High-throughput Ab Initio Calculations Research Articles

Phase stability and mechanical property trends for MAB phases by high-throughput ab initio calculations

MAB phases (MABs) are atomically-thin laminates of ceramic/metallic-like layers, having made a breakthrough in the development of 2D materials. Though offering a vast chemical and phase space, relatively few MABs have been synthesised. To guide experiments, we perform high-throughput ab initio screening of MABs that combine group 4–7 transition metals (M); Al, Si, Ga, Ge, or In (A); and boron (B) focusing on their phase stability trends and mechanical properties. Considering the 1:1:1, 2:1:1, 2:1:2, 3:1:2, 3:1:3, and 3:1:4 M:A:B ratios and 10 phase prototypes, synthesisability of a single-phase compound for each elemental combination is estimated through formation energy spectra of competing dynamically stable MABs. Based on the volumetric proximity of energetically-close phases, we identify systems in which volume-changing deformations may facilitate transformation toughening. Subsequently, chemistry- and phase-structure-related trends in the elastic stiffness and ductility are predicted using elastic-constants-based descriptors. The analysis of directional Cauchy pressures and Young's moduli allows comparing mechanical response parallel and normal to M–B/A layers. The suggested promising MABs include Nb3AlB4, Cr2SiB2, Mn2SiB2 or the already synthesised MoAlB.

High-throughput ab initio calculations and machine learning to discover SrFeO3-δ-based perovskites for chemical-looping applications

High-throughput ab initio calculations and machine learning to discover SrFeO3-δ-based perovskites for chemical-looping applications

First-principles investigation on twin-related lattice reorientation in hexagonal metals and alloys

High-throughput ab initio calculations were employed to study the lattice reorientation process accompanying twinning under plastic deformation in hexagonal metals, as well as in alloyed Mg and Ti. The results indicate that, in unalloyed hexagonal metals, reorientation consumes the highest and lowest energies in Be and Mg, respectively, with intermediate energies in Sc, Co, Ti, Zr and Dy. The shuffle component always contributes a significant part of the reorientation energy in Mg, whereas in Ti with sufficient shear strain, subsequent reorientation process becomes energy downhill. In alloyed Mg and Ti, appropriate alloying significantly reduces the reorientation energy, and especially in alloyed Ti, there is significant negative correlation between the reorientation energy and certain properties of the alloying elements. The results are consistent with the fact that reorientation has been experimentally observed in compressed Mg nanopillars, but only in atomic scale simulations under high strain rate in Ti.

High-Throughput Exploration of Half-Heusler Phases for Thermoelectric Applications

As a result of the high-throughput ab initiocalculations, the set of 34 stable and novel half-Heusler phases was revealed. The electronic structure and the elastic, transport, and thermoelectric properties of these systems were carefully investigated, providing some promising candidates for thermoelectric materials. The complementary nature of the research is enhanced by the deformation potential theory applied for the relaxation time of carriers (for power factor, PF) and the Slack formula for the lattice thermal conductivity (for figure of merit, ZT). Moreover, two exchange-correlation parametrizations were used (GGA and MBJGGA), and a complete investigation was provided for both p- and n-type carriers. The distribution of the maximum PF and ZT for optimal doping at 300 K in all systems was disclosed. Some chemical trends in electronic and transport properties were discussed. The results suggest TaFeAs, TaFeSb, VFeAs, and TiRuAs as potentially valuable thermoelectric materials. TaFeAs revealed the highest values of both PF and ZT at 300 K (PFp = 1.67 mW/K2m, ZTp = 0.024, PFn = 2.01 mW/K2m, and ZTp = 0.025). The findings presented in this work encourage further studies on the novel phases, TaFeAs in particular.

High-throughput identification of materials for silicon tandem solar cells

High-throughput ab initio calculations are employed to identify the most promising materials for Si tandem solar cells from over 100 000 candidates.

Revisit the VEC criterion in high entropy alloys (HEAs) with high-throughput ab initio calculations: A case study with Al-Co-Cr-Fe-Ni system

Valence electron concentration (VEC) was treated as a useful parameter to predict the stability of solid solution phases. However, the available experimental data to support this criterion is far from enough. In the current study, the high-throughput ab initio modeling is applied to investigate the relative stability of FCC and BCC single crystals of the Al-Co-Cr-Fe-Ni high entropy alloys (HEAs) by using the special quasi-random structure (SQS) approach. The predictions start with pure elements of the Al-Co-Cr-Fe-Ni system and are continued with binaries, ternaries, and quaternary compositions, which come up with 180 compositions (360 structures). After that, the reliability of the VEC criterion is testified. The results show that the VEC criterion not only works for the stable structure but also works effectively for metastable structure when both FCC and BCC are not thermodynamic stable. However, it is found that the old VEC criterion proposed by Guo et al. fails to work effectively for compositions containing high concentrations of light-weight metals such as Al at VEC< 5. To solve this problem, the present work proposed a new VEC rule to define the stability of FCC and BCC structures at the ground state. With the implementation of the new VEC rule, the effectiveness of the VEC rule (EVEC) of both FCC and BCC structures is enhanced, especially for pure elements and binary compositions, indicating that this rule does not only work effectively for multicomponent systems but also works for low-order systems. • The Effectiveness of empirical VEC rule in the Al-Co-Cr-Fe-Ni system was systematically discussed. • The HT-ab initio calculations were performed to investigate the relative stability of FCC and BCC structure in the Al-Co-Cr-Fe-Ni system. • 180 compositions (360 structures) were investigated based on the Special Quasi-random Structures (SQS) method. • A new VEC criterion was proposed based on the results of the HT-calculations.

Ab initio Cu alloy design for high-gradient accelerating structures

Operation of normal conducting accelerator structures at high accelerating gradients is beneficial for many accelerator applications in basic science, industry, medicine, and National Security. RF breakdown is the major factor that limits the achievable accelerating gradients. Previous experiments on copper (Cu) have demonstrated that RF breakdown probability can be significantly decreased by hardening the material and alloying Cu with solutes such as silver (Ag). In this paper, we propose a figure-of-merit (FOM) that characterizes the ability of Cu alloys to withstand high-gradients. The FOM represents a trade-off between hardening through solid solution strengthening and the additional thermal stress induced by incremental RF pulse heating resulting from changes in electronic properties induced by alloying. We performed high-throughput ab initio calculations and computed the FOM for a large number of binary Cu alloys. Several promising candidate alloys for high-gradient accelerating structures were identified, such as CuAg, CuCd, CuHg, CuAu, CuIn, and CuMg. CuAg alloys have previously exhibited low RF breakdown rates in experiments. The results provide guidance for selecting alloys for the future high-gradient normal conducting accelerating structures operating at very high gradients.

Revisit the Vec Criterion in High Entropy Alloys (Heas) with High-Throughput Ab Initio Calculations: A Case Study with Al-Co-Cr-Fe-Ni System

Revisit the Vec Criterion in High Entropy Alloys (Heas) with High-Throughput Ab Initio Calculations: A Case Study with Al-Co-Cr-Fe-Ni System

Compositional effects on the mechanical and thermal properties of MoNbTaTi refractory complex concentrated alloys

Refractory complex concentrated alloys are an emerging class of materials that attracts attention due to their stability and performance at high temperatures. In this study, we investigate the variations in the mechanical and thermal properties across a broad compositional space for the refractory MoNbTaTi quaternary using high-throughput ab-initio calculations and experimental characterization. For all the properties surveyed, we note a good agreement between our modeling predictions and the experimentally measured values. We reveal the particular role of molybdenum (Mo) to achieve high strength when in high concentration. We trace the origin of this phenomenon to a shift from metallic to covalent bonding when the Mo content is increased. Additionally, a mechanistic, dislocation-based description of the yield strength further explains such high strength due to a combination of high bulk and shear moduli, accompanied by the relatively small size of the Mo atom compared to the other atoms in the alloy. Our analysis of the thermodynamics properties shows that regardless of the composition, this class of quaternary alloys shows good stability and low sensitivity to temperature. Taken together, these results pave the way for the design of new high-performance refractory alloys beyond the equimolar composition found in high-entropy alloys.

Prediction of thermoelectric performance for layered IV-V-VI semiconductors by high-throughput ab initio calculations and machine learning

Layered IV-V-VI semiconductors have immense potential for thermoelectric (TE) applications due to their intrinsically ultralow lattice thermal conductivity. However, it is extremely difficult to assess their TE performance via experimental trial-and-error methods. Here, we present a machine-learning-based approach to accelerate the discovery of promising thermoelectric candidates in this chalcogenide family. Based on a dataset generated from high-throughput ab initio calculations, we develop two highly accurate-and-efficient neural network models to predict the maximum ZT (ZTmax) and corresponding doping type, respectively. The top candidate, n-type Pb2Sb2S5, is successfully identified, with the ZTmax over 1.0 at 650 K, owing to its ultralow thermal conductivity and decent power factor. Besides, we find that n-type Te-based compounds exhibit a combination of high Seebeck coefficient and electrical conductivity, thereby leading to better TE performance under electron doping than hole doping. Whereas p-type TE performance of Se-based semiconductors is superior to n-type, resulting from large Seebeck coefficient induced by high density-of-states near valence band edges.

Different types of spin currents in the comprehensive materials database of nonmagnetic spin Hall effect

Spin Hall effect (SHE) has its special position in spintronics. To gain new insight into SHE and to identify materials with substantial spin Hall conductivity (SHC), we performed high-precision high-throughput ab initio calculations of the intrinsic SHC for over 20,000 nonmagnetic crystals. The calculations revealed a strong relationship between the magnitude of the SHC and the crystalline symmetry, where a large SHC is typically associated with mirror symmetry-protected nodal line band structures. This database includes 11 materials with an SHC comparable to or even larger than that of Pt. Materials with different types of spin currents were additionally identified. Furthermore, we found that different types of spin current can be obtained by rotating applied electrical fields. This improves our understanding and is expected to facilitate the design of new types of spin-orbitronic devices.

High-efficient ab initio Bayesian active learning method and applications in prediction of two-dimensional functional materials.

Beyond the conventional trial-and-error method, machine learning offers a great opportunity to accelerate the discovery of functional materials, but still often suffers from difficulties such as limited materials data and the unbalanced distribution of target properties. Here, we propose the ab initio Bayesian active learning method that combines active learning and high-throughput ab initio calculations to accelerate the prediction of desired functional materials with ultrahigh efficiency and accuracy. We apply it as an instance to a large family (3119) of two-dimensional hexagonal binary compounds with unbalanced materials properties, and accurately screen out the materials with maximal electric polarization and proper photovoltaic band gaps, respectively, whereas the computational costs are significantly reduced by only calculating a few tenths of the possible candidates in comparison with a random search. This approach shows the enormous advantages for the cases with unbalanced distribution of target properties. It can be readily applied to seek a broad range of advanced materials.

Large family of two-dimensional ferroelectric metals discovered via machine learning

Ferroelectricity and metallicity are usually believed not to coexist because conducting electrons would screen out static internal electric fields. In 1965, Anderson and Blount proposed the concept of “ferroelectric metal”, however, it is only until recently that very rare ferroelectric metals were reported. Here, by combining high-throughput ab initio calculations and data-driven machine learning method with new electronic orbital based descriptors, we systematically investigated a large family (2964) of two-dimensional (2D) bimetal phosphates, and discovered 60 stable ferroelectrics with out-of-plane polarization, including 16 ferroelectric metals and 44 ferroelectric semiconductors that contain seven multiferroics. The ferroelectricity origins from spontaneous symmetry breaking induced by the opposite displacements of bimetal atoms, and the full-d-orbital coinage metal elements cause larger displacements and polarization than other elements. For 2D ferroelectric metals, the odd electrons per unit cell without spin polarization may lead to a half-filled energy band around Fermi level and is responsible for the metallicity. It is revealed that the conducting electrons mainly move on a single-side surface of the 2D layer, while both the ionic and electric contributions to polarization come from the other side and are vertical to the above layer, thereby causing the coexistence of metallicity and ferroelectricity. Van der Waals heterostructures based on ferroelectric metals may enable the change of Schottky barrier height or the Schottky-Ohmic contact type and induce a dramatic change of their vertical transport properties. Our work greatly expands the family of 2D ferroelectric metals and will spur further exploration of 2D ferroelectric metals.

Predicting material properties by integrating high-throughput experiments, high-throughput ab-initio calculations, and machine learning

ABSTRACT High-throughput experiments (HTEs) have been powerful tools to obtain many materials data. However, HTEs often require expensive equipment. Although high-throughput ab-initio calculation (HTC) has the potential to make materials big data easier to collect, HTC does not represent the actual materials data obtained by HTEs in many cases. Here we propose using a combination of simple HTEs, HTC, and machine learning to predict material properties. We demonstrate that our method enables accurate and rapid prediction of the Kerr rotation mapping of an FexCoyNi1-x-y composition spread alloy. Our method has the potential to quickly predict the properties of many materials without a difficult and expensive HTE and thereby accelerate materials development.

Accelerated Discovery of Two-Dimensional Optoelectronic Octahedral Oxyhalides via High-Throughput Ab Initio Calculations and Machine Learning.

Traditional trial-and-error methods are obstacles for large-scale searching of new optoelectronic materials. Here, we introduce a method combining high-throughput ab initio calculations and machine-learning approaches to predict two-dimensional octahedral oxyhalides with improved optoelectronic properties. We develop an effective machine-learning model based on an expansive data set generated from density functional calculations including the geometric and electronic properties of 300 two-dimensional octahedral oxyhalides. Our model accelerates the screening of potential optoelectronic materials of 5000 two-dimensional octahedral oxyhalides. The distorted stacked octahedral factors proposed in our model play essential roles in the machine-learning prediction. Several potential two-dimensional optoelectronic octahedral oxyhalides with moderate band gaps, high electron mobilities, and ultrahigh absorbance coefficients are successfully hypothesized.

Reliable electrochemical phase diagrams of magnetic transition metals and related compounds from high-throughput ab initio calculations

Magnetic transition metals (mTM = Cr, Mn, Fe, Co, and Ni) and their complex compounds (oxides, hydroxides, and oxyhydroxides) are highly important material platforms for diverse technologies, where electrochemical phase diagrams with respect to electrode potential and solution pH can be used to effectively understand their corrosion and oxidation behaviors in relevant aqueous environments. Many previous decades-old mTM–Pourbaix diagrams are inconsistent with various direct electrochemical observations, because experimental complexities associated with extracting reliable free energies of formation (ΔfG) lead to inaccuracies in the data used for modeling. Here, we develop a high-throughput simulation approach based on density-functional theory (DFT), which quickly screens structures and compounds using efficient DFT methods and calculates accurate ΔfG values, using high-level exchange-correlation functions to obtain ab initio Pourbaix diagrams in comprehensive and close agreement with various important electrochemical, geological, and biomagnetic observations reported over the last few decades. We also analyze the microscopic mechanisms governing the chemical trends among the ΔfG values and Pourbaix diagrams to further understand the electrochemical behaviors of mTM-based materials. Last, we provide probability profiles at variable electrode potential and solution pH to show quantitatively the likely coexistence of multiple-phase areas and diffuse phase boundaries.

Identifying rhenium substitute candidate multiprincipal-element alloys from electronic structure and thermodynamic criteria

Abstract

Identifying optimal dopants for Sb2Te3 phase-change material by high-throughput ab initio calculations with experiments

Doping is used to improve the overall performance of rhombohedral Sb2Te3 for the application in phase-change memory (PCM). However, it is difficult and laborious to obtain the optimal dopants using the time-consuming trial and error way. By high-throughput ab initio calculations with experiments, the present work identified Scandium, Yttrium and Mercury as the optimal dopants for Sb2Te3 to reduce the power consumption of Sb2Te3-based PCM. Using Sc as an example the doping effect on the structure and property of Sb2Te3 has been extensively investigated. The band gap of Sb2Te3 linearly increases with increasing the Sc concentration by ab initio calculations, and our experimental results confirm the theoretical findings. Furthermore, based on the semi-classical Boltzmann transport theory, Sc can significantly decrease the electrical conductivity and thermal conductivity of Sb2Te3, and therefore reduced energy consumption of Sb2Te3-based PCM is expected. Finally, the ab initio molecular dynamics simulations demonstrate that Scandium can significantly improve the thermal stability of amorphous Sc-doped Sb2Te3, and hence the enhanced data reliability of Sb2Te3-based PCM. The present work provides fundamentals for fast screening optimal dopants to enhance the overall performance of Sb2Te3 for PCM, and the present method can be extended to other semiconductor materials.

The chemical space of B, N-substituted polycyclic aromatic hydrocarbons: Combinatorial enumeration and high-throughput first-principles modeling.

Combinatorial introduction of heteroatoms in the two-dimensional framework of aromatic hydrocarbons opens up possibilities to design compound libraries exhibiting desirable photovoltaic and photochemical properties. Exhaustive enumeration and first-principles characterization of this chemical space provide indispensable insights for rational compound design strategies. Here, for the smallest seventy-seven Kekulean-benzenoid polycyclic systems, we reveal combinatorial substitution of C atom pairs with the isosteric and isoelectronic B, N pairs to result in 7 453 041 547 842 (7.4 tera) unique molecules. We present comprehensive frequency distributions of this chemical space, analyze trends, and discuss a symmetry-controlled selectivity manifestable in synthesis product yield. Furthermore, by performing high-throughput ab initio density functional theory calculations of over thirty-three thousand (33k) representative molecules, we discuss quantitative trends in the structural stability and inter-property relationships across heteroarenes. Our results indicate a significant fraction of the 33k molecules to be electronically active in the 1.5-2.5 eV region, encompassing the most intense region of the solar spectrum, indicating their suitability as potential light-harvesting molecular components in photo-catalyzed solar cells.

Ni-stabilizing additives for completion of Ni-rich layered cathode systems in lithium-ion batteries: An Ab initio study

We propose the development of Ni-stabilizing electrolyte additives to fundamentally prevent the degradation of Ni-rich layered cathode systems in lithium-ion batteries because unstable surface Ni and the dissolved Ni2+ are the major problems of those systems. The Ni2+-affinity is investigated as a key factor of the Ni-stabilizing additives. However, when providing a noble function to the electrolyte additive, the redox stability of the additives should be also understood. Thus, in addition to the intrinsic oxidation energy, the protonation and dehydrogenation energies of the additive molecules are calculated to determine the H-transfer-driven electrolyte oxidation. The Li+-complexation is considered to model the electrolyte reduction. We investigate the molecular-leveled computed factors of electrolyte materials using fully automated high-throughput ab initio calculations. Those computed factors for representative molecules based on CO3, SO4, SO3, SO2, PC3, PO3, and OPO3, which are of great interest as major parts of electrolyte materials, are discussed to guide the additive development. In particular, SO2 and OPO3 molecules, which can strongly stabilize Ni2+ in a structurally stable form, have great advantages as Ni-stabilizing electrolyte additives for completion of Ni-rich layered cathode systems.