Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds

Fragment Orbitals Extracted from First-Principles Plane-Wave Calculations

Decomposing extended structures into smaller, molecular, even functional groups or simple fragments has a long tradition in chemistry because it allows for understanding certain electronic peculiarities in truly chemical terms. By doing so, invaluable property information is chemically accessible, for example, needed to rationalize catalytic or magnetic or optical nature. In order to also follow that train of thought for periodic materials, we have developed a tool which in a straightforward manner derives fragment molecular orbitals from plane-wave electronic-structure data of whatever kind of solid-state material. We here report on the mathematical apparatus of the method dubbed linear combination of fragment orbitals (LCFO) used for that purpose, implemented within the LOBSTER code. The method is illustrated from various sorts of molecular entities contained in such crystalline materials, together with an assessment of both accuracy and robustness of the new tool.

Spin dependent tunneling and strain sensitivity in a Co2MnSb/HfIrSb magnetic tunneling junction: a first-principles study

Half-metallic Co-based full Heusler alloys have captured considerable attention of researchers in the realm of spintronic applications, owing to their remarkable characteristics such as exceptionally high spin polarization at the Fermi level, ultra-low Gilbert damping, and a high Curie temperature. In this comprehensive study, employing the density functional theory, we delve into the electronic stability and ballistic spin transport properties of a magnetic tunneling junction (MTJ) comprising a Co2MnSb/HfIrSb interface. An in-depth investigation of k-dependent spin transmissions uncovers the occurrence of coherent tunneling for the Mn-Mn/Ir interface, particularly when a spacer layer beyond a certain thickness is employed. It has been found that the Co-terminated Co2MnSb/HfIrSb interface shows perpendicular magnetic anisotropy, while those with Mn-Sb and Mn-Mn termination exhibit in-plane magnetic anisotropy. Furthermore, our spin-dependent transmission calculations demonstrate that the Mn-Mn/Ir interface manifests strain-sensitive transmission properties under both compressive and tensile strain and yields a remarkable three-fold increase in majority spin transmission under tensile strain conditions. We find a tunnel magnetoresistance of ∼500% under a bi-axial strain of -3%, beyond which the tunnel resistance is found to be theoretically infinite. These compelling outcomes place the Co2MnSb/HfIrSb junction among the highly promising candidates for nanoscale spintronic devices, emphasizing the potential significance of the system in the advancement of the field.

Design of Co-Cured Multi-Component Thermosets with Enhanced Heat Resistance, Toughness, and Processability via a Machine Learning Approach

Designing heat-resistant thermosets with excellent comprehensive performance has been a long-standing challenge. Co-curing of various high-performance thermosets is an effective strategy, however, the traditional trial-and-error experiments have long research cycles for discovering new materials. Herein, a two-step machine learning (ML) assisted approach is proposed to design heat-resistant co-cured resins composed of polyimide (PI) and silicon-containing arylacetylene (PSA), that is, poly(silicon-alkyne imide) (PSI). First, two ML prediction models are established to evaluate the processability of PIs and their compatibility with PSA. Then, another two ML models are developed to predict the thermal decomposition temperature and flexural strength of the co-cured PSI resins. The optimal molecular structures and compositions of PSI resins are high-throughput screened. The screened PSI resins are experimentally verified to exhibit enhanced heat resistance, toughness, and processability. The research framework established in this work can be generalized to the rational design of other advanced multi-component polymeric materials.

Computational insights into the structure and decomposition behaviors of 2,4,6-triamino-5-nitropyrimidine-1,3-dioxide under high pressure up to 10 GPa

CONTEXT

Inspired by the recent successful synthesis of the energetic compound 2,4,6-triamino-5-nitropyrimidine-1,3-dioxide (ICM-102), which displayed a good balance between high energy and sensitivity, the response of the structure and decomposition behaviors of ICM-102 to high pressure was systematically investigated using first principle calculations. ICM-102 exhibited a graphite-like layer structure, with the c-axis and the a-axis mainly contributing to the distance between the molecular planes. As the pressure increased from 1 atm to 10 GPa, this distance decreased from 3.166 to 2.689 Ǻ. The hydrogen bonds had the most contribution to the non-covalent interactions within the same molecular planes, resulting in the b-axis discontinuity. However, van der Waals interactions gradually appeared between molecular planes as the pressure increased to 2.5 GPa. Based on the analysis of crystal orbitals, the distribution of π bonds and the Laplacian bond order (LBO), it was determined that the generation mechanism of H2O molecules involved the cleavage of N-Oc (coordinated oxygen atoms), followed by intermolecular hydrogen transfer reactions, and ultimately the formation of H2O molecules through competition with H atoms in the amino groups within the same molecules. More importantly, the pressure dependence of LBO values for N-Oc revealed that high pressure could inhibit the ICM-102 decomposition process due to reinforcing hydrogen bonds and van der Waals interactions. This work will deepen our understanding of the stability of ICM-102 under high pressure and provide a helpful reference for its potential detonation applications.

METHODS

All simulations, including geometry optimization and vibration analysis under quasi-hydrostatic pressure, were conducted using the CP2K code. The PBE function and the Goldk-Teter-Hutter (GTH) pseudopotential with the double-ζ-with-polarization (DZVP) basis set were employed. Additionally, the semiempirical dispersion correction D3 (BJ) was used to account for the intermolecular dispersion force. The simulations were performed under periodic boundary conditions, with a finest grid level cutoff set to 400 Ry for the Γ point. The Broyden-Flecher-Goldfarb-Shanno (BFGS) optimization method was used, with tighter convergence criteria applied for the subsequent calculations of infrared spectra. Finally, the wave-function analysis, such as non-covalent interaction and LBO, was conducted using the Multiwfn and VMD packages.

Emin: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability

Predicting the synthesizability of a new molecule remains an unsolved challenge that chemists have long tackled with heuristic approaches. Here, we report a new method for predicting synthesizability using a simple yet accurate thermochemical descriptor. We introduce Emin, the energy difference between a molecule and its lowest energy constitutional isomer, as a synthesizability predictor that is accurate, physically meaningful, and first-principles based. We apply Emin to 134,000 molecules in the QM9 data set and find that Emin is accurate when used alone and reduces incorrect predictions of "synthesizable" by up to 52% when used to augment commonly used prediction methods. Our work illustrates how first-principles thermochemistry and heuristic approximations for molecular stability are complementary, opening a new direction for synthesizability prediction methods.

Mechanistically Guided Materials Chemistry: Synthesis of Ternary Nitrides, CaZrN2 and CaHfN2

Recent computational studies have predicted many new ternary nitrides, revealing synthetic opportunities in this underexplored phase space. However, synthesizing new ternary nitrides is difficult, in part because intermediate and product phases often have high cohesive energies that inhibit diffusion. Here, we report the synthesis of two new phases, calcium zirconium nitride (CaZrN2) and calcium hafnium nitride (CaHfN2), by solid state metathesis reactions between Ca3N2 and MCl4 (M = Zr, Hf). Although the reaction nominally proceeds to the target phases in a 1:1 ratio of the precursors via Ca3N2 + MCl4 → CaMN2 + 2 CaCl2, reactions prepared this way result in Ca-poor materials (CaxM2-xN2, x < 1). A small excess of Ca3N2 (ca. 20 mol %) is needed to yield stoichiometric CaMN2, as confirmed by high-resolution synchrotron powder X-ray diffraction. In situ synchrotron X-ray diffraction studies reveal that nominally stoichiometric reactions produce Zr3+ intermediates early in the reaction pathway, and the excess Ca3N2 is needed to reoxidize Zr3+ intermediates back to the Zr4+ oxidation state of CaZrN2. Analysis of computationally derived chemical potential diagrams rationalizes this synthetic approach and its contrast from the synthesis of MgZrN2. These findings additionally highlight the utility of in situ diffraction studies and computational thermochemistry to provide mechanistic guidance for synthesis.

Identification of High-Reliability Regions of Machine Learning Predictions Based on Materials Chemistry

Progress in the application of machine learning (ML) methods to materials design is hindered by the lack of understanding of the reliability of ML predictions, in particular, for the application of ML to small data sets often found in materials science. Using ML prediction for transparent conductor oxide formation energy and band gap, dilute solute diffusion, and perovskite formation energy, band gap, and lattice parameter as examples, we demonstrate that (1) construction of a convex hull in feature space that encloses accurately predicted systems can be used to identify regions in feature space for which ML predictions are highly reliable; (2) analysis of the systems enclosed by the convex hull can be used to extract physical understanding; and (3) materials that satisfy all well-known chemical and physical principles that make a material physically reasonable are likely to be similar and show strong relationships between the properties of interest and the standard features used in ML. We also show that similar to the composition-structure-property relationships, inclusion in the ML training data set of materials from classes with different chemical properties will not be beneficial for the accuracy of ML prediction and that reliable results likely will be obtained by ML model for narrow classes of similar materials even in the case where the ML model will show large errors on the data set consisting of several classes of materials.

Vacancy-mediated anomalous phononic and electronic transport in defective half-Heusler ZrNiBi

Studies of vacancy-mediated anomalous transport properties have flourished in diverse fields since these properties endow solid materials with fascinating photoelectric, ferroelectric, and spin-electric behaviors. Although phononic and electronic transport underpin the physical origin of thermoelectrics, vacancy has only played a stereotyped role as a scattering center. Here we reveal the multifunctionality of vacancy in tailoring the transport properties of an emerging thermoelectric material, defective n-type ZrNiBi. The phonon kinetic process is mediated in both propagating velocity and relaxation time: vacancy-induced local soft bonds lower the phonon velocity while acoustic-optical phonon coupling, anisotropic vibrations, and point-defect scattering induced by vacancy shorten the relaxation time. Consequently, defective ZrNiBi exhibits the lowest lattice thermal conductivity among the half-Heusler family. In addition, a vacancy-induced flat band features prominently in its electronic band structure, which is not only desirable for electron-sufficient thermoelectric materials but also interesting for driving other novel physical phenomena. Finally, better thermoelectric performance is established in a ZrNiBi-based compound. Our findings not only demonstrate a promising thermoelectric material but also promote the fascinating vacancy-mediated anomalous transport properties for multidisciplinary explorations.

Opening the Bandgap of Metallic Half-Heuslers via the Introduction of d-d Orbital Interactions

Half-Heusler compounds with semiconducting behavior have been developed as high-performance thermoelectric materials for power generation. Many half-Heusler compounds also exhibit metallic behavior without a bandgap and thus inferior thermoelectric performance. Here, taking metallic half-Heusler MgNiSb as an example, a bandgap opening strategy is proposed by introducing the d-d orbital interactions, which enables the opening of the bandgap and the improvement of the thermoelectric performance. The width of the bandgap can be engineered by tuning the strength of the d-d orbital interactions. The conduction type and the carrier density can also be modulated in the Mg1- x Tix NiSb system. Both improved n-type and p-type thermoelectric properties are realized, which are much higher than that of the metallic MgNiSb. The proposed bandgap opening strategy can be employed to design and develop new half-Heusler semiconductors for functional and energy applications.

Location of Artinite (Mg2CO3(OH)2·3H2O) within the MgO-CO2-H2O system using ab initio thermodynamics

The MgO-CO₂-H₂O system have a variety of important industrial applications including in catalysis, immobilisation of radionuclides and heavy metals, construction, and mineralisation and permanent storage of anthropogenic CO₂. Here, we develop a computational approach to generate phase stability plots for the MgO-CO₂-H₂O system that do not rely on traditional experimental corrections for the solid phases. We compare the predictions made by several dispersion-corrected density-functional theory schemes, and we include the temperature-dependent Gibbs free energy through the quasi-harmonic approximation. We locate the Artinite phase (Mg₂CO₃(OH)₂·3H₂O) within the MgO-CO₂-H₂O phase stability plot, and we demonstrate that this widely-overlooked hydrated and carbonated phase is metastable and can be stabilised by inhibiting the formation of fully-carbonated stable phases. Similar considerations may apply more broadly to other lesser known phases. These findings provide new insight to explain conflicting results from experimental studies, and demonstrate how this phase can potentially be stabilised by optimising the synthesis conditions.

Spin and current transport in the robust half-metallic magnetc-CoFeGe

Spintronics is an emerging form of electronics based on the electrons' spin degree of freedom for which materials with robust half-metallic ferromagnet character are very attractive. Here we determine the structural stability, electronic, magnetic, and mechanical properties of the half-Heusler (hH) compound CoFeGe, in particular also in its cubic form. The first-principles calculations suggest that the electronic structure is robust with 100% spin polarization at the Fermi level under hydrostatic pressure and uni-axial strain. Both the longitudinal and Hall current polarization are calculated and the longitudinal current polarization (PL) is found to be>99%and extremely robust under uniform pressure and uni-axial strain. The anomalous Hall conductivity and spin Hall conductivity of hH cubic CoFeGe (c-CoFeGe) are found to be∼-100S cm^-1and∼39 ℏ/eS cm^-1, respectively. Moreover, the Curie temperature of the alloy is calculated to be ∼524 K with a 3μBmagnetic moment. Lastly, the calculated mechanical properties indicate thatc-CoFeGe is ductile and mechanically stable with a bulk modulus of ≈154 GPa. Overall, this analysis reveals that cubic CoFeGe is a robust half-metallic ferromagnet and an interesting material for spintronic applications.

High-Throughput Design of Magnetocaloric Materials for Energy Applications: MM´X alloys

Magnetic refrigeration offers an energy efficient and environmental friendly alternative to conventional vapor-cooling. However, its adoption depends on materials with tailored magnetic and structural properties. Here a high-throughput computational workflow for the design of magnetocaloric materials is introduced. Density functional theory calculations are used to screen potential candidates in the family of MM'X (M/M' = metal, X = main group element) compounds. Out of 274 stable compositions, 46 magnetic compounds are found to stabilize in both an austenite and martensite phase. Following the concept of Curie temperature window, nine compounds are identified as potential candidates with structural transitions, by evaluating and comparing the structural phase transition and magnetic ordering temperatures. Additionally, the use of doping to tailor magnetostructural coupling for both known and newly predicted MM'X compounds is predicted and isostructural substitution as a general approach to engineer magnetocaloric materials is suggested.

The rise of MAX phase alloys - large-scale theoretical screening for the prediction of chemical order and disorder

MAX phases (M = metal, A = A-group element, X = C and/or N) are layered materials, combining metallic and ceramic attributes. They are also parent materials for the two-dimensional (2D) derivative, MXene, realized from selective etching of the A-element. In this work, we present a historical survey of MAX phase alloying to date along with an extensive theoretical investigation of MAX phase alloys (M = Sc, Y, Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, W, Mn, Fe, Co, and Ni, A = Al, Ga, In, Si, Ge, Sn, Ni, Cu, Zn, Pd, Ag, Pt, and Au, and X = C). We assess both in-plane chemical ordering (in the so-called i-MAX phases) and solid solution. Out of the 2702 compositions, 92 i-MAX and 291 solid solution MAX phases are predicted to be thermodynamically stable. A majority of these have not yet been experimentally reported. In general, i-MAX is favored for a smaller size of A and a large difference in metal size, while solid solution is favored for a larger size of A and with comparable size of the metals. The results thus demonstrate avenues for a prospective and substantial expansion of the MAX phase and MXene chemistries.

Examination of a Structural Preference in Quaternary Alkali-Metal (A) Rare-Earth (R) Copper Tellurides by Combining Experimental and Quantum-chemical Means

In the quest for materials addressing the grand challenges of the future, there is a critical need for a broad understanding of their electronic structures because the knowledge of the electronic structure of a given solid allows us to recognize its structural preferences and to rationalize its properties. As previous research on quaternary chalcogenides containing active metals (a group-I- or -II-element), early transition-metals, and late transition-metals indicated that such materials could pose as alluring systems in the developments of thermoelectrics, our impetus was stimulated to probe the suitability of tellurides belonging to the prolific A₃R₄Cu₅Te₁₀-family. In doing so, we first used quantum-chemical techniques to explore the electronic and vibrational properties of representatives crystallizing with different A₃R₄Cu₅Te₁₀ structure types. The outcome of these explorations indicated that the aspects that control the formation of a given type of A₃R₄Cu₅Te₁₀ structure are rather subtle so that transitions between different types of A₃R₄Cu₅Te₁₀ structures could be induced by manipulating the ambient conditions. To probe this prediction, we explored the thermal behavior for the example of one quaternary telluride, that is, Rb₃Er₄Cu₅Te₁₀, and thereby identified a new type of A₃R₄Cu₅Te₁₀ structure. Because understanding the structural features of the A₃R₄Cu₅Te₁₀ family plays an important role in the analyses of the aforementioned explorations, we also present an overview about the structural features and the members of this class of quaternary tellurides. In this connection, we also provide a structural report of four tellurides, that is, K₃Tb₄Cu₅Te₁₀ and Rb₃R₄Cu₅Te₁₀ (R = Tb, Dy, Ho), which have been obtained from high-temperature solid-state reactions for the very first time.

Designing a multilayer film via machine learning of scientific literature

Scientists who design chemical substances often use materials informatics (MI), a data-driven approach with either computer simulation or artificial intelligence (AI). MI is a valuable technique, but applying it to layered structures is difficult. Most of the proposed computer-aided material search techniques use atomic or molecular simulations, which are limited to small areas. Some AI approaches have planned layered structures, but they require a physical theory or abundant experimental results. There is no universal design tool for multilayer films in MI. Here, we show a multilayer film can be designed through machine learning (ML) of experimental procedures extracted from chemical-coating articles. We converted material names according to International Union of Pure and Applied Chemistry rules and stored them in databases for each fabrication step without any physicochemical theory. Compared with experimental results which depend on authors, experimental protocol is superiority at almost unified and less data loss. Connecting scientific knowledge through ML enables us to predict untrained film structures. This suggests that AI imitates research activity, which is normally inspired by other scientific achievements and can thus be used as a general design technique.

Recent Progress and Future Prospects on All-Organic Polymer Dielectrics for Energy Storage Capacitors

With the development of advanced electronic devices and electric power systems, polymer-based dielectric film capacitors with high energy storage capability have become particularly important. Compared with polymer nanocomposites with widespread attention, all-organic polymers are fundamental and have been proven to be more effective choices in the process of scalable, continuous, and large-scale industrial production, leading to many dielectric and energy storage applications. In the past decade, efforts have intensified in this field with great progress in newly discovered dielectric polymers, fundamental production technologies, and extension toward emerging computational strategies. This review summarizes the recent progress in the field of energy storage based on conventional as well as heat-resistant all-organic polymer materials with the focus on strategies to enhance the dielectric properties and energy storage performances. The key parameters of all-organic polymers, such as dielectric constant, dielectric loss, breakdown strength, energy density, and charge-discharge efficiency, have been thoroughly studied. In addition, the applications of computer-aided calculation including density functional theory, machine learning, and materials genome in rational design and performance prediction of polymer dielectrics are reviewed in detail. Based on a comprehensive understanding of recent developments, guidelines and prospects for the future development of all-organic polymer materials with dielectric and energy storage applications are proposed.

Decoding the conductance of disordered nanostructures: a quantum inverse problem

Obtaining conductance spectra for a concentration of disordered impurities distributed over a nanoscale device with sensing capabilities is a well-defined problem. However, to do this inversely, i.e., extracting information about the scatters from the conductance spectrum alone, is not an easy task. In the presence of impurities, even advanced techniques of inversion can become particularly challenging. This article extends the applicability of a methodology we proposed capable of extracting composition information about a nanoscale sensing device using the conductance spectrum. The inversion tool decodes the conductance spectrum to yield the concentration and nature of the disorders responsible for conductance fluctuations in the spectra. We present the method for simple one-dimensional systems like an electron gas with randomly distributed delta functions and a linear chain of atoms. We prove the generality and robustness of the method using materials with complex electronic structures like hexagonal boron nitride, graphene nanoribbons, and carbon nanotubes. We also go on to probe distribution of disorders on the sublattice structure of the materials using the proposed inversion tool.

John P, Gorai P. Predicting energy and stability of known and hypothetical crystals using graph neural network

The discovery of new inorganic materials in unexplored chemical spaces necessitates calculating total energy quickly and with sufficient accuracy. Machine learning models that provide such a capability for both ground-state (GS) and higher-energy structures would be instrumental in accelerated screening. Here, we demonstrate the importance of a balanced training dataset of GS and higher-energy structures to accurately predict total energies using a generic graph neural network architecture. Using ∼ 16,500 density functional theory calculations from the National Renewable Energy Laboratory (NREL) Materials Database and ∼ 11,000 calculations for hypothetical structures as our training database, we demonstrate that our model satisfactorily ranks the structures in the correct order of total energies for a given composition. Furthermore, we present a thorough error analysis to explain failure modes of the model, including both prediction outliers and occasional inconsistencies in the training data. By examining intermediate layers of the model, we analyze how the model represents learned structures and properties.

High thermoelectric performance of half-Heusler ZrXPb (X= Ni, Pd, and Pt) compounds from first principle calculation

Half-Heusler compounds have distinguished themselves as outstanding thermoelectric materials on account of high temperature stability and large thermopower. However, the dimensionless figure of merit of traditional half-Heusler alloys remains low. In this study, we investigate the thermoelectric performance of novel ZrXPb (X= Ni, Pd, and Pt) ternary compounds by semi-classical Boltzmann transport theory combining with deformation potential. The n-type ZrNiPb and ZrPtPb exhibits obviously largeZTvalues of 1.71 around 650 K and 1.75 around 1200 K, with 1.17 × 10²⁰ cm^-3and 3.43 × 10²⁰ cm^-3, respectively. The electron and phonon structure calculations demonstrate that for the n-type ZrXPb (X= Ni, Pd, and Pt) compounds, doping at Pb site can not only modify the carrier concentrations but also significantly decrease the lattice thermal conductivity. These investigations are expected to be beneficial to the exploration of novel highZTthermoelectric materials.

How to Look for Compounds: Predictive Screening and in situ Studies in Na-Zn-Bi System

Here, the combination of theoretical computations followed by rapid experimental screening and in situ diffraction studies is demonstrated as a powerful strategy for novel compounds discovery. When applied for the previously “empty” Na−Zn−Bi system, such an approach led to four novel phases. The compositional space of this system was rapidly screened via the hydride route method and the theoretically predicted NaZnBi (PbClF type, P4/nmm) and Na₁₁Zn₂Bi₅ (Na₁₁Cd₂Sb₅ type, P1‾ ) phases were successfully synthesized, while other computationally generated compounds on the list were rejected. In addition, single crystal X‐ray diffraction studies of NaZnBi indicate minor deviations from the stoichiometric 1 : 1 : 1 molar ratio. As a result, two isostructural (PbClF type, P4/nmm) Zn‐deficient phases with similar compositions, but distinctly different unit cell parameters were discovered. The vacancies on Zn sites and unit cell expansion were rationalized from bonding analysis using electronic structure calculations on stoichiometric “NaZnBi”. In‐situ synchrotron powder X‐ray diffraction studies shed light on complex equilibria in the Na−Zn−Bi system at elevated temperatures. In particular, the high‐temperature polymorph HT‐Na₃Bi (BiF₃ type, Fm3‾m) was obtained as a product of Na₁₁Zn₂Bi₅ decomposition above 611 K. HT‐Na₃Bi cannot be stabilized at room temperature by quenching, and this type of structure was earlier observed in the high‐pressure polymorph HP‐Na₃Bi above 0.5 GPa. The aforementioned approach of predictive synthesis can be extended to other multinary systems.

Electronic transport descriptors for the rapid screening of thermoelectric materials

The discovery of novel materials for thermoelectric energy conversion has potential to be accelerated by data-driven screening combined with high-throughput calculations. One way to increase the efficacy of successfully choosing a candidate material is through its evaluation using transport descriptors. Using a data-driven screening, we selected 12 potential candidates in the trigonal ABX₂ family, followed by charge transport property simulations from first principles. The results suggest that carrier scattering processes in these materials are dominated by ionised impurities and polar optical phonons, contrary to the oft-assumed acoustic-phonon-dominated scattering. Using these data, we further derive ground-state transport descriptors for the carrier mobility and the thermoelectric powerfactor. In addition to low carrier mass, high dielectric constant was found to be an important factor towards high carrier mobility. A quadratic correlation between dielectric constant and transport performance was established and further validated with literature. Looking ahead, dielectric constant can potentially be exploited as an independent criterion towards improved thermoelectric performance. Combined with calculations of thermal conductivity including Peierls and inter-branch coherent contributions, we conclude that the trigonal ABX₂ family has potential as high performance thermoelectrics in the intermediate temperature range for low grade waste heat harvesting.

Defect engineering in thermoelectric materials: what have we learned?

Thermoelectric energy conversion is an all solid-state technology that relies on exceptional semiconductor materials that are generally optimized through sophisticated strategies involving the engineering of defects in their structure. In this review, we summarize the recent advances of defect engineering to improve the thermoelectric (TE) performance and mechanical properties of inorganic materials. First, we introduce the various types of defects categorized by dimensionality, i.e. point defects (vacancies, interstitials, and antisites), dislocations, planar defects (twin boundaries, stacking faults and grain boundaries), and volume defects (precipitation and voids). Next, we discuss the advanced methods for characterizing defects in TE materials. Subsequently, we elaborate on the influences of defect engineering on the electrical and thermal transport properties as well as mechanical performance of TE materials. In the end, we discuss the outlook for the future development of defect engineering to further advance the TE field.

Strain effect on topological and thermoelectric properties of half Heusler compoundsXPtS (XSr, Ba)

In this article, we report theoretical investigations of topological and thermoelectric (TE) properties of non-centrosymmetric half Heusler compoundsXPtS (X= Sr, Ba) using first principles calculations. In addition, we also investigated the effect of static strain (up to 10%) on its topological and TE properties. Our detailed investigations show that the XPtS compounds are topological insulators (TIs) and continue as TIs up to a strain of 10%. However, the band gap becomes a maximum of 0.213 eV under a strain of 3% for SrPtS and 0.164 eV at a strain of 5% for BaPtS. TE investigations show that the figure of merit (a measure of TE performance) ZT becomes maximum (0.222) at room temperature for BaPtS under a strain of 1%. The detailed theoretical investigations ofXPtS with and without strain provide a theoretical platform for experiments and its possible applications in spintronics and thermoelectricity.

Computational discovery of stable and metastable ternary oxynitrides

Materials design from first principles enables exploration of uncharted chemical spaces. Extensive computational searches have been performed for mixed-cation ternary compounds, but mixed-anion systems are gaining increased interest as well. Central to computational discovery is the crystal structure prediction, where the trade-off between reliance on prototype structures and size limitations of unconstrained sampling has to be navigated. We approach this challenge by letting two complementary structure sampling approaches compete. We use the kinetically limited minimization approach for high-throughput unconstrained crystal structure prediction in smaller cells up to 21 atoms. On the other hand, ternary-and, more generally, multinary-systems often assume structures formed by atomic ordering on a lattice derived from a binary parent structure. Thus, we additionally sample atomic configurations on prototype lattices with cells up to 56 atoms. Using this approach, we searched 65 different charge-balanced oxide-nitride stoichiometries, including six known systems as the control sample. The convex hull analysis is performed both for the thermodynamic limit and for the case of synthesis with activated nitrogen sources. We identified 34 phases that are either on the convex hull or within a viable energy window for potentially metastable phases. We further performed structure sampling for "missing" binary nitrides whose energies are needed for the convex hull analysis. Among these, we discovered metastable Ce₃N₄ as a nitride analog of the tetravalent cerium oxide, which becomes stable under slightly activated nitrogen condition Δμ_N > +0.07 eV. Given the outsize role of CeO₂ in research and application, Ce₃N₄ is a potentially important discovery.

Explanation for the conductivity difference of half-Heusler transparent conductors via ionization energy

To further understand the less-studied half-Heusler transparent conductors, we have considered four 18-electron ABX compounds (TaIrGe, TaIrSn, ZrIrSb, and TiIrSb) to focus on their carrier effective masses and ionization energies. The novelty of this work lies in two aspects: (i) we discover that hole-killer defects are more likely to form in TaIrGe than in ZrIrSb, which leads to a lower concentration of the holes in TaIrGe. This is the fundamental reason for the conductivity of TaIrGe being much lower than that of ZrIrSb; (ii) we propose that the hole effective mass near the sub-valence band maximum (Sub-VBM) could be used to forecast the potential transport performance of the materials. The obtained results show that the transport performance of TaIrGe & TaIrSn is potentially more promising than that of TiIrSb and ZrIrSb. Besides, this work firstly studies the mechanical properties of the considered ABX compounds, offering strong evidence that TaIrGe, TaIrSn, ZrIrSb, and TiIrSb could be potentially flexible and ductile TCMs.

Probing the martensite transition and thermoelectric properties of CoxTaZ(ZSi, Ge, Sn andx1, 2): a study based on density functional theory

In this work, using density functional theory based electronic structure calculations, we carry out a comparative study of geometric, mechanical, electronic, magnetic, and thermoelectric properties of Co_xTaZalloys, whereZ= Si, Ge and Sn andx= 1 and 2. In the present study, a systematic approach has been taken to perform calculations to probe the possibility of existence of a tetragonal (martensite) phase in these alloys and also to perform a comparative study of various physical properties of the six systems, mentioned above, in the cubic and possible tetragonal phases. From our calculations, a tetragonal phase has been found to be stable up to about 400 K in case of Co₂TaSi and Co₂TaGe alloys, and up to about 115 K for Co₂TaSn, indicating the presence of room temperature cubic phase in the latter alloy unlike the former two. Further, the results based on the energetics and electronic structure have been found to corroborate well with the elastic properties. All the above-mentioned full Heusler alloys (FHAs) show magnetic behavior with metallicity in both the phases. However, their half Heusler counterparts exhibit non-magnetic semi-conducting behavior in the cubic phase. We calculate and compare the thermoelectric properties, in detail, of all the materials in the cubic and possible tetragonal phases. In the cubic phase, the half Heusler alloys exhibit improved thermoelectric properties compared to the respective FHAs. Furthermore, it is observed that the FHAs exhibit higher (by about an order of magnitude) values of Seebeck coefficients in their cubic phases, compared to those in the tetragonal phases (which are of the order of only a few micro-volts/Kelvin). The observed behaviors of the transport properties of the probed materials have been analyzed using the topology of the Fermi surface.

Autonomous intelligent agents for accelerated materials discovery

We present an end-to-end computational system for autonomous materials discovery. The system aims for cost-effective optimization in large, high-dimensional search spaces of materials by adopting a sequential, agent-based approach to deciding which experiments to carry out. In choosing next experiments, agents can make use of past knowledge, surrogate models, logic, thermodynamic or other physical constructs, heuristic rules, and different exploration-exploitation strategies. We show a series of examples for (i) how the discovery campaigns for finding materials satisfying a relative stability objective can be simulated to design new agents, and (ii) how those agents can be deployed in real discovery campaigns to control experiments run externally, such as the cloud-based density functional theory simulations in this work. In a sample set of 16 campaigns covering a range of binary and ternary chemistries including metal oxides, phosphides, sulfides and alloys, this autonomous platform found 383 new stable or nearly stable materials with no intervention by the researchers.

High thermoelectric performance of topological half-Heusler compound LaPtBi achieved by hydrostatic pressure

The topological phase transition and thermoelectric performance of LaPtBi under hydrostatic pressure up to 34.6 GPa have been systematically investigated using first-principles calculations based on density functional theory. The results indicate that the band structure can be tuned by applying hydrostatic pressure. As the energy band gap is opened under the hydrostatic pressure, a topological phase transition occurs in this material, changing from a topologically nontrivial semimetal to a trivial semiconductor. In addition, the hydrostatic pressure also has a remarkable effect on the thermoelectric properties of the topological half-Heusler compound LaPtBi. Though the lattice thermal conductivity shows a continuous increase with increasing hydrostatic pressure, the power factor is greatly enhanced due to the increase of the Seebeck coefficient. As a result, a maximum ZT value of 1.74 at 1000 K is achieved in n-type LaPtBi under pressure of 21.0 GPa. It is obvious that the thermoelectric figure of merit of LaPtBi is far beyond that of state-of-the-art half-Heusler thermoelectric materials, such as ZrNiSn, FeNbSb and TiCoSb. The realization of high thermoelectric performance in the half-Heusler compound LaPtBi under hydrostatic pressure could provide a new way to further explore other topological thermoelectric materials.

New polymorphism for BaTi(IO3)6 with two polymorphs crystallizing in the same space group

Polymorphic α- and β-BaTi(IO₃)₆ have been synthesized. They crystallize in the same space group and exhibit highly similar structures. But the different powder X-ray patterns and crystal morphologies indicate that they belong to different phases, which represents the first phase transitions with two polymorphs possessing the same space group and similar cells.

Micro-Scale Device-An Alternative Route for Studying the Intrinsic Properties of Solid-State Materials: The Case of Semiconducting TaGeIr

An efficient application of a material is only possible if we know its physical and chemical properties, which is frequently obstructed by the presence of micro- or macroscopic inclusions of secondary phases. While sometimes a sophisticated synthesis route can address this issue, often obtaining pure material is not possible. One example is TaGeIr, which has highly sample-dependent properties resulting from the presence of several impurity phases, which influence electronic transport in the material. The effect of these minority phases was avoided by manufacturing, with the help of focused-ion-beam, a μm-scale device containing only one phase-TaGeIr. This work provides evidence for intrinsic semiconducting behavior of TaGeIr and serves as an example of selective single-domain device manufacturing. This approach gives a unique access to the properties of compounds that cannot be synthesized in single-phase form, sparing costly and time-consuming synthesis efforts.

Developing Highly Tough, Heat-Resistant Blend Thermosets Based on Silicon-Containing Arylacetylene: A Material Genome Approach

Silicon-containing arylacetylene (PSA) resins exhibit excellent heat resistance, yet their brittleness limits the applications. We proposed using acetylene-terminated polyimides (ATPI) as an additive to enhance the toughness of the PSA resins and maintain excellent heat resistance. A material genome approach (MGA) was first established for designing and screening the acetylene-terminated polyimides, and a polyimide named ATPI was filtered out by using this MGA. The ATPI was synthesized and blended with PSA resins to improve the toughness of the thermosets. Influences of the added ATPI contents and prepolymerization temperature on the properties were examined. The result shows that the blend resin can resist high temperature and bear excellent mechanical properties. The molecular dynamics simulations were carried out to understand the mechanism behind the improvement of toughness. The present work provides a method for the rapid design and screening of high-performance polymeric materials.

Bandgaps of noble and transition metal/ZIF-8 electro/catalysts: a computational study

Zeolitic imidazolate frameworks (ZIFs) are designed with metals as center atoms, connected by imidazole-like linkers. The created structures have been employed considerably in the field of advanced energy materials, including catalysis/electrocatalysis and energy storage and harvesting applications. In the present study, the bandgaps of pristine and doped ZIF-8 (using noble and transition metal dopants such as Pd, Pt, Ni, Mn, Co, Cu, Fe, and Ti) are determined. This can result in a promising approach to enhance the corresponding electronic properties while applying noble metal-free dopants. To determine the bandgap values, a quantum mechanical modeling based on density functional theory (DFT) was applied. Then, due to the time-consuming and complicated nature of this approach, the obtained results from the DFT study were then employed to develop the support vector machine (SVM) model to estimate the bandgap of the resulting nanostructure. The outcomes of the proposed model showed its high accuracy, with R² of 0.98 and root mean squared error (RMSE) of 0.04. The developed model could have great value in designing various ZIF-8-based nanostructures, particularly when applied in electro/catalytic reactions, e.g., electrocatalytic hydrogen evolution reaction or catalytic hydrogenation reaction, through a simple approach.

Band gap estimation for metal/ZIF-8 framework electro/catalysts by hybrid DFT and machine learning technique.

Probing the Validity of the Zintl-Klemm Concept for Alkaline-Metal Copper Tellurides by Means of Quantum-Chemical Techniques

Understanding the nature of bonding in solid-state materials is of great interest for their designs, because the bonding nature influences the structural preferences and chemical as well as physical properties of solids. In the cases of tellurides, the distributions of valence-electrons are typically described by applying the Zintl-Klemm concept. Yet, do these Zintl-Klemm treatments provide adequate pictures that help us understanding the bonding nature in tellurides? To answer this question, we followed up with quantum-chemical examinations on the electronic structures and the bonding nature of three alkaline-metal copper tellurides, i.e., NaCu3Te2, K2Cu2Te5, and K2Cu5Te5. In doing so, we accordingly probed the validity of the Zintl-Klemm concept for these ternary tellurides, based on analyses of the respective projected crystal orbital Hamilton populations (-pCOHP) and Mulliken as well as Löwdin charges. Since all of the inspected tellurides are expected to comprise Cu-Cu interactions, we also paid particular attention to the possible presence of closed-shell interactions.

Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials

Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors, which is known to be correlated to valley degeneracy in the electronic band structure. However, there are over 50 known semiconducting half-Heusler phases, and it is not clear how the chemical composition affects the electronic structure. While all the n-type electronic structures have their conduction band minimum at either the Γ- or X-point, there is more diversity in the p-type electronic structures, and the valence band maximum can be at either the Γ-, L-, or W-point. Here, we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W-point to the valence band maximum. We do this by constructing an "orbital phase diagram" to cluster the variety of electronic structures expressed by these phases into groups, based on the atomic orbitals that contribute most to their valence bands. Then, with the aid of machine learning, we develop new chemical rules that predict the location of the valence band maximum in each of the phases. These rules can be used to engineer band structures with band convergence and high valley degeneracy.

n-Type TaCoSn-Based Half-Heuslers as Promising Thermoelectric Materials

Half-Heusler compounds are recognized as promising thermoelectric materials for high-temperature power generation, but their relatively high lattice thermal conductivity impedes further improvement of ZT. Here, we report the synthesis of a new half-Heusler compound TaCoSn with a low thermal conductivity. Experimentally, the pristine TaCoSn exhibits a low lattice thermal conductivity of ∼5.7 W m^-1 K^-1 at 300 K, which is lower than that of most of the other half-Heusler compounds. Phonon calculations by density functional theory indicate that the low phonon velocity, small Debye temperature, and large Grüneisen parameter are the contributors to the low thermal conductivity of TaCoSn. Importantly, intense point-defect scattering can be induced by alloying Nb at the Ta site, which further suppresses the thermal conductivity of TaCoSn. Additionally, Sb has been identified as an efficient dopant for supplying high electron concentration. Finally, the optimized n-type Ta_0.6Nb_0.4CoSn_0.94Sb_0.06 demonstrates a peak ZT above 0.7 at 973 K. Our work demonstrates that the TaCoSn-based half-Heuslers are promising thermoelectric materials.

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Atomic-scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic-structure methods such as density-functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materials modeling: by "learning" electronic-structure data, ML-based interatomic potentials give access to atomistic simulations that reach similar accuracy levels but are orders of magnitude faster. A brief introduction to the new tools is given, and then, applications to some select problems in materials science are highlighted: phase-change materials for memory devices; nanoparticle catalysts; and carbon-based electrodes for chemical sensing, supercapacitors, and batteries. It is hoped that the present work will inspire the development and wider use of ML-based interatomic potentials in diverse areas of materials research.

Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible

Designing future-proof materials goes beyond a quest for the best. The next generation of materials needs to be adaptive, multipurpose, and tunable. This is not possible by following the traditional experimentally guided trial-and-error process, as this limits the search for untapped regions of the solution space. Here, a computational data-driven approach is followed for exploring a new metamaterial concept and adapting it to different target properties, choice of base materials, length scales, and manufacturing processes. Guided by Bayesian machine learning, two designs are fabricated at different length scales that transform brittle polymers into lightweight, recoverable, and supercompressible metamaterials. The macroscale design is tuned for maximum compressibility, achieving strains beyond 94% and recoverable strengths around 0.1 kPa, while the microscale design reaches recoverable strengths beyond 100 kPa and strains around 80%. The data-driven code is available to facilitate future design and analysis of metamaterials and structures (https://github.com/mabessa/F3DAS).