mkite: A distributed computing platform for high-throughput materials simulations

Advances in high-throughput simulation (HTS) software enabled computational databases and big data to become common resources in materials science. However, while computational power is increasingly larger, software packages orchestrating complex workflows in heterogeneous environments are scarce. This paper introduces mkite, a Python package for performing HTS in distributed computing environments. The mkite toolkit is built with the server-client pattern, decoupling production databases from client runners. When used in combination with message brokers, mkite enables any available client to perform calculations without prior hardware specification on the server side. Furthermore, the software enables the creation of complex workflows with multiple inputs and branches, facilitating the exploration of combinatorial chemical spaces. Software design principles are discussed in detail, highlighting the usefulness of decoupling simulations and data management tasks to diversify simulation environments. To exemplify how mkite handles simulation workflows of combinatorial systems, case studies on zeolite synthesis and surface catalyst discovery are provided. Finally, key differences with other atomistic simulation workflows are outlined. The mkite suite can enable HTS in distributed computing environments, simplifying workflows with heterogeneous hardware and software, and helping deployment of calculations at scale.Comment: preprint; code available soo

From isomorphism to polymorphism: connecting interzeolite transformations to structural and graph similarity

Zeolites are nanoporous crystalline materials with abundant industrial applications. Despite sustained research, only 235 different zeolite frameworks have been realized out of millions of hypothetical ones predicted by computational enumeration. Structure-property relationships in zeolite synthesis are very complex and only marginally understood. Here, we apply structure and graph-based unsupervised machine learning to gain insight on zeolite frameworks and how they relate to experimentally observed polymorphism and phase transformations. We begin by describing zeolite structures using the Smooth Overlap of Atomic Positions method, which clusters crystals with similar cages and density in a way consistent with traditional hand-selected composite building units. To also account for topological differences, zeolite crystals are represented as multigraphs and compared by isomorphism tests. We find that fourteen different pairs and one trio of known frameworks are graph isomorphic. Based on experimental interzeolite conversions and occurrence of competing phases, we propose that the availability of kinetic-controlled transformations between metastable zeolite frameworks is related to their similarity in the graph space. When this description is applied to enumerated structures, over 3,400 hypothetical structures are found to be isomorphic to known frameworks, and thus might be realized from their experimental counterparts. Using a continuous similarity metric, the space of known zeolites shows additional overlaps with experimentally observed phase transformations. Hence, graph-based similarity approaches suggest a venue for realizing novel zeolites from existing ones by providing a relationship between pairwise structure similarity and experimental transformations.Comment: 11 pages, 6 figure

Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data. However, NN predictions are only reliable within well-learned training domains, and show volatile behavior when extrapolating. Uncertainty quantification approaches can flag atomic configurations for which prediction confidence is low, but arriving at such uncertain regions requires expensive sampling of the NN phase space, often using atomistic simulations. Here, we exploit automatic differentiation to drive atomistic systems towards high-likelihood, high-uncertainty configurations without the need for molecular dynamics simulations. By performing adversarial attacks on an uncertainty metric, informative geometries that expand the training domain of NNs are sampled. When combined to an active learning loop, this approach bootstraps and improves NN potentials while decreasing the number of calls to the ground truth method. This efficiency is demonstrated on sampling of kinetic barriers and collective variables in molecules, and can be extended to any NN potential architecture and materials system.Comment: 12 pages, 4 figures, supporting informatio

Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances

Zeolites are inorganic materials known for their diversity of applications, synthesis conditions, and resulting polymorphs. Although their synthesis is controlled both by inorganic and organic synthesis conditions, computational studies of zeolite synthesis have focused mostly on organic template design. In this work, we use a strong distance metric between crystal structures and machine learning (ML) to create inorganic synthesis maps in zeolites. Starting with 253 known zeolites, we show how the continuous distances between frameworks reproduce inorganic synthesis conditions from the literature without using labels such as building units. An unsupervised learning analysis shows that neighboring zeolites according to our metric often share similar inorganic synthesis conditions, even in template-based routes. In combination with ML classifiers, we find synthesis-structure relationships for 14 common inorganic conditions in zeolites, namely Al, B, Be, Ca, Co, F, Ga, Ge, K, Mg, Na, P, Si, and Zn. By explaining the model predictions, we demonstrate how (dis)similarities towards known structures can be used as features for the synthesis space. Finally, we show how these methods can be used to predict inorganic synthesis conditions for unrealized frameworks in hypothetical databases and interpret the outcomes by extracting local structural patterns from zeolites. In combination with template design, this work can accelerate the exploration of the space of synthesis conditions for zeolites

Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances

Zeolites are inorganic materials known for their diversity of applications, synthesis conditions, and resulting polymorphs. Although their synthesis is controlled both by inorganic and organic synthesis conditions, computational studies of zeolite synthesis have focused mostly on the design of organic structure-directing agents (OSDAs). In this work, we combine distances between crystal structures and machine learning (ML) to create inorganic synthesis maps in zeolites. Starting with 253 known zeolites, we show how the continuous distances between frameworks reproduce inorganic synthesis conditions from the literature without using labels such as building units. An unsupervised learning analysis shows that neighboring zeolites according to two different representations often share similar inorganic synthesis conditions, even in OSDA-based routes. In combination with ML classifiers, we find synthesis-structure relationships for 14 common inorganic conditions in zeolites, namely Al, B, Be, Ca, Co, F, Ga, Ge, K, Mg, Na, P, Si, and Zn. By explaining the model predictions, we demonstrate how (dis)similarities towards known structures can be used as features for the synthesis space, thus quantifying the intuition that similar structures often share inorganic synthesis routes. Finally, we show how these methods can be used to predict inorganic synthesis conditions for unrealized frameworks in hypothetical databases and interpret the outcomes by extracting local structural patterns from zeolites. In combination with OSDA design, this work can accelerate the exploration of the space of synthesis conditions for zeolites

Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure-Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx

[EN] A novel ab initio methodology based on high-throughput simulations has permitted designing unique biselective organic structure-directing agents (OSDAs) that allow the efficient synthesis of CHA/AEI zeolite intergrowth materials with controlled phase compositions. Distinctive local crystallographic ordering of the CHA/AEI intergrowths was revealed at the nanoscale level using integrated differential phase contrast scanning transmission electron microscopy (iDPC STEM). These novel CHA/AEI materials have been tested for the selective catalytic reduction (SCR) of NOx, presenting an outstanding catalytic performance and hydrothermal stability, even surpassing the performance of the well-established commercial CHA-type catalyst. This methodology opens the possibility for synthetizing new zeolite intergrowths with more complex structures and unique catalytic properties.E.B.-J., C.P., M.M. and A.C. acknowledge financial support by the Spanish Government [Grant RTI2018-101033-B-I00 (MCIU/AEI/FEDER, UE)], and by CSIC [I-link+ Program (LINKA20381)]. D.S.-K. and R.G.-B. acknowledge the Energy Initiative (MITEI) and MIT International Science and Technology Initiatives (MISTI) Seed Funds. D.S.-K. was additionally funded by the MIT Energy Fellowship. Y.R.-L acknowledges support by the U.S. Department of Energy, Office of Basic Energy Sciences under Award DE-SC0016214. E.B.-J. acknowledges the Spanish Government for an FPI scholarship (PRE2019-088360). T.W. acknowledges financial support by the Swedish Research Council (Grant No. 2019-05465). T.W. and T.U. acknowledge funding from the Swedish Strategic Res. Foundation (project nr. ITM17-0301). The Electron Microscopy Service of the UPV is also acknowledged for their help in sample characterization. Computer calculations were executed at the Massachusetts Green High-Performance Computing Center with support from MIT Research Computing, and at the Extreme Sci. and Eng. Discovery Environment (XSEDE)[33] Expanse through allocation TG-DMR200068.Bello-Jurado, E.; Schwalbe-Koda, D.; Nero, M.; Paris, C.; Uusimäki, T.; Román-Leshkov, Y.; Corma Canós, A.... (2022). Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure-Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx. Angewandte Chemie International Edition. 61(28):1-6. https://doi.org/10.1002/anie.20220183716612

A priori control of zeolite phase competition and intergrowth with high-throughput simulations

Zeolites are versatile catalysts and molecular sieves with large topological diversity, but managing phase competition in zeolite synthesis is an empirical, labor-intensive task. In this work, we controlled phase selectivity in templated zeolite synthesis from first principles by combining high-throughput atomistic simulations, literature mining, human-computer interaction, synthesis, and characterization. Proposed binding metrics distilled from more than 586,000 zeolite-molecule simulations reproduced the extracted literature and rationalized framework competition in the design of organic structure-directing agents. Energetic, geometric, and electrostatic descriptors of template molecules were found to regulate synthetic accessibility windows and aluminum distributions in pure-phase zeolites. Furthermore, these parameters allowed us to realize an intergrowth zeolite through a single bi-selective template. The computation-first approach enables control of both zeolite synthesis and structure composition using a priori theoretical descriptors.D.S.-K. and R.G.-B. acknowledge the Energy Initiative (MITEI) and MIT International Science and Technology Initiatives (MISTI) Seed Funds. D.S.-K. was also funded by the MIT Energy Fellowship. C.P., E.B.-J., M.M., and A.C. acknowledge financial support by the Spanish government through the “Severo Ochoa” program (SEV-2016-0683, MINECO) and grant RTI2018-101033-B-I00 (MCIU/AEI/FEDER, UE). E.B.-J. acknowledges the Spanish government for an FPI scholarship (PRE2019-088360). Z.J., E.O., S.K., and Y.R.-L. acknowledge partial funding from Designing Materials to Revolutionize and Engineer our Future (DMREF) from the National Science Foundation (NSF); awards 1922311, 1922372, and 1922090; and the Office of Naval Research (ONR) under contract N00014-20-1-2280. S.K. was additionally funded by the Kwanjeong Educational Fellowship. Z.J. was also supported by the Department of Defense (DoD) through the National Defense Science Engineering Graduate (NDSEG) fellowship program. T.W. acknowledges financial support by the Swedish Research Council (grant no. 2019-05465). Computer calculations were executed at the Massachusetts Green High-Performance Computing Center with support from MIT Research Computing and at the Extreme Science and Engineering Discovery Environment (XSEDE) (53) Expanse through allocation TG-DMR200068

dskoda/Zeolites-AMD: Public release

Public release of the data for the manuscript. Connects with Zenodo for generation of a DOI