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The design/discovery of new materials is highly nontrivial owing to the near-infinite possibilities of material candidates and multiple required property/performance objectives. Thus, machine learn...

Developing a large database of polymers structures and properties, for which suitable polymer structural models are a prerequisite, is critical for polymer informatics. We present a simple strategy, referred to as polymer structure predictor (PSP), for predicting the crystal structural models of linear polymers, given their chain-level atomic connectivity information. The PSP, which was designed specifically for polymers, relies on properly defining and sampling the configuration space. Using this approach, we have successfully recovered eight known crystal structures of six common linear polymers, including polyethylene, polyvinylidene fluoride, poly(vinyl chloride), poly(p-phenylene sulfide), polyacrylonitrile, and poly-2,5-benzoxazole, while discovering some new stable structures of three of them, i.e., polyvinylidene fluoride, polyacrylonitrile, and poly(p-phenylene sulfide). The PSP is very simple, highly scalable, suitable for automatic workflows, and comparable to the best major structure prediction method in terms of efficiency in polymer crystal structure prediction. Although challenges in comprehensively accounting for possible chain-level conformations remain, the PSP will be very useful in efficiently generating polymer data and strengthening the emerging polymer informatics ecosystems.

We develop a probabilistic machine learning model and use it to screen for new hybrid organic-inorganic perovskites (HOIPs) with targeted electronic band gap. The data set used for this work is highly diverse, containing multiple atomic structures for each of 192 chemically distinct HOIP formulas. Therefore, any property prediction on a given formula must be associated with an irreducible "uncertainty" that comes from its unknown atomic details. As a result, dozens of new HOIP formulas with band gap falling between 1.25 and 1.50 eV were identified and validated against suitable first-principles computations. Through this demonstration we show that the probabilistic deep learning approach is robust, versatile, and can be used to properly quantify this uncertainty. In conclusion, the probabilistic standpoint and approach described herein could be widely useful for the very common and inevitable data uncertainty which is rooted at the incompleteness of information during experiments and/or computations.

Emerging data-driven approaches in materials science have triggered the development of numerous machine-learning force fields. In practice, they are constructed by training a statistical model on a reference database to predict potential energy and/or atomic forces. Although most of the force fields can accurately recover the properties of the training set, some of them are becoming useful for actual molecular dynamics simulations. In this work, we employ a simple iterative-learning strategy for the development of machine-learning force fields targeted at specific simulations (applications). The strategy involves (1) preparing and fingerprinting a diverse reference database of atomic configurations and forces, (2) generating a pool of machine-learning force fields by learning the reference data, (3) validating the force fields against a series of targeted applications, and (4) selectively and recursively improving the force fields that are unsuitable for a given application while keeping their performance...

Within materials informatics, a rapidly developing subfield of materials research, past (curated) data are mined and learned for either discovering new materials or identifying new functionalities of known materials. This paper provides an example of this process. Starting from a recently developed (very diverse) dataset which includes 1346 hybrid organic-inorganic perovskites (HOIPs), we downselect a subset of 350 three dimensional HOIPs to a final set of four lead-free HOIPs, including CH3NH3SnI3, HC(NH2)2SnI3, NH2NH3SnI3, and NH2(CH2)3SnI3, in which the first two were experimentally synthesized and the others remain hypothetical. Using first-principles based computational methods, we show that these HOIPs have preferable electronic band structures and carrier effective mass, good optical properties, and high spectroscopic limited maximum efficiency. Compared to the experimental data, we find that state-of-the-art numerical methods can predict the electronic and optical properties fairly well, while the current model for the spectroscopic limited maximum efficiency is inadequate for capturing the power conversion efficiency of a solar absorber. We suggest that the HOIP dataset should be expanded to include larger structures of HOIPs, thereby being more useful for future data-mining and machine-learning approaches.

The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational methodologies, data descriptors, and machine learning. Polymers have long suffered from a lack of data on electronic, mechanical, and dielectric properties across large chemical spaces, causing a stagnation in the set of suitable candidates for various applications. Extensive efforts over the last few years have seen the fruitful application of MGI principles toward the accelerated discovery of attractive polymer dielectrics for capacitive energy storage. Here, we review these efforts, highlighting the importance of computational data generation and screening, targeted synthesis and characterization, polymer fingerprinting and machine-learning prediction models, and the creation of an online knowledgebase to guide ongoing and future polymer discovery and design. We lay special emphasis on the fingerprinting of polymers in terms of their genome or constituent atomic and molecular fragments, an idea that pays homage to the pioneers of the human genome project who identified the basic building blocks of the human DNA. By scoping the polymer genome, we present an essential roadmap for the design of polymer dielectrics, and provide future perspectives and directions for expansions to other polymer subclasses and properties.

Bornite materials are naturally occurring systems composed of earth-abundant constituents. Bournonite, a representative of this class of materials, is of interest for thermoelectric applications due to its inherently low thermal conductivity, which has been attributed to the lattice distortions due to stereochemically active electron lone pair distributions. In this computational and experimental study, we present analyses of the lattice structure, electron and phonon dynamics, and charge localization and transfer properties for undoped and Ni and Zn doped bournonites. The results from our simulations reveal complex relations between bond length and bond angle characteristics, chemical bonding, and charge transfer upon doping. Analysis of the experimental results indicate that a microscopic description for bournonite and its doped compositions is necessary for a complete understanding of these materials, as well as for effective control of the transport properties for targeted applications.

Mesoporous α-MoO3–x combined with poly(diallyldimethylammonium chloride)–functionalized reduced graphene oxide (PDDA–rGO) is introduced as an inexpensive and efficient oxygen reduction reaction (ORR) catalyst. The mesoporous catalysts are wrapped by conductive rGO sheets via an electrostatic interaction induced by a PDDA polyelectrolyte. The thermal interaction of PDDA with MoO3 efficiently reduces the metal oxide to MoO3–x at 400–600 °C, creating a surface oxygen vacancy. Through a combination of density functional theory and experiments, the role of the surface oxygen vacancy sites in the ORR activity of MoO3–x is identified. For the first time, all the energy barriers against ORR are calculated at each step for MoO3 with no oxygen vacancies and MoO3–x with surface oxygen vacancies. It is shown that the presence of an Mo4+‐vO•• oxygen vacancy site on the surface significantly reduces the energy barriers against ORR in the reaction pathways. An overpotential of 0.86 V (vs a reversible hydrogen electrode)...

Three known binary compounds of tin (Sn) and sulfur (S), namely, SnS, SnS2, and Sn2S3, have been extensively studied for potential application in energy generation and conversion applications. Inspired by the existence of many metastable phases of SnS, we explore the chemical space of nine crystalline solids with chemical composition SnxS1–x (x falls between 0.25 and 0.75), predicting that Sn3S is thermodynamically stable in a metallic Pmn21 phase. Due to the layered structure of this phase, Sn3S is a quasi two-dimensional material, characterized by highly anisotropic electronic-related properties. Moreover, the discovered metastable structures of Sn3S2, Sn2S, and Sn5S2 are just about 5 meV/atom above the stability limit, and may potentially be realized. The data set of 369 low-energy structures of nine SnxS1–x crystalline solids reported in this work is a reliable sample of the low-energy sector of the chemical space, and thus being useful for the currently established materials databases, providing a pl...

The recent successes of the Materials Genome Initiative have opened up new opportunities for data-centric informatics approaches in several subfields of materials research, including in polymer science and engineering. Polymers, being inexpensive and possessing a broad range of tunable properties, are widespread in many technological applications. The vast chemical and morphological complexity of polymers though gives rise to challenges in the rational discovery of new materials for specific applications. The nascent field of polymer informatics seeks to provide tools and pathways for accelerated property prediction (and materials design) via surrogate machine learning models built on reliable past data. We have carefully accumulated a data set of organic polymers whose properties were obtained either computationally (bandgap, dielectric constant, refractive index, and atomization energy) or experimentally (glass transition temperature, solubility parameter, and density). A fingerprinting scheme that capt...

The presence of oxygen vacancy sites fundamentally affects physical and chemical properties of materials. In this study, a dipole-containing interaction between poly(diallyldimethylammonium chloride) PDDA and α-MoO3 is found to enable high-concentrations of surface oxygen vacancies. Thermal annealing under Ar resulted in negligible reduction of MoO3 to MoO3–x with x = 0.03 at 600 °C. In contrast, we show that the thermochemical reaction with PDDA polyelectrolyte under Ar can significantly reduce MoO3 to MoO3–x with x = 0.36 (MoO2.64) at 600 °C. Thermal annealing under H2 gas enhanced the substoichiometry of MoO3–x from x = 0.62 to 0.98 by using PDDA at the same conditions. Density functional theory calculations, supported by experimental analysis, suggest that the vacancy sites are created through absorption of terminal site oxygen (Ot) upon decomposition of the N–C bond in the pentagonal ring of PDDA during the thermal treatment. Ot atoms are absorbed as ionic O– and neutral O2–, creating Mo5+-vO· and Mo...

Nanoporous materials are emerging as potential candidates for a wide range of technological applications in environment, electronic, and optoelectronics, to name just a few. Within this active research area, experimental works are predominant while theoretical/computational prediction and study of these materials face some intrinsic challenges, one of them is how to predict porous structures. We propose a computationally and technically feasible approach for predicting zinc oxide structures with hollows at the nano scale. The designed zinc oxide hollow structures are studied with computations using the density functional tight binding and conventional density functional theory methods, revealing a variety of promising mechanical and electronic properties, which can potentially find future realistic applications.

Mining of currently available and evolving materials databases to discover structure–chemistry–property relationships is critical to developing an accelerated materials design framework. The design of new and advanced polymeric dielectrics for capacitive energy storage has been hampered by the lack of sufficient data encompassing wide enough chemical spaces. Here, data mining and analysis techniques are applied on a recently presented computational data set of around 1100 organic polymers, organometallic polymers, and related molecular crystals, in order to obtain qualitative understanding of the origins of dielectric and electronic properties. By probing the relationships between crucial chemical and structural features of materials and their dielectric constant and band gap, design rules are devised for optimizing either property. Learning from this data set provides guidance to experiments and to future computations, as well as a way of expanding the pool of promising polymer candidates for dielectric ...

Emerging machine learning (ML)-based approaches provide powerful and novel tools to study a variety of physical and chemical problems. In this contribution, we outline a universal strategy to create ML-based atomistic force fields, which can be used to perform high-fidelity molecular dynamics simulations. This scheme involves (1) preparing a big reference dataset of atomic environments and forces with sufficiently low noise, e.g., using density functional theory or higher-level methods, (2) utilizing a generalizable class of structural fingerprints for representing atomic environments, (3) optimally selecting diverse and non-redundant training datasets from the reference data, and (4) proposing various learning approaches to predict atomic forces directly (and rapidly) from atomic configurations. From the atomistic forces, accurate potential energies can then be obtained by appropriate integration along a reaction coordinate or along a molecular dynamics trajectory. Based on this strategy, we have created model ML force fields for six elemental bulk solids, including Al, Cu, Ti, W, Si, and C, and show that all of them can reach chemical accuracy. The proposed procedure is general and universal, in that it can potentially be used to generate ML force fields for any material using the same unified workflow with little human intervention. Moreover, the force fields can be systematically improved by adding new training data progressively to represent atomic environments not encountered previously. A machine learning-based strategy for calculating atomic force fields could expand the range of molecular dynamics simulations. Molecular dynamics simulations are a powerful tool for exploring how atomic-scale chemical and physical processes evolve over time. To perform such simulations, an initial atomic configuration needs to be defined, and atomic forces are input for each time step. Whilst quantum mechanics-based methods for calculating the fields are available, these approaches cannot easily be applied to large systems over long timescales. A team of researchers from the University of Connecticut, led by Rampi Ramprasad, now present a general and universal strategy for using machine learning-based methods to generate highly accurate atomic force fields that may provide a pathway for performing efficient molecular dynamics simulations on nanometer-sized systems over several nanoseconds.

The electronic structure of an insulator encodes essential signatures of its short-term electrical performance and long-term reliability. A critical long-standing challenge though is that key features of the electronic structure of an insulator (and its evolution) under realistic conditions have not been entirely accessible, either via experimental or computational approaches, due to the inherent complexities involved. In this comprehensive study, we reveal the role of chemical and morphological imperfections that inevitably exist within the technologically important prototypical and pervasive insulator, polyethylene (PE), and at electrode/PE interfaces. Large-scale density functional theory computations and long-time molecular dynamics simulations were employed to accurately recover, explain and unravel a wide variety of experimental data obtained during the electrical degradation of PE. This scheme has allowed us to directly and realistically address the role of chemical, morphological and interfacial complexity in determining electronic structure. These efforts take us a step closer to understanding and potentially controlling dielectric degradation and breakdown.

Inorganic clathrates with the type II crystal structure are of interest as potential materials for high temperature thermoelectric applications. In this study we present ab initio calculations for the electronic and phonon properties of several Sn type II clathrate compositions with partial Ga substitution on the framework, empty cage Sn136, and compounds of Sn136 filled with inert Xe atoms. It is found that cage disorder due to atomic substitution and guest encapsulation affect the fundamental characteristics of these materials in profound ways. We determine that the stability of these materials is enhanced by the presence of guests and lack of direct Ga Ga bonds in disordered clathrates. Inert Xe atoms provide a unique opportunity to preserve the overall electronic structure of Sn136 and take advantage of the loosely bound guest rattling for enhanced phonon scattering. The calculated energy bands and density of states, as well as phonon band structure and mode Gruneisen parameter, enable further analysis of type II Sn clathrates and reveal interesting structure-property relations.

The surprising ferroelectricity displayed by hafnia thin films has been attributed to a metastable polar orthorhombic (Pca21) phase. Nevertheless, the conditions under which this (or another competing) ferroelectric phase may be stabilized remain unresolved. It has been hypothesized that a variety of factors, including strain, grain size, electric field, impurities and dopants, may contribute to the observed ferroelectricity. Here, we use first-principles computations to examine the influence of mechanical and electrical boundary conditions (i.e., strain and electric field) on the relative stability of a variety of relevant nonpolar and polar phases of hafnia. We find that although strain or electric field, independently, do not lead to a ferroelectric phase, the combined influence of in-plane equibiaxial deformation and electric field results in the emergence of the polar Pca21 structure as the equilibrium phase. The results provide insights for better controlling the ferroelectric characteristics of haf...

Dielectric polymers are widely used in electronics and energy technologies, but their performance is severely limited by the electrical breakdown under a high electric field. Dielectric breakdown is commonly understood as an avalanche of processes such as carrier multiplication and defect generation that are triggered by field-accelerated hot electrons and holes. However, how these processes are initiated remains elusive. Here, nonadiabatic quantum molecular dynamics simulations reveal microscopic processes induced by hot electrons and holes in a slab of an archetypal dielectric polymer, polyethylene, under an electric field of 600 MV/m. We found that electronic-excitation energy is rapidly dissipated within tens of femtoseconds because of strong electron-phonon scattering, which is consistent with quantum-mechanical perturbation calculations. This in turn excites other electron-hole pairs to cause carrier multiplication. We also found that the key to chemical damage is localization of holes that travel to a slab surface and weaken carbon-carbon bonds on the surface. Such quantitative information can be incorporated into first-principles-informed, predictive modeling of dielectric breakdown.

Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymers. Surrogate models are trained on available polymer data for instant property prediction, allowing screening of promising polymer candidates with specific target property requirements. Questions regarding synthesizability, and potential (retro)synthesis steps to create a target polymer, are being explored using statistical means. Data-driven strategies to tackle unique challenges resulting from the extraordinary chemical and physical diversity of polymers at small and large scales are being explored. Other major hurdles for polymer informatics are the lack of widespread availability of curated and organized data, and approaches to create machine-readable representations that capture not just the structure of complex polymeric situations but also synthesis and processing conditions. Methods to solve inverse problems, wherein polymer recommendations are made using advanced AI algorithms that meet application targets, are being investigated. As various parts of the burgeoning polymer informatics ecosystem mature and become integrated, efficiency improvements, accelerated discoveries and increased productivity can result. Here, we review emergent components of this polymer informatics ecosystem and discuss imminent challenges and opportunities.

Hollow spherical structures of ternary bismuth molybdenum oxide doped with samarium (Bi2–xSmxMoO6) were successfully synthesized via development of a Pluronic P123 (PEO20-PPO70-PEO20)-assisted solvothermal technique. Density functional theory calculations have been performed to improve our understanding of the effects of Sm doping on the electronic band structure, density of states, and band gap of the material. The calculations for 0 ≤ x ≤ 0.3 revealed a considerably flattened conduction band minimum near the Γ point, suggesting that the material can be considered to possess a quasi-direct band gap. In contrast, for x = 0.5, the conduction band minimum is deflected toward the U point, making it a distinctly indirect band gap material. The effects of a hollow structure as well as Sm substitution on the absorbance and fluorescence properties of the materials produced increased emission intensities at low Sm concentrations (x = 0.1 and 0.3), with x = 0.1 displaying a peak photoluminescence intensity 13.2 ti...

This article provides an overview of the present state-of-the-art pertaining to polymer capacitor dielectrics appropriate for high electrostatic energy density applications. The challenges and opportunities surrounding capacitor materials development/discovery in a practical context are reviewed. It is pointed out that several hierarchical considerations ranging from dielectric, electronic, morphological, processing, reliability and electrical characteristics need to be confronted and addressed adequately before one can progress from “standard” materials (such as biaxially oriented polypropylene) toward next generation materials. Nevertheless, it is argued that the prospects for systematic approaches toward polymer dielectrics discovery appear to be strong, especially given recent successes and evidence that a cooperative computation-synthesis-processing-characterization paradigm can bear fruit. It is hoped that this article will be particularly useful for a new researcher entering the field as it presents a snapshot of various critical aspects of this emerging field in one comprehensive narrative.

Although traditional materials discovery has historically benefited from intuition-driven experimental approaches and serendipity, computational strategies have risen in prominence and proven to be a powerful complement to experiments in the modern materials research environment. It is illustrated here how one may harness a rational co-design approach-involving synergies between high-throughput computational screening and experimental synthesis and testing-with the example of polymer dielectrics design for electrostatic energy storage applications. Recent co-design efforts that can potentially enable going beyond present-day "standard" polymer dielectrics (such as biaxially oriented polypropylene) are highlighted. These efforts have led to the identification of several new organic polymer dielectrics within known generic polymer subclasses (e.g., polyurea, polythiourea, polyimide), and the recognition of the untapped potential inherent in entirely new and unanticipated chemical subspaces offered by organometallic polymers. The challenges that remain and the need for additional methodological developments necessary to further strengthen the co-design concept are then presented.

Two cage-like nanoporous polymorphs composed of spheroidal cages (ZnO)24 as building block in high symmetrical cubic lattice frameworks are proposed. By using this cluster, instead of atom, as primary building block we studied various scheme of its assembling via bottom up cluster-cluster coalescence schemes. First principles and density-functional tight binding (DFTB+) calculations confirm that the new polymorphs are energetically, dynamically, and mechanically stable. The two newly found nanoporous phases possess an intrinsic wide direct band gap preserving from wurtzite tetragonal-based bonding. The high cubic symmetry wide bandgap semiconductor and its derivatives are expected to have broad applications in photocatalysis, and biomedicine.

The fidelity of data is of paramount importance in the construction of reliable and accurate machine learning (ML) models. Low-fidelity data, although noisy, can usually be obtained for a large number of material samples. High-fidelity data, on the other hand, is time-consuming and oftentimes, only available for a limited number of target samples. While the former can provide useful information to help generalize the ML models over large materials space, the latter is useful to build more accurate surrogate models. Information fusion schemes that utilize the data available at multiple levels of fidelity can outperform traditional single fidelity based ML methods, such as Gaussian process regression. In this work, a variant of the multi-fidelity information fusion scheme, namely multi-fidelity co-kriging, is used to build powerful prediction models of polymer bandgaps. To benchmark this strategy, we utilize a bandgap dataset of 382 polymers, obtained at two levels of fidelity: using the Perdew-Burke-Ernzerhof (PBE) exchange-correlational functional (“low-fidelity”) and the Heyd-Scuseria-Ernzerhof (HSE06) functional(“high-fidelity”) of density functional theory. The multi-fidelity model, trained on both PBE and HSE06 data, outperforms a single-fidelity Gaussian process regression model trained on just HSE06 band-gaps in a number of scenarios and is also able to generalize better to a more diverse chemical space.

The family of binary compounds composed of magnesium and silicon is rather rich. In addition to the well-known magnesium silicide ${\mathrm{Mg}}_{2}\mathrm{Si}$, other compounds, including ${\mathrm{MgSi}}_{2}$, ${\mathrm{Mg}}_{4}{\mathrm{Si}}_{7}$, ${\mathrm{Mg}}_{5}{\mathrm{Si}}_{6}$, MgSi, and ${\mathrm{Mg}}_{9}{\mathrm{Si}}_{5}$, have also been identified and/or proposed in precipitated Al-Mg-Si solid solutions. Nevertheless, computational studies show that only ${\mathrm{Mg}}_{2}\mathrm{Si}$ is thermodynamically stable at ambient conditions while certain nonzero hydrostatic pressure can stabilize ${\mathrm{Mg}}_{9}{\mathrm{Si}}_{5}$ so that it can coexist with ${\mathrm{Mg}}_{2}\mathrm{Si}$. We conduct a comprehensive search for viable binary compounds of ${\mathrm{Mg}}_{x}{\mathrm{Si}}_{1\ensuremath{-}x}$ $(1/3\ensuremath{\le}x\ensuremath{\le}2/3)$, discovering numerous low-energy structures for all the compounds. On one hand, we find that ${\mathrm{MgSi}}_{2}$, MgSi, and ${\mathrm{Mg}}_{9}{\mathrm{Si}}_{5}$ are likely pressure-stabilized materials, while, on the other hand, supporting previous studies, we raise doubt on the existence of ${\mathrm{Mg}}_{5}{\mathrm{Si}}_{6}$, and claim that the existence of ${\mathrm{Mg}}_{4}{\mathrm{Si}}_{7}$ remains an open question. Therefore, we recommend that (hydrostatic and/or nonhydrostatic) pressure should be explicitly considered when discussing the stability of these solids (and maybe other solids as well) by computations. We also find that ${\mathrm{MgSi}}_{2}$ can potentially exhibit superconducting behaviors within a wide range of pressure with the critical temperature of up to 7 K.

Charge injection barriers at metal/polymer interfaces are affected by many factors, including the physical, chemical, and electronic structure of the metal, the polymer, and the interfacial region. Here, we consider a diverse set of metals (Al, Ag, Au, Pd, and Pt), and a few metal/polyethylene interfacial configurations in an attempt to span situations encountered in real metal/polyethylene systems. Several relevant electronic properties and the charge injection barriers are computed for these cases using density functional theory computations. The calculations reveal important trends and correlations, and identify the favored mechanism of charge transport (as mediated by the charge injection barriers). While satisfactory correspondences of the computations with available measurements are achieved, quantitative discrepancies still remain between the computed and measured injection barriers. These issues may be resolved when more realistic models of the interface, inclusive of its morphological complexities, are utilized.

Large dielectric constant and band gap are essential for insulating materials used in applications such as capacitors, transistors and photovoltaics. Of the most common polymers utilized for these applications, polyvinyldiene fluoride (PVDF) offers a good balance between dielectric constant, >10, and band gap, 6 eV, but suffers from being a ferroelectric material. Herein, we investigate a series of aliphatic organotin polymers, p[DMT(CH2)n], to increase the dipolar and ionic part of the dielectric constant while maintaining a large band gap. We model these polymers by performing first-principles calculations based on density functional theory (DFT), to predict their structures, electronic and total dielectric constants and energy band gaps. The modeling and experimental values show strong correlation, in which the polymers exhibit both high dielectric constant, ≥5.3, and large band gap, ≥4.7 eV with one polymer displaying a dielectric constant of 6.6 and band gap of 6.7 eV. From our work, we can identify ...

Metal sulfate formation is one of the major challenges to the emissions aftertreatment catalysts. Unlike the incredibly sulfation prone nature of Pd to form PdSO$_4$, no experimental evidence exits for the PtSO$_4$ formation. Given the mystery of nonexistence of the PtSO$_4$, we explore the PtSO$_4$ using a combined approach of evolutionary algorithm based search technique and quantum mechanical computations. Experimentally known PdSO$_4$ is considered for the comparison and validation of our results. We predict many possible low-energy phases of the PtSO$_4$ and PdSO$_4$ at 0K, which are further investigated under wide range of temperature-pressure conditions. An entirely new low-energy (tetragonal $P4_2/m$) structure of the PtSO$_4$ and PdSO$_4$ is predicted, which appears to be the most stable phase of the PtSO$_4$ and a competing phase of the experimentally known monoclinic $C_2/c$ phase of PdSO$_4$. Phase stability at finite temperature is further examined and verified by free energy calculations of sulfates towards their possible decomposition products. Finally, temperature-pressure phase diagrams are computationally established for both PtSO$_4$ and PdSO$_4$.

Hybrid organic-inorganic perovskites (HOIPs) have been attracting a great deal of attention due to their versatility of electronic properties and fabrication methods. We prepare a dataset of 1,346 HOIPs, which features 16 organic cations, 3 group-IV cations and 4 halide anions. Using a combination of an atomic structure search method and density functional theory calculations, the optimized structures, the bandgap, the dielectric constant, and the relative energies of the HOIPs are uniformly prepared and validated by comparing with relevant experimental and/or theoretical data. We make the dataset available at Dryad Digital Repository, NoMaD Repository, and Khazana Repository (http://khazana.uconn.edu/), hoping that it could be useful for future data-mining efforts that can explore possible structure-property relationships and phenomenological models. Progressive extension of the dataset is expected as new organic cations become appropriate within the HOIP framework, and as additional properties are calculated for the new compounds found.

To date semiconductor nanowire (NW) heterostructures (HS) have attracted extensive attention as important components of electronic and optoelectronic nanodevices. Further NWs also show promising potency to enhance the solar energy harvesting, e.g. improving both light trapping, photo-carrier collection, and contacting surface area. In this work we show theoretically that the \(d^{0}\)-ferromagnetism and NW HS bandgap can be turned by engineering the HS interfaces in non-magnetic ZnO/GaN core/shell NW HS. In that NW HS the incorporation of one compound into the other leads to the bandgap narrowing in the nonisovalent alloy because of the type II band alignment betwwen ZnO and GaN. The \(d^{0}\)-ferromagnetic interface can be developed by creating \(p\)-type defect with \(N\) and/or \(n\)-type defect with Zn in Ga--O interface bonds due to the defect-induced polar discontinuity. It's noted that the GaN/ZnO NW HS itself without defect or with same number defects of both types are not ferromagnetic. So that the induced magnetic moment is suggested to be related to the missing charge introduced at these defects. In our study we focused on the effects of GaN/ZnO interfaces on the electronic and magnetic properties, e.g. interface states within the bandgap and interface-induced ferromagnetism and impact of surface reconstruction and quantum confinement. The origin of this \(d^{0}\)-FM is revealed by analyses of spin-polarized bandstructure indicated by the asymmetrical spin-up and spin-down states near the Fermi level, the projected densities of states (PDOSs) and the spin-polarized mulliken charge differences, indicated that most spin-polarized states are dominated by the interface defect site N$p$ electrons. The calculated GaN/ZnO interface magnetism, have been compared with FM at the LaAlO\(-SrTiO\(_{3}\) interface which are theoretically predicted [30] and experimentally confirmed [31], where the magnetic moments also arise from the polar discontinuity.

authoren Lithium/potassium borohydride LiK(BH) was recently synthesized and reported to be in a Pnma phase at ambient conditions. Subsequent theoretical studies show that this phase is metastable at finite temperatures up to 400 K. Studying the Pnma phase of LiK(BH) by first-principles calculations, we show that this phase is dynamically stable. Although the Pnma phase is metastable at finite temperatures as reported by previous studies, we find that it can be significantly stabilized by compression. Based on the structural similarity between the reactant LiBH and the product LiK(BH) of the synthesization reaction, we suggest that the formation of the Pnma phase of LiK(BH) may be driven by the kinetic of the reaction, according to the empirical Ostwald's steps rule in crystal nucleation. Therefore, it may be interesting to explore other possible low-energy phases of LiK(BH) by recently developed structure prediction methods.

We present a first-principles study on the impacts of the surface on the ferromagnetism (FM) induced by non-magnetic defects (lithium substitutional dopants at zinc sites ${\rm Li}_{\rm Zn}$ and zinc vacancies $V_{\rm Zn}$ ) of zinc oxide (ZnO) nanowires. We show that unlike bulk ZnO structures, both ${\rm Li}_{\rm Zn}$ and $V_{\rm Zn}$ are able to induce $d^{0}$ FM in ZnO nanowires. While the structural confinement at the nanowire surface is found to play a key role in the FM induced by ${\rm Li}_{\rm Zn}$ , the reconstruction of the surface tunes the defect-induced FM. By suppressing the surface reconstruction, we find that a ${\rm Li}_{\rm Zn}$ and a $V_{\rm Zn}$ , regardless of their position, induce the same magnetic moments of 0.71 and 2.00 $\mu_{\rm B}$ ( $\mu_{\rm B}$ is the magneton Bohr), respectively. The steady correlation between the defect-induced FM, the dangling bonds introduced at these defects, and the local structural reconstruction surrounding these defects is unveiled. Finally, we discuss the saturation of the magnetic moment as the defect density increases.

This work describes our discovery of the dominant role of highly charged interfaces on the electrothermal transport properties of PbS, along with a method to reduce the barrier potential for charge carriers by an order of magnitude. High temperature thermoelectrics such as PbS are inevitably exposed to elevated temperatures during postsynthesis treatment as well as operation. However, we observed that as the material was heated, large concentrations of sulfur vacancy (VS̈) sites were formed at temperatures as low as 266 °C. This loss of sulfur doped the PbS n-type and increased the carrier concentration, where these excess electrons were trapped and immobilized at interfacial defect sites in polycrystalline PbS with an abundance of grain boundaries. Sulfur deficient PbS0.81 exhibited a large barrier potential for charge carriers of 0.352 eV, whereas annealing the material under a sulfur-rich environment prevented VS̈ formation and lowered the barrier by an order of magnitude to 0.046 eV. Through ab initio calculations, the formation of VS̈ was found to be more favorable on the surface compared to the bulk of the material with a 1.72 times lower formation energy barrier. These observations underline the importance of controlling interface-vacancy effects in the preparation of bulk materials comprised of nanoscale constituents.

Emerging computation- and data-driven approaches are particularly useful for rationally designing materials with targeted properties. Generally, these approaches rely on identifying structure-property relationships by learning from a dataset of sufficiently large number of relevant materials. The learned information can then be used to predict the properties of materials not already in the dataset, thus accelerating the materials design. Herein, we develop a dataset of 1,073 polymers and related materials and make it available at http://khazana.uconn.edu/. This dataset is uniformly prepared using first-principles calculations with structures obtained either from other sources or by using structure search methods. Because the immediate target of this work is to assist the design of high dielectric constant polymers, it is initially designed to include the optimized structures, atomization energies, band gaps, and dielectric constants. It will be progressively expanded by accumulating new materials and including additional properties calculated for the optimized structures provided.

Artificial van der Waals heterostructures constitute an emerging field that promises to design systems with properties on demand. Stacking patterns and the utilization of different types of chemically inert layers can deliver novel materials and devices. Despite the relatively weak van der Waals interaction, which does not affect the electronic properties around the Fermi level, our first-principles calculations show significant changes in the higher conduction and deeper valence regions in the considered graphene/silicene, graphene/MoS2, and silicene/MoS2 systems. Such changes are linked to strong out-of-plane hybridization effects and van der Waals interactions. We also find that the interface coupling significantly affects the vibrational properties of the heterostructures when compared to the individual constituents. Specifically, the van der Waals coupling is found to be a major factor for the stability of the system. The emergence of shear and breathing modes, as well as the transformation of flexural modes, are also found.

The ability to efficiently design new and advanced dielectric polymers is hampered by the lack of sufficient, reliable data on wide polymer chemical spaces and the difficulty of generating such data given time and computational/experimental constraints. Here, we address the issue of accelerating polymer dielectrics design by extracting learning models from data generated by accurate state-of-the-art first principles computations for polymers occupying an important part of the chemical subspace. The polymers are ‘fingerprinted’ as simple, easily attainable numerical representations, which are mapped to the properties of interest using a machine learning algorithm to develop an on-demand property prediction model. Further, a genetic algorithm is utilised to optimise polymer constituent blocks in an evolutionary manner, thus directly leading to the design of polymers with given target properties. While this philosophy of learning to make instant predictions and design is demonstrated here for the example of polymer dielectrics, it is equally applicable to other classes of materials as well.

Physical properties of a nano structure are dramatically affected by its surface reconstruction and quantum confinement. For a core/shell structured nanowire (NW), the core/shell interface also plays a key role. The effects induced solely by the surface reconstruction can be estimated by using a reference model in which the dangling bonds on the NW's surface are saturated by a number of pseudo hydrogen atoms with fractional charge. We present in this paper a first principles study on the role of the surface reconstruction in several series of gallium nitride/aluminum nitride core/shell structured NWs. We also show that the model of pseudo hydrogen atoms does suppress the effects of surface reconstruction on both structural and electronic properties of the NWs, thus the model can be used to estimate the role of surface reconstruction.

We report a first-principles study on several series of zinc oxide prismatic nanoparticles (NPs) with triangular and hexagonal cross-section geometries and various diameters up to 2.4 nm. Structural and electronic properties of the NPs are calculated using density functional theory, focusing on the effects induced by the surfaces reconstruction and the quantum sizes of the structures. We have observed a transition from the rocksalt structure for short NPs to the wurtzite structure for long NPs. These two structures are found to correspond to two mechanisms of surface stabilization, which are also observed in short and long NPs. For short NPs, the surfaces are stabilized by merging two polar facets of each layer into one while for long NPs, an “effective charge transfer” between two outer base surfaces is the key mechanism for surface stabilization. Effects of sizes and surfaces on electronic properties of the structures, e.g., the highest occupied molecular orbital (HOMO)–lowest unoccupied molecular orbital (LUMO) gap, are examined to support the discussion of the structural transition and the polar surface stability.

Chemical defects in polyethylene (PE) can deleteriously downgrade its electrical properties and performance. Although these defects usually leave spectroscopic signatures in terms of characteristic luminescence peaks, it is nontrivial to make unambiguous assignments of the peaks to specific defect types. In this work, we go beyond traditional density functional theory calculations to determine intra-defect state transition and charge recombination process derived emission and absorption energies in PE. By calculating the total energy differences of the neutral defect at excited and ground states, the emission energies from intra-defect state transition are obtained, reasonably explaining the photoluminescence peaks in PE. In order to study the luminescence emitted in charge recombination processes, we characterize PE defect levels in terms of thermodynamic and optical charge transition levels that involve total energy calculations of neutral and charged defects. Calculations are performed at several levels of theory including those involving (semi)local and hybrid electron exchange-correlation functionals, and many-body perturbation theory. With these critical elements, the emission energies are computed and further used to clarify and confirm the origins of the observed electroluminescence and thermoluminescence peaks.

Data-driven approaches are particularly useful for computational materials discovery and design as they can be used for rapidly screening over a very large number of materials, thus suggesting lead candidates for further in-depth investigations. A central challenge of such approaches is to develop a numerical representation, often referred to as a fingerprint, of the materials. Inspired by recent developments in cheminformatics, we propose a class of hierarchical motif-based topological fingerprints for materials composed of elements such as C, O, H, N, F, etc., whose coordination preferences are well understood. We show that these fingerprints, when representing either molecules or crystals, may be effectively mapped onto a variety of properties using a similarity-based learning model and hence can be used to predict the relevant properties of a material, given that its fingerprint can be defined. Two simple machine-learning-based procedures are introduced to demonstrate that the learning model can be inverted to identify the desired fingerprints and then to reconstruct molecules which possess a set of targeted properties.