Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Towards Predictive Synthesis of Inorganic Materials Using Network Science.

ABSTRACT: Accelerating materials discovery is the cornerstone of modern technological competitiveness. Yet, the inorganic synthesis of new compounds is often an important bottleneck in this quest. Well-established quantum chemistry and experimental synthesis methods combined with consolidated network science approaches might provide revolutionary knowledge to tackle this challenge. Recent pioneering studies in this direction have shown that the topological analysis of material networks hold great potential to effectively explore the synthesizability of inorganic compounds. In this Perspective we discuss the most exciting work in this area, in particular emerging new physicochemical insights and general concepts on how network science can significantly help reduce the timescales required to discover new materials and find synthetic routes for their fabrication. We also provide a perspective on outstanding problems, challenges and open questions.

SUBMITTER: Aziz A

PROVIDER: S-EPMC8724131 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Towards Predictive Synthesis of Inorganic Materials Using Network Science.

Aziz Alex A Carrasco Javier J

Frontiers in chemistry 20211221

Accelerating materials discovery is the cornerstone of modern technological competitiveness. Yet, the inorganic synthesis of new compounds is often an important bottleneck in this quest. Well-established quantum chemistry and experimental synthesis methods combined with consolidated network science approaches might provide revolutionary knowledge to tackle this challenge. Recent pioneering studies in this direction have shown that the topological analysis of material networks hold great potentia ...[more]

PMID: 34993176

Similar Datasets

Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials.

Project description:Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and secondary competition, to assess the favorability of target/impurity phase formation in solid-state reactions. We used these metrics to analyze 3520 solid-state reactions in the literature, ranking existing approaches to popular target materials. Additionally, we implemented these metrics in a data-driven synthesis planning workflow and demonstrated its application in the synthesis of barium titanate (BaTiO3). Using an 18-element chemical reaction network with first-principles thermodynamic data from the Materials Project, we identified 82985 possible BaTiO3 synthesis reactions and selected 9 for experimental testing. Characterization of reaction pathways via synchrotron powder X-ray diffraction reveals that our selectivity metrics correlate with observed target/impurity formation. We discovered two efficient reactions using unconventional precursors (BaS/BaCl2 and Na2TiO3) that produce BaTiO3 faster and with fewer impurities than conventional methods, highlighting the importance of considering complex chemistries with additional elements during precursor selection. Our framework provides a foundation for predictive inorganic synthesis, facilitating the optimization of existing recipes and the discovery of new materials, including those not easily attainable with conventional precursors.

| S-EPMC10604012 | biostudies-literature

Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation.

Project description:While advances in computational techniques have accelerated virtual materials design, the actual synthesis of predicted candidate materials is still an expensive and slow process. While a few initial studies attempted to predict the synthesis routes for inorganic crystals, the existing models do not yield the priority of predictions and could produce thermodynamically unrealistic precursor chemicals. Here, we propose an element-wise graph neural network to predict inorganic synthesis recipes. The trained model outperforms the popularity-based statistical baseline model for the top-k exact match accuracy test, showing the validity of our approach for inorganic solid-state synthesis. We further validate our model by the publication-year-split test, where the model trained based on the materials data until the year 2016 is shown to successfully predict synthetic precursors for the materials synthesized after 2016. The high correlation between the probability score and prediction accuracy suggests that the probability score can be interpreted as a measure of confidence levels, which can offer the priority of the predictions.

| S-EPMC10793203 | biostudies-literature

The phase stability network of all inorganic materials.

Project description:One of the holy grails of materials science, unlocking structure-property relationships, has largely been pursued via bottom-up investigations of how the arrangement of atoms and interatomic bonding in a material determine its macroscopic behavior. Here, we consider a complementary approach, a top-down study of the organizational structure of networks of materials, based on the interaction between materials themselves. We unravel the complete "phase stability network of all inorganic materials" as a densely connected complex network of 21,000 thermodynamically stable compounds (nodes) interlinked by 41 million tie line (edges) defining their two-phase equilibria, as computed by high-throughput density functional theory. Analyzing the topology of this network of materials has the potential to uncover previously unidentified characteristics inaccessible from traditional atoms-to-materials paradigms. Using the connectivity of nodes in the phase stability network, we derive a rational, data-driven metric for material reactivity, the "nobility index," and quantitatively identify the noblest materials in nature.

| S-EPMC7048430 | biostudies-literature

Text-mined dataset of inorganic materials synthesis recipes.

Project description:Materials discovery has become significantly facilitated and accelerated by high-throughput ab-initio computations. This ability to rapidly design interesting novel compounds has displaced the materials innovation bottleneck to the development of synthesis routes for the desired material. As there is no a fundamental theory for materials synthesis, one might attempt a data-driven approach for predicting inorganic materials synthesis, but this is impeded by the lack of a comprehensive database containing synthesis processes. To overcome this limitation, we have generated a dataset of "codified recipes" for solid-state synthesis automatically extracted from scientific publications. The dataset consists of 19,488 synthesis entries retrieved from 53,538 solid-state synthesis paragraphs by using text mining and natural language processing approaches. Every entry contains information about target material, starting compounds, operations used and their conditions, as well as the balanced chemical equation of the synthesis reaction. The dataset is publicly available and can be used for data mining of various aspects of inorganic materials synthesis.

| S-EPMC6794279 | biostudies-literature

Thermodynamic limit for synthesis of metastable inorganic materials.

Project description:Realizing the growing number of possible or hypothesized metastable crystalline materials is extremely challenging. There is no rigorous metric to identify which compounds can or cannot be synthesized. We present a thermodynamic upper limit on the energy scale, above which the laboratory synthesis of a polymorph is highly unlikely. The limit is defined on the basis of the amorphous state, and we validate its utility by effectively classifying more than 700 polymorphs in 41 common inorganic material systems in the Materials Project for synthesizability. The amorphous limit is highly chemistry-dependent and is found to be in complete agreement with our knowledge of existing polymorphs in these 41 systems, whether made by the nature or in a laboratory. Quantifying the limits of metastability for realizable compounds, the approach is expected to find major applications in materials discovery.

| S-EPMC5930398 | biostudies-literature

XElemNet: towards explainable AI for deep neural networks in materials science.

Project description:Recent progress in deep learning has significantly impacted materials science, leading to accelerated material discovery and innovation. ElemNet, a deep neural network model that predicts formation energy from elemental compositions, exemplifies the application of deep learning techniques in this field. However, the "black-box" nature of deep learning models often raises concerns about their interpretability and reliability. In this study, we propose XElemNet to explore the interpretability of ElemNet by applying a series of explainable artificial intelligence (XAI) techniques, focusing on post-hoc analysis and model transparency. The experiments with artificial binary datasets reveal ElemNet's effectiveness in predicting convex hulls of element-pair systems across periodic table groups, indicating its capability to effectively discern elemental interactions in most cases. Additionally, feature importance analysis within ElemNet highlights alignment with chemical properties of elements such as reactivity and electronegativity. XElemNet provides insights into the strengths and limitations of ElemNet and offers a potential pathway for explaining other deep learning models in materials science.

| S-EPMC11502843 | biostudies-literature

Flagellar filament bio-templated inorganic oxide materials - towards an efficient lithium battery anode.

Project description:Designing a new generation of energy-intensive and sustainable electrode materials for batteries to power a variety of applications is an imperative task. The use of biomaterials as a nanosized structural template for these materials has the potential to produce hitherto unachievable structures. In this report, we have used genetically modified flagellar filaments of the extremely halophilic archaea species Halobacterium salinarum to synthesize nanostructured iron oxide composites for use as a lithium-ion battery anode. The electrode demonstrated a superior electrochemical performance compared to existing literature results, with good capacity retention of 1032 mAh g(-1) after 50 cycles and with high rate capability, delivering 770 mAh g(-1) at 5 A g(-1) (~5 C) discharge rate. This unique flagellar filament based template has the potential to provide access to other highly structured advanced energy materials in the future.

| S-EPMC4291565 | biostudies-literature

Broad virus inactivation using inorganic micro/nano-particulate materials.

Project description:Inorganic materials can provide a set of tools to decontaminate solid, liquid or air containing viral particles. The use of disinfectants can be limited or not practical in scenarios where continuous cleaning is not feasible. Physicochemical differences between viruses raise the need for effective formulations for all kind of viruses. In the present work we describe two types of antimicrobial inorganic materials: i) a novel soda-lime glass (G3), and ii) kaolin containing metals nanoparticles (Ag or CuO), as materials to disable virus infectivity. Strong antiviral properties can be observed in G3 glass, and kaolin-containing nanoparticle materials showing a reduction of viral infectivity close to 99%. in the first 10 min of contact of vesicular stomatitis virus (VSV). A potent virucidal activity is also present in G3 and kaolin containing Ag or CuO nanoparticles against all kinds of viruses tested, reducing more than 99% the amount of HSV-1, Adenovirus, VSV, Influenza virus and SARS-CoV-2 exposed to them. Virucidal properties could be explained by a direct interaction of materials with viruses as well as inactivation by the presence of virucidal elements in the material lixiviates. Kaolin-based materials guarantee a controlled release of active nanoparticles with antiviral activity. Current coronavirus crisis highlights the need for new strategies to remove viruses from contaminated areas. We propose these low-cost inorganic materials as useful disinfecting antivirals in the actual or future pandemic threats.

| S-EPMC8733340 | biostudies-literature

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds.

Project description:Materials scientists increasingly employ machine or statistical learning (SL) techniques to accelerate materials discovery and design. Such pursuits benefit from pooling training data across, and thus being able to generalize predictions over, k-nary compounds of diverse chemistries and structures. This work presents a SL framework that addresses challenges in materials science applications, where datasets are diverse but of modest size, and extreme values are often of interest. Our advances include the application of power or Hölder means to construct descriptors that generalize over chemistry and crystal structure, and the incorporation of multivariate local regression within a gradient boosting framework. The approach is demonstrated by developing SL models to predict bulk and shear moduli (K and G, respectively) for polycrystalline inorganic compounds, using 1,940 compounds from a growing database of calculated elastic moduli for metals, semiconductors and insulators. The usefulness of the models is illustrated by screening for superhard materials.

| S-EPMC5046120 | biostudies-literature

Neural network training method for materials science based on multi-source databases.

Project description:The fourth paradigm of science has achieved great success in material discovery and it highlights the sharing and interoperability of data. However, most material data are scattered among various research institutions, and a big data transmission will consume significant bandwidth and tremendous time. At the meanwhile, some data owners prefer to protect the data and keep their initiative in the cooperation. This dilemma gradually leads to the "data island" problem, especially in material science. To attack the problem and make full use of the material data, we propose a new strategy of neural network training based on multi-source databases. In the whole training process, only model parameters are exchanged and no any external access or connection to the local databases. We demonstrate its validity by training a model characterizing material structure and its corresponding formation energy, based on two and four local databases, respectively. The results show that the obtained model accuracy trained by this method is almost the same to that obtained from a single database combining all the local ones. Moreover, different communication frequencies between the client and server are also studied to improve the model training efficiency, and an optimal frequency is recommended.

| S-EPMC9468338 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data