Autonomous Design of Photoferroic Ruddlesden-Popper Perovskites for Water Splitting Devices

The use of ferroelectric materials for light-harvesting applications is a possible solution for increasing the efficiency of solar cells and photoelectrocatalytic devices. In this work, we establish a fully autonomous computational workflow to identify light-harvesting materials for water splitting devices based on properties such as stability, size of the band gap, position of the band edges, and ferroelectricity. We have applied this workflow to investigate the Ruddlesden-Popper perovskite class and have identified four new compositions, which show a theoretical efficiency above 5%.

Computational Workflow for Three-Dimension Printing in Construction: Digital Tools and Methodological Limitations

Three-dimensional printing in construction (3DPiC) is known as a trending technology in the construction industry. While scholars and practitioners seek to learn more about the applications of 3DPiC, there are no efficient workflows and open data sets available for further investigations. This paper intends to present the data produced in a laboratory for creating new models. The paper first presents the experimentation data collected from 60 models, and selected thermal digital images can be used for further sustainability analysis. The recorded data includes the time of crafting each layer of the model, the total time of creating a model and thermal measures. Based on the 60 experimentations and an intensive literature review, the paper presents a proposed computational workflow, including the use of Revit, Dynamo, Fusion 360, Navisworks and a selected 3D printer, which can be utilised for further data collection and analysis in the field. This model will assist in automating the cost estimation as an upgrade for 3DPiC. This paper is helpful for scholars and practitioners since it shows how laboratory data can be helpful for construction operation design.

Freeform 3D Printing: A Sustainable, Efficient Construction Alternative

<p>Since the beginning of the 20th century, modernism introduced to the world an architectural composite that consists of concrete, steel and glass. Heading into the 21st century, the use of these three materials has only expanded as it continues to be the most economically efficient means of construction. While digital technology in design and construction continues to evolve, the materials at which we construct architecture has remained the same. Given the rapid growth of the human population, new and more sustainable approaches to construction methodologies and materials need to be explored and utilised. This research will demonstrate the potential of freeform 3D printing as a sustainable and efficient alternative building method. It outlines contemporary digital design techniques including computation and simulation tools as a means to define and test this proposed building method including structural optimisation tools to create the most structurally efficient form from additive manufacturing. The computational methods described are then applied to a manufacturing process that includes a 6-axis robotic arm. The final result is a building methodology that supports a computational workflow from design conception to manufacture.</p>

Freeform 3D Printing: A Sustainable, Efficient Construction Alternative

<p>Since the beginning of the 20th century, modernism introduced to the world an architectural composite that consists of concrete, steel and glass. Heading into the 21st century, the use of these three materials has only expanded as it continues to be the most economically efficient means of construction. While digital technology in design and construction continues to evolve, the materials at which we construct architecture has remained the same. Given the rapid growth of the human population, new and more sustainable approaches to construction methodologies and materials need to be explored and utilised. This research will demonstrate the potential of freeform 3D printing as a sustainable and efficient alternative building method. It outlines contemporary digital design techniques including computation and simulation tools as a means to define and test this proposed building method including structural optimisation tools to create the most structurally efficient form from additive manufacturing. The computational methods described are then applied to a manufacturing process that includes a 6-axis robotic arm. The final result is a building methodology that supports a computational workflow from design conception to manufacture.</p>

Stock-constrained truss design exploration through combinatorial equilibrium modeling

Reusing structural components has potential to reduce environmental impacts of building structures because it reduces new material use, energy consumption, and waste. When designing structures through reuse, available element characteristics become a design input. This paper presents a new computational workflow to design structures made of reused and new elements. The workflow combines Combinatorial Equilibrium Modeling, efficient Best-Fit heuristics, and Life Cycle Assessment to explore different design options in a user-interactive way and with almost real-time feedback. The method applicability is demonstrated by a realistic case study. Results show that structures combining reused and new elements have a significantly lower environmental impact than solutions made of new material only.

FA-nf: A Functional Annotation Pipeline for Proteins from Non-Model Organisms Implemented in Nextflow

Functional annotation allows adding biologically relevant information to predicted features in genomic sequences, and it is, therefore, an important procedure of any de novo genome sequencing project. It is also useful for proofreading and improving gene structural annotation. Here, we introduce FA-nf, a pipeline implemented in Nextflow, a versatile computational workflow management engine. The pipeline integrates different annotation approaches, such as NCBI BLAST+, DIAMOND, InterProScan, and KEGG. It starts from a protein sequence FASTA file and, optionally, a structural annotation file in GFF format, and produces several files, such as GO assignments, output summaries of the abovementioned programs and final annotation reports. The pipeline can be broken easily into smaller processes for the purpose of parallelization and easily deployed in a Linux computational environment, thanks to software containerization, thus helping to ensure full reproducibility.

Performance comparison of r2SCAN and SCAN metaGGA density functionals for solid materials via an automated, high-throughput computational workflow

Computational materials discovery efforts utilize hundreds or thousands of density functional theory (DFT) calculations to predict material properties. Historically, such efforts have performed calculations at the generalized gradient approximation (GGA) level of theory due to its efficient compromise between accuracy and computational reliability. However, high-throughput calculations at the higher metaGGA level of theory are becoming feasible. The Strongly Constrainted and Appropriately Normed (SCAN) metaGGA functional offers superior accuracy to GGA across much of chemical space, making it appealing as a general-purpose metaGGA functional, but it suffers from numerical instabilities that impede it's use in high-throughput workflows. The recently-developed r2SCAN metaGGA functional promises accuracy similar to SCAN in addition to more robust numerical performance. However, its performance compared to SCAN has yet to be evaluated over a large group of solid materials. In this work, we compared r2SCAN and SCAN predictions for key properties of approximately 6,000 solid materials using a newly-developed high-throughput computational workflow. We find that r2SCAN predicts formation energies more accurately than SCAN and PBEsol for both strongly- and weakly-bound materials and that r2SCAN predicts systematically larger lattice constants than SCAN. We also find that r2SCAN requires modestly fewer computational resources than SCAN and offers significantly more reliable convergence. Thus, our large-scale benchmark confirms that r2SCAN has delivered on its promises of numerical efficiency and accuracy, making it a preferred choice for high-throughput metaGGA calculations.

Performance comparison of r2SCAN and SCAN metaGGA density functionals for solid materials via an automated, high-throughput computational workflow

Computational materials discovery efforts utilize hundreds or thousands of density functional theory (DFT) calculations to predict material properties. Historically, such efforts have performed calculations at the generalized gradient approximation (GGA) level of theory due to its efficient compromise between accuracy and computational reliability. However, high-throughput calculations at the higher metaGGA level of theory are becoming feasible. The Strongly Constrainted and Appropriately Normed (SCAN) metaGGA functional offers superior accuracy to GGA across much of chemical space, making it appealing as a general-purpose metaGGA functional, but it suffers from numerical instabilities that impede it's use in high-throughput workflows. The recently-developed r2SCAN metaGGA functional promises accuracy similar to SCAN in addition to more robust numerical performance. However, its performance compared to SCAN has yet to be evaluated over a large group of solid materials. In this work, we compared r2SCAN and SCAN predictions for key properties of approximately 6,000 solid materials using a newly-developed high-throughput computational workflow. We find that r2SCAN predicts formation energies more accurately than SCAN and PBEsol for both strongly- and weakly-bound materials and that r2SCAN predicts systematically larger lattice constants than SCAN. We also find that r2SCAN requires modestly fewer computational resources than SCAN and offers much more reliable convergence. Thus, our large-scale benchmark confirms that r2SCAN has delivered on its promises of numerical efficiency and accuracy, making it an ideal choice for high-throughput metaGGA calculations.