jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kazuki Morita | Daniel W. Davies | Keith T. Butler | Aron Walsh
Source:  (preprint, 2021-11-12)
Field: Materials Science — Materials Chemistry

Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals While traditional crystallographic representations of structure play an important role in materials science, they are unsuitable for efficient machine learning. A range of effective numerical descriptors have been developed for molecular and crystal structures. We are interested in a special case, where distortions emerge relative to an ideal high-symmetry parent structure. We demonstrate that irreducible representations form an efficient basis for the featurisation of polyhedral deformations with respect to such an aristotype. Applied to dataset of 552 octahedra in ABO3 perovskite-type materials, we use unsupervised machine learning with irreducible representation descriptors to identify four distinct classes of behaviour, associated with predominately corner, edge, face, and mixed connectivity between neighbouring octahedral units. Through this analysis, we identify SrCrO3 as a material with tuneable multiferroic behaviour. We further show, through supervised machine learning, that thermally activated structural distortions of CsPbI3 are well described by this approach. Octahedron ; Irreducible representation ; Representation (politics) ; Crystal structure ; Multiferroics ; Ideal (ethics) ; Perovskite (structure) ; Computer science ; Crystallography ; Materials science ; Theoretical physics ; Artificial intelligence ; Physics ; Chemistry ; Mathematics ; Pure mathematics ; Quantum mechanics Machine Learning in Materials Science ; Multiferroics and related materials ; Perovskite Materials and Applications
Authors: B. Gopala krishnan
Source: NVEO - NATURAL VOLATILES & ESSENTIAL OILS Journal | NVEO (article, 2021-11-07)
Field: Chemistry — Analytical Chemistry

Dense Neural Network For Optimal Prediction Of Bioactivity In Essential Oil-Producing Plants Now a days this deep learning technique is playing a vital role in different science sectors and also in biological fields.This technique is being implemented because it is good at deploying big data which are syllabified for processing non-linear and complicated relationships.The main aim of this research work is to predict the biological actions of the essential oil producing plants and also to classify them in subject to their chemical compositions by implementing Convolutional Neural Networks which is one of the deep learning technique. This study is based on the chemical content present in the Egyptian essential oil plants and their activities.As like the one more algorithm is also used on essential oil dataset called Multiclass Neural Network(MNN). The main concept of our study is to CNN algorithm.while testing using both the algorithms CNN and MNN is 98.13% and 81.88%.At the end this CNN model have been taken as the reliable model for analysing the bio activities of the Egyptian EC-containing plants.At last we would get 97% approximately as the prediction. Convolutional neural network ; Artificial neural network ; Artificial intelligence ; Computer science ; Deep learning ; Machine learning ; Essential oil ; Botany ; Biology Spectroscopy and Chemometric Analyses
Authors: Rima Suzuki
Source: The Chemical Society of Japan (article, 2021-01-18)
Field: Chemistry — Physical and Theoretical Chemistry

Prediction of Gas Phase Acidity for in Silico Design of Super Acids with High Precision Quantum Chemical Calculations and Machine Learning Quantum chemical ; In silico ; Gas phase ; Machine learning ; Chemistry ; Quantum ; Phase (matter) ; Computer science ; Artificial intelligence ; Computational chemistry ; Organic chemistry ; Physics ; Quantum mechanics ; Biochemistry ; Molecule Various Chemistry Research Topics ; Thermal and Kinetic Analysis
Authors: Kazuki Morita | Daniel W. Davies | Keith T. Butler | Aron Walsh
Source: ChemRxiv (preprint, 2021-12-16)
Field: Materials Science — Materials Chemistry

Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals While traditional crystallographic representations of structure play an important role in materials science, they are unsuitable for efficient machine learning. A range of effective numerical descriptors have been developed for molecular and crystal structures. We are interested in a special case, where distortions emerge relative to an ideal high-symmetry parent structure. We demonstrate that irreducible representations form an efficient basis for the featurisation of polyhedral deformations with respect to such an aristotype. Applied to dataset of 552 octahedra in ABO3 perovskite-type materials, we use unsupervised machine learning with irreducible representation descriptors to identify four distinct classes of behaviour, associated with predominately corner, edge, face, and mixed connectivity between neighbouring octahedral units. Through this analysis, we identify SrCrO3 as a material with tuneable multiferroic behaviour. We further show, through supervised machine learning, that thermally activated structural distortions of CsPbI3 are well described by this approach. Octahedron ; Multiferroics ; Perovskite (structure) ; Representation (politics) ; Irreducible representation ; Ideal (ethics) ; Crystal structure ; Crystal (programming language) ; Face (sociological concept) ; Enhanced Data Rates for GSM Evolution ; Range (aeronautics) ; Symmetry (geometry) ; Crystallography ; Materials science ; Computer science ; Artificial intelligence ; Theoretical physics ; Mathematics ; Physics ; Geometry ; Chemistry ; Pure mathematics ; Ferroelectricity ; Optoelectronics ; Sociology Machine Learning in Materials Science ; Multiferroics and related materials ; Perovskite Materials and Applications
Authors: WaiChing Sun | Nikolas Vlassis
Source:  (article, 2021-03-04)
Field: Materials Science — Materials Chemistry

Thermodynamic-informed machine learning for pressure-sensitive plasticity model &amp;lt;p&amp;gt;This talk will present a machine learning framework that builds interpretable macroscopic surrogate elasto-plasticity models inferred from sub-scale direction numerical simulations (DNS) or experiments with limited data. To circumvent the lack of interpretability of the classical black-box neural network, we introduce a higher-order supervised machine learning technique that generates components of elasto-plastic models such as elasticity functional, yield function, hardening mechanisms, and plastic flow. The geometrical interpretation in the principal stress space allows us to use convexity and smoothness to ensure thermodynamic consistency. The speed function from the Hamilton-Jacobi equation is deduced from the DNS data to formulate hardening and non-associative plastic flow rules governed by the evolution of the low-dimensional descriptors. By incorporating a non-cooperative game that determines the necessary data to calibrate material models, the machine learning generated model is continuously tested, calibrated, and improved as new data guided by the adversarial agents are generated. A graph convolutional neural network is used to deduce low-dimensional descriptors that encodes the evolutional of particle topology under path-dependent deformation and are used to replace internal variables. The resultant constitutive laws can be used in a finite element solver or incorporated as a loss function for the physical-informed neural network run physical simulations.&amp;lt;/p&amp;gt; Interpretability ; Artificial neural network ; Artificial intelligence ; Computer science ; Solver ; Boltzmann machine ; Convexity ; Machine learning ; Convolutional neural network ; Applied mathematics ; Algorithm ; Mathematical optimization ; Topology (electrical circuits) ; Mathematics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Force Microscopy Techniques and Applications
Authors: Umberto Raucci | Valerio Rizzi | Michele Parrinello
Source: ChemRxiv (preprint, 2021-12-08)
Field: Materials Science — Materials Chemistry

Discover, Sample and Refine: ExploringChemistry with Enhanced Sampling Techniques Over the last few decades enhanced sampling methods have made great strides. Here, we exploit this progress and propose a modular workflow for blind reaction discovery and characterization of reaction paths. Central to our strategy is the use of the recently developed explore variant of the on-the-fly probability enhanced sampling method. Like metadynamics, this method is based on the identification of appropriate collective variables. Our first step is the discovery of new chemical reactions and it is performed biasing a one dimensional collective variable derived from spectral graph theory. Once new reaction pathways are detected, we construct ad-hoc tailored neural-network based collective variables to improve sampling of specific reactions and finally we refine the results using free energy perturbation theory. Our workflow has been successfully applied to both intramolecular and intermolecular reactions. Without any chemical hypothesis, we discovered several possible products, computed the free energy surface at semiempirical level, and finally refined it with a more accurate Hamiltonian. Our workflow requires minimal user input, and thanks to its modularity and flexibility, can extend the scope of ab initio molecular dynamics for the exploration and characterization of reaction space. Computer science ; Metadynamics ; Workflow ; Chemical space ; Theoretical computer science ; Potential energy surface ; Chemistry ; Computational chemistry ; Molecular dynamics ; Ab initio ; Drug discovery Machine Learning in Materials Science ; Advanced Text Analysis Techniques ; Computational Drug Discovery Methods
Authors: Akshansh Mishra | Tarushi Pathak
Source: Indian Journal of Data Mining (article, 2021-04-30)
Field: Materials Science — Materials Chemistry

Deep Convolutional Generative Modeling for Artificial Microstructure Development ofAluminum-Silicon Alloy Machine learning which is a sub-domain of an Artificial Intelligence which is finding various applications in manufacturing and material science sectors. In the present study, Deep Generative Modeling which a type of unsupervised machine learning technique has been adapted for the constructing the artificial microstructure of Aluminium-Silicon alloy. Deep Generative Adversarial Networks has been used for developing the artificial microstructure of the given microstructure image dataset. The results obtained showed that the developed models had learnt to replicate the lining near the certain images of the microstructures. Generative grammar ; Deep learning ; Microstructure ; Development (topology) ; Pattern recognition (psychology) ; Convolutional neural network ; Function (biology) Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Model Reduction and Neural Networks
Source: Journal of Physics Conference Series (article, 2021-05-01)
Field: Materials Science — Materials Chemistry

Retraction: An Intelligent System for Waste Materials Segregation Using IoT and Deep Learning (J. Phys.: Conf. Ser. 1916 012028) This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022 Publishing ; Citation ; Volume (thermodynamics) ; Process (computing) ; Computer science ; Quality (philosophy) ; Library science ; Political science ; Law ; Epistemology ; Philosophy Machine Learning in Materials Science
Authors: Yusuf Shaidu | Emine Küçükbenli | Ruggero Lot | Franco Pellegrini | Efthimios Kaxiras | Stefano de Gironcoli
Source: npj Computational Materials (preprint, 2021-04-14)
Field: Materials Science — Materials Chemistry

A systematic approach to generating accurate neural network potentials: the case of carbon Abstract Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling. Artificial neural network-based approaches for generating potentials are promising; however, neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive, and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite, and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases. Artificial neural network ; Interatomic potential ; Computer science ; Formalism (music) ; Construct (python library) ; Diamond ; Graphene ; Artificial intelligence ; Statistical physics ; Materials science ; Molecular dynamics ; Nanotechnology ; Computational chemistry ; Chemistry ; Physics Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; Advanced Physical and Chemical Molecular Interactions
Authors: Leopoldo Molina‐Luna
Source: Proceedings of the European Microscopy Congress 2020 (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Insights into structure-property-performance correlations in functional materials: from MEMS-based in-situ/operando (S)TEM to Machine Learning In situ ; Property (philosophy) ; Microelectromechanical systems ; Materials science ; Computer science ; Nanotechnology ; Chemistry Machine Learning in Materials Science
Authors: Kazuki Morita | Daniel W. Davies | Keith T. Butler | Aron Walsh
Source: ChemRxiv (preprint, 2021-08-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Breaking the aristotype: featurisation of polyhedral distortions in perovskite crystals While traditional crystallographic representations of structure play an important role in materials science, they are unsuitable for efficient machine learning. A range of effective numerical descriptors have been developed for molecular and crystal structures. We are interested in a special case, where distortions emerge relative to an ideal high-symmetry parent structure. We demonstrate that irreducible representations form an efficient basis for the featurisation of polyhedral deformations with respect to such an aristotype. Applied to dataset of 552 octahedra in ABO3 perovskite-type materials, we use unsupervised machine learning with irreducible representation descriptors to identify four distinct classes of behaviour, associated with predominately corner, edge, face, and mixed connectivity between neighbouring octahedral units. Through this analysis, we identify SrCrO3 as a material with tuneable multiferroic behaviour. Octahedron ; Multiferroics ; Irreducible representation ; Representation (politics) ; Perovskite (structure) ; Ideal (ethics) ; Crystal structure ; Crystal (programming language) ; Face (sociological concept) ; Enhanced Data Rates for GSM Evolution ; Range (aeronautics) ; Symmetry (geometry) ; Type (biology) ; Materials science ; Crystallography ; Theoretical physics ; Computer science ; Mathematics ; Physics ; Geometry ; Pure mathematics ; Artificial intelligence ; Chemistry ; Geology ; Ferroelectricity ; Sociology ; Epistemology Multiferroics and related materials ; Machine Learning in Materials Science ; Advanced Condensed Matter Physics
Authors: R. E. Ryltsev | N. M. Chtchelkatchev
Source: arXiv (Cornell University) (preprint, 2021-10-26)
Field: Materials Science — Materials Chemistry

Deep machine learning potentials for multicomponent metallic melts: development, predictability and compositional transferability The use of machine learning interatomic potentials (MLIPs) in simulations of materials is a state-of-the-art approach, which allows achieving nearly \textit{ab initio} accuracy with orders of magnitude less computational cost. Multicomponent disordered systems have a highly complicated potential energy surface due to both topological and compositional disorder. That arises issues in MLIPs developing, such as optimal design strategy of potentials and their predictability and transferability. Here we address MLIPs for multicomponent metallic melts taking the ternary Al-Cu-Ni ones as a convenient example. We use many-body deep machine learning potentials as implemented in the DeePMD-kit to build MLIP that allows describing both atomic structure and dynamics of the system in the whole composition range. Doing that we consider different sets of neural networks hyperparameters and learning schemes to create an optimal MLIP, which allows archiving good accuracy in comparison with both \textit{ab initio} and experimental data. We find that developed MLIP demonstrates good compositional transferability, which extends far beyond compositional fluctuations in the training configurations. The results obtained open up prospects for simulating structural and dynamical properties of multicomponent metallic alloys with MLIPs. Transferability ; Predictability ; Ternary operation ; Computer science ; Ab initio ; Hyperparameter ; Interatomic potential ; Machine learning ; Artificial intelligence ; Artificial neural network ; Statistical physics ; Algorithm ; Materials science ; Physics ; Chemistry ; Molecular dynamics ; Computational chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; X-ray Diffraction in Crystallography
Authors: Wesley F. Reinhart
Source: Computational Materials Science (preprint, 2021-05-03)
Field: Materials Science — Materials Chemistry

Unsupervised learning of atomic environments from simple features Computer science ; Unsupervised learning ; Simple (philosophy) ; Invariant (physics) ; Embedding ; Artificial intelligence ; Crystal (programming language) ; Binary number ; Artificial neural network ; A priori and a posteriori ; Machine learning ; Biological system ; Physics ; Mathematics ; Biology Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis ; Calcium Carbonate Crystallization and Inhibition
Authors: Naoki Iinuma | Fusataka Kuniyoshi | Jun Ozawa | Makoto Miwa
Source: Research Square (preprint, 2021-11-30)
Field: Materials Science — Materials Chemistry

Annotation and Classification of Graphs of Property Values Reported in Material Science Literature Abstract Building a system for extracting information from the scientific literature is an important research topic in the field of inorganic materials science. However, conventional extraction systems have a limitation in that they do not extract characteristic values from nontextual components, such as charts, diagrams, and tables, which provide key information in many scientific documents. Although there have been several studies on identifying the characteristic values of graphs in the literature, there is no general method that classifies graphs according to the property conditions of the values in the field of materials science. Therefore, in this study, we focus on graphs that are figures representing graphically numerical data, such as a bar graph and line graph, as the first step toward developing a framework for extracting material property information from such noncontextual components. We propose deep-learning-based classification models for identifying the types of graph properties, such as temperature and time, by combining graph images, text in graphs, and captions in neural networks. To train and evaluate the models, we construct a material graph dataset with different types of material properties from a large collection of data from journals in the field of materials science. By using cloud sourcing, we annotate 16,668 images. Our experimental results demonstrate that the best model can achieve high performance with a microaveraged F-score of 0.961. Property (philosophy) ; Annotation ; Computer science ; Information retrieval ; Data science ; Natural language processing ; Artificial intelligence ; Epistemology ; Philosophy Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Material Selection and Properties
Authors: Sauradeep Majumdar | Seyed Mohamad Moosavi | Kevin Maik Jablonka | Daniele Ongari | Berend Smit
Source:  (preprint, 2021-09-28)
Field: Chemistry — Inorganic Chemistry

Diversifying databases of metal organic frameworks for high-throughput computational screening By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. When making new databases of such hypothetical MOFs, we need to assure that they not only contribute towards the growth of the count of structures but also add different chemistry to existing databases. In this study, we designed a database of ~20,000 hypothetical MOFs which are diverse in terms of their chemical design space—metal nodes, organic linkers, functional groups and pore geometries. Using Machine Learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications—post combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post combustion carbon capture) and MOF-5 (for hydrogen storage). Computer science ; Database ; Benchmark (surveying) ; Metal-organic framework ; Chemical space ; Hydrogen storage ; Hydrogen ; Chemistry ; Drug discovery Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science
Authors: Ying Shi Teh | Swarnava Ghosh | Kaushik Bhattacharya
Source: Mechanics of Materials (preprint, 2021-09-27)
Field: Materials Science — Materials Chemistry

Machine-learned prediction of the electronic fields in a crystal Lattice (music) ; Electronic structure ; Computer science ; Machine learning ; Materials science ; Crystal structure prediction ; Unit (ring theory) ; Deformation (meteorology) ; Artificial intelligence ; Density functional theory ; Statistical physics ; Crystal structure ; Algorithm ; Condensed matter physics ; Physics ; Mathematics ; Crystallography ; Composite material ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques