jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Weiyi Xia | Ling Tang | Huaijun Sun | Chao Zhang | Kai‐Ming Ho | Gayatri Viswanathan | Kirill Kovnir | Cai‐Zhuang Wang
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Accelerating materials discovery using integrated deep machine learning approaches Our work introduces an innovative deep machine learning framework to significantly accelerate novel materials discovery, as demonstrated by its application to the La–Si–P system where new ternary and quaternary compounds were successfully identified. Deep learning ; Computer science ; Artificial intelligence ; Ternary operation ; Machine learning ; Programming language Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Fu‐Xiang Rikudo Chen | Chia‐Yu Lin | Hui-Ying Siao | Cheng-Yuan Jian | Yongcheng Yang | Chun‐Liang Lin
Source: Scientific Data (article, 2023-02-14)
Field: Materials Science — Materials Chemistry

Deep learning based atomic defect detection framework for two-dimensional materials Defects to popular two-dimensional (2D) transition metal dichalcogenides (TMDs) seriously lower the efficiency of field-effect transistor (FET) and depress the development of 2D materials. These atomic defects are mainly identified and researched by scanning tunneling microscope (STM) because it can provide precise measurement without harming the samples. The long analysis time of STM for locating defects in images has been solved by combining feature detection with convolutional neural networks (CNN). However, the low signal-noise ratio, insufficient data, and a large amount of TMDs members make the automatic defect detection system hard to be applied. In this study, we propose a deep learning-based atomic defect detection framework (DL-ADD) to efficiently detect atomic defects in molybdenum disulfide (MoS<sub>2</sub>) and generalize the model for defect detection in other TMD materials. We design DL-ADD with data augmentation, color preprocessing, noise filtering, and a detection model to improve detection quality. The DL-ADD provides precise detection in MoS<sub>2</sub> (F2-scores is 0.86 on average) and good generality to WS<sub>2</sub> (F2-scores is 0.89 on average). Molybdenum disulfide ; Convolutional neural network ; Computer science ; Preprocessor ; Artificial intelligence ; Noise (video) ; Scanning tunneling microscope ; Deep learning ; Pattern recognition (psychology) ; Transistor ; Materials science ; Optoelectronics ; Nanotechnology ; Electrical engineering ; Image (mathematics) ; Engineering 2D Materials and Applications ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Claudio Pereti | Kévin Bernot | Thierry Guizouarn | František Laufek | Anna Vymazalová | Luca Bindi | Roberta Sessoli | Duccio Fanelli
Source: npj Computational Materials (article, 2023-05-02)
Field: Materials Science — Materials Chemistry

From individual elements to macroscopic materials: in search of new superconductors via machine learning Abstract An approach to supervised classification and regression of superconductive materials is proposed which builds on the DeepSet technology. This enables us to provide the chemical constituents of the examined compounds as an input to the algorithm, while avoiding artefacts that could originate from the chosen ordering in the list. The performance of the method are successfully challenged for both classification (tag a given material as superconducting) and regression (quantifying the associated critical temperature). We then searched through the International Mineralogical Association list with the trained neural network. Among the obtained superconducting candidates, three materials were selected to undergo a thorough experimental characterization. Superconductivity has been indeed confirmed for the synthetic analogue of michenerite, PdBiTe, and observed for the first time in monchetundraite, Pd 2 NiTe 2 , at critical temperatures in good agreement with the theory predictions. This latter is the first certified superconducting material to be identified by artificial intelligence methodologies. Superconductivity ; Artificial neural network ; Regression ; Artificial intelligence ; Computer science ; Characterization (materials science) ; Machine learning ; Materials science ; Condensed matter physics ; Physics ; Nanotechnology ; Mathematics ; Statistics Machine Learning in Materials Science ; Rare-earth and actinide compounds ; Iron-based superconductors research
Authors: Agrim Babbar | Sriram Ragunathan | Debirupa Mitra | Arnab Dutta | Tarak K. Patra
Source: Journal of Polymer Science (article, 2023-12-10)
Field: Materials Science — Materials Chemistry

Explainability and extrapolation of machine learning models for predicting the glass transition temperature of polymers Abstract Machine learning (ML) offers promising tools to develop surrogate models for polymers' structure–property relations. Surrogate models can be built upon existing polymer data and are useful for rapidly predicting the properties of unknown polymers. The accuracy of such ML models appears to depend on the feature space representation of polymers, the range of training data, and learning algorithms. Here, we establish connections between these factors for predicting the glass transition temperature ( T g ) of polymers. Our analysis suggests linear models with fewer fitting parameters are as accurate as nonlinear models with many hidden and unexplainable parameters. Also, the performance of a monomer topology‐based ML model is found to be qualitatively identical to that of a physicochemical descriptor‐based ML model. We find that the ML models's performance in the extrapolative region is enhanced as the property range of the training data increases. Moreover, we establish new T g – polymer chemistry correlations via ML. Our work illustrates how ML can advance the fundamental understanding of polymer structure–property correlations and its efficacy for extrapolation problems. Extrapolation ; Polymer ; Representation (politics) ; Property (philosophy) ; Glass transition ; Computer science ; Range (aeronautics) ; Monomer ; Biological system ; Nonlinear system ; Training set ; Machine learning ; Artificial intelligence ; Materials science ; Mathematics ; Chemistry ; Physics ; Organic chemistry ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Chengwei Wu | Fan Li | Yu‐Jia Zeng | Hongwei Zhao | Guofeng Xie | Wu‐Xing Zhou | Qingquan Liu | Gang Zhang
Source: Applied Surface Science (article, 2023-07-03)
Field: Materials Science — Materials Chemistry

Machine learning accelerated design of 2D covalent organic frame materials for thermoelectrics Thermoelectric effect ; Thermoelectric materials ; Materials science ; Phonon ; Porphyrin ; Thermal stability ; Thermal conductivity ; Azulene ; Optoelectronics ; Thermodynamics ; Condensed matter physics ; Chemistry ; Physics ; Organic chemistry ; Composite material Covalent Organic Framework Applications ; Advanced Thermoelectric Materials and Devices ; Advanced Photocatalysis Techniques
Authors: Xiaoxi Mi | Xuerui Jing | Hailian Wang | Jianbin Xu | Jia She | Aitao Tang | Bjørn Holmedal | Fusheng Pan
Source: Journal of Materials Research and Technology (article, 2023-02-21)
Field: Materials Science — Biomaterials

A machine learning enabled ultra-fine grain design strategy of Mg–Mn-based alloys Grain size is the critical characteristic of ultra-fine grain Magnesium (Mg), which is a concrete representation of the whole heat deformation procedure. In this paper, a design strategy was proposed to quantitatively investigate the composition and process conditions for the preparation of ultrafine grains. Herein, a dataset of Mg–Mn-based wrought alloys was constructed, and the average grain size was set as the design target. Based on this dataset, five machine learning (ML) algorithms, including the k-nearest neighbor (kNN), support vector machine (SVM), decision tree (DT), random forest (RF), and artificial neural network (ANN), were integrated to conduct in-depth analysis and make predictions. Among these models, the computational accuracy of both the DT classifier and ANN predictor is around 90%. The main factors affecting the formation of ultrafine grains were found by ML, and the interrelationships between the features were quantitatively analyzed as well. Then, four suggested routes with conditions were extracted from the tree models for preparing ultrafine grain Mg alloys. And four new Mg alloys were designed through these routes and taken as experimental validation. After testing, the actual grain sizes are close to the predictions, and the accuracy of the experimental verification exceeds 80%. Compared with conventional “trial and error” design methods, the grain design strategy proposed in this paper brings new thought and good prior guidance for developing high-performance commercial Mg alloys. Materials science ; Support vector machine ; Random forest ; Machine learning ; Grain size ; Artificial neural network ; Decision tree ; Artificial intelligence ; Algorithm ; Computer science ; Metallurgy Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: Rodrigo Louzada Campos | Seung-Chul Yoon | Soo Chung | Suchendra M. Bhandarkar
Source: Sensors (article, 2023-08-08)
Field: Chemistry — Analytical Chemistry

Semisupervised Deep Learning for the Detection of Foreign Materials on Poultry Meat with Near-Infrared Hyperspectral Imaging A novel semisupervised hyperspectral imaging technique was developed to detect foreign materials (FMs) on raw poultry meat. Combining hyperspectral imaging and deep learning has shown promise in identifying food safety and quality attributes. However, the challenge lies in acquiring a large amount of accurately annotated/labeled data for model training. This paper proposes a novel semisupervised hyperspectral deep learning model based on a generative adversarial network, utilizing an improved 1D U-Net as its discriminator, to detect FMs on raw chicken breast fillets. The model was trained by using approximately 879,000 spectral responses from hyperspectral images of clean chicken breast fillets in the near-infrared wavelength range of 1000-1700 nm. Testing involved 30 different types of FMs commonly found in processing plants, prepared in two nominal sizes: 2 × 2 mm<sup>2</sup> and 5 × 5 mm<sup>2</sup>. The FM-detection technique achieved impressive results at both the spectral pixel level and the foreign material object level. At the spectral pixel level, the model achieved a precision of 100%, a recall of over 93%, an F1 score of 96.8%, and a balanced accuracy of 96.9%. When combining the rich 1D spectral data with 2D spatial information, the FM-detection accuracy at the object level reached 96.5%. In summary, the impressive results obtained through this study demonstrate its effectiveness at accurately identifying and localizing FMs. Furthermore, the technique's potential for generalization and application to other agriculture and food-related domains highlights its broader significance. Hyperspectral imaging ; Discriminator ; Artificial intelligence ; Pixel ; Computer science ; Pattern recognition (psychology) ; Deep learning ; Computer vision ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: H. Mohit | Sanjay Mavinkere Rangappa | Suchart Siengchin | Belal Muneeb Kanaan | Vakkar Ali | Ibrahim M. Alarifi | Tarek M. A. A. EL-Bagory
Source: Polymer Composites (article, 2023-09-28)
Field: Materials Science — Polymers and Plastics

Machine learning‐based prediction of mechanical and thermal properties of nickel/cobalt/ferrous and dried leaves fiber‐reinforced polymer hybrid composites Abstract Dried leaves are the outstanding origin of cellulosic plant matter, and it is securing reputation as a renewable resource. Dried leaves fiber is suggested to possess the capability to substitute synthetic fibers in polymer laminates as a reinforcing component. The novelty of the present study reveals the effect of dried leaves fiber, cobalt, nickel, and ferrous reinforcement on the physical, mechanical, and thermal characteristics of epoxy, vinyl‐ester, and polyester polymers using artificial neural network (ANN) technique. These composites were fabricated using ultrasonication bath‐assisted wet layup method under ambient condition. The outcomes of this research exhibit that the dried leaves‐cobalt fillers reinforced in all three polymers possess higher mechanical and thermal stability characteristics when compared with other samples. The reason may be assigned to producing novel hydroxyl functional groups and strong interfacial bonding of fillers within the matrix as observed from Fourier‐transform infrared (FTIR) spectra and scanning electron microscope (SEM) micrographs, respectively. Moreover, as observed from the thermogravimetric analysis, the dried leaves‐ferrous filler‐reinforced polymer hybrid composites provided higher thermal stability. Statistical analysis was performed using the one‐way ANOVA technique and found that outcomes were significant statistically under the confidence level of 95%. Hence, this investigation not only emphasize the significance of investigating new polymer composites but also highlight the benefits of engaging advanced modeling to forecast the material characteristics precisely. Highlights Dried leaves and cobalt/nickel/ferrous are applied reinforcement to polymers. Composites fabricated using ultrasonication bath‐assisted wet layup technique. LM Algorithm‐based ANN selected for predicting the best composite. Higher mechanical and thermal stability with dried leaves‐cobalt filler. One‐way ANOVA proved statistically significant within the material properties. Materials science ; Composite material ; Thermogravimetric analysis ; Polymer ; Thermal stability ; Fourier transform infrared spectroscopy ; Epoxy ; Chemical engineering Natural Fiber Reinforced Composites ; Electrospun Nanofibers in Biomedical Applications ; Tribology and Wear Analysis
Authors: Yougen Xu | Feixiao Ren | Lebin Su | Zhaoping Xiong | Xinwei Zhu | Xinyuan Lin | Nan Qiao | Hao Tian | Chang-En Tian | Kuangbiao Liao
Source: Organic Chemistry Frontiers (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

HTE and machine learning-assisted development of iridium(<scp>i</scp>)-catalyzed selective O–H bond insertion reactions toward carboxymethyl ketones By combining HTE and machine learning technologies, an iridium( i )-catalyzed highly selective O–H bond insertion reaction of carboxylic acids and sulfoxonium ylides was developed, and an extensive reaction space exploration was accomplished. Chemistry ; Iridium ; Catalysis ; Combinatorial chemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Synthesis and Catalytic Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: N.V. Viet | Wael Zaki
Source: Mechanics of Materials (article, 2023-05-10)
Field: Materials Science — Materials Chemistry

Artificial neural network model of the mechanical behaviour of shape memory alloy Schwartz primitive lattice architectures SMA* ; Shape-memory alloy ; Homogenization (climate) ; Artificial neural network ; Pseudoelasticity ; Materials science ; Relative density ; Lattice (music) ; Computer science ; Topology (electrical circuits) ; Mechanical engineering ; Composite material ; Artificial intelligence ; Algorithm ; Mathematics ; Acoustics ; Microstructure ; Engineering ; Physics ; Martensite Shape Memory Alloy Transformations ; Polymer composites and self-healing ; Cellular and Composite Structures
Authors: H. Mohit | Sanjay Mavinkere Rangappa | Suchart Siengchin | Belal Muneeb Kanaan | Vakkar Ali | Ibrahim M. Alarifi | Tarek M. A. A. EL-Bagory
Source: Construction and Building Materials (article, 2023-10-27)
Field: Materials Science — Polymers and Plastics

Predicting physico-mechanical and thermal properties of loofa cylindrica fibers and Al2O3/Al-SiC reinforced polymer hybrid composites using artificial neural network techniques Materials science ; Composite material ; Ultimate tensile strength ; Flexural strength ; Fiber ; Fibre-reinforced plastic ; Silicon carbide ; Composite number Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Tribology and Wear Analysis
Authors: Andrés Felipe Usuga | C. S. Praveen | Aleix Comas‐Vives
Source: Journal of Materials Chemistry A (article, 2023-12-04)
Field: Materials Science — Materials Chemistry

Local descriptors-based machine learning model refined by cluster analysis for accurately predicting adsorption energies on bimetallic alloys The CatBoost method, combined with cluster filtering, accurately predicts adsorption energies on metal alloys. The approach uses local chemical descriptors to understand chemisorption on metal alloys, which is essential for catalytic applications. Bimetallic strip ; Chemisorption ; Cluster (spacecraft) ; Adsorption ; Cluster analysis ; Metal ; Artificial intelligence ; Materials science ; Computer science ; Machine learning ; Chemistry ; Metallurgy ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Di‐Fan Liu | Yongxin Zhang | Wen-Zhuo Dong | Qi‐Kun Feng | Shao‐Long Zhong | Zhi‐Min Dang
Source: Journal of Chemical Information and Modeling (article, 2023-12-07)
Field: Materials Science — Materials Chemistry

High-Temperature Polymer Dielectrics Designed Using an Invertible Molecular Graph Generative Model Generating new molecules with the desired physical or chemical properties is the key challenge of computational material design. Deep learning techniques are being actively applied in the field of data-driven material informatics and provide a promising way to accelerate the discovery of innovative materials. In this work, we utilize an invertible graph generative model to generate hypothetical promising high-temperature polymer dielectrics. A molecular graph generative model based on the invertible normalizing flow is trained on a data set containing 250k polymer molecular graphs (mostly generated by an RNN-based generative model) to learn the invertible transformations between latent distributions and molecular graph structures. When generating molecular graphs, a sample vector is drawn from the latent space, and then an adjacency tensor and node attribute matrix are generated through two invertible flows in two steps and assembled into a molecular graph. The model has the merits of exact likelihood training and an efficient one-shot generation process. The learned latent space is used to generate polymers with a high glass-transition temperature (<i>T</i><sub>g</sub>) and a wide band gap (<i>E</i><sub>g</sub>) for the application of high-temperature energy storage film capacitors. This work contributes to the efficient design of high-temperature polymer dielectrics by using deep generative models. Invertible matrix ; Molecular graph ; Computer science ; Generative model ; Graph ; Adjacency matrix ; Theoretical computer science ; Algorithm ; Generative grammar ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Carbon dioxide utilization in catalysis
Authors: Haixiao Wan | Sai Li | Yachen Wang | Zhudan Chen | Junwei He | Chunhua Li | Gengxin Liu | Jun Liu | Xiaodong Wang | Thomas P. Russell | Liqun Zhang
Source: Nano Energy (article, 2023-09-22)
Field: Materials Science — Polymers and Plastics

All-polymer nanocomposites having superior strength, toughness and ultralow energy dissipation Materials science ; Dissipation ; Toughness ; Polymer ; Elastomer ; Composite material ; Nanocomposite ; Toughening ; Thermodynamics ; Physics Polymer Nanocomposites and Properties ; Polymer composites and self-healing ; Advanced Polymer Synthesis and Characterization
Authors: Diego Milardovich | Christoph Wilhelmer | Dominic Waldhoer | Lukas Cvitkovich | Ganesh Sivaraman | Tibor Grasser
Source: The Journal of Chemical Physics (article, 2023-05-15)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential for silicon-nitride (Si3N4) by active learning Silicon nitride (Si3N4) is an extensively used material in the automotive, aerospace, and semiconductor industries. However, its widespread use is in contrast to the scarce availability of reliable interatomic potentials that can be employed to study various aspects of this material on an atomistic scale, particularly its amorphous phase. In this work, we developed a machine learning interatomic potential, using an efficient active learning technique, combined with the Gaussian approximation potential (GAP) method. Our strategy is based on using an inexpensive empirical potential to generate an initial dataset of atomic configurations, for which energies and forces were recalculated with density functional theory (DFT); thereafter, a GAP was trained on these data and an iterative re-training algorithm was used to improve it by learning on-the-fly. When compared to DFT, our potential yielded a mean absolute error of 8 meV/atom in energy calculations for a variety of liquid and amorphous structures and a speed-up of molecular dynamics simulations by 3-4 orders of magnitude, while achieving a first-rate agreement with experimental results. Our potential is publicly available in an open-access repository. Interatomic potential ; Molecular dynamics ; Atom (system on chip) ; Materials science ; Density functional theory ; Semiconductor ; Gaussian ; Statistical physics ; Silicon nitride ; Amorphous solid ; Computer science ; Computational physics ; Chemical physics ; Silicon ; Computational chemistry ; Chemistry ; Physics ; Optoelectronics ; Parallel computing Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ion-surface interactions and analysis
Authors: Basile Herzog | Bastien Casier | Sébastien Lebègue∥ | Dario Rocca
Source: Journal of Chemical Theory and Computation (article, 2023-04-12)
Field: Materials Science — Materials Chemistry

Solving the Schrödinger Equation in the Configuration Space with Generative Machine Learning The configuration interaction approach provides a conceptually simple and powerful approach to solve the Schrödinger equation for realistic molecules and materials but is characterized by an unfavorable scaling, which strongly limits its practical applicability. Effectively selecting only the configurations that actually contribute to the wave function is a fundamental step toward practical applications. We propose a machine learning approach that iteratively trains a generative model to preferentially generate the important configurations. By considering molecular applications it is shown that convergence to chemical accuracy can be achieved much more rapidly with respect to random sampling or the Monte Carlo configuration interaction method. This work paves the way to a broader use of generative models to solve the electronic structure problem. Computer science ; Generative grammar ; Convergence (economics) ; Configuration space ; Generative model ; Simple (philosophy) ; Scaling ; Space (punctuation) ; Train ; Monte Carlo method ; Wave function ; Function (biology) ; Statistical physics ; Artificial intelligence ; Algorithm ; Machine learning ; Theoretical computer science ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics