Bharat Medasani

FireWorks: a dynamic workflow system designed for high-throughput applications

This paper introduces FireWorks, a workflow software for running high‐throughput calculation workflows at supercomputing centers. FireWorks has been used to complete over 50 million CPU‐hours worth of computational chemistry and materials science calculations at the National Energy Research Supercomputing Center. It has been designed to serve the demanding high‐throughput computing needs of these applications, with extensive support for (i) concurrent execution through job packing, (ii) failure detection and correction, (iii) provenance and reporting for long‐running projects, (iv) automated duplicate detection, and (v) dynamic workflows (i.e., modifying the workflow graph during runtime). We have found that these features are highly relevant to enabling modern data‐driven and high‐throughput science applications, and we discuss our implementation strategy that rests on Python and NoSQL databases (MongoDB). Finally, we present performance data and limitations of our approach along with planned future work. Copyright

PyDII: A python framework for computing equilibrium intrinsic point defect concentrations and extrinsic solute site preferences in intermetallic compounds ✩

a b s t r a c t Point defects play an important role in determining the structural stability and mechanical behavior of intermetallic compounds. To help quantitatively understand the point defect properties in these compounds, we developed PyDII, a Python program that performs thermodynamic calculations of equilibrium intrinsic point defect concentrations and extrinsic solute site preferences in intermetallics. The algorithm implemented in PyDII is built upon a dilute-solution thermodynamic formalism with a set of defect excitation energies calculated from first-principles density-functional theory methods. The analysis module in PyDII enables automated calculations of equilibrium intrinsic antisite and vacancy concentrations as a function of composition and temperature (over ranges where the dilute solution formalism is accurate) and the point defect concentration changes arising from addition of an extrinsic substitutional solute species. To demonstrate the applications of PyDII, we provide examples for intrinsic point defect concentrations in NiAl and Al3 V and site preferences for Ti, Mo and Fe solutes in NiAl.

Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

We present a combination of machine learning and high throughput calculations to predict the points defects behavior in binary intermetallic (A–B) compounds, using as an example systems with the cubic B2 crystal structure (with equiatomic AB stoichiometry). To the best of our knowledge, this work is the first application of machine learning-models for point defect properties. High throughput first principles density functional calculations have been employed to compute intrinsic point defect energies in 100 B2 intermetallic compounds. The systems are classified into two groups: (i) those for which the intrinsic defects are antisites for both A and B rich compositions, and (ii) those for which vacancies are the dominant defect for either or both composition ranges. The data was analyzed by machine learning-techniques using decision tree, and full and reduced multiple additive regression tree (MART) models. Among these three schemes, a reduced MART (r-MART) model using six descriptors (formation energy, minimum and difference of electron densities at the Wigner–Seitz cell boundary, atomic radius difference, maximal atomic number and maximal electronegativity) presents the highest fit (98 %) and predictive (75 %) accuracy. This model is used to predict the defect behavior of other B2 compounds, and it is found that 45 % of the compounds considered feature vacancies as dominant defects for either A or B rich compositions (or both). The ability to predict dominant defect types is important for the modeling of thermodynamic and kinetic properties of intermetallic compounds, and the present results illustrate how this information can be derived using modern tools combining high throughput calculations and data analytics.Machine learning a defect’s effectA method for quickly predicting the dominant equilibrium atomic-level defects in a material is developed by researchers in the USA. Crystalline materials derive many of their attributes from the regular and symmetric arrangement of their atoms. Consequently, a missing or an impurity atom can noticeably change these properties. A quantum physics method known as density functional theory calculations has proven to be a powerful method for predicting the influence of these so-called point defects. However, the brute-force application of these methods requires significant computing power, thus hindering its application in high throughput screening of thousands of materials for properties influenced by point defects. Bharat Medasani from the Lawrence Berkeley National Laboratory and co-workers combine machine learning with a few hundred density functional theory calculations to make this process much faster. They demonstrate the power of their approach by examining the properties of a family of binary intermetallic alloys.

PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators

Author(s): Broberg, D; Medasani, B; Zimmermann, NER; Yu, G; Canning, A; Haranczyk, M; Asta, M; Hautier, G | Abstract: