36 research outputs found
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