Showing papers by "Anubhav Jain published in 2021"

Best practices in machine learning for chemistry.

Abstract: Statistical tools based on machine learning are becoming integrated into chemistry research workflows. We discuss the elements necessary to train reliable, repeatable and reproducible models, and recommend a set of guidelines for machine learning reports.

Recent Advances and Applications of Deep Learning Methods in Materials Science

Abstract: Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. Recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep-learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. The application of DL methods in materials science presents an exciting avenue for future materials discovery and design.

A framework for quantifying uncertainty in DFT energy corrections.

Abstract: In this work, we demonstrate a method to quantify uncertainty in corrections to density functional theory (DFT) energies based on empirical results. Such corrections are commonly used to improve the accuracy of computational enthalpies of formation, phase stability predictions, and other energy-derived properties, for example. We incorporate this method into a new DFT energy correction scheme comprising a mixture of oxidation-state and composition-dependent corrections and show that many chemical systems contain unstable polymorphs that may actually be predicted stable when uncertainty is taken into account. We then illustrate how these uncertainties can be used to estimate the probability that a compound is stable on a compositional phase diagram, thus enabling better-informed assessments of compound stability.

Experimental validation of high thermoelectric performance in RECuZnP2 predicted by high-throughput DFT calculations

Abstract: Accurate density functional theory calculations of the interrelated properties of thermoelectric materials entail high computational cost, especially as crystal structures increase in complexity and size. New methods involving ab initio scattering and transport (AMSET) and compressive sensing lattice dynamics are used to compute the transport properties of quaternary CaAl2Si2-type rare-earth phosphides RECuZnP2 (RE = Pr, Nd, Er), which were identified to be promising thermoelectrics from high-throughput screening of 20 000 disordered compounds. Experimental measurements of the transport properties agree well with the computed values. Compounds with stiff bulk moduli (>80 GPa) and high speeds of sound (>3500 m s−1) such as RECuZnP2 are typically dismissed as thermoelectric materials because they are expected to exhibit high lattice thermal conductivity. However, RECuZnP2 exhibits not only low electrical resistivity, but also low lattice thermal conductivity (∼1 W m−1 K−1). Contrary to prior assumptions, polar-optical phonon scattering was revealed by AMSET to be the primary mechanism limiting the electronic mobility of these compounds, raising questions about existing assumptions of scattering mechanisms in this class of thermoelectric materials. The resulting thermoelectric performance (zT of 0.5 for ErCuZnP2 at 800 K) is among the best observed in phosphides and can likely be improved with further optimization.

Benchmarking Coordination Number Prediction Algorithms on Inorganic Crystal Structures.

Abstract: Coordination numbers and geometries form a theoretical framework for understanding and predicting materials properties. Algorithms to determine coordination numbers automatically are increasingly used for machine learning (ML) and automatic structural analysis. In this work, we introduce MaterialsCoord, a benchmark suite containing 56 experimentally derived crystal structures (spanning elements, binaries, and ternary compounds) and their corresponding coordination environments as described in the research literature. We also describe CrystalNN, a novel algorithm for determining near neighbors. We compare CrystalNN against seven existing near-neighbor algorithms on the MaterialsCoord benchmark, finding CrystalNN to perform similarly to several well-established algorithms. For each algorithm, we also assess computational demand and sensitivity toward small perturbations that mimic thermal motion. Finally, we investigate the similarity between bonding algorithms when applied to the Materials Project database. We expect that this work will aid the development of coordination prediction algorithms as well as improve structural descriptors for ML and other applications.

Optimal band structure for thermoelectrics with realistic scattering and bands

Abstract: Understanding how to optimize electronic band structures for thermoelectrics is a topic of long-standing interest in the community. Prior models have been limited to simplified bands and/or scattering models. In this study, we apply more rigorous scattering treatments to more realistic model band structures—upward-parabolic bands that inflect to an inverted-parabolic behavior—including cases of multiple bands. In contrast to common descriptors (e.g., quality factor and complexity factor), the degree to which multiple pockets improve thermoelectric performance is bounded by interband scattering and the relative shapes of the bands. We establish that extremely anisotropic “flat-and-dispersive” bands, although best-performing in theory, may not represent a promising design strategy in practice. Critically, we determine optimum bandwidth, dependent on temperature and lattice thermal conductivity, from perfect transport cutoffs that can in theory significantly boost zT beyond the values attainable through intrinsic band structures alone. Our analysis should be widely useful as the thermoelectric research community eyes zT > 3.

Efficient calculation of carrier scattering rates from first principles

Abstract: The electronic transport behaviour of materials determines their suitability for technological applications. We develop a computationally efficient method for calculating carrier scattering rates of solid-state semiconductors and insulators from first principles inputs. The present method extends existing polar and non-polar electron-phonon coupling, ionized impurity, and piezoelectric scattering mechanisms formulated for isotropic band structures to support highly anisotropic materials. We test the formalism by calculating the electronic transport properties of 23 semiconductors, including the large 48 atom CH3NH3PbI3 hybrid perovskite, and comparing the results against experimental measurements and more detailed scattering simulations. The Spearman rank coefficient of mobility against experiment (rs = 0.93) improves significantly on results obtained using a constant relaxation time approximation (rs = 0.52). We find our approach offers similar accuracy to state-of-the art methods at approximately 1/500th the computational cost, thus enabling its use in high-throughput computational workflows for the accurate screening of carrier mobilities, lifetimes, and thermoelectric power.

A study on the relationship of abusive supervision and fear based silence in India the mediating role of dimensions of emotional intelligence

Abstract: This study examines the relationship between abusive supervision and fear based silence and turnover intentions and how emotional intelligence (EI) dimensions mediate this relationship. Using “relationship theory” in high power distance work context of India, the authors predicted that abusive supervision is positively related to EI and also positively related to fear based silence and turnover intention. Data were collected from 347 employees from Indian manufacturing and retail industry in two stages. Results have supported the mediating impact of others’ emotional appraisal on the relationship of abusive supervision and fear based silence. However other EI dimensions (self emotional appraisal, regulation of emotions and use of emotions) did not produce a significant mediating effect. Furthermore, abusive supervision had a negative relationship with self emotional appraisal and a positive relationship with others’ emotional appraisal. The implications are discussed for understanding the relevance of others’ emotional appraisal in diminishing the fear based silence among the employees. The study advances the use of relationship theory and its practices in Indian work context.

Phase-Transition-Enhanced Thermoelectric Transport in Rickardite Mineral Cu3‒xTe2

Abstract: The binary copper chalcogenides Cu2−δX (X = S, Se, and Te) have recently gained significant interest due to their high thermoelectric performance at moderate temperatures. In an effort to unveil ne...

Nondestructive Characterization of Antireflective Coatings on PV Modules

Abstract: Antireflective coatings (ARCs) are used on the vast majority of solar photovoltaic (PV) modules to increase power production. However, ARC longevity can vary from less than 1 to over 15 years depending on coating quality and deployment conditions. A technique that can quantify ARC degradation nondestructively on commercial modules would be useful both for in-field diagnostics and accelerated aging tests. In this article, we demonstrate that accurate measurements of ARC spectral reflectance can be performed using a modified commercially available integrating-sphere probe. The measurement is fast, accurate, nondestructive, and can be performed outdoors in full-sun conditions. We develop an interferometric model that estimates coating porosity, thickness, and fractional area coverage from the measured reflectance spectrum for a uniform single-layer coating. We demonstrate the measurement outdoors on an active PV installation, identify the presence of an ARC, and estimate the properties of the coating.

A simple model for the entropy of melting of monatomic liquids

Abstract: We present an analytical model for calculating the entropy at melt of monatomic liquids. The model is motivated by the concept of a rough potential energy surface. It offers a simple, physical explanation for Richard's melting rule and provides a material-dependent correction to Trouton's vaporization rule. Without employing any adjustable parameters, the model agrees closely with experimental entropy of melting values for monatomic liquids. When combined with the phonon theory of liquids, it allows for estimation of entropy over the entire liquid range.

When band convergence is not beneficial for thermoelectrics.

Abstract: Band convergence is considered a clear benefit to thermoelectric performance because it increases the charge carrier concentration for a given Fermi level, which typically enhances charge conductivity while preserving the Seebeck coefficient. However, this advantage hinges on the assumption that interband scattering of carriers is weak or insignificant. With first-principles treatment of electron-phonon scattering in the CaMg2Sb2-CaZn2Sb2 Zintl system and full Heusler Sr2SbAu, we demonstrate that the benefit of band convergence can be intrinsically negated by interband scattering depending on the manner in which bands converge. In the Zintl alloy, band convergence does not improve weighted mobility or the density-of-states effective mass. We trace the underlying reason to the fact that the bands converge at a one k-point, which induces strong interband scattering of both the deformation-potential and the polar-optical kinds. The case contrasts with band convergence at distant k-points (as in the full Heusler), which better preserves the single-band scattering behavior thereby successfully leading to improved performance. Therefore, we suggest that band convergence as thermoelectric design principle is best suited to cases in which it occurs at distant k-points.

A metamodel for competence assessment: Co.S.M.O.© competences software management for organizations

Abstract: This paper aims to propose an explorative metamodel of the key organizational competences management and presents a Web-based tool (Co.S.M.O.© Competences Software Management for Organizations) for all-around assessment of the identified competences.,Building on the Great Eight Competencies Model- GEC, the European Qualifications Framework-EQF and focus group feedback, an online questionnaire was developed to manage the key organizational competences and to adapt the competence metamodel to the Italian context.,The competence metamodel described in this study and its newly designed tool (software with online questionnaire) could be used at the organizational level to improve productivity and efficiency by allowing an easy identification of key organizational competences and facilitating their acquisition and sharing.,Currently, the metamodel is mainly theoretical and the software sustained only a partial validation.,The developed tool is a dynamic, easy to use and interactive Web-based software useful for managing the competences in both for-profit and not-for-profit organizations.,European official documents invite companies and institutions to work together and share human capital: the European Qualifications Framework-EQF, at the base of this model, facilitates a common organizational language for human resources management.,Managerial competence literature indicates that a comprehensive model capturing a link between the EQF and a managerial competence model has not yet been considered in the literature.

Zero-Shot Text-Guided Object Generation with Dream Fields.

Abstract: We combine neural rendering with multi-modal image and text representations to synthesize diverse 3D objects solely from natural language descriptions. Our method, Dream Fields, can generate the geometry and color of a wide range of objects without 3D supervision. Due to the scarcity of diverse, captioned 3D data, prior methods only generate objects from a handful of categories, such as ShapeNet. Instead, we guide generation with image-text models pre-trained on large datasets of captioned images from the web. Our method optimizes a Neural Radiance Field from many camera views so that rendered images score highly with a target caption according to a pre-trained CLIP model. To improve fidelity and visual quality, we introduce simple geometric priors, including sparsity-inducing transmittance regularization, scene bounds, and new MLP architectures. In experiments, Dream Fields produce realistic, multi-view consistent object geometry and color from a variety of natural language captions.