We demonstrate the application of deep neural networks as a machine-learning tool for the analysis of a large collection of crystallographic data contained in the crystal structure repositories. Using input data in the form of multiperspective atomic fingerprints, which describe coordination topology around unique crystallographic sites, we show that the neural-network model can be trained to effectively distinguish chemical elements based on the topology of their crystallographic environment. The model also identifies structurally similar atomic sites in the entire data set of ∼50000 crystal structures, essentially uncovering trends that reflect the periodic table of elements. The trained model was used to analyze templates derived from the known crystal structures in order to predict the likelihood of forming new compounds that could be generated by placing elements into these structural templates in a combinatorial fashion. Statistical analysis of predictive performance of the neural-network model, which was applied to a test set of structures never seen by the model during training, indicates its ability to predict known elemental compositions with a high likelihood of success. In ∼30% of cases, the known compositions were found among the top 10 most likely candidates proposed by the model. These results suggest that the approach developed in this work can be used to effectively guide the synthetic efforts in the discovery of new materials, especially in the case of systems composed of three or more chemical elements.

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Department of Defense, Washington Headquarters Services, Directorate for Information AFRL-ML-WP-TP-2006-435 DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTLinerless composite tanks made from continuous carbon fiber reinforced polymers will enable significant mass and cost savings over lined, composite overwrapped tanks. The key technical challenge in developing these linerless tanks will be to choose and/or design the material to resist microcracks that may lead to leakage. Microcracks are known to form in the matrix of a composite due to mechanical stresses transverse to the reinforcing fiber direction. This paper presents an approach for characterizing the accumulation of microcracks in linerless composite tank materials under cyclic mechanical loading associated with multiple fill-and-drain pressure cycles. The model assumes that the rate of microcrack-damage accumulation is related to the microcracking fracture toughness of the material through a modified Paris-law formulation. A key artifact of this model is that microcrack-damage accumulation under cyclic load can be predicted from only two material constants. This damage accumulation model is validated through a series of coupon tests, and an illustrative example is presented to demonstrate how the model can be used to predict the microcracking performance of a linerless composite tank subjected to fatigue cycles. SUBJECT TERMS Air Force Research Laboratory (AFRL/VSSV), Kirtland AFB, NM 87117-5776Linerless composite tanks made from continuous carbon fiber reinforced polymers will enable significant mass and cost savings over lined, composite overwrapped tanks. The key technical challenge in developing these linerless tanks will be to choose and/or design the material to resist microcracks that may lead to leakage. Microcracks are known to form in the matrix of a composite due to mechanical stresses transverse to the reinforcing fiber direction. This paper presents an approach for characterizing the accumulation of microcracks in linerless composite tank materials under cyclic mechanical loading associated with multiple fill-and-drain pressure cycles. The model assumes that the rate of microcrack-damage accumulation is related to the microcracking fracture toughness of the material through a modified Paris-law formulation. A key artifact of this model is that microcrack-damage accumulation under cyclic load can be predicted from only two material constants. This damage accumulation model is validated through a seri...

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