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  《npj 计算材料学》是在线出版、完全開放獲取的国际学术期刊。发表结合计算模拟与设计的材料学一流的研究成果。本刊由中國科學院上海矽酸鹽研究所与英国自然出版集团(Nature Publishing Group,NPG)以伙伴关系合作出版。
  主編爲陳龍慶博士,美國賓州大學材料科學與工程系、工程科學與力學系、數學系的傑出教授。
  共同主编为陈立东研究员,中國科學院上海矽酸鹽研究所研究员高性能陶瓷与超微结构国家重点实验室主任。
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  《npj 计算材料学》是在线出版、完全開放獲取的国际...
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Theoretical analysis of spectral lineshapes from molecular dynamics (譜線形的分子動力學理論分析)
Andrew CupoDamien TristantKyle Rego & Vincent Meunier
118彩票 5:82(2019)
doi:s41524-019-0220-1
Published online:07 August 2019

Abstract| Full Text | PDF OPEN

摘要:用于計算非諧聲子特性的傳統方法,計算起來十分昂貴。爲解決這個問題,本研究開發了一種理論方法,用于加速從有限時間分子動力學獲得的波普振動線形的計算。該方法提供了研究非諧誘導的頻移和壽命影響的方法,及其模擬擴展。我們的研究證明,采用簡約模型(Toy model)獲得收斂的振動特性所需的模擬步驟數量,與標准提取程序相比,幾乎在所有情況下都減少了至少一個數量級。對于石墨烯、六方氮化硼和矽,本研究還從理論上證明了,在密度泛函理論水平上,在振動頻率和延時中,達到收斂所需的模擬時間,均減少了近9倍。一般來說,當非諧性足夠弱時,我們期望新開發的方法優于標准程序,從而得出明確定義的重正化聲子准粒子。我們將信號分析擴展到材料振動,代表了計算與溫度相關的聲子特性的最新研究進展,並且可以在計算材料發現工具包中實現諸如熱電材料的搜索,因爲熱導率對ZT的貢獻強烈依賴于這些特征   

Abstract:Conventional methods for calculating anharmonic phonon properties are computationally expensive. To address this issue, a theoretical approach was developed for the accelerated calculation of vibrational lineshapes for spectra obtained from finite-time molecular dynamics. The method gives access to the effect of anharmonicity-induced frequency shift and lifetime, as well as simulation broadening. For a toy model we demonstrate at least an order of magnitude reduction in the number of simulation steps needed to obtain converged vibrational properties in nearly all cases considered as compared to the standard extraction procedure. The theory is also illustrated for graphene, hexagonal boron nitride, and silicon at the density functional theory level, with up to nearly a factor of 9 reduction in the required simulation time to reach convergence in the vibrational frequencies and lifetimes. In general, we expect the newly developed method to outperform the standard procedure when the anharmonicity is sufficiently weak so that well-defined renormalized phonon quasiparticles emerge. Our extension of signal analysis to material vibrations represents a state-of-the-art advance in calculating temperature-dependent phonon properties and could be implemented in computational materials discovery packages that search for thermoelectric materials for instance, since the thermal conductivity contribution to ZT depends strongly on these characteristics. 

Editorial Summary

Phononic anharmonicity: captured by molecular dynamics聲子非諧性:獨立特行但卻被分子動力學拿下

從分子動力學計算的波普推導出了一種新方法,用來研究聲子振動線形,包括非諧性引起的頻移效應、壽命效應,以及模擬展寬。來自美國倫斯勒理工學院物理、應用物理和天文學系Vincent Meunier教授領導的團隊,由分子動力學推導獲得了波普精確解析表達式,以及速度的簡單擾動正態模式的表達式。與標准提取程序相比,我們證明了簡約模型(Toy model)在幾乎所有情況下,所獲得收斂振動特性的模擬步驟數量,至少降低了一個數量級。在50 K時,使用這兩種方法可以收斂壽命,新方法將所需的模擬時間縮短了大約1.4倍。他們將推導獲得的擬合函數應用于簡單模型,石墨烯、六方氮化硼(hBN)和矽,以研究振動頻率和壽命與模擬時間的收斂性。他們所提出的方法,在強相關系統到生物材料的各領域,都將對分子動力學聲子特性的確定産生深遠影響

A new approach to understand phononic vibrational lineshapes is derived for spectra calculated from molecular dynamics and include the effect of anharmonicity-induced frequency shift and lifetime, as well as the simulation broadening. A team led by Vincent Meunier from the Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, USA, derived exact analytical expressions for spectra obtained from molecular dynamics starting with a simple perturbed normal mode expression for the velocities. They applied the derived fitting functions to simple models, graphene, hexagonal boron nitride (hBN), and silicon to study the convergence of the vibrational frequency and lifetime with simulation time. For a toy model they demonstrated at least an order of magnitude reduction in the number of simulation steps to obtain converged vibrational properties in nearly all cases considered as compared to the standard extraction procedure. At 50K the lifetime can be converged using both methods, with the new method reducing the required simulation time by a factor of about 1.4. Their proposed method could have far reaching impact on the determination of phononic properties from MD, in areas ranging from strongly correlated systems to biological materials.

Recent advances and applications of machine learning in solid-state materials science機器學習在固體材料科學中的新進展和新應用
Jonathan SchmidtMário R. G. MarquesSilvana Botti & Miguel A. L. Marques
118彩票 5:83(2019)
doi:s41524-019-0221-0
Published online:08 August 2019

Abstract| Full Text | PDF OPEN

摘要:近年來材料科學中最激動人心的工具手段之一便是機器學習。事實證明,這種基于統計學的研究方法能夠大大加快基礎研究和應用研究的速度。目前,我們目睹了大量的最新研究進展,這些進展將機器學習開發、應用于固態系統。就機器學習在固體材料研究和應用中的作用這一主題,我們提供了新近研究的全面概述和分析。作爲起點,我們介紹了應用于材料科學的機器學習原理、算法、描述符和數據庫。接著,我們描述了用以發現穩定材料和預測其晶體結構的各種不同的機器學習方法,繼而討論了許多定量結構-屬性關系的研究,以及通過機器學習取代第一原理計算的各種方法。我們綜述了如何應用主動學習和如何基于代理優化,來改進理性設計過程和相關應用的實例。本文始終以1)機器學習模型的可解釋性和2)機器學習模型的物理認識,作爲兩個主題。因此,我們關注了可解釋性的不同方面,及其在材料科學中的重要作用。最後,我們爲計算材料科學中的各種挑戰提出了解決方案和未來研究途徑   

Abstract:One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science. 

Editorial Summary

Review on the whole machine learning: becoming superman, human, application, and ideal機器學習:是神、是人、是應用、是理想

該文綜述了機器學習在材料科學領域的最新應用。來自德國馬丁路德大學物理研究所的Miguel A. L. Marques教授專注于詳細討論和分析固態材料科學(特別是最新的固態材料科學)機器學習的各種應用。由于機器學習算法在幾個不同的科學和技術領域中取得了無與倫比的成功(神一般的成功),這些應用在過去幾年中一直在蓬勃發展。該綜述首先介紹了機器學習,特別是材料科學中的機器學習原理、算法、描述符和數據庫(人的理論貢獻)。然後,介紹了固態材料科學中機器學習的衆多應用(應用是目的和推動力):新穩定材料的發現及其結構的預測、材料特性的機器學習計算、材料科學模擬的機器學習力場的發展、通過機器學習方法構建DFT功能、通過主動學習優化自適應設計過程,以及機器學習模型的可解釋性和物理認識。最後,討論了機器學習在材料科學中面臨的挑戰和局限,並提出了一些克服或規避它們的研究策略。作者堅信,這一系列高效的統計工具確實能夠大大加快基礎研究和應用研究的速度(理想)。 因此,它們顯然不僅僅是一種短暫作用于材料科學的方式,而肯定一直是未來幾年塑造材料科學的力量

The latest applications of machine learning in the field of materials science is reviewed. A team led by Miguel A. L. Marques from the Institut für Physik, Martin-Luther-Universitat, Germany, concentrated on the various applications of machine learning in solid-state materials science (especially the most recent ones) and discussed and analyzed them in detail. These applications have been mushrooming in the past couple of years, fueled by the unparalleled success that machine learning algorithms have found in several different fields of science and technology. As a starting point, they provide an introduction to machine learning, and in particular to machine learning principles, algorithms, descriptors, and databases in materials science. They then review numerous applications of machine learning in solid-state materials science: the discovery of new stable materials and the prediction of their structure, the machine learning calculation of material properties, the development of machine learning force fields for simulations in material science, the construction of DFT functionals by machine learning methods, the optimization of the adaptive design process by active learning, and the interpretability of, and the physical understanding gained from, machine learning models. Finally, they discuss the challenges and limitations machine learning faces in materials science and suggest a few research strategies to overcome or circumvent them. It is authors’ firm conviction that this collection of efficient statistical tools are indeed capable of speeding up considerably both fundamental and applied research. As such, they are clearly more than a temporary fashion and will certainly shape materials science for the years to come.

Machine learning enables polymer cloud-point engineering via inverse design (機器學習通過逆向設計實現聚合物濁點工程)
Jatin N. KumarQianxiao LiKaren Y. T. TangTonio BuonassisiAnibal L. Gonzalez-Oyarce & Jun Ye
118彩票 5:73(2019)
doi:s41524-019-0209-9
Published online:12 July 2019

Abstract| Full Text | PDF OPEN

摘要:諸如聚合物多種尺度無序系統的逆向設計是個重大挑戰,在設計具有所需相行爲的聚合物時,挑戰尤爲重大。本研究通過機器學習證實了聚(2-惡唑啉)濁點的高精度調整。我們在四個重複單元和一系列分子質量的設計空間中,采用梯度增強決策樹于24~90範圍內,實現了4均方根誤差(RMSE)的精度。RMSE比線性和多項式回歸好3倍。通過粒子群優化進行逆向設計,我們在37~804個目標濁點下預測和合成了17種具有約束設計的聚合物。本方法通過機器學習算法優于現有的聚合物設計方法,相應的算法能夠快速、系統地發現新聚合物   

Abstract:Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. Here we demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4°C root mean squared error (RMSE) in a temperature range of 24–90°C, employing gradient boosting with decision trees. The RMSE is >3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80°C. Our approach challenges the status quo in polymer design with a machine learning algorithm that is capable of fast and systematic discovery of new polymers. 

Editorial Summary

Machine learning: polymer inverse design機器學習:逆向設計聚合物

該研究報道了通過合理應用機器學習方法,在聚合物設計方面實現了概念上的重大進步。由新加坡材料研究與工程研究所的Jatin N. Kumar教授領導的團隊,通過三個重要步驟實現了聚合物的設計:首先,策劃並分類曆史數據和新數據;其次,選擇並微調基于梯度增強回歸和決策樹的機器學習模型,從而得到3.9的濁點預測精度RMSE),該模型能夠很好地規範化明確定義的曆史數據集,以及新合成的分子量呈不對稱分布的聚合物;第三步,經粒子群優化的聚合物逆向設計,該步驟基于所訓練數據(37℃、45℃、60℃、80℃)的濁點範圍擴展的期望濁點,對新聚合物的設計進行預測。作者討論了逆向設計方法如何擴展用于多個目標函數,演示了如何通過神經網絡集合作爲交叉驗證技術,以便推斷超出訓練集之外的數據,從而篩選出17種具有最低預測方差的聚合物。預測聚合物的RMSE與正向模型的RMSE相似。該方法提供了前所未有的對聚合物主動設計的實例,有望顯著加速依據濁點等多種目標性能的聚合物設計

A significant conceptual advance in polymer design via judicious application of machine-learning methods is reported. A team led by Prof. Jatin N. Kumar from the Institute of Materials Research & Engineering, Singapore, achieved polymer design in three important steps: firstly, curated and categorized historical and new data; secondly, selected and fine-tuned a machine-learning model based on gradient boosting regression with decision trees, resulting in a cloud point predictive accuracy of 3.9°C (RMSE), which was able to generalize well with both well-defined historic data sets as well as newly synthesized polymers of unsymmetrical molecular weight distributions; and thirdly, polymer inverse design by particle-swarm optimization which predicted the design of new polymers based on desired cloud points spread over the range of the cloud points of the training data (37, 45, 60, 80°C). They discussed how the inverse-design methodology is scalable to more than one objective function. It could extrapolate beyond the training set via an ensemble of neural networks as a cross-validation technique to downselect 17 polymers with the lowest variance across predictions. The RMSE of predicted polymers were similar to those of the forward model. This methodology offers unprecedented control of polymer design, which may significantly accelerate polymer design for one or more objective properties well beyond cloud points.

High-throughput prediction of the ground-state collinear magnetic order of inorganic materials using Density Functional Theory(用密度泛函理論高通量預測無機材料的基態共線磁序)
Matthew Kristofer HortonJoseph Harold Montoya, Miao Liu & Kristin Aslaug Persson
118彩票 5:64(2019)
doi:s41524-019-0199-7
Published online:06 June 2019

Abstract| Full Text | PDF OPEN

摘要:在共線自旋極化密度泛函理論的框架下,我們提出了一種魯棒的、自動化的高通量工作流程,用于計算固態無機晶體(無論是鐵磁性、反鐵磁性還是亞鐵磁性)的磁性基態和相關磁矩。這是通過一個計算效率很高的方案來實現的,其中首先列舉所有合理的磁序,並根據對稱性進行優先排序,然後通過傳統的DFT + U計算來弛豫結構和計算能量。這種自動化工作流程使用atomatecode進行形式化,以適用于數千種材料以可靠、系統的方式使用,也可完全自定義。該工作流程以64種實驗已知的非平凡磁序離子磁性材料作爲基准,通過計算500多種不同的磁序,對工作流程的性能進行了評估。該流程正確預測了基准材料中95%的非鐵磁基態,正確預測了60%的實驗測定的基態排序。對基態磁序的大規模預測爲基于磁性能的高通量篩選研究開辟了可能性,從而加速了新功能材料的發現和認識   

Abstract:We present a robust, automatic high-throughput workflow for the calculation of magnetic ground state of solid-state inorganic crystals, whether ferromagnetic, antiferromagnetic or ferrimagnetic, and their associated magnetic moments within the framework of collinear spin-polarized Density Functional Theory. This is done through a computationally efficient scheme whereby plausible magnetic orderings are first enumerated and prioritized based on symmetry, and then relaxed and their energies determined through conventional DFT+U calculations. This automated workflow is formalized using the atomatecode for reliable, systematic use at a scale appropriate for thousands of materials and is fully customizable. The performance of the workflow is evaluated against a benchmark of 64 experimentally known mostly ionic magnetic materials of non-trivial magnetic order and by the calculation of over 500 distinct magnetic orderings. A non-ferromagnetic ground state is correctly predicted in 95% of the benchmark materials, with the experimentally determined ground state ordering found exactly in over 60% of cases. Knowledge of the ground state magnetic order at scale opens up the possibility of high-throughput screening studies based on magnetic properties, thereby accelerating discovery and understanding of new functional materials. 

Editorial Summary

High-throughput prediction: ground-state collinear magnetic order高通量預測:基態共線磁序

該研究報道了一種工作流程和支持分析,爲基于磁性能的篩選開辟了道路,並爲以前未被實驗研究的磁性材料開展新的研究提供了一個起點。來自美國勞倫斯伯克利國家實驗室和加州科學大學伯克利分校的Kristin Aslaug Persson教授領導的團隊,提出了一種基于純共線DFT模擬的工作流程,包括在高通量條件下確定材料是否爲鐵磁性的判據並試圖找到在0 K溫度下給定材料的基態磁序。作者討論了兩個關鍵成果:1)提出並實現了一種對給定材料進行合理的磁性排序的方案,並確定了計算的優先級;2)使用基于一組成熟磁性材料的傳統DFT + U工作流來評估這些生成的順序,計算和保存不同磁序造成的能量差,從而確定DFT預測的基態順序。基于磁性能的高通量篩選研究有助于加速新功能材料的發現和理解

A workflow and supporting analyses opened up opportunities for screening based on magnetic properties, providing a starting point for the investigation of magnetic materials previously unstudied by experimental techniques is reported. A team led by Kristin Aslaug Persson from the Lawrence Berkeley National Laboratory, Berkeley, and the Science University of California Berkeley, USA, presented a workflow based on purely collinear DFT simulations with the modest but crucial goal of determining whether a material is ferromagnetic or not in a high-throughput context, and then of attempting to find the ground state magnetic order of a given material at 0K, presupposing that such a ground state exhibits collinear spin. There are two key advances addressed in this paper. Firstly, they proposed and implemented a scheme for enumerating plausible magnetic orderings for a given material, and decided on a ranking for prioritizing calculations. Secondly, they evaluated these generated orderings using a workflow based on conventional DFT+U for a set of well-established magnetic materials, stored the differences in energy between the calculated orderings and thus determine the ground-state ordering predicted by DFT. So now high-throughput screening studies based on magnetic properties may accelerate discovery and understanding of new functional materials.

Atom table convolutional neural networks for an accurate prediction of compounds properties (原子表卷積神經網絡准確預測化合物性質)
Shuming Zeng, Yinchang Zhao, Geng Li, Ruirui Wang, Xinming Wang&Jun Ni 
Abstract| Full Text | PDF OPEN

118彩票 5:84(2019)
doi:s41524-019-0223-y
Published online:08 August 2019

摘要:機器學習技術在材料科學中具有廣泛的應用。然而,大多數的機器學習模型需要很多先驗知識來手動構建特征向量。本研究提出了一種原子表卷積神經網絡模型,它僅需要組分信息就能自動構建特征向量,進而學習化合物的實驗性質。對于帶隙和形成能的預測,該模型的精度超過了標准DFT計算的結果。通過數據增強的方法,這種模型不僅能夠准確預測超導體的超導轉變溫度,還能夠區分超導體和非超導體。利用這種模型,我們從數據庫中篩選出了20種可能具有高超導轉變溫度的材料。此外,從模型學習到的特征向量中,我們提取了主族元素的性質並重現了它們的化學趨勢。此框架對于高通量材料篩選以及挖掘潛在的物理提供了有效手段   

Abstract:Machine learning techniques are widely used in materials science. However, most of the machine learning models require a lot of prior knowledge to manually construct feature vectors. Here, we develop an atom table convolutional neural networks that only requires the component information to directly learn the experimental properties from the features constructed by itself. For band
gap and formation energy prediction, the accuracy of our model exceeds the standard DFT calculations. Besides, through data enhanced technology, our model not only accurately predicts superconducting transition temperatures, but also distinguishes superconductors and non-superconductors. Utilizing the trained model, we have screened 20 compounds that are potential superconductors with high superconducting transition temperature from the existing database. In addition, from the learned features, we extract the properties of the elements and reproduce the chemical trends. This framework is valuable for high throughput screening and helpful to understand the underlying physics
.
 

Editorial Summary

Machine Learning: Accurate Prediction of Compounds Properties with only composition input機器學習:僅需組分信息就能准確預測化合物性質

該研究提出了一种称为原子表卷积神经网络的机器学习方法,可以在训练中不断学习合适的特征来预测化合物的形成能、带隙和超导转变温度。来自清华大学物理系倪军教授領導的團隊,报道了一种端对端的机器学习方案,可以在材料结构數据缺乏的情况下,有效地预测材料的实验性质。利用化合物的组分信息,构造出一张与之对应的的原子表。材料的特征向量通过一个卷积网络来学习并直接用于性质的预测。整个网络是同步训练的,既避免了构造特征的麻烦,又搜索了更大的参數空间,能够提高准确率。这种模型在预测超导转变温度、带隙和形成能上相对于手动精心构造特征向量的方法准确率更高。模型还能够区别超导体和非超导体。同时,对学习到的特征向量的分析表明,模型捕捉到了相关性质内在的物理机制。該研究方法提出的模型可用于材料的高通量筛选和解释内在的物理机制

In this study, a machine learning framework called atom table convolutional neural networks is proposed, which can learn appropriate features in training to predict the formation energy, band gap and superconducting transition temperature of compounds. A team led by Professor Ni Jun, Department of Physics, Tsinghua University, reported an end-to-end machine learning scheme that can effectively predict the experimental properties of materials in the absence of structural data. A corresponding atomic table is constructed by using the component information of compounds. Material features are learned through a convolution network and are directly used for prediction. The whole network is trained synchronously, which not only avoids the difficulties of constructing features, but also searches for a larger parameter space, and can improve the accuracy. This model is more accurate in predicting superconducting transition temperature, band gap and formation energy than the common method of carefully constructing features manually. The model can also distinguish the superconductors and non-superconductors. At the same time, the analysis of the learned features shows that the model captures the intrinsic physical mechanism of the related properties. The proposed model can be used for high throughput screening and explaining the intrinsic physical mechanism of materials.

An electrostatic spectral neighbor analysis potential for lithium nitride (氮化锂的靜電譜相鄰分析電位)
Zhi DengChi ChenXiang-Guo Li & Shyue Ping Ong
118彩票 5:75(2019)
doi:s41524-019-0212-1
Published online:16 July 2019

Abstract| Full Text | PDF OPEN

摘要:基于局部環境描述符的機器學習原子間勢,在預測精度上較基于剛性函數形式的傳統勢有了革命性的飛躍。然而,它們在離子體系中的應用面臨的一個挑戰是長程靜電相互作用的處理。本研究提出了利用離子α-Li3N的高精度靜電光譜鄰域分析電位(eSNAP)來解決這一問題,離子α-Li3N是一種經典的锂離子快離子導體,可作爲可充電锂離子電池的固體電解質或塗層。研究表明,優化後的eSNAP模型在能量和力的預測以及晶格常數、彈性常數、聲子色散關系等各種性質的預測方面,明顯優于傳統的Coulomb–Buckingham勢模型。該研究還展示了eSNAPLi3N中長時、大尺度的Li擴散研究中的應用,爲測量協同離子運動(例如Haven比率)和晶界擴散提供了原子層次的認識。這項工作旨在提供一種方法,以發展量子精確力場的多組分離子導體體系下的SNAP形式,從而實現這種系統的大尺度模擬   

Abstract:Machine-learned interatomic potentials based on local environment descriptors represent a transformative leap over traditional potentials based on rigid functional forms in terms of prediction accuracy. However, a challenge in their application to ionic systems is the treatment of long-ranged electrostatics. Here, we present a highly accurate electrostatic Spectral Neighbor Analysis Potential (eSNAP) for ionic α-Li3N, a prototypical lithium superionic conductor of interest as a solid electrolyte or coating for rechargeable lithium-ion batteries. We show that the optimized eSNAP model substantially outperforms traditional Coulomb–Buckingham potential in the prediction of energies and forces, as well as various properties, such as lattice constants, elastic constants, and phonon dispersion curves. We also demonstrate the application of eSNAP in long-time, large-scale Li diffusion studies in Li3N, providing atomistic insights into measures of concerted ionic motion (e.g., the Haven ratio) and grain boundary diffusion. This work aims at providing an approach to developing quantum-accurate force fields for multi-component ionic systems under the SNAP formalism, enabling large-scale atomistic simulations for such systems. 

Editorial Summary

Lithium nitride: electrostatic spectral neighbor analysis potential氮化锂:做好電池的新算法

利用局部環境描述符,如光譜鄰域分析勢(SNAP),本研究證明了通過引入長程靜電相互作用可適用于快離子體系的最新勢函數。來自美國加州大學聖叠戈分校的Shyue Ping Ong教授領導的團隊發現,靜電SNAP (eSNAP)模型在預測能量和力,以及晶格常數、彈性常數和聲子色散曲線等各種性質方面,顯著優于傳統的Coulomb-Buckingham勢模型。他們應用eSNAP模型對複雜的α-Li3N模型(500~5000個原子)進行了長時間(~1 ns)的模擬。他們發現通過直接計算電荷擴散系數和顆粒邊界得到的α-Li3NHaven比率在晶界處具有更快的擴散通道(相對于塊體材料)。電荷擴散率的計算在從頭算分子動力學模擬中非常難以收斂,而該參數的計算可使他們能夠估算出更加可靠的α-Li3N各向異性擴散系數。有趣的是,盡管他們發現c方向的電導率通常比ab平面的電導率低,但與單晶的測量結果相比,電導率僅低一個數量級。該研究提供了一種在SNAP形式下發展多組分離子體系的量子精確力場的方法,爲此類體系的大規模原子模擬提供了可能

Modern potentials based on local environment descriptors such as the Spectral Neighbor Analysis Potential (SNAP) that can be adapted for ionic systems by incorporating long-range electrostatics is demonstrated. A team led by Prof. Shyue Ping Ong from the University of California San Diego, USA, showed that the electrostatic SNAP (eSNAP) model substantially outperforms traditional Coulomb–Buckingham potential in the prediction of energies and forces, as well as various properties, such as lattice constants, elastic constants, and phonon dispersion curves. They applied the eSNAP model to conduct long-time-scale (~1ns) simulations of complex models (500–5000 atoms) of α-Li3N. They found the Haven ratio of α-Li3N by directly calculating charge diffusivity and the grain boundaries providing faster diffusion pathways (relative to bulk). The calculation of charge diffusivity, which is difficult to converge in ab initio molecular dynamics simulations, enables them to compute much more reliable estimates of the anisotropic diffusivities of α-Li3N. Interestingly, though they find that conductivity in the c-crystallographic direction is in general slower than the ab plane, the value is only one order of magnitude lower, contrary to single crystal measurements. This study provides an approach to developing quantum-accurate force fields for multi-component ionic systems under the SNAP formalism, enabling large-scale atomistic simulations for such systems.

Ab initio vibrational free energies including anharmonicity for multicomponent alloys (多組分合金非諧性振動自由能的從頭算)
Blazej GrabowskiYuji IkedaPrashanth SrinivasanFritz KormannChristoph FreysoldtAndrew Ian DuffAlexander Shapeev & Jorg Neugebauer
118彩票 5:80(2019)
doi:s41524-019-0218-8
Published online:26 July 2019

Abstract| Full Text | PDF OPEN

摘要:多種主成分合金的獨特和意想不到的特性使合金設計重新煥發活力,並引起了科學界的濃厚興趣。通過計算設計,巨大的成分參數空間使這些合金成爲一個獨特的探索領域。然而,截至目前,還沒有一種計算效率高、精度高的合金熱力學性質的方法。其中一個根本原因是缺乏精確和有效的方法來計算這些多組分、化學上複雜的合金的振動自由能(包括其非諧性)。在這項工作中,通過密度泛函理論的方法來解決這個問題,其原理是熱力學積分和機器學習勢的結合使用。通過計算典型五組分VNbMoTaW難熔高熵合金的非諧自由能,證明了該方法的有效性   

Abstract:The unique and unanticipated properties of multiple principal component alloys have reinvigorated the field of alloy design and drawn strong interest across scientific disciplines. The vast compositional parameter space makes these alloys a unique area of exploration by means of computational design. However, as of now a method to compute efficiently, yet with high accuracy the thermodynamic properties of such alloys has been missing. One of the underlying reasons is the lack of accurate and efficient approaches to compute vibrational free energies—including anharmonicity—for these chemically complex multicomponent alloys. In this work, a density-functional-theory based approach to overcome this issue is developed based on a combination of thermodynamic integration and a machine-learning potential. We demonstrate the performance of the approach by computing the anharmonic free energy of the prototypical five-component VNbMoTaW refractory high entropy alloy. 

Editorial Summary

Multicomponent alloys: Ab initio vibrational free energies多組分合金:非諧性振動自由能

本文提出了一種新的算法,結合TU-TILD方法與力矩張量勢(MTPs),這是目前計算化學複雜合金振動自由能貢獻最有效的方法。由德國斯圖加特大學的Blazej Grabowski教授領導的團隊,证明了TU-TILD + MTP組合是一種理想的協同互作組合,可有效、准確地計算無序多組分合金的完全振動自由能。他們在TU-TILD中將MTP作爲參考電位,用于化學複雜的無序VNbMoTaW高熵合金中,結果表明MTP明顯優于其它參考電位。TU-TILD + MTP組合的優異性能的內在物理機制是,振動自由能是由相空間中一個定義明確、足夠平滑且局部(雖然嚴格來說是非諧的)相空間的一部分來確定。本研究表明,該組合方法不僅適用于本研究中涉及的等原子比例的組成,也同樣適用于任意非等原子比例的組成,且還適用于不同的晶格類型,如hcpfcc等,甚至適用于液相

A new algorithm combining the TU-TILD method with moment tensor potentials (MTPs), a presently most efficient combination to compute the vibrational free energy contribution of chemically complex alloys, is developed. A team led by Prof. Blazej Grabowski from the University of Stuttgart, Germany, demonstrated that the TU-TILD+MTP combination is an ideal symbiosis for an efficient and accurate calculation of the full vibrational free energy of disordered multicomponent alloys. In particular, they applied an MTP as a reference potential within TU-TILD for the chemically complex disordered VNbMoTaW HEA and showed that it is clearly superior to alternative reference potentials. The underlying physical reason for the excellent performance of the TU-TILD+MTP combination is the fact that the vibrational free energy is determined by a rather well-defined, sufficiently smooth, and local—although strictly anharmonic—part of the phase space. The present study indicates that this applies not only to equiatomic compositions such as the one studied in the present study, but likewise to arbitrary nonequiatomic compositions, and further also to different crystallographic lattice types such as hcp or fcc and even to the liquid phase.

3D non-isothermal phase-field simulation of microstructure evolution during selective laser sintering (选区激光烧结微结构演化的三维非等温相场模拟)
Yangyiwei Yang, Olav Ragnvaldsen, Yang Bai, Min Yi & Bai-Xiang Xu
118彩票 5:81(2019)
doi:s41524-019-0219-7
Published online:06 August 2019

Abstract| Full Text | PDF OPEN

摘要:選區激光燒結(SLS)增材制造過程中,微結構演化極度依賴于局部溫度的急劇變化,故而常規的等溫相場模型很難適用于SLS的模擬。本研究報道了一種新的非等溫相場模型,該模型從熵出發,熱力學自洽地推導出了控制微結構序參量演化的非等溫動力學方程,以及耦合微結構演化的熱傳導方程,並考慮了SLS局部極高溫導致的局部熔化以及激光-粉末相互作用。該模型經三維有限元數值化後,被用于模擬單次掃描的SLS。爲了減小計算量並加快計算速度,提出了一種類似于求解最小著色數問題的新算法,結合晶粒追蹤方法,該算法可僅用8個序參量來模擬具有多達200個晶粒的系統。特別地,將該非等溫相場模型用于SLS處理316L不鏽鋼粉末的研究,揭示了激光功率和掃描速度對孔隙率、表面形貌、溫度分布、晶粒幾何形狀以及致密度等微觀結構特征的影響規律。此外,模擬結果驗證了致密化過程中孔隙率變化的一階動力學特征,並證實了該模型可用于預測SLS過程中致密化因子與激光比能量之間的關聯   

Abstract:During selective laser sintering (SLS), the microstructure evolution and local temperature variation interact mutually. Application of conventional isothermal sintering model is thereby insufficient to describe SLS. In this work, we construct our model from entropy level, andderive the non-isothermal kinetics for order parameters along with the heat transfer equation coupled with microstructure evolution. Influences from partial melting and laser-powder interaction are also addressed. We then perform 3D finite element non-isothermal phase-field simulations of the SLS single scan. To confront the high computation cost, we propose a novel algorithm analogy to minimum coloring problem and manage to simulate a system of 200 grains with grain tracking algorithm using as low as 8 non-conserved order parameters. Specifically, applying the model to SLS of the stainless steel 316L powder, we identify the influences of laser power and scan speed on microstructural features, including the porosity, surface morphology, temperature profile, grain geometry, and densification. We further validate the first-order kinetics of the transient porosity during densification, and demonstrate the applicability of the developed model in predicting the linkage of densification factor to the specific energy input during SLS. 

Editorial Summary

Additive Manufacturing: Sino-German cooperation predicts complex microstructures增材制造:中德合作預測複雜微結構

該研究提出了一种热力学自洽的非等温相场模型以及相应的三维高效數值方法,可以模拟選區激光燒結(SLS)增材制造中複雜微結構的演化過程。德國達姆施塔特工業大學的終身教授胥柏香領導的團隊,與南京航空航天大學的青年千人易敏教授合作,報道了一種熱力學自洽的非等溫相場模型,考慮微結構與熱傳導的強耦合、SLS局部極高溫導致的局部熔化以及激光-粉末相互作用。他們提出了一種類似于求解最小著色數問題的新解決方案,結合晶粒追蹤方法,該方案可僅用8個序參量來模擬具有多達200個晶粒的系統。研究人員還使用了基于LM算法的非線性優化方法,同時擬合模型與實驗中表面能、晶界能隨溫度變化的趨勢,以獲取用于非恒溫相場的模型參數。特別地,將該非等溫相場模型用于SLS處理316L不鏽鋼粉末的研究,揭示了激光功率和掃描速度對孔隙率、表面形貌、溫度分布、晶粒幾何形狀以及致密度等微觀結構特征的影響規律,並證實了該模型可用于預測SLS過程中致密化因子與激光比能量之間的關聯。他们的研究为基于SLS的增材制造的建模及計算模擬提供了有效方法或工具

An appropriate consideration of complex temperature profile and its extreme gradient which are the most prevailing feature of selective laser sintering (SLS)-additive manufacturing (AM) process for simulating the microstructure evolution during the SLS based AM is reported. A team led by Bai-Xiang Xu from Technical University of Darmstadt, Germany, cooperating with Min Yi from Nanjing University of Aeronautics and Astronautics (NUAA), developed a thermodynamically consistent non-isothermal phase-field model to simulate the microstructure evolution during SLS-AM. In order to save the high computation cost, the authors proposed a novel algorithm analogy to minimum coloring problem and manage to simulate a system of 200 grains with grain tracking algorithm using as low as 8 non-conserved order parameters. After applying the model to SLS of the stainless steel 316L powder, the influences of laser power and scan speed on microstructural features (i.e. the porosity, surface morphology, temperature profile, grain geometry, and densification) are successfully identified. Their work provides a phase-field model and the associated numeric scheme which are promising for the large-scale simulation of SLS-AM process.

Unconventional topological phase transition in non-symmorphic material KHgX (X=As, Sb, Bi)(非對稱材料中的非常規拓撲相變KHgXX = AsSbBi)
Chin-Shen KuoTay-Rong ChangSu-Yang Xu & Horng-Tay Jeng
118彩票 5:65(2019)
doi:s41524-019-0201-4
Published online:06 June 2019

Abstract| Full Text | PDF OPEN

摘要:傳統的拓撲相變描述了從拓撲平凡到拓撲非平凡態的演化。我們在這項工作中提出了由Dirac無間隙態介導的兩個拓撲非平凡絕緣態之間的非常規拓撲相變體系,源于非對稱型晶體對稱性,不同于傳統的拓撲相變。KHgXX = AsSbBi)族是第一個實驗上實現的拓撲非同態晶體絕緣體(TNCI),其中拓撲表面態以Mobius扭曲連通性爲特征。基于第一性原理計算,我們通過在KHgX上施加外部壓力,提出了從TNCI到狄拉克半金屬(DSM)的拓撲絕緣體-金屬轉變。我們發現在非同態晶體結構中KHgXDSM相具有不尋常的鏡面ChernCm=-3,其在拓撲上不同于傳統的DSM,例如Na3BiCd3As2。此外,我們通過對稱性破壞預測KHgX中的新TNCI相。這個新的TNCI相的拓撲表面狀態顯示鋸齒形連通性,不同于無應力的連通性。我們的研究結果爲理解拓撲表面狀態如何從量子演化提供了全面的研究   

Abstract:Traditionally topological phase transition describes an evolution from topological trivial to topological nontrivial state. Originated from the non-symmorphic crystalline symmetry, we propose in this work an unconventional topological phase transition scheme between two topological nontrivial insulating states mediated by a Dirac gapless state, differing from the traditional topological phase transition. The KHgX (X=As, Sb, Bi) family is the first experimentally realized topological non-symmorphic crystalline insulator (TNCI), where the topological surface states are characterized by the Mobius-twisted connectivity. Based on first-principles calculations, we present a topological insulator–metal transition from TNCI into a Dirac semimetal (DSM) via applying an external pressure on KHgX. We find an unusual mirror Chern number Cm=-3 for the DSM phase of KHgX in the non-symmorphic crystal structure, which is topologically distinct from the traditional DSM such as Na3Bi and Cd3As2. Furthermore, we predict a new TNCI phase in KHgX via symmetry breaking. The topological surface states in this new TNCI phase display zigzag connectivity, different from the unstressed one. Our results offer a comprehensive study for understanding how the topological surface states evolve from a quantum. 

Editorial Summary

Non-symmorphic material KHgX: Unconventional topological phase transition非對稱材料KHgX:非常規拓撲相變

該研究提出了由Dirac無間隙態介導的兩個拓撲非平凡絕緣態之間的非常規拓撲相變體系,該體系源于非對稱型晶體對稱性,不同于傳統的拓撲相變。來自中國台灣兩所大學的Tay-Rong ChangHorng-Tay Jeng等,基于第一行原理計算提出了非常規拓撲相轉變。KHgXX = AsSbBi)族是第一個實驗上實現的拓撲非同態晶體絕緣體,其中拓撲表面態以莫比烏斯扭曲連接爲特征。他們基于第一原理計算,通過引入兩個新相來使KHgX的拓撲相圖多樣化。通過施加應力,KHgX經曆拓撲絕緣體-金屬的轉變,從拓撲非同態晶體絕緣體相轉變爲Cm = -2DSM相,在非對稱晶體結構中的非平凡鏡ChernCm = -3。通過對稱性破壞,DSM相轉換爲另一個新的拓撲非同態晶體絕緣體相,其中Cm = -3主導著QSH效應。表面能帶的連通性的變化,提供了拓撲相變的直接證明,而且要實現這些預測的新拓撲相,操縱帶隙是其關鍵

An unconventional topological phase transition scheme between two topological non-trivial insulating states mediated by a Dirac gapless state, originated from non-symmorphic crystalline symmetry, differing from traditional topological phase transitions. A team co-led by Tay-Rong Chang and Horng-Tay Jeng from universities in Taiwan, China, proposed the unconventional topological phase transition based on the first-principles calculations. A KHgX (X = As, Sb, Bi) family which they studied is the first experimentally realized topologically non-homomorphic crystal insulator (TNCI) where the topological surface states are characterized by Mobius-twisted connectivity. Based on the first principles calculations, they diversify the topological phase diagram of KHgX by introducing two new phases. By applying stress, KHgX undergoes a topological insulator-metal transition from the TNCI phase with Cm = -2 into the DSM phase with a non-trivial mirror Chern number Cm = -3 in the non-symmorphic crystal structure. Through symmetry breakong, the DSM phase transforms into another new TNCI phase with Cm = -3 hosting the QSH effect. The change in the connectivity of the surface bands provides a direct justification of the topological phase transition, and manipulating the band gap is the key to realize these predicted new topological phases.

Tunable ferromagnetic Weyl fermions from a hybrid nodal ring (源于雜化節點環的可調鐵磁外爾費米子)
Baobing Zheng, Bowen Xia, Rui Wang, Jinzhu Zhao, Zhongjia Chen, Yujun Zhao Hu Xu
118彩票 5:74(2019)
doi:s41524-019-0214-z
Published online:15 July 2019

Abstract| Full Text | PDF OPEN

摘要:近年來,實現非平庸的能帶拓撲結構是凝聚態系統中一個極受關注的熱點。基于第一性原理計算和對稱性分析,本研究報道了在鐵磁半金屬氧化物CrP2O7中的可調外爾費米子的拓撲相。忽略自旋軌道耦合的情況下,CrP2O7能帶中不同類型的節點形成雜化的節點環。考慮自旋軌道耦合的情況下,體系的自旋翻轉對稱性破缺,因此,雜化的節點環縮減爲離散的節點,形成了不同類型的外爾點。該體系投影在(100)面的費米弧清晰可見,有助于在實驗上研究CrP2O7的拓撲性質。此外,計算得到的准粒子幹涉圖樣對實驗研究也很有幫助。本工作提供了一種良好的鐵磁外爾半金屬候選材料,並有望應用于拓撲相關領域   

Abstract:Realization of nontrivial band topology in condensed matter systems is of great interest in recent years. Using first-principles calculations and symmetry analysis, we propose an exotic topological phase with tunable ferromagnetic Weyl fermions in a half-metallic oxide CrP2O7. In the absence of spin–orbit coupling (SOC), we reveal that CrP2O7 possesses a hybrid nodal ring. When SOC is present, the spin-rotation symmetry is broken. As a result, the hybrid nodal ring shrinks to discrete nodal points and forms different types of Weyl points. The Fermi arcs projected on the (100) surface are clearly visible, which can contribute to the experimental study for the topological properties of CrP2O7. In addition, the calculated quasiparticle interference patterns are also highly desirable for the experimental study of CrP2O7. Our findings provide a good candidate of ferromagnetic Weyl semimetals, and are expected to realize related topological applications with their attracted features. 

Editorial Summary

Ferromagnetic Weyl Semimetal CrP2O7: From a hybrid nodal ring to tunable Weyl fermions新型鐵磁外爾半金屬:來自南科大慢悠悠的老虎

該研究提出了CrP2O7 是一種第一類和第二類外爾點共存的鐵磁外爾半金屬材料,共存的不同類型外爾點來源于沒有自旋軌道耦合時的雜化節點環。來自南方科技大學的徐虎教授領導的團隊(簡稱慢悠悠老虎團),基于第一性原理計算和對稱性分析,研究了鐵磁材料CrP2O7 的拓撲能帶結構。在考虑自旋轨道耦合的情况下,体系的杂化节点环缩减为不同类型的外尔点。通过外加磁场改变磁化方向,可以调节外尔点的數量和类型。此外,计算得到的费米弧和准粒子干涉图样非常有助于实验的进一步观测。該研究结果为深入研究磁性和拓扑之间的相互作用提供了一种理想候选材料,并且加深了人们对铁磁拓扑半金属材料的认识

CrP2O7 is demonstrated to be a ferromagnetic Weyl semimetal with the coexistence of type-I and type-II Weyl fermions, which originates from a hybrid nodal ring without spin-orbital coupling (SOC). A team led by Hu Xu from the Southern University of Science and Technology, reported the nontrivial band topology of CrP2O7 by using first-principles calculations and symmetry analysis. The hybrid nodal ring of CrP2O7 without SOC shrinks to different types of Weyl points when SOC is included, and the numbers and types of Weyl points can be tuned by external magnetic field. In addition, the calculated Fermi arcs and quasiparticle interference patterns facilitate the experimental study of the topological properties of CrP2O7. Their findings provide a good candidate of studying the interplay between magnetism and topology physics, and deepen the understanding of ferromagnetic Weyl semimetal.

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