Elastic Properties of Alloyed Cementite M3X (M = Fe, Cr; X = C, B) Phases from First-Principle Calculations and CALPHAD Model.

Fe-Cr-C-B wear-resistant steels are widely used as wear-resistant alloys in harsh environments. The M3X (M = Fe, Cr; X = C, B) cementite-type material is a commonly used strengthening phase in these alloys. This study investigated the mechanical properties of cementite (Fe, Cr)3(C, B) using the first-principle density functional theory. We constructed crystal structures of (Fe, Cr)3(C, B) with different concentrations of Cr and B. The bulk modulus, shear modulus, Young's modulus, Poisson's ratio, and hardness of the material were calculated, and a comprehensive mechanical property database based on CALPHAD modeling of the full composition was established. The optimal concentrations of the (Fe, Cr)3(C, B) phase were systematically evaluated across its entire composition range. The material exhibited the highest hardness, shear modulus, and Young's modulus at Cr and B concentrations in the range of 70-95 at% and 40 at%, respectively, rendering it difficult to compress and relatively poor in machinability. When the B content exceeded 90 at%, and the Cr content was zero, the shear modulus and hardness were low, resulting in poor resistance to deformation, reduced stiffness, and ease of plastic processing. This study provides an effective alloying strategy for balancing the brittleness and toughness of (Fe, Cr)3(C, B) phases.

Accelerating the Discovery of Hybrid Perovskites with Targeted Band Gaps via Interpretable Machine Learning.

The band gap of hybrid organic-inorganic perovskites (HOIPs) is a key factor affecting the light absorption characteristics and thus the performance of perovskite solar cells (PSCs). However, band gap engineering, using experimental trial and error and high-throughput density functional theory calculations, is blind and costly. Therefore, it is critical to statistically identify the multiple factors influencing band gaps and to rationally design perovskites with targeted band gaps. This study combined feature engineering, the gradient-boosted regression tree (GBRT) algorithm, and the genetic algorithm-based symbolic regression (GASR) algorithm to develop an interpretable machine learning (ML) strategy for predicting the band gap of HOIPs accurately and quantitatively interpreting the factors affecting the band gap. Seven best physical features were selected to construct a GBRT model with a root-mean-square error of less than 0.060 eV, and the most important feature is the electronegativity difference between the B-site and the X-site (χB-X). Further, a mathematical formula (Eg = χB-X2 + 0.881χB-X) was constructed with GASR for a quantitative interpretation of the band gap influence patterns. According to the ML model, the HOIP MA0.23FA0.02Cs0.75Pb0.59Sn0.41Br0.24I2.76 was obtained, with a suitable band gap of 1.39 eV. Our proposed interpretable ML strategy provides an effective approach for developing HOIP structures with targeted band gaps, which can also be applied to other material fields.

Correlation analysis of materials properties by machine learning: illustrated with stacking fault energy from first-principles calculations in dilute fcc-based alloys.

Advances in machine learning (ML), especially in the cooperation between ML predictions, density functional theory (DFT) based first-principles calculations, and experimental verification are emerging as a key part of a new paradigm to understand fundamentals, verify, analyze, and predict data, and design and discover materials. Taking stacking fault energy (γSFE) as an example, we perform a correlation analysis ofγSFEin dilute Al-, Ni-, and Pt-based alloys by descriptors and ML algorithms. TheseγSFEvalues were predicted by DFT-based alias shear deformation approach, and up to 49 elemental descriptors and 21 regression algorithms were examined. The present work indicates that (i) the variation ofγSFEaffected by alloying elements can be quantified through 14 elemental attributes based on their statistical significances to decrease the mean absolute error (MAE) in ML predictions, and in particular, the number of p valence electrons, a descriptor second only to the covalent radius in importance to model performance, is unexpected; (ii) the alloys with elements close to Ni and Co in the periodic table possess higherγSFEvalues; (iii) the top four outliers of DFT predictions ofγSFEare for the alloys of Al23La, Pt23Au, Ni23Co, and Al23Be based on the analyses of statistical differences between DFT and ML predictions; and (iv) the best ML model to predictγSFEis produced by Gaussian process regression with an average MAE < 8 mJ m-2. Beyond detailed analysis of the Al-, Ni-, and Pt-based alloys, we also predict theγSFEvalues using the present ML models in other fcc-based dilute alloys (i.e., Cu, Ag, Au, Rh, Pd, and Ir) with the expected MAE < 17 mJ m-2and observe similar effects of alloying elements onγSFEas those in Pt23X or Ni23X.

Numerical Optimization for the Geometric Configuration of Ceramics Perform in HCCI/ZTAP Wear-Resistant Composites Based on Actual Particle Model.

In order to reduce the thermal stress in high chromium cast iron (HCCI) matrix composites reinforced by zirconia toughened alumina (ZTA) ceramic particles, finite element simulation is performed to optimize the geometric configuration of ceramics perform. The previous model simplifies the overall structure of the ceramic particle preform and adds boundary conditions to simulate the particles, which will cause uncontrollable error in the results. In this work, the equivalent grain models are used to describe the actual preform, making the simulation results closer to the actual experimental results. The solidification process of composite material is simulated, and the infiltration between molten iron and ceramic particles is realized. Thermal stress in solidification process and compression stress distribution are obtained. The results show that adding 10-mm round holes on the preform can improve the performance of the composite, which is helpful to prevent the cracks and increases the plasticity of the material.

Sub-1.4eV bandgap inorganic perovskite solar cells with long-term stability.

State-of-the-art halide perovskite solar cells have bandgaps larger than 1.45 eV, which restricts their potential for realizing the Shockley-Queisser limit. Previous search for low-bandgap (1.2 to 1.4 eV) halide perovskites has resulted in several candidates, but all are hybrid organic-inorganic compositions, raising potential concern regarding device stability. Here we show the promise of an inorganic low-bandgap (1.38 eV) CsPb0.6Sn0.4I3 perovskite stabilized via interface functionalization. Device efficiency up to 13.37% is demonstrated. The device shows high operational stability under one-sun-intensity illumination, with T80 and T70 lifetimes of 653 h and 1045 h, respectively (T80 and T70 represent efficiency decays to 80% and 70% of the initial value, respectively), and long-term shelf stability under nitrogen atmosphere. Controlled exposure of the device to ambient atmosphere during a long-term (1000 h) test does not degrade the efficiency. These findings point to a promising direction for achieving low-bandgap perovskite solar cells with high stability.

Achieving high thermoelectric performance of Cu1.8S composites with WSe2 nanoparticles.

Polycrystalline p-type Cu1.8S composites with WSe2 nanoparticles were fabricated by the mechanical alloying method combined with the spark plasma sintering technique. The Seebeck coefficient was significantly enhanced by the optimized carrier concentration, while the thermal conductivity was simultaneously decreased due to the refined grain and WSe2 nanoparticles. An enhanced Seebeck coefficient of 110 µV K-1 and a reduced thermal conductivity of 0.68 W m-1 K-1 were obtained for the Cu1.8S + 1 wt% WSe2 sample at 773 K, resulting in a remarkably enhanced peak ZT of 1.22 at 773 K, which is 2.5 times higher than that (0.49 at 773 K) of a pristine Cu1.8S sample. The cheap and environmentally friendly Cu1.8S-based materials with enhanced properties may find promising applications in thermoelectric devices.

A first-principles calculation of structural, mechanical, thermodynamic and electronic properties of binary Ni-Y compounds.

The intermetallic compounds between rare earth (RE) elements and transition metal elements have been comprehensively researched due to their appealing magnetic, electronic, optical and thermal properties, in which Ni-Y alloys are one kind of important system. In this work, a systematic investigation concerned with structures, elastic, and thermodynamic properties of Ni17Y2, Ni5Y, Ni7Y2, Ni3Y, Ni2Y, NiY, Ni2Y3 and NiY3 in Ni-Y systems is implemented by means of first-principles calculations. NiY has the lowest formation enthalpy within -0.49 kJ per mol per atom. Ni5Y has the largest bulk modulus, shear modulus and Young's modulus of 181.71 GPa, 79.75 GPa and 208.70 GPa, respectively. Furthermore, the effects of different concentrations of yttrium on the mechanical and thermal properties of Ni-Y compounds are estimated by using the Voigt-Reuss method. The electronic density of states and chemical bonding between Ni and Y are key factors that determine mechanical and thermodynamic properties of these compounds. What's more, results indicate that all compounds are dynamically stable as shown by the calculated phonon dispersions.

Boosting the Thermoelectric Performance of (Na,K)-Codoped Polycrystalline SnSe by Synergistic Tailoring of the Band Structure and Atomic-Scale Defect Phonon Scattering.

We report the high thermoelectric performance of p-type polycrystalline SnSe obtained by the synergistic tailoring of band structures and atomic-scale defect phonon scattering through (Na,K)-codoping. The energy offsets of multiple valence bands in SnSe are decreased after Na doping and further reduced by (Na,K)-codoping, resulting in an enhancement in the Seebeck coefficient and an increase in the power factor to 492 µW m-1 K-2. The lattice thermal conductivity of polycrystalline SnSe is decreased by the introduction of effective phonon scattering centers, such as point defects and antiphase boundaries. The lattice thermal conductivity of the material is reduced to values as low as 0.29 W m-1 K-1 at 773 K, whereas ZT is increased from 0.3 for 1% Na-doped SnSe to 1.2 for 1% (Na,K)-codoped SnSe.

Highly Enhanced Thermoelectric Properties of Bi/Bi2S3 Nanocomposites.

Bismuth sulfide (Bi2S3) has been of high interest for thermoelectric applications due to the high abundance of sulfur on Earth. However, the low electrical conductivity of pristine Bi2S3 results in a low figure of merit (ZT). In this work, Bi2S3@Bi core-shell nanowires with different Bi shell thicknesses were prepared by a hydrothermal method. The core-shell nanowires were densified to Bi/Bi2S3 nanocomposite by spark plasma sintering (SPS), and the structure of the nanowire was maintained as the nanocomposite due to rapid SPS processing and low sintering temperature. The thermoelectric properties of bulk samples were investigated. The electrical conductivity of a bulk sample after sintering at 673 K for 5 min using Bi2S3@Bi nanowire powders prepared by treating Bi2S3 nanowires in a hydrazine solution for 3 h is 3 orders of magnitude greater than that of a pristine Bi2S3 sample. The nanocomposite possessed the highest ZT value of 0.36 at 623 K. This represents a new strategy for densifying core-shell powders to enhance their thermoelectric properties.

The effects of ordered carbon vacancies on stability and thermo-mechanical properties of V8C7 compared with VC.

The ordered non-stoichiometric V8C7 can form in the VCy carbides by the disorder-order phase transformation. The intrusion of ordered carbon vacancies can affect their stability, mechanical, thermal and electronic properties. The relatively thermodynamic stability and mechanical properties at high temperature for the ordered stoichiometric VC and non-stoichiometric V8C7 are investigated in this paper by first-principle calculations combined with the quasi-harmonic approximation. The difference between the properties of VC and V8C7 can be obtained. We find that the V8C7 is thermodynamic more stable than VC, but has weaker elastic heat resistance than VC. Moreover, the minimum thermal conductivity of VC is a little larger than V8C7 and a simple way is proposed to characterize the anisotropy of lattice thermal conductivity based on the Cahill's model.

Pressure dependence of electronic structure and superconductivity of the MnX (X = N, P, As, Sb).

A recently experimental discovered (Cheng et al., Phys. Rev. Lett. 114, 117001 (2015)) of superconductivity on the border of long-range magnetic order in the itinerant-electron helimagnet MnP via the application of high pressure makes MnP the first Mn-based superconductor. In this paper, we carry out first-principles calculations on MnX (X = N, P, As, Sb) and find superconducting critical temperature TC of MnP sharply increases near the critical pressure PC ≈ 8 GPa, which is in good agreement with the experiments. Electron-phonon coupling constant λ and electronic density of states at the Fermi level N (EF) are found to increase with pressure for MnP, which lead to the increase of TC of MnP. Moreover, we also find that the TC of MnAs and MnSb are higher than MnP, implying that the MnAs and MnSb may be the more potential Mn-based superconducting materials.