942 research outputs found
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
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
Equivariant Networks for Porous Crystalline Materials
Porous crystalline materials have the potential to play a key role in
developing solutions for molecular storage, gas separation and carbon
adsorption. For these solutions, we need to develop new materials with specific
properties. Estimating the properties of such porous materials involves first
principle simulation using classical molecular simulations. The computational
complexity of these methods can be a barrier to high throughput screening of
the potential materials as the space of possible materials is vast. Data-driven
methods, specifically machine learning methods based on deep neural networks
offer a significant opportunity to significantly scale the simulation of the
behavior of these materials. However, to effectively achieve this the Deep
Learning models need to utilize the symmetries present in the crystals.
Crystals pose specific symmetries that are present in their space group.
Existing methods for crystal property prediction either have symmetry
constraints that are too restrictive or only incorporate symmetries between
unit cells. In addition, these models do not explicitly model the porous
structure of the crystal. In this paper, we develop a model which incorporates
the symmetries of the unit cell of a crystal in its architecture and explicitly
models the porous structure. We evaluate our model by predicting the heat of
adsorption of CO for different configurations of the Mordenite and ZSM-5
zeolites. Our results confirm that our method performs better than existing
methods for crystal property prediction and that the inclusion of pores results
in a more efficient model.Comment: Added additional figures as well as additional experiments for MF
Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks
The combination of high throughput computation and machine learning has led
to a new paradigm in materials design by allowing for the direct screening of
vast portions of structural, chemical, and property space. The use of these
powerful techniques leads to the generation of enormous amounts of data, which
in turn calls for new techniques to efficiently explore and visualize the
materials space to help identify underlying patterns. In this work, we develop
a unified framework to hierarchically visualize the compositional and
structural similarities between materials in an arbitrary material space with
representations learned from different layers of graph convolutional neural
networks. We demonstrate the potential for such a visualization approach by
showing that patterns emerge automatically that reflect similarities at
different scales in three representative classes of materials: perovskites,
elemental boron, and general inorganic crystals, covering material spaces of
different compositions, structures, and both. For perovskites, elemental
similarities are learned that reflects multiple aspects of atom properties. For
elemental boron, structural motifs emerge automatically showing characteristic
boron local environments. For inorganic crystals, the similarity and stability
of local coordination environments are shown combining different center and
neighbor atoms. The method could help transition to a data-centered exploration
of materials space in automated materials design.Comment: 22 + 7 pages, 6 + 5 figure
Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery
[EN] CONSPECTUS: Zeolites are microporous crystalline materials with well-defined cavities and pores, which can be prepared under different pore topologies and chemical compositions. Their preparation is typically defined by multiple interconnected variables (e.g., reagent sources, molar ratios, aging treatments, reaction time and temperature, among others), but unfortunately their distinctive influence, particularly on the nucleation and crystallization processes, is still far from being understood. Thus, the discovery and/or optimization of specific zeolites is closely related to the exploration of the parametric space through trial-and-error methods, generally by studying the influence of each parameter individually.
In the past decade, machine learning (ML) methods have rapidly evolved to address complex problems involving highly nonlinear or massively combinatorial processes that conventional approaches cannot solve. Considering the vast and interconnected multiparametric space in zeolite synthesis, coupled with our poor understanding of the mechanisms involved in their nucleation and crystallization, the use of ML is especially timely for improving zeolite synthesis. Indeed, the complex space of zeolite synthesis requires draWing inferences from incomplete and imperfect information, for which ML methods are very well-suited to replace the intuition-based approaches traditionally used to guide experimentation.
In this Account, we contend that both existing and new ML approaches can provide the "missing link" needed to complete the traditional zeolite synthesis workflow used in our quest to rationalize zeolite synthesis. Within this context, we have made important efforts on developing ML tools in different critical areas, such as (1) data-mining tools to process the large amount of data generated using high-throughput platforms; (2) novel complex algorithms to predict the formation of energetically stable hypothetical zeolites and guide the synthesis of new zeolite structures; (3) new "ab initio" organic structure directing agent predictions to direct the synthesis of hypothetical or known zeolites; (4) an automated tool for nonsupervised data extraction and classification from published research articles.
ML has already revolutionized many areas in materials science by enhancing our ability to map intricate behavior to process variables, especially in the absence of well-understood mechanisms. Undoubtedly, ML is a burgeoning field with many future opportunities for further breakthroughs to advance the design of molecular sieves. For this reason, this Account includes an outlook of future research directions based on current challenges and opportunities. We envision this Account will become a hallmark reference for both well-established and new researchers in the field of zeolite synthesis.This work has been supported by the EU through ERC-AdG2014-671093, by the Spanish Government through SEV-20160683 and RTI2018-101033-B-I00 (MCIU/AEI/FEDER, UE), and by La Caixa-Foundation through MIT -SPAIN MISTI program (LCF/PR/MIT17/11820002). Y.R.-L. thanks the DoE for funding through the Office of Basic Energy Sciences (DE-SC0016214).Moliner Marin, M.; Román-Leshkov, Y.; Corma Canós, A. (2019). Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery. Accounts of Chemical Research. 52(10):2971-2980. https://doi.org/10.1021/acs.accounts.9b00399S29712980521
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
By combining metal nodes with organic linkers we can potentially synthesize
millions of possible metal organic frameworks (MOFs). At present, we have
libraries of over ten thousand synthesized materials and millions of in-silico
predicted materials. The fact that we have so many materials opens many
exciting avenues to tailor make a material that is optimal for a given
application. However, from an experimental and computational point of view we
simply have too many materials to screen using brute-force techniques. In this
review, we show that having so many materials allows us to use big-data methods
as a powerful technique to study these materials and to discover complex
correlations. The first part of the review gives an introduction to the
principles of big-data science. We emphasize the importance of data collection,
methods to augment small data sets, how to select appropriate training sets. An
important part of this review are the different approaches that are used to
represent these materials in feature space. The review also includes a general
overview of the different ML techniques, but as most applications in porous
materials use supervised ML our review is focused on the different approaches
for supervised ML. In particular, we review the different method to optimize
the ML process and how to quantify the performance of the different methods. In
the second part, we review how the different approaches of ML have been applied
to porous materials. In particular, we discuss applications in the field of gas
storage and separation, the stability of these materials, their electronic
properties, and their synthesis. The range of topics illustrates the large
variety of topics that can be studied with big-data science. Given the
increasing interest of the scientific community in ML, we expect this list to
rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures
Multivariate Analysis Applications in X-ray Diffraction
: Multivariate analysis (MA) is becoming a fundamental tool for processing in an efficient
way the large amount of data collected in X-ray diffraction experiments. Multi-wedge data
collections can increase the data quality in case of tiny protein crystals; in situ or operando setups
allow investigating changes on powder samples occurring during repeated fast measurements;
pump and probe experiments at X-ray free-electron laser (XFEL) sources supply structural
characterization of fast photo-excitation processes. In all these cases, MA can facilitate the extraction
of relevant information hidden in data, disclosing the possibility of automatic data processing even
in absence of a priori structural knowledge. MA methods recently used in the field of X-ray
diffraction are here reviewed and described, giving hints about theoretical background and possible
applications. The use of MA in the framework of the modulated enhanced diffraction technique is
described in detail
Multi-label prediction method for lithology, lithofacies and fluid classes based on data augmentation by cascade forest
Predicting the lithology, lithofacies and reservoir fluid classes of igneous rocks holds significant value in the domains of CO2 storage and reservoir evaluation. However, no precedent exists for research on the multi-label identification of igneous rocks. This study proposes a multi-label data augmented cascade forest method for the prediction of multilabel lithology, lithofacies and fluid using 9 conventional logging data features of cores collected from the eastern depression of the Liaohe Basin in northeastern China. Data augmentation is performed on an unbalanced multi-label training set using the multi-label synthetic minority over-sampling technique. Sample training is achieved by a multi-label cascade forest consisting of predictive clustering trees. These cascade structures possess adaptive feature selection and layer growth mechanisms. Given the necessity to focus on all possible outcomes and the generalization ability of the method, a simulated well model is built and then compared with 6 typical multi-label learning methods. The outperformance of this method in the evaluation metrics validates its superiority in terms of accuracy and generalization ability. The consistency of the predicted results and geological data of actual wells verifies the reliability of our method. Furthermore, the results show that it can be used as a reliable means of multi-label prediction of igneous lithology, lithofacies and reservoir fluids.Document Type: Original articleCited as: Han, R., Wang, Z., Guo, Y., Wang, X., A, R., Zhong, G. Multi-label prediction method for lithology, lithofacies and fluid classes based on data augmentation by cascade forest. Advances in Geo-Energy Research, 2023, 9(1): 25-37. https://doi.org/10.46690/ager.2023.07.0
Machine learning to empower electrohydrodynamic processing
Electrohydrodynamic (EHD) processes are promising healthcare fabrication technologies, as evidenced by the number of commercialised and food-and-drug administration (FDA)-approved products produced by these processes. Their ability to produce both rapidly and precisely nano-sized products provides them with a unique set of qualities that cannot be matched by other fabrication technologies. Consequently, this has stimulated the development of EHD processing to tackle other healthcare challenges. However, as with most technologies, time and resources will be needed to realise fully the potential EHD processes can offer. To address this bottleneck, researchers are adopting machine learning (ML), a subset of artificial intelligence, into their workflow. ML has already made ground-breaking advancements in the healthcare sector, and it is anticipated to do the same in the materials domain. Presently, the application of ML in fabrication technologies lags behind other sectors. To that end, this review showcases the progress made by ML for EHD workflows, demonstrating how the latter can benefit greatly from the former. In addition, we provide an introduction to the ML pipeline, to help encourage the use of ML for other EHD researchers. As discussed, the merger of ML with EHD has the potential to expedite novel discoveries and to automate the EHD workflow
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