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An isotropic zero thermal expansion alloy with super-high toughness
Zero thermal expansion (ZTE) alloys with high mechanical response are crucial for their practical usage. Yet, unifying the ZTE behavior and mechanical response in one material is a grand obstacle, especially in multicomponent ZTE alloys. Herein, we report a near isotropic zero thermal expansion (α l = 1.10 × 10 -6 K -1 , 260–310 K) in the natural heterogeneous LaFe 54 Co 3.5 Si 3.35 alloy, which exhibits a super-high toughness of 277.8 ± 14.7 J cm -3 . Chemical partition, in the dual-phase structure, assumes the role of not only modulating thermal expansion through magnetic interaction but also enhancing mechanical properties via interface bonding. The comprehensive analysis reveals that the hierarchically synergistic enhancement among lattice, phase interface, and heterogeneous structure is significant for strong toughness. Our findings pave the way to tailor thermal expansion and obtain prominent mechanical properties in multicomponent alloys, which is essential to ultra-stable functional materials.
Finding predictive models for singlet fission by machine learning
Singlet fission (SF), the conversion of one singlet exciton into two triplet excitons, could significantly enhance solar cell efficiency. Molecular crystals that undergo SF are scarce. Computational exploration may accelerate the discovery of SF materials. However, many-body perturbation theory (MBPT) calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physical primary features. The best models are selected by linear regression with cross-validation. The SISSO models successfully predict the SF driving force with errors below 0.2 eV. Based on the cost, accuracy, and classification performance of SISSO models, we propose a hierarchical materials screening workflow. Three potential SF candidates are found in the PAH101 set.
Composition design of high-entropy alloys with deep sets learning
High entropy alloys (HEAs) are an important material class in the development of next-generation structural materials, but the astronomically large composition space cannot be efficiently explored by experiments or first-principles calculations. Machine learning (ML) methods might address this challenge, but ML of HEAs has been hindered by the scarcity of HEA property data. In this work, the EMTO-CPA method was used to generate a large HEA dataset (spanning a composition space of 14 elements) containing 7086 cubic HEA structures with structural properties, 1911 of which have the complete elastic tensor calculated. The elastic property dataset was used to train a ML model with the Deep Sets architecture. The Deep Sets model has better predictive performance and generalizability compared to other ML models. Association rule mining was applied to the model predictions to describe the compositional dependence of HEA elastic properties and to demonstrate the potential for data-driven alloy design.
Identification of high-dielectric constant compounds from statistical design
Abstract The discovery of high-dielectric materials is crucial to increasing the efficiency of electronic devices and batteries. Here, we report three previously unexplored materials with very high dielectric constants (69 < ϵ < 101) and large band gaps (2.9 < E g (eV) < 5.5) obtained by screening materials databases using statistical optimization algorithms aided by artificial neural networks (ANN). Two of these new dielectrics are mixed-anion compounds (Eu 5 SiCl 6 O 4 and HoClO) and are shown to be thermodynamically stable against common semiconductors via phase diagram analysis. We also uncovered four other materials with relatively large dielectric constants (20 < ϵ < 40) and band gaps (2.3 < E g (eV) < 2.7). While the ANN training-data are obtained from the Materials Project, the search-space consists of materials from the Open Quantum Materials Database (OQMD)—demonstrating a successful implementation of cross-database materials design. Overall, we report the dielectric properties of 17 materials calculated using ab initio calculations, that were selected in our design workflow. The dielectric materials with high-dielectric properties predicted in this work open up further experimental research opportunities.
Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning
Characterization of material structure with X-ray or neutron scattering using e.g. Pair Distribution Function (PDF) analysis most often rely on refining a structure model against an experimental dataset. However, identifying a suitable model is often a bottleneck. Recently, automated approaches have made it possible to test thousands of models for each dataset, but these methods are computationally expensive and analysing the output, i.e. extracting structural information from the resulting fits in a meaningful way, is challenging. Our Machine Learning based Motif Extractor (ML-MotEx) trains an ML algorithm on thousands of fits, and uses SHAP (SHapley Additive exPlanation) values to identify which model features are important for the fit quality. We use the method for 4 different chemical systems, including disordered nanomaterials and clusters. ML-MotEx opens for a type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML.
Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table
Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneck with the developed Elemental Spatial Density Neural Network Force Field, namely Elemental-SDNNFF. The effectiveness and precision of our Elemental-SDNNFF approach are demonstrated on 11,866 full, half, and quaternary Heusler structures spanning 55 elements in the periodic table by prediction of complete phonon properties. Self-improvement schemes including active learning and data augmentation techniques provide an abundant 9.4 million atomic data for training. Deep insight into predicted ultralow lattice thermal conductivity (<1 Wm –1 K –1 ) of 774 Heusler structures is gained by p–d orbital hybridization analysis. Additionally, a class of two-band charge-2 Weyl points, referred to as “double Weyl points”, are found in 68% and 87% of 1662 half and 1550 quaternary Heuslers, respectively.
Substrate effects on spin relaxation in two-dimensional Dirac materials with strong spin-orbit coupling
Understanding substrate effects on spin dynamics and relaxation is of key importance for spin-based information technologies. However, the key factors that determine such effects, in particular for materials with strong spin-orbit coupling (SOC), have not been well understood. Here we performed first-principles real-time density-matrix dynamics simulations with SOC and the electron phonon and electron-impurity scattering for spin lifetimes (τ s ) of supported/free-standing germanene, a prototypical strong SOC 2D Dirac material. We show that the effects of different substrates on τ s can surprisingly differ by two orders of magnitude. We find that substrate effects on τ s are closely related to substrate-induced modifications of the SOC-field anisotropy, which changes the spin flip scattering matrix elements. We propose a new electronic quantity, named spin-flip angle θ ↑↓ , to characterize spin relaxation through intervalley spin-flip scattering. We find that $τ^{-1}_s$ is approximately proportional to the averaged value of sin 2 (θ ↑↓ /2), which serves as a guiding parameter of controlling spin relaxation.
Multi-scale investigation of short-range order and dislocation glide in MoNbTi and TaNbTi multi-principal element alloys
Refractory multi-principal element alloys (RMPEAs) are promising materials for high-temperature structural applications. Here, we investigate the role of short-range ordering (SRO) on dislocation glide in the MoNbTi and TaNbTi RMPEAs using a multi-scale modeling approach. Monte carlo/molecular dynamics simulations with a moment tensor potential show that MoNbTi exhibits a much greater degree of SRO than TaNbTi and the local composition has a direct effect on the unstable stacking fault energies (USFEs). From mesoscale phase-field dislocation dynamics simulations, we find that increasing SRO leads to higher mean USFEs and stress required for dislocation glide. The gliding dislocations experience significant hardening due to pinning and depinning caused by random compositional fluctuations, with higher SRO decreasing the degree of USFE dispersion and hence, amount of hardening. Finally, we show how the morphology of an expanding dislocation loop is affected by the applied stress.
An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems
Materials design aims to identify the material features that provide optimal properties for various engineering applications, such as aerospace, automotive, and naval. One of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal properties. This paper proposes an end-to-end artificial intelligence (AI)-driven microstructure optimization framework for elastic properties of materials. In this work, the microstructure is represented by the Orientation Distribution Function (ODF) that determines the volume densities of crystallographic orientations. The framework was evaluated on two crystal systems, cubic and hexagonal, for Titanium (Ti) in Joint Automated Repository for Various Integrated Simulations (JARVIS) database and is expected to be widely applicable for materials with multiple crystal systems. The proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time.
Computational alchemy clarifies origins of alloy strengthening
Solid solution strengthening (SSS) is widely used to enhance mechanical properties of metals. Originally developed for dilute alloys, classical SSS theories are presently challenged by the rise of complex concentrated alloys (CCA) with nearly equiatomic compositions. Here, we propose and develop a method of “computational alchemy” in which interatomic interactions are modified to systematically vary two key physical parameters defining SSS - atomic size misfit and elastic stiffness misfit - over a maximally wide range of two misfits. The resulting alchemical alloys are subjected to massive (~10 8 atoms) molecular dynamics (MD) simulations reproducing full complexity of plastic strength response. At variance with prevailing views, stiffness misfit is observed to contribute to SSS on par if not more than size misfit. Furthermore, depending on exactly how two misfits are combined, they result in synergistic (amplification) or antagonistic (compensation) effect on alloy strengthening. Unlike real CCAs in which each component element comes with its own specific size and stiffness, our alchemical model alloys span the space of two misfits continuously revealing trends in alloy strengthening unrecognized so far. Our study demonstrates unique value of intentionally unrealistic models for gaining deep physical insights into material behaviors that are difficult to reveal otherwise.
Unconventional excitonic states with phonon sidebands in layered silicon diphosphide
Complex correlated states emerging from many-body interactions between quasiparticles (electrons, excitons and phonons) are at the core of condensed matter physics and material science. In low-dimensional materials, quantum confinement affects the electronic, and subsequently, optical properties for these correlated states. Here, by combining photoluminescence, optical reflection measurements and ab initio theoretical calculations, we demonstrate an unconventional excitonic state and its bound phonon sideband in layered silicon diphosphide (SiP 2 ), where the bound electron–hole pair is composed of electrons confined within one-dimensional phosphorus–phosphorus chains and holes extended in two-dimensional SiP 2 layers. The excitonic state and emergent phonon sideband show linear dichroism and large energy redshifts with increasing temperature. Our ab initio many-body calculations confirm that the observed phonon sideband results from the correlated interaction between excitons and optical phonons. With these results, we propose layered SiP2 as a platform for the study of excitonic physics and many-particle effects.
Giant spin Seebeck effect through an interface organic semiconductor
Interfacing an organic semiconductor C 60 with a non-magnetic metallic thin film (Cu or Pt) has created a novel heterostructure that is ferromagnetic at ambient temperature, while its interface with a magnetic metal (Fe or Co) can tune the anisotropic magnetic surface property of the material. Here, we demonstrate that sandwiching C60 in between a magnetic insulator (Y 3 Fe 5 O 12 :YIG) and a non-magnetic, strong spin–orbit metal (Pt) promotes highly efficient spin current transport via the thermally driven spin Seebeck effect (SSE). Experiments and first principles calculations consistently show that the presence of C 60 reduces significantly the conductivity mismatch between YIG and Pt and the surface perpendicular magnetic anisotropy of YIG, giving rise to enhanced spin mixing conductance across YIG/C 60 /Pt interfaces. As a result, a 600% increase in the SSE voltage (V LSSE ) has been realized in YIG/C 60 /Pt relative to YIG/Pt. Temperature-dependent SSE voltage measurements on YIG/C 60 /Pt with varying C 60 layer thicknesses also show an exponential increase in V LSSE at low temperatures below 200 K, resembling the temperature evolution of spin diffusion length of C 60 . Our report emphasizes the important roles of the magnetic anisotropy and the spin diffusion length of the intermediate layer in the SSE in YIG/C 60 /Pt structures, providing a new pathway for developing novel spin-caloric materials.
Chemically controlled shape-morphing of elastic sheets
Two-dimensional responsive materials that change shape into complex three-dimensional structures are valuable for creating systems ranging from wearable electronics to soft robotics. Typically, the final 3D structure is unique and predetermined through the materials’ processing. Here, we use theory and simulation to devise a distinctive approach for driving shape changes of 2D elastic sheets in fluid-filled microchambers. The sheets are coated with catalyst to generate controllable fluid flows, which transform the sheets into complex 3D shapes. A given shape can be achieved by patterning the arrangement of the catalytic domains on the sheet and introducing the appropriate reactant to initiate a specific catalytic reaction. Moreover, a single sheet that encompasses multiple catalytic domains can be transformed into a variety of 3D shapes through the addition of one or more reactants. Materials systems that morph on-demand into a variety of distinct structures can simplify manufacturing processes and broaden the utility of soft materials.
Understanding, quantifying, and controlling the molecular ordering of semiconducting polymers: from novices to experts and amorphous to perfect crystals
Molecular packing and texture of semiconducting polymers are often critical to the performance of devices using these materials. Although frameworks exist to quantify the ordering, interpretations are often just qualitative, resulting in imprecise use of terminology. Here, we reemphasize the significance of quantifying molecular ordering in terms of degree of crystallinity (volume fractions that are ordered) and quality of ordering and their relation to the size scale of an ordered region. We are motivated in part by our own imprecise and inconsistent use of terminology in the past, as well as the need to have a primer or tutorial reference to teach new group members. We strive to develop and use consistent terminology with regards to crystallinity, semicrystallinity, paracrystallinity, and related characteristics. To account for vastly different quality of ordering along different directions, we classify paracrystals into 2D and 3D paracrystals and use paracrystallite to describe the spatial extent of molecular ordering in 1–10 nm. We show that a deeper understanding of molecular ordering can be achieved by combining grazing-incidence wide-angle X-ray scattering and differential scanning calorimetry, even though not all aspects of these measurements are consistent, and some classification appears to be method dependent. We classify a broad range of representative polymers under common processing conditions into five categories based on the quantitative analysis of the paracrystalline disorder parameter (g) and thermal transitions. A small database is presented for 13 representative conjugated and insulating polymers ranging from amorphous to semi-paracrystalline. Lastly, we outline the challenges to rationally design more perfect polymer crystals and propose a new molecular design approach that envisions conceptual molecular grafting that is akin to strained and unstrained hetero-epitaxy in classic (compound) semiconductors thin film growth.
Coupled hard–soft spinel ferrite-based core–shell nanoarchitectures: magnetic properties and heating abilities
The heating abilities of bi-magnetic core–shell nanostructures are studied depending on core size, nature of the shell, and shell thickness.
Lineshape characterization of excitons in monolayer WS 2 by two-dimensional electronic spectroscopy
The optical properties of monolayer transition metal dichalcogenides (TMDCs), an important family of two-dimensional (2D) semiconductors for optoelectronic applications, are dominated by two excitons A (XA) and B (XB) located at K/K's valleys. The lineshape of the excitons is an indicator of the interaction of the excitons with other particles and also largely determines the performance of TMDC-based optoelectronic devices. In this work, we apply 2D electronic spectroscopy (2DES), which enables separation of the intrinsic homogeneous linewidth and the extrinsic inhomogeneous linewidth, to dissect the lineshape of XA in monolayer WS 2 . With a home-built broadband optical parametric amplifier, the 2D spectra give the exciton linewidth values for extensive ranges of excitation densities and temperatures, reflecting inter-exciton and exciton–phonon interactions. Meanwhile, the time-domain evolution of the lineshape reveals a similar rate of spectral diffusion to that in quantum wells (QWs) based on III–V semiconductors.
On the synthesis of bi-magnetic manganese ferrite-based core–shell nanoparticles
Dissolution phenomena in seed-mediated growth approaches of MnFe 2 O 4 -based bi-magnetic core–shell nanoheterostructures with a cobalt ferrite or spinel iron oxide shell.