Sofia Sheikh, Brent Vela, Pejman Honarmandi, Peter Morcos, David Shoukr, Ibrahim Karaman, Alaa Elwany, Raymundo Arróyave
{"title":"An automated computational framework to construct printability maps for additively manufactured metal alloys","authors":"Sofia Sheikh, Brent Vela, Pejman Honarmandi, Peter Morcos, David Shoukr, Ibrahim Karaman, Alaa Elwany, Raymundo Arróyave","doi":"10.1038/s41524-024-01436-x","DOIUrl":"https://doi.org/10.1038/s41524-024-01436-x","url":null,"abstract":"<p>In metal additive manufacturing (AM), processing parameters can affect the probability of macroscopic defect formation (lack-of-fusion, keyholing, balling), which can, in turn, jeopardize the final product’s integrity. A printability map classifies regions in the processing space where an alloy can be printed with or without porosity defects. However, the creation of these printability maps is resource-intensive. Previous efforts to generate printability maps have required single-track experiments on pre-alloyed powder, limiting the utilization of these printability maps for the high-throughput design of printable alloys. We address these challenges in the case of Laser Powder Bed Fusion AM (L-PBF-AM) by introducing a fully computational, predictive approach to create printability maps for arbitrary alloys. Our framework uses physics-based thermal models and a variety of defect formation criteria. We benchmark the predictive ability of the proposed framework against literature data for the following commonly printed alloys: 316 Stainless Steel, Inconel 718, Ti-6Al-4V, AF96, and Ni-5Nb. Furthermore, we deploy the framework on NiTi-based Shape Memory Alloys (SMAs) as a case study. We scrutinize the accuracy of various sets of defect criteria and use these accuracy measurements to create an uncertainty-aware probabilistic framework capable of predicting the printability maps of arbitrary alloys. This framework has the potential to guide alloy designers to potentially easy-to-print alloys, enabling the co-design of high-performing printable alloys.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142588781","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}
Michael H. Prince, Henry Chan, Aikaterini Vriza, Tao Zhou, Varuni K. Sastry, Yanqi Luo, Matthew T. Dearing, Ross J. Harder, Rama K. Vasudevan, Mathew J. Cherukara
{"title":"Opportunities for retrieval and tool augmented large language models in scientific facilities","authors":"Michael H. Prince, Henry Chan, Aikaterini Vriza, Tao Zhou, Varuni K. Sastry, Yanqi Luo, Matthew T. Dearing, Ross J. Harder, Rama K. Vasudevan, Mathew J. Cherukara","doi":"10.1038/s41524-024-01423-2","DOIUrl":"https://doi.org/10.1038/s41524-024-01423-2","url":null,"abstract":"<p>Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities’ users and accelerate scientific output.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580299","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}
Ebert Alvares, Kai Sellschopp, Bo Wang, ShinYoung Kang, Thomas Klassen, Brandon C. Wood, Tae Wook Heo, Paul Jerabek, Claudio Pistidda
{"title":"Multiscale modeling of metal-hydride interphases—quantification of decoupled chemo-mechanical energies","authors":"Ebert Alvares, Kai Sellschopp, Bo Wang, ShinYoung Kang, Thomas Klassen, Brandon C. Wood, Tae Wook Heo, Paul Jerabek, Claudio Pistidda","doi":"10.1038/s41524-024-01424-1","DOIUrl":"https://doi.org/10.1038/s41524-024-01424-1","url":null,"abstract":"<p>The quantification of interphase properties between metals and their corresponding hydrides is crucial for modeling the thermodynamics and kinetics of the hydrogenation processes in solid-state hydrogen storage materials. In particular, interphase boundary energies assume a pivotal role in determining the kinetics of nucleation, growth, and coarsening of hydrides, alongside accompanying morphological evolution during hydrogenation. The total interphase energy arises from both chemical bonding and mechanical strains in these solid-state systems. Since these contributions are usually coupled, it is challenging to distinguish via conventional computational approaches. Here, a comprehensive atomistic modeling methodology is developed to decouple chemical and mechanical energy contributions using first-principles calculations, of which feasibility is demonstrated by quantifying chemical and elastic strain energies of key interfaces within the FeTi metal-hydride system. Derived materials parameters are then employed for mesoscopic micromechanical analysis, predicting crystallographic orientations in line with experimental observations. The multiscale approach outlined verifies the importance of the chemo-mechanical interplay in the morphological evolution of growing hydride phases, and can be generalized to investigate other systems. In addition, it can streamline the design of atomistic models for the quantitative evaluation of interphase properties between dissimilar phases and allow for efficient predictions of their preferred phase boundary orientations.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142489692","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":"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><sub>0</sub>) 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><sub>0</sub> on a 3D grid and then expanding the input features to include atomic fingerprints beyond <i>ρ</i><sub>0</sub>. 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":null,"pages":null},"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}
Jiaqi Zhou, Samuel Poncé, Jean-Christophe Charlier
{"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 Sc<sub>2</sub>CCl<sub>2</sub> 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":null,"pages":null},"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}
Hoje Chun, Jaclyn R. Lunger, Jeung Ku Kang, Rafael Gómez-Bombarelli, Byungchan Han
{"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":null,"pages":null},"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":null,"pages":null},"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}
Rasmus Fromsejer, Bjørn Maribo-Mogensen, Georgios M. Kontogeorgis, Xiaodong Liang
{"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<sub>f</sub><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<sub>f</sub><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<sub>f</sub><i>H</i> of 29.6 meV atom<sup>−1</sup> 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":null,"pages":null},"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}
Alireza Sadri, Timothy C. Petersen, Emmanuel W. C. Terzoudis-Lumsden, Bryan D. Esser, Joanne Etheridge, Scott D. Findlay
{"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":null,"pages":null},"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}
Nicolas Roisin, Guillaume Brunin, Gian-Marco Rignanese, Denis Flandre, Jean-Pierre Raskin, Samuel Poncé
{"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><sub>11</sub> and <i>π</i><sub>12</sub> 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 cm<sup>2 </sup>V<sup>−1 </sup>s<sup>−1</sup> at 0.3% uniaxial tensile strain while we observe a monotonous increase of the longitudinal transport, reaching a value of 2200 cm<sup>2 </sup>V<sup>−1 </sup>s<sup>−1</sup> 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":null,"pages":null},"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}