Purpose This paper aims to present the methodology and results of the experimental characterization of three-dimensional (3D) printed acrylonitrile butadiene styrene (ABS) and polycarbonate (PC) parts utilizing digital image correlation (DIC). Design/methodology/approach Tensile and shear characterizations of ABS and PC 3D-printed parts were performed to determine the extent of anisotropy present in 3D-printed materials. Specimens were printed with varying raster ([+45/−45], [+30/−60], [+15/−75] and [0/90]) and build orientations (flat, on-edge and up-right) to determine the directional properties of the materials. Tensile and Iosipescu shear specimens were printed and loaded in a universal testing machine utilizing two-dimensional (2D) DIC to measure strain. The Poisson’s ratio, Young’s modulus, offset yield strength, tensile strength at yield, elongation at break, tensile stress at break and strain energy density were gathered for each tensile orientation combination. Shear modulus, offset yield strength and shear strength at yield values were collected for each shear combination. Findings Results indicated that raster and build orientations had negligible effects on the Young’s modulus or Poisson’s ratio in ABS tensile specimens. Shear modulus and shear offset yield strength varied by up to 33 per cent in ABS specimens, signifying that tensile properties are not indicative of shear properties. Raster orientation in the flat build samples reveals anisotropic behavior in PC specimens as the moduli and strengths varied by up to 20 per cent. Similar variations were observed in shear for PC. Changing the build orientation of PC specimens appeared to reveal a similar magnitude of variation in material properties. Originality/value This article tests tensile and shear specimens utilizing DIC, which has not been employed previously with 3D-printed specimens. The extensive shear testing conducted in this paper has not been previously attempted, and the results indicate the need for shear testing to understand the 3D-printed material behavior fully.

Polymer Genome is a web-based machine-learning capability to perform near-instantaneous predictions of a variety of polymer properties. The prediction models are trained on (and interpolate between) an underlying database of polymers and their properties obtained from first principles computations and experimental measurements. In this contribution, we first provide an overview of some of the critical technical aspects of Polymer Genome, including polymer data curation, representation, learning algorithms, and prediction model usage. Then, we provide a series of pedagogical examples to demonstrate how Polymer Genome can be used to predict dozens of polymer properties, appropriate for a range of applications. This contribution is closed with a discussion on the remaining challenges and possible future directions.

Tuning the structure of metal–organic frameworks (MOFs) is a promising pathway toward the development of high-performing materials for methane storage. To aid such discoveries, we introduce techniques for the machine-learned prediction of methane isotherms in MOFs. We demonstrate that our predictors surpass prior benchmarks. We use these models to search for novel (from both a structural and chemical point of view), high-performing MOFs and test them using density functional theory (DFT)-based structural relaxation and molecular simulation of methane adsorption. These simulations reveal that our model generalizes to chemistries not seen during training. One novel candidate, predicted to surpass the 2008 world record for volumetric methane uptake in MOFs, is proposed. Our simulations also reveal that DFT relaxation has a systematic effect on the uptake value. Finally, we interpret the models to discover and present potential MOF–methane uptake structure–property relationships.

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