jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hiroaki Taniwaki | Hiromasa Kaneko
Source: Macromolecular Theory and Simulations (article, 2023-03-17)
Field: Materials Science — Materials Chemistry

Retrosynthetic and Synthetic Reaction Prediction Model Based on Sequence‐to‐Sequence with Attention for Polymer Designs Abstract Polymer designs, especially monomer designs, can be performed with machine learning and artificial intelligence using a polymer dataset, however, it is meaningless if the designed monomer structures cannot be synthesized and the polymer compound cannot be polymerized. In this study, a retrosynthesis prediction model based on sequence‐to‐sequence (Seq2Seq) with attention is developed, which is originally used in language transformation, to predict reactants from monomer structures corresponding to polymers. In addition, Seq2Seq with an attention‐based synthetic reaction prediction model that predicts monomer structures from reactants is also developed to propose monomer structures with free bonds for polymer design. Through case studies using an actual polymer dataset, it is confirmed that appropriate polymer designs can be achieved by using the proposed method, including the generation of valid monomer structures, the selection of the monomer structures with promising polymer properties, and the prediction of reactants for the monomer structures. Monomer ; Polymer ; Sequence (biology) ; Retrosynthetic analysis ; Polymerization ; Materials science ; Set (abstract data type) ; Computer science ; Transformation (genetics) ; Biological system ; Chemistry ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Qingyang Dong | Jacqueline M. Cole
Source: Journal of Chemical Information and Modeling (article, 2023-11-07)
Field: Materials Science — Materials Chemistry

Snowball 2.0: Generic Material Data Parser for ChemDataExtractor The ever-growing amount of chemical data found in the scientific literature has led to the emergence of data-driven materials discovery. The first step in the pipeline, to automatically extract chemical information from plain text, has been driven by the development of software toolkits such as ChemDataExtractor. Such data extraction processes have created a demand for parsers that efficiently enable text mining. Here, we present Snowball 2.0, a sentence parser based on a semisupervised machine-learning algorithm. It can be used to extract any chemical property without additional training. We validate its precision, recall, and <i>F</i>-score by training and testing a model with sentences of semiconductor band gap information curated from journal articles. Snowball 2.0 builds on two previously developed Snowball algorithms. Evaluation of Snowball 2.0 shows a 15-20% increase in recall with marginally reduced precision over the previous version which has been incorporated into ChemDataExtractor 2.0, giving Snowball 2.0 better performance in most configurations. Snowball 2.0 offers more and better parsing options for ChemDataExtractor, and it is more capable in the pipeline of automated data extraction. Snowball 2.0 also features better generalizability, performance, learning efficiencies, and user-friendliness. Parsing ; Computer science ; Pipeline (software) ; Information extraction ; Artificial intelligence ; Snowball sampling ; Generalizability theory ; Sentence ; Automatic summarization ; Precision and recall ; Natural language processing ; Machine learning ; Recall ; Snowball Earth ; Data mining ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Roman Kempt | Agnieszka Kuc | Thomas Brumme | Thomas Heine
Source: Small Structures (article, 2023-11-12)
Field: Materials Science — Materials Chemistry

Edge Conductivity in PtSe<sub>2</sub>Nanostructures PtSe 2 is a promising 2D material for nanoelectromechanical sensing and photodetection in the infrared regime. One of its most compelling features is the facile synthesis at temperatures below 500 °C, which is compatible with current back‐end‐of‐line semiconductor processing. However, this process generates polycrystalline thin films with nanoflake‐like domains of 5–100 nm size. To investigate the lateral quantum confinement effect in this size regime, a deep neural network is trained to obtain an interatomic potential at density functional theory accuracy and it is used to model ribbons, surfaces, nanoflakes, and nanoplatelets of PtSe 2 with lateral widths between 5 and 15 nm. Which edge terminations are the most stable are determined and evidence is found that the electrical conductivity is localized on the edges for lateral sizes below 10 nm. This suggests that the transport channels in thin films of PtSe 2 might be dominated by networks of edges, instead of transport through the layers themselves. Photodetection ; Materials science ; Conductivity ; Nanostructure ; Enhanced Data Rates for GSM Evolution ; Semiconductor ; Crystallite ; Optoelectronics ; Condensed matter physics ; Quantum dot ; Thin film ; Nanotechnology ; Chemistry ; Physics ; Photodetector ; Computer science 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Fengyuan Wei | Lin Zhuang
Source: Science Bulletin (article, 2023-12-09)
Field: Materials Science — Materials Chemistry

Unsupervised machine learning reveals eigen reactivity of metal surfaces Reactivity (psychology) ; Metal ; Ionic bonding ; Chemistry ; Chemical physics ; Covalent bond ; Eigenvalues and eigenvectors ; Valence (chemistry) ; Computational chemistry ; Materials science ; Physics ; Quantum mechanics ; Ion ; Organic chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Mouna Gharbi | Émilie Chouzenoux | Jean‐Christophe Pesquet
Source: Signal Processing (article, 2023-12-29)
Field: Chemistry — Analytical Chemistry

An unrolled half-quadratic approach for sparse signal recovery in spectroscopy Computer science ; Hyperparameter ; Regularization (linguistics) ; Quadratic equation ; Artificial neural network ; Robustness (evolution) ; Algorithm ; Neural coding ; Quadratic programming ; Artificial intelligence ; Stability (learning theory) ; Deep learning ; Machine learning ; Mathematical optimization ; Mathematics Spectroscopy and Chemometric Analyses ; NMR spectroscopy and applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Zoé Faure Beaulieu | Thomas C. Nicholas | John L. A. Gardner | Andrew L. Goodwin | Volker L. Deringer
Source: Chemical Communications (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Coarse-grained <i>versus</i> fully atomistic machine learning for zeolitic imidazolate frameworks Zeolitic imidazolate frameworks are widely thought of as being analogous to inorganic AB<sub>2</sub> phases. We test the validity of this assumption by comparing simplified and fully atomistic machine-learning models for local environments in ZIFs. Our work addresses the central question to what extent chemical information can be "coarse-grained" in hybrid framework materials. Zeolitic imidazolate framework ; Imidazolate ; Computer science ; Work (physics) ; Materials science ; Nanotechnology ; Metal-organic framework ; Chemistry ; Physics ; Adsorption ; Physical chemistry ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Polyoxometalates: Synthesis and Applications
Authors: Alice E. A. Allen | Nicholas Lubbers | Sakib Matin | Justin S. Smith | Richard A. Messerly | Sergei Tretiak | Kipton Barros
Source: arXiv (Cornell University) (preprint, 2023-07-08)
Field: Materials Science — Materials Chemistry

Learning Together: Towards foundational models for machine learning interatomic potentials with meta-learning The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable to leverage the plethora of data available as they require that each dataset be generated using the same QM method. Taking machine learning interatomic potentials (MLIPs) as an example, we show that meta-learning techniques, a recent advancement from the machine learning community, can be used to fit multiple levels of QM theory in the same training process. Meta-learning changes the training procedure to learn a representation that can be easily re-trained to new tasks with small amounts of data. We then demonstrate that meta-learning enables simultaneously training to multiple large organic molecule datasets. As a proof of concept, we examine the performance of a MLIP refit to a small drug-like molecule and show that pre-training potentials to multiple levels of theory with meta-learning improves performance. This difference in performance can be seen both in the reduced error and in the improved smoothness of the potential energy surface produced. We therefore show that meta-learning can utilize existing datasets with inconsistent QM levels of theory to produce models that are better at specializing to new datasets. This opens new routes for creating pre-trained, foundational models for interatomic potentials. Machine learning ; Leverage (statistics) ; Artificial intelligence ; Computer science ; Process (computing) ; Representation (politics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Xiaolin Liu | Chunlei Zhu | Ben Zhong Tang
Source: Nature Reviews Chemistry (review, 2023-03-13)
Field: Materials Science — Materials Chemistry

Informatics colourizes polymers Informatics ; Materials informatics ; Computer science ; Polymer ; Health informatics ; Data science ; Space (punctuation) ; Nanotechnology ; Artificial intelligence ; Engineering informatics ; Engineering ; Materials science ; Medicine ; Electrical engineering Luminescence and Fluorescent Materials ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Amir Rezaei
Source: Anti-Corrosion Methods and Materials (article, 2023-11-29)
Field: Materials Science — Metals and Alloys

Prediction of hot corrosion behavior of Inconel 617 via machine learning Purpose This paper aims to study the feasibility of using machine learning in hot corrosion prediction of Inconel 617 alloy. Design/methodology/approach By examination of the experimental studies on hot corrosion of Inconel 617, a data set was built for machine learning models. Apart from the alloy composition, this paper included the condition of hot corrosion like time and temperature, and the composition of the saline medium as independent features, while the specific mass change is set as the target feature. In this paper, linear regression, random forest and XGBoost are used to predict the specific mass gain of Inconel 617. Findings XGBoost yields the coefficient of determination ( R 2 ) of 0.98, which was highest among models. Also, this model recorded the lowest value of mean absolute error (0.20). XGBoost had the best performance in predicting specific mass gain of the alloy in different times at temperature of 900°C. In sum, XGBoost shows highest accuracy in predicting specific mass gain for Inconel 617. Originality/value Using machine learning to predict hot corrosion in Inconel 617 marks a substantial progress in this domain and holds promise for simplifying the development and evaluation of novel materials featuring enhanced hot corrosion resilience. Inconel ; Corrosion ; Random forest ; Linear regression ; Mean absolute percentage error ; Alloy ; Materials science ; Set (abstract data type) ; Correlation coefficient ; Metallurgy ; Artificial intelligence ; Computer science ; Machine learning ; Artificial neural network Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Additive Manufacturing Materials and Processes
Authors: Andrey A. Stepashkin | Suresh Chavhan | S. Gromov | Ashish Khanna | Victor V. Tcherdyntsev | Deepak Gupta | Hussam Mohammad | Е. В. Медведева | Namita Gupta | S. Alexandrova
Source: Alexandria Engineering Journal (article, 2023-10-11)
Field: Materials Science — General Materials Science

ANN-based structure peciliaties evaluation of polymer composite reinforced with unidirectional carbon fiber At the moment, there is a growing interest in composite materials with matrices based on thermoplastic polymers; these materials are superior to traditional carbon fiber plastics due to higher fracture toughness and impact strength, low smoke generation during combustion, and high thermal and chemical resistance. The deformation behavior of such composite materials due to the high plastic deformation of the matrix differs from the behavior of traditional composites, which must be considered when performing calculations. In our study, as a model object, the features of the microstructure of single carbon thread impregnated with polysulfone were studied in order to evaluate the distribution of misorientation angles of elementary fibers, the degree of their damage, the thickness of the polymer matrix interlayers between elementary fibers in threads impregnated with more viscous thermoplastic binders. Statistical analysis of a large array of micrographs was carried out using specially developed algorithms for computer processing of electron microscopic images of a composite material. Algorithms for processing arrays of images using machine vision algorithms are proposed. Based on the analysis of the data array, the distributions of carbon fibers impregnated with polysulfone were plotted over the misorientation angle of the filaments and over the interfilament distance in the longitudinal and cross sections of the fiber. Using the machine learning method, an algorithm for detecting violations of the filament structure was implemented. The data obtained can be used to refine the calculations of the strength and deformation characteristics of composite materials with thermoplastic matrices. Materials science ; Composite material ; Composite number ; Thermoplastic ; Misorientation ; Polysulfone ; Toughness ; Microstructure ; Polymer ; Deformation (meteorology) Material Properties and Applications ; Innovations in Concrete and Construction Materials ; Industrial Engineering and Technologies
Authors: Fatin Abrar Shams | Rashed Hasan Ratul | Ahnaf Islam Naf | Syed Shaek Hossain Samir | Mirza Muntasir Nishat | Fahim Faisal | Md. Ashraful Hoque
Source: arXiv (Cornell University) (preprint, 2023-08-02)
Field: Materials Science — Materials Chemistry

Investigation on Machine Learning Based Approaches for Estimating the Critical Temperature of Superconductors Superconductors have been among the most fascinating substances, as the fundamental concept of superconductivity as well as the correlation of critical temperature and superconductive materials have been the focus of extensive investigation since their discovery. However, superconductors at normal temperatures have yet to be identified. Additionally, there are still many unknown factors and gaps of understanding regarding this unique phenomenon, particularly the connection between superconductivity and the fundamental criteria to estimate the critical temperature. To bridge the gap, numerous machine learning techniques have been established to estimate critical temperatures as it is extremely challenging to determine. Furthermore, the need for a sophisticated and feasible method for determining the temperature range that goes beyond the scope of the standard empirical formula appears to be strongly emphasized by various machine-learning approaches. This paper uses a stacking machine learning approach to train itself on the complex characteristics of superconductive materials in order to accurately predict critical temperatures. In comparison to other previous accessible research investigations, this model demonstrated a promising performance with an RMSE of 9.68 and an R2 score of 0.922. The findings presented here could be a viable technique to shed new insight on the efficient implementation of the stacking ensemble method with hyperparameter optimization (HPO). Superconductivity ; Hyperparameter ; Machine learning ; Stacking ; Computer science ; Artificial intelligence ; Scope (computer science) ; Range (aeronautics) ; Statistical physics ; Condensed matter physics ; Materials science ; Physics Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism
Authors: Alexander E. Mayer
Source: Scripta Materialia (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Influence of preliminary compressive deformation on the spall strength of aluminum single crystal Spall ; Materials science ; Deformation (meteorology) ; Ultimate tensile strength ; Shock (circulatory) ; Compressive strength ; Compression (physics) ; Composite material ; Plasticity ; Single crystal ; Microstructure ; Crystallography High-Velocity Impact and Material Behavior ; High-pressure geophysics and materials ; Microstructure and mechanical properties
Authors: Palanichamy Gayathri | Saurabh Ghosh | Ayana Ghosh
Source: Chemistry of Materials (article, 2023-12-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predictive Design of Hybrid Improper Ferroelectric Double Perovskite Oxides The computational design of suitable multiferroic double perovskite oxides requires finding materials that exhibit sizable polarization, magnetization, and coupling between them. Oxides with the chemical formula of AA′BB′O6 with building blocks of ABO3 single perovskite oxides in centrosymmetric Pnma symmetry are strong candidates that have been reported to satisfy such criteria. The system lowers to noncentrosymmetric, polar P21 symmetry if A/A′ layered and B/B′ rocksalt cation orderings are imposed. A detailed compositional search over a variety of chemical spaces followed by evaluating their polarization may lead to the identification of more of these compounds with ferroelectric ordering. The standard density functional theory practices to estimate polarization within the Berry phase formalism require the systems to be perfectly insulating. The number of compounds that can be evaluated using this method is therefore limited. In this work, we introduce a predictive learning strategy based on importance sampling to build a series of machine learning models using results from first-principles simulations to predict polarization and the corresponding switching barrier. The geometry-driven features related to charge states and cationic radii play key roles in predicting the switching barrier with complementary contributions from the key structural mode-based order parameters. These modes become important to draw reasonable predictions of polarization components from machine learning models. Our predictive models identify candidates with high polarizations and low switching barriers from a pool of double perovskite oxides, suitable for future investigation for their potential applications in spintronic devices. Multiferroics ; Ferroelectricity ; Polarization (electrochemistry) ; Perovskite (structure) ; Spintronics ; Materials science ; Formalism (music) ; Condensed matter physics ; Chemical physics ; Computer science ; Ferromagnetism ; Chemistry ; Physics ; Optoelectronics ; Crystallography ; Dielectric ; Physical chemistry Multiferroics and related materials ; Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Rochan Bajpai | Atharva Shukla | Janish Kumar | Abhishek Tewari
Source: Computational Materials Science (article, 2023-09-22)
Field: Materials Science — Materials Chemistry

A scalable crystal representation for reverse engineering of novel inorganic materials using deep generative models Scalability ; Crystal structure prediction ; Computer science ; Autoencoder ; Representation (politics) ; Generality ; Artificial intelligence ; Crystal (programming language) ; Theoretical computer science ; Deep learning ; Crystal structure ; Chemistry ; Database Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Reservoir Engineering and Simulation Methods
Authors: Ruihong Zhang | Tayyaba Mubashir | Muhammad Sulaman | Najam Ul Hassan | Sajjad Haider | Mudassir Hussain Tahir
Source: Computational and Theoretical Chemistry (article, 2023-07-14)
Field: Materials Science — Polymers and Plastics

Rational polymer design for organic solar cells through similarity index-based data mining Similarity (geometry) ; Index (typography) ; Chemistry ; Polymer ; Work (physics) ; Experimental data ; Energy (signal processing) ; Data mining ; Artificial intelligence ; Machine learning ; Computer science ; Mechanical engineering ; Engineering ; Organic chemistry Conducting polymers and applications ; Machine Learning in Materials Science ; Organic Electronics and Photovoltaics
Authors: Yerin Jang | Choong H. Kim | Ara Go
Source: Scientific Reports (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Classification of magnetic order from electronic structure by using machine learning Abstract Identifying the magnetic state of materials is of great interest in a wide range of applications, but direct identification is not always straightforward due to limitations in neutron scattering experiments. In this work, we present a machine-learning approach using decision-tree algorithms to identify magnetism from the spin-integrated excitation spectrum, such as the density of states. The dataset was generated by Hartree–Fock mean-field calculations of candidate antiferromagnetic orders on a Wannier Hamiltonian, extracted from first-principle calculations targeting BaOsO $$_3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>3</mml:mn> </mml:msub> </mml:math> . Our machine learning model was trained using various types of spectral data, including local density of states, momentum-resolved density of states at high-symmetry points, and the lowest excitation energies from the Fermi level. Although the density of states shows good performance for machine learning, the broadening method had a significant impact on the model’s performance. We improved the model’s performance by designing the excitation energy as a feature for machine learning, resulting in excellent classification of antiferromagnetic order, even for test samples generated by different methods from the training samples used for machine learning. Artificial intelligence ; Machine learning ; Antiferromagnetism ; Excitation ; Computer science ; Hamiltonian (control theory) ; Magnetism ; Physics ; Density of states ; Condensed matter physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; Physics of Superconductivity and Magnetism
Authors: Connor Hainje | Cody A. Nizinski | Shane W. Jackson | Richard A. Clark | Forrest D. Heller | Ian J. Schwerdt | Edgar C. Buck | David E. Meier | Alexander Hagen
Source: Chemometrics and Intelligent Laboratory Systems (article, 2023-07-11)
Field: Materials Science — Materials Chemistry

Investigation of process history and underlying phenomena associated with the synthesis of plutonium oxides using Vector Quantizing Variational Autoencoder Biological system ; Autoencoder ; Particle (ecology) ; Scanning electron microscope ; Particle size ; Pattern recognition (psychology) ; Oxalic acid ; Materials science ; Computer science ; Process (computing) ; Artificial neural network ; Artificial intelligence ; Chemistry ; Inorganic chemistry Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear reactor physics and engineering