao, Shenglan Yang, Lijun Zhang","doi":"10.1038/s41524-024-01457-6","DOIUrl":"https://doi.org/10.1038/s41524-024-01457-6","url":null,"abstract":"<p>Lightweight refractory high-entropy alloys (LW-RHEAs) hold significant potential in the fields of aviation, aerospace, and nuclear energy due to their low density, high strength, high hardness, and corrosion resistance. However, the enormous composition space has severely hindered the development of novel LW-RHEAs with excellent comprehensive performance. In this paper, an machine learning (ML)-based alloy design strategy combined with a multi-objective optimization method was proposed and applied for a rational design of Al-Nb-Ti-V-Zr-Cr-Mo-Hf LW-RHEAs. The quantitative relation of “composition-structure-property” was first established by ML modeling. Then, feature analysis reveals that Cr content greater than 12 at.% is a key criterion for alloys with high corrosion resistance. The phase structure, density, melting point, hardness and corrosion resistance of the alloys were screened layer by layer, and finally, three LW-RHEAs with superb hard and corrosion resistance were successfully designed. Key experimental validation indicates that three target alloys have densities around 6.5 g/cm³, and all alloys are disordered bcc_A2 single-phase with the highest hardness of 593 HV and the largest pitting potential of 2.5 V_SCE, which far exceeds all the literature reports. The successful demonstration in this paper clearly demonstrates that the present design strategy driven by the ML technique should be generally applicable to other RHEA systems.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"6 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142610238","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}