显示样式:     当前期刊: npj Computational Materials    加入关注    导出
我的关注
我的收藏
您暂时未登录!
登录
  • Theoretical potential for low energy consumption phase change memory utilizing electrostatically-induced structural phase transitions in 2D materials
    npj Comput. Mater. Pub Date : 2018-01-17
    Daniel A. Rehn, Yao Li, Eric Pop, Evan J. Reed

    Structural phase-change materials are of great importance for applications in information storage devices. Thermally driven structural phase transitions are employed in phase-change memory to achieve lower programming voltages and potentially lower energy consumption than mainstream nonvolatile memory technologies. However, the waste heat generated by such thermal mechanisms is often not optimized, and could present a limiting factor to widespread use. The potential for electrostatically driven structural phase transitions has recently been predicted and subsequently reported in some two-dimensional materials, providing an athermal mechanism to dynamically control properties of these materials in a nonvolatile fashion while achieving potentially lower energy consumption. In this work, we employ DFT-based calculations to make theoretical comparisons of the energy required to drive electrostatically-induced and thermally-induced phase transitions. Determining theoretical limits in monolayer MoTe2 and thin films of Ge2Sb2Te5, we find that the energy consumption per unit volume of the electrostatically driven phase transition in monolayer MoTe2 at room temperature is 9% of the adiabatic lower limit of the thermally driven phase transition in Ge2Sb2Te5. Furthermore, experimentally reported phase change energy consumption of Ge2Sb2Te5 is 100–10,000 times larger than the adiabatic lower limit due to waste heat flow out of the material, leaving the possibility for energy consumption in monolayer MoTe2-based devices to be orders of magnitude smaller than Ge2Sb2Te5-based devices.

    更新日期:2018-01-17
  • Understanding the physical metallurgy of the CoCrFeMnNi high-entropy alloy: an atomistic simulation study
    npj Comput. Mater. Pub Date : 2018-01-10
    Won-Mi Choi, Yong Hee Jo, Seok Su Sohn, Sunghak Lee, Byeong-Joo Lee

    Although high-entropy alloys (HEAs) are attracting interest, the physical metallurgical mechanisms related to their properties have mostly not been clarified, and this limits wider industrial applications, in addition to the high alloy costs. We clarify the physical metallurgical reasons for the materials phenomena (sluggish diffusion and micro-twining at cryogenic temperatures) and investigate the effect of individual elements on solid solution hardening for the equiatomic CoCrFeMnNi HEA based on atomistic simulations (Monte Carlo, molecular dynamics and molecular statics). A significant number of stable vacant lattice sites with high migration energy barriers exists and is thought to cause the sluggish diffusion. We predict that the hexagonal close-packed (hcp) structure is more stable than the face-centered cubic (fcc) structure at 0 K, which we propose as the fundamental reason for the micro-twinning at cryogenic temperatures. The alloying effect on the critical resolved shear stress (CRSS) is well predicted by the atomistic simulation, used for a design of non-equiatomic fcc HEAs with improved strength, and is experimentally verified. This study demonstrates the applicability of the proposed atomistic approach combined with a thermodynamic calculation technique to a computational design of advanced HEAs.

    更新日期:2018-01-10
  • Machine learning in materials informatics: recent applications and prospects
    npj Comput. Mater. Pub Date : 2017-12-13
    Rampi Ramprasad, Rohit Batra, Ghanshyam Pilania, Arun Mannodi-Kanakkithodi, Chiho Kim

    Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as “descriptors”, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.

    更新日期:2017-12-14
  • Virtual screening of inorganic materials synthesis parameters with deep learning
    npj Comput. Mater. Pub Date : 2017-12-01
    Edward Kim, Kevin Huang, Stefanie Jegelka, Elsa Olivetti

    Virtual materials screening approaches have proliferated in the past decade, driven by rapid advances in first-principles computational techniques, and machine-learning algorithms. By comparison, computationally driven materials synthesis screening is still in its infancy, and is mired by the challenges of data sparsity and data scarcity: Synthesis routes exist in a sparse, high-dimensional parameter space that is difficult to optimize over directly, and, for some materials of interest, only scarce volumes of literature-reported syntheses are available. In this article, we present a framework for suggesting quantitative synthesis parameters and potential driving factors for synthesis outcomes. We use a variational autoencoder to compress sparse synthesis representations into a lower dimensional space, which is found to improve the performance of machine-learning tasks. To realize this screening framework even in cases where there are few literature data, we devise a novel data augmentation methodology that incorporates literature synthesis data from related materials systems. We apply this variational autoencoder framework to generate potential SrTiO3 synthesis parameter sets, propose driving factors for brookite TiO2 formation, and identify correlations between alkali-ion intercalation and MnO2 polymorph selection.

    更新日期:2017-12-14
  • First-principles screening of structural properties of intermetallic compounds on martensitic transformation
    npj Comput. Mater. Pub Date : 2017-11-24
    Joohwi Lee, Yuji Ikeda, Isao Tanaka

    Martensitic transformation with good structural compatibility between parent and martensitic phases are required for shape memory alloys (SMAs) in terms of functional stability. In this study, first-principles-based materials screening is systematically performed to investigate the intermetallic compounds with the martensitic phases by focusing on energetic and dynamical stabilities as well as structural compatibility with the parent phase. The B2, D03, and L21 crystal structures are considered as the parent phases, and the 2H and 6M structures are considered as the martensitic phases. In total, 3384 binary and 3243 ternary alloys with stoichiometric composition ratios are investigated. It is found that 187 alloys survive after the screening. Some of the surviving alloys are constituted by the chemical elements already widely used in SMAs, but other various metallic elements are also found in the surviving alloys. The energetic stability of the surviving alloys is further analyzed by comparison with the data in Materials Project Database (MPD) to examine the alloys whose martensitic structures may cause further phase separation or transition to the other structures.

    更新日期:2017-12-14
  • First-principles prediction of high-entropy-alloy stability
    npj Comput. Mater. Pub Date : 2017-11-21
    Rui Feng, Peter K. Liaw, Michael C. Gao, Michael Widom

    High entropy alloys (HEAs) are multicomponent compounds whose high configurational entropy allows them to solidify into a single phase, with a simple crystal lattice structure. Some HEAs exhibit desirable properties, such as high specific strength, ductility, and corrosion resistance, while challenging the scientist to make confident predictions in the face of multiple competing phases. We demonstrate phase stability in the multicomponent alloy system of Cr–Mo–Nb–V, for which some of its binary subsystems are subject to phase separation and complex intermetallic-phase formation. Our first-principles calculation of free energy predicts that the configurational entropy stabilizes a single body-centered cubic (BCC) phase from T = 1700 K up to melting, while precipitation of a complex intermetallic is favored at lower temperatures. We form the compound experimentally and confirm that it develops as a single BCC phase from the melt, but that it transforms reversibly at lower temperatures.

    更新日期:2017-12-14
  • Rethinking phonons: The issue of disorder
    npj Comput. Mater. Pub Date : 2017-11-16
    Hamid Reza Seyf, Luke Yates, Thomas L. Bougher, Samuel Graham, Baratunde A. Cola, Theeradetch Detchprohm, Mi-Hee Ji, Jeomoh Kim, Russell Dupuis, Wei Lv, Asegun Henry

    Current understanding of phonons treats them as plane waves/quasi-particles of atomic vibration that propagate and scatter. The problem is that conceptually, when any level of disorder is introduced, whether compositional or structural, the character of vibrational modes in solids changes, yet nearly all theoretical treatments continue to assume phonons are still waves. For example, the phonon contributions to alloy thermal conductivity (TC) rely on this assumption and are most often computed from the virtual crystal approximation (VCA). Good agreement is obtained in some cases, but there are many instances where it fails—both quantitatively and qualitatively. Here, we show that the conventional theory and understanding of phonons requires revision, because the critical assumption that all phonons/normal modes resemble plane waves with well-defined velocities is no longer valid when disorder is introduced. Here we show, surprisingly, that the character of phonons changes dramatically within the first few percent of impurity concentration, beyond which phonons more closely resemble the modes found in amorphous materials. We then utilize a different theory that can treat modes with any character and experimentally confirm its new insights.

    更新日期:2017-12-14
  • Theory, simulations and the design of functionalized nanoparticles for biomedical applications: A Soft Matter Perspective
    npj Comput. Mater. Pub Date : 2017-11-13
    Stefano Angioletti-Uberti

    Functionalised nanoparticles for biomedical applications represents an incredibly exciting and rapidly growing field of research. Considering the complexity of the nano–bio interface, an important question is to what extent can theory and simulations be used to study these systems in a realistic, meaningful way. In this review, we will argue for a positive answer to this question. Approaching the issue from a “Soft Matter” perspective, we will consider those properties of functionalised nanoparticles that can be captured within a classical description. We will thus not concentrate on optical and electronic properties, but rather on the way nanoparticles’ interactions with the biological environment can be tuned by functionalising their surface and exploited in different contexts relevant to applications. In particular, we wish to provide a critical overview of theoretical and computational coarse-grained models, developed to describe these interactions and present to the readers some of the latest results in this fascinating area of research.

    更新日期:2017-12-14
  • Publisher Correction: First-principles calculation of intrinsic defect chemistry and self-doping in PbTe
    npj Comput. Mater. Pub Date : 2017-11-10
    Anuj Goyal, Prashun Gorai, Eric S. Toberer, Vladan Stevanović

    Publisher Correction: First-principles calculation of intrinsic defect chemistry and self-doping in PbTe Publisher Correction: First-principles calculation of intrinsic defect chemistry and self-doping in PbTe, Published online: 10 November 2017; doi:10.1038/s41524-017-0054-7 Publisher Correction: First-principles calculation of intrinsic defect chemistry and self-doping in PbTe

    更新日期:2017-12-14
  • Author Correction: Chemically intuited, large-scale screening of MOFs by machine learning techniques
    npj Comput. Mater. Pub Date : 2017-11-08
    Giorgos Borboudakis, Taxiarchis Stergiannakos, Maria Frysali, Emmanuel Klontzas, Ioannis Tsamardinos, George E. Froudakis

    Author Correction: Chemically intuited, large-scale screening of MOFs by machine learning techniques Author Correction: Chemically intuited, large-scale screening of MOFs by machine learning techniques, Published online: 08 November 2017; doi:10.1038/s41524-017-0051-x Author Correction: Chemically intuited, large-scale screening of MOFs by machine learning techniques

    更新日期:2017-12-14
Some contents have been Reproduced with permission of the American Chemical Society.
Some contents have been Reproduced by permission of The Royal Society of Chemistry.
化学 • 材料 期刊列表
Wiley论文编辑服务,每月大奖送不停!
深圳大学-同济大学联合招聘博士后(税后年薪30万)
华中师范大学第一届国际青年学者化学科学论坛
【问答】大分子介晶有哪些重要意义?
X-MOL近期新增451种期刊(20171216)
2017年中科院JCR分区化学大类列表
试剂库存管理
化合物查询
down
wechat
bug