Search for: All records

Active learning (AL) is a powerful sequential optimization approach that has shown great promise in the discovery of new materials. However, a major challenge remains the acquisition of the initial data and the development of workflows to generate new data at each iteration. In this study, we demonstrate a significant speedup in an optimization task by reusing a published simulation workflow available for online simulations and its associated data repository, where the results of each workflow run are automatically stored. Both the workflow and its data follow FAIR (findable, accessible, interoperable, and reusable) principles using nanoHUB’s infrastructure. The workflow employs molecular dynamics to calculate the melting temperature of multi-principal component alloys. We leveraged all prior data not only to develop an accurate machine learning model to start the sequential optimization but also to optimize the simulation parameters and accelerate convergence. Prior work showed that finding the alloy composition with the highest melting temperature required testing several alloy compositions, and establishing the melting temperature for each composition took, on average, multiple simulations. By developing a workflow that utilizes the FAIR data in the nanoHUB database, we reduced the number of simulations per composition to one and found the alloy with the lowest melting temperature testing only three compositions. This second optimization, therefore, shows a speedup of 10x as compared to models that do not access the FAIR databases 

Abstract MXenes are an emerging class of 2D materials of interest in applications ranging from energy storage to electromagnetic shielding. MXenes are synthesized by selective etching of layered bulk MAX phases into sheets of 2D MXenes. Their chemical tunability has been significantly expanded with the successful synthesis of double transition metal MXenes. While knowledge of the structure and energetics of double transition metal MAX phases is critical to designing and optimizing new MXenes, only a small subset of these materials been explored. We present a comprehensive dataset of key properties of MAX phases obtained using density functional theory within the generalized gradient approximation exchange-correlation functionals. Energetics and structure of 8,712 MAX phases have been calculated and stored in a queryable, open database hosted at nanoHUB. 

2D rare-earth metal carbides (MXenes) are attractive due to their novel electronic and magnetic properties and their potential as scalable 2D magnets. In this study, we used density functional theory with the Hubbard U correction to characterize the structure, termination, and magnetism in an out-of-plane ordered rare-earth containing M 3 C 2 T x MXene, Mo 2 NdC 2 T 2 (T = O or OH). We investigated the effect of the U parameter on the stability and magnetism of two possible termination sites: the hollow sites aligned with the inner Nd atoms (Nd-hollow sites) and those aligned with the closest C atoms (C-hollow sites). We found that increasing U Mo stabilized the Nd hollow sites, which minimized electrostatic repulsion between C and O atoms. Using U Mo  = 3.0 eV and U Nd  = 5.6 eV, obtained via the linear response method, we found that the energetically preferred termination site was C-hollow in Mo 2 NdC 2 O 2 and Nd-hollow in Mo 2 NdC 2 (OH) 2 . Regardless of termination and the Hubbard U value, we found Mo 2 NdC 2 O 2 and Mo 2 NdC 2 (OH) 2 to be magnetic. The C-hollow termination resulted in ferromagnetic states for all Hubbard U tested with no magnetic moment in Mo. In the case of Nd-hollow, Mo became magnetic for U Mo  ≥ 4 eV. The difference of Mo magnetism in Nd-hollow and C-hollow was explained by crystal field splitting of the Mo d orbital caused by a distorted ligand. 

Refractory complex concentrated alloys (RCCAs) are a relatively new class of materials that can exhibit excellent mechanical properties at high temperatures, and determining their melting temperature (Tm) is critical to assess their range of operation. Unfortunately, the experimental determination of this property is challenging and computational tools to predict the Tm of RCCAs from first-principles calculations are highly desirable. We quantify the uncertainties associated with such predictions for two methods that can be used with density functional theory-based molecular dynamics and apply them to predict the melting temperature of equiatomic NbMoTaW. We find that a combination of free energy calculations of individual phases with a dynamical coexistence method can provide accurate results with the minimum possible computational cost. We predict the melting temperature for the RCCA NbMoTaW to be between 3000 and 3100 K. 

Graphical abstract 

Blockchain technology enables the creation of a distributed and tamper-proof ledger, even in the presence of untrusted agents. While much financial resources and attention are devoted to blockchain tools, the underlying technology is not well understood by the general population. This paper presents a newly developed online tool that allows users to learn and create their own blockchain, with a graphical user interface and code. The module is freely available on nanoHUB.org and describes all components of the blockchain, including the SHA256, Proof of Work, and other features that enable the blockchain to function as a tamper-proof ledger. This tool has been utilized to instruct students without prior knowledge of blockchain technology, and the survey of students’ responses demonstrates that this tool is an effective way of teaching the general population about blockchain technology.