Mining Materials Design Rules from Data: The Example of Polymer Dielectrics

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 applications

Mining Materials Design Rules from Data: The Example of Polymer Dielectrics

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 applications

Dopants in Lanthanum Manganite: Insights from First-Principles Chemical Space Exploration

The dopant chemical space in LaMnO₃ (LMO) is systematically explored using first-principles computations. We study a range of cationic dopants including alkali, alkaline earth metals, 3d, 4d, and 5d transition metal elements without and with an adjacent O vacancy. A linear programming approach is employed to access the energetically favorable decomposition pathway and the corresponding decomposition energy of doped LaMnO₃. The decomposition energy is then used to classify the dopants for stability, site preference and tendency of O vacancy formation. We find that La site doping is more favored compared to Mn site doping. We also identify dopants previously not considered, such as K, Rb, Cs, and In, which lead to stable doped LMO and are also excellent O vacancy formers. Employing data mining techniques, we identify the dopant features that are critical to the stability of a doped oxide

Why Pt Survives but Pd Suffers From SO_<i>x</i> Poisoning?

Pd is more prone to sulfation compared to Pt. Given the chemical similarity between Pt and Pd, the radical divide in their tendencies for sulfation remains a puzzle. We explain this intriguing difference using an extensive first-principles thermodynamics analysis and computed bulk and surface phase diagrams. In practically relevant temperatures and O₂ and SO₃ partial pressures, we find that Pt and Pd show significantly different tendencies for oxidation and sulfation. PdO formation is favored even at low oxygen chemical potential; however, PtO₂ formation is not favorable in catalytically relevant conditions. Similarly, PdSO₄, and adsorbed SO₃ and oxygen species on clean and oxidized surfaces are highly favored, whereas PtSO₄ formation does not occur at typical temperature and pressure conditions. Finally, several descriptors are identified that correlate to heightened sulfation tendencies, such as the critical O chemical potential for bulk oxide and surface oxide formation, chemical potentials O and SO₃ for bulk sulfate formation, and SO₃ binding strength on metal surface-oxide layers, which can be used to explore promising sulfur resistant catalysts

Why Pt Survives but Pd Suffers From SO_<i>x</i> Poisoning?

Pd is more prone to sulfation compared to Pt. Given the chemical similarity between Pt and Pd, the radical divide in their tendencies for sulfation remains a puzzle. We explain this intriguing difference using an extensive first-principles thermodynamics analysis and computed bulk and surface phase diagrams. In practically relevant temperatures and O₂ and SO₃ partial pressures, we find that Pt and Pd show significantly different tendencies for oxidation and sulfation. PdO formation is favored even at low oxygen chemical potential; however, PtO₂ formation is not favorable in catalytically relevant conditions. Similarly, PdSO₄, and adsorbed SO₃ and oxygen species on clean and oxidized surfaces are highly favored, whereas PtSO₄ formation does not occur at typical temperature and pressure conditions. Finally, several descriptors are identified that correlate to heightened sulfation tendencies, such as the critical O chemical potential for bulk oxide and surface oxide formation, chemical potentials O and SO₃ for bulk sulfate formation, and SO₃ binding strength on metal surface-oxide layers, which can be used to explore promising sulfur resistant catalysts

Informatics-Driven Selection of Polymers for Fuel-Cell Applications

Modern fuel cell technologies use Nafion as the material of choice for the proton exchange membrane (PEM) and as the binding material (ionomer) used to assemble the catalyst layers of the anode and cathode. These applications demand high proton conductivity as well as other requirements. For example, PEM is expected to block electrons, oxygen, and hydrogen from penetrating and diffusing while the anode/cathode ionomer should allow hydrogen/oxygen to move easily, so that they can reach the catalyst nanoparticles. Given some of the well-known limits of Nafion, such as low glass-transition temperature, the community is in the midst of an active search for Nafion replacements. In this work, we present an informatics-based scheme to search large polymer chemical spaces, which includes establishing a list of properties needed for the targeted applications, developing predictive machine-learning models for these properties, defining a search space, and using the developed models to screen the search space. Using the scheme, we have identified 60 new polymer candidates for PEM, anode ionomer, and cathode ionomer that we hope will be advanced to the next step, i.e., validating the designs through synthesis and testing. The proposed informatics scheme is generic, and it can be used to select polymers for multiple applications in the future

Factors Favoring Ferroelectricity in Hafnia: A First-Principles Computational Study

The surprising ferroelectricity displayed by hafnia thin films has been attributed to a metastable polar orthorhombic (<i>Pca</i>2₁) 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 <i>Pca</i>2₁ structure as the equilibrium phase. The results provide insights for better controlling the ferroelectric characteristics of hafnia thin films by adjusting the growth conditions and electrical history

Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions

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 captures atomistic to morphological structural features was developed to numerically represent the polymers. Machine learning models were then trained by mapping the fingerprints (or features) to properties. Once developed, these models can rapidly predict properties of new polymers (within the same chemical class as the parent data set) and can also provide uncertainties underlying the predictions. Since different properties depend on different length-scale features, the prediction models were built on an optimized set of features for each individual property. Furthermore, these models are incorporated in a user-friendly online platform named Polymer Genome (www.polymergenome.org). Systematic and progressive expansion of both chemical and property spaces are planned to extend the applicability of Polymer Genome to a wide range of technological domains

Informatics-Driven Design of Superhard B–C–O Compounds

Materials containing B, C, and O, due to the advantages of forming strong covalent bonds, may lead to materials that are superhard, i.e., those with a Vicker’s hardness larger than 40 GPa. However, the exploration of this vast chemical, compositional, and configurational space is nontrivial. Here, we leverage a combination of machine learning (ML) and first-principles calculations to enable and accelerate such a targeted search. The ML models first screen for potentially superhard B–C–O compositions from a large hypothetical B–C–O candidate space. Atomic-level structure search using density functional theory (DFT) within those identified compositions, followed by further detailed analyses, unravels on four potentially superhard B–C–O phases exhibiting thermodynamic, mechanical, and dynamic stability

A polymer dataset for accelerated property prediction and design

This tarball includes 1073 CIF files, each of them provides the optimized structure and the accompanied properties calculated with first-principles computations. The README.txt file provides details on the inputs of the runs used to calculate the properties reportes