jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sonja Grönroos | Jari Rinta-aho | Tuomas Koskinen | Gonçalo Sorger
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Combination of Non-Destructive Methods in Estimating Irradiation-Induced Reactor Pressure Vessel Steel Alloy Embrittlement with Machine Learning Embrittlement ; Charpy impact test ; Reactor pressure vessel ; Materials science ; Artificial neural network ; Pressure vessel ; Brittleness ; Irradiation ; Alloy steel ; Metallurgy ; Alloy ; Machine learning ; Nuclear engineering ; Composite material ; Computer science ; Engineering ; Nuclear physics ; Microstructure Nuclear Materials and Properties ; Welding Techniques and Residual Stresses ; Material Properties and Failure Mechanisms
Authors: Yan Huo | Tao Liu | Zhenyu Jiang | Ji Fan
Source:  (article, 2021-07-26)
Field: Materials Science — Materials Chemistry

U-net based Deep-Learning Image monitoring of Crystal Size Distribution during L-Glutamic Acid Crystallization For monitoring the crystal size distribution (CSD) during L-glutamic acid (LGA) crystallization process, a deep-learning image analysis method is proposed based on the U-net network. The proposed method consists of crystal image preprocessing, crystal image segmentation, and CSD measurement. To cope with the negative influence on image analysis from continuous stirring of solution and particle motion in a crystallizer, the U-net network is adopted to perform deep learning for crystal image segmentation, with no need to prepare a large amount of training samples of real crystal images. Moreover, this approach could be implemented in real time for analysis of in-situ captured microscopic images, thus facilitating on-line measurement of CSD. Consequently, a log-normal distribution model is established for effectively depicting the one-dimensional (i.e. length) distribution of β-form LGA crystals during crystallization. A numerical example and experimental study on the cooling crystallization process of β-form LGA are shown to demonstrate the effectiveness the proposed method. Crystallization ; Crystal (programming language) ; Artificial intelligence ; Image segmentation ; Preprocessor ; Image (mathematics) ; Materials science ; Segmentation ; Computer science ; Biological system ; Physics ; Thermodynamics Crystallization and Solubility Studies ; Spectroscopy and Chemometric Analyses ; Image Processing Techniques and Applications
Authors: Ajith Pattammattel | Ryan Tappero | Yong S. Chu | Mingyuan Ge | Dmitri Gavrilov | Xiaojing Huang | Hanfei Yan
Source:  (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Hard x-ray nano-XANES and implementation deep learning tools for multi-modal chemical imaging Spectromicroscopy techniques allow the study of local chemical states along with morphology information. At the hard X-ray nanoprobe (HXN) beamline at NSLS-II, we developed nanoscale chemical imaging with high chemical state sensitivity and micron-scale penetration depth. In addition to the chemical images, XRF and phase-contrast images collected simultaneously offer multi-modal, correlative image analysis. We also developed a highly interactive, python-based graphical user interface (NSLS-II MIDAS) that allows multi-modal analysis of nano-XANES and XRF images. Advanced supervised and unsupervised learning algorithms enable users to explore the traditional XANES analysis along with standard machine-learning tools XANES ; Computer science ; Chemical imaging ; Nanoscopic scale ; Nanoprobe ; Beamline ; Chemical state ; Python (programming language) ; Artificial intelligence ; Materials science ; Optics ; Nanotechnology ; Spectroscopy ; Nanoparticle ; Hyperspectral imaging ; X-ray photoelectron spectroscopy ; Physics ; Chemical engineering ; Engineering ; Operating system Machine Learning in Materials Science ; Nuclear Physics and Applications ; Advanced X-ray Imaging Techniques
Authors: Shih‐Han Wang | Hemanth Somarajan Pillai | Siwen Wang | Luke E. K. Achenie | Hongliang Xin
Source: Nature Communications (preprint, 2021-09-06)
Field: Materials Science — Materials Chemistry

Infusing theory into deep learning for interpretable reactivity prediction Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties. Generalizability theory ; Artificial intelligence ; Artificial neural network ; Machine learning ; Deep neural networks ; Deep learning ; Computer science ; Labeled data ; Reactivity (psychology) ; Chemisorption ; Catalysis ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: X. L. Lei | Andrew J. Medford
Source: Research Square (preprint, 2021-10-05)
Field: Materials Science — Materials Chemistry

A universal framework for featurization of atomistic systems Abstract Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat large systems or long timescales. Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales, but these force fields require substantial effort to construct and are highly specific to a given chemical composition and application. A significant limitation of machine learning models is the use of element-specific features, leading to models that scale poorly with the number of elements. This work introduces the Gaussian multipole (GMP) featurization scheme that utilizes physically-relevant multipole expansions of the electron density around atoms to yield feature vectors that interpolate between element types and have a fixed dimension regardless of the number of elements present. We combine GMP with neural networks to directly compare it to the widely used Behler-Parinello symmetry functions for the MD17 dataset, revealing that it exhibits improved accuracy and computational efficiency. Further, we demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements. Finally, we test GMP-based models for the Open Catalysis Project (OCP) dataset, revealing comparable performance to graph convolutional deep learning models. The results indicate that this featurization scheme fills a critical gap in the construction of efficient and transferable machine-learned force fields. Computer science ; Statistical physics ; Physics Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Advanced Materials Characterization Techniques
Authors: Alessandra Toniato | Philippe Schwaller | Antonio Cardinale | Joppe Geluykens | Teodoro Laino
Source: Nature Machine Intelligence (preprint, 2021-03-29)
Field: Materials Science — Materials Chemistry

Unassisted noise reduction of chemical reaction datasets Computer science ; Metric (unit) ; Reduction (mathematics) ; Set (abstract data type) ; Noise (video) ; Artificial intelligence ; Data mining ; Noise reduction ; Data set ; Machine learning ; Training set ; Value (mathematics) ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: William Borrelli | Joshua Schrier
Source:  (preprint, 2021-06-29)
Field: Materials Science — Materials Chemistry

Evaluating the Performance of a Transformer-based Organic Reaction Prediction Model Forward and retrosynthetic organic reaction prediction are challenging applications of artificial intelligence (AI) research in chemistry. IBM’s freely available RXN for Chemistry (https://rxn.res.ibm.com) treats reaction prediction as a translation problem, by using transformer-based machine learning models trained on patent data to convert reactant SMILES string sequences into product strings. Here we characterize the performance of transformer models on 100 undergraduate text-book problems to expose reaction classes where the fundamentals of organic chemistry are violated. The forward prediction model is generally successful in predicting outcomes for substitution reactions and unsuccessful for elimination and organocopper reactions. For the retrosynthesis model, we found characteristic examples of a lack of atom conservation and nonsensical chemical transformations. We also compared the dif- ferences in molecular complexity and synthetic accessibility between predicted and literature reactions to probe how AI plans reactions compared to humans. Forward predictions replicated a similar distribution of differences in molecular complexity as the human reactions from the literature, whereas retrosynthetic predictions resulted in both positive and negative deviations from literature complexity. Finally, we analyzed the atom mapping in test reactions to expose errors in how the model identifies reactive atoms and species. Retrosynthetic analysis ; Transformer ; Computer science ; IBM ; Artificial intelligence ; Chemistry ; Machine learning ; Biochemical engineering ; Nanotechnology ; Engineering ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ninja Begum | Manuj Kumar Hazarika
Source: Agricultural Reviews (review, 2021-09-11)
Field: Chemistry — Analytical Chemistry

Deep Learning based Image Processing Solutions in Food Engineering: A Review Image based assessment of food quality for wholesomeness, nutritional composition, suitability as raw material for processing, degree of processing, product aesthetics, consumer attractiveness etc., are some of the aspirations for applying machine learning in food technology. The initial contributions made by machine learning in the field of artificial intelligence are now more prominent through the techniques of deep learning. This review presents the contributions of machine learning in obtaining image processing based solutions in food technology and the relative advantages of deep learning over machine learning as the technique for solving complex problems like image recognition and image classification. The deep learning based solutions to the problems of image processing are highlighted as the enablers of disruptions in the design and development of different sorting, grading and dietary assessment tools. Artificial intelligence ; Image processing ; Deep learning ; Computer science ; Machine learning ; Food processing ; Sorting ; Field (mathematics) ; Image (mathematics) ; Mathematics ; Food science ; Algorithm Spectroscopy and Chemometric Analyses
Authors: Alessandra Toniato | Philippe Schwaller | Antonio Cardinale | Joppe Geluykens | Teodoro Laino
Source: ChemRxiv (preprint, 2021-02-01)
Field: Materials Science — Materials Chemistry

Unassisted Noise-Reduction of Chemical Reactions Data Sets Existing deep learning models applied to reaction prediction in organic chemistry can reach high levels of accuracy (> 90% for Natural Language Processing-based ones).With no chemical knowledge embedded than the information learnt from reaction data, the quality of the data sets plays a crucial role in the performance of the prediction models. While human curation is prohibitively expensive, the need for unaided approaches to remove chemically incorrect entries from existing data sets is essential to improve artificial intelligence models' performance in synthetic chemistry tasks. Here we propose a machine learning-based, unassisted approach to remove chemically wrong entries from chemical reaction collections. We applied this method to the collection of chemical reactions Pistachio and to an open data set, both extracted from USPTO (United States Patent Office) patents. Our results show an improved prediction quality for models trained on the cleaned and balanced data sets. For the retrosynthetic models, the round-trip accuracy metric grows by 13 percentage points and the value ofthe cumulative Jensen Shannon divergence decreases by 30% compared to its original record. The coverage remains high with 97%, and the value of the class-diversity is not affected by the cleaning. The proposed strategy is the first unassisted rule-free technique to address automatic noise reduction in chemical data sets. Computer science ; Metric (unit) ; Reduction (mathematics) ; Set (abstract data type) ; Noise (video) ; Artificial intelligence ; Data mining ; Noise reduction ; Machine learning ; Training set ; Data set ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Rui Xin | Edirisuriya M. Dilanga Siriwardane | Yuqi Song | Yong Zhao | Steph-Yves Louis | Alireza Nasiri | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2021-02-28)
Field: Materials Science — Materials Chemistry

Active learning based generative design for the discovery of wide bandgap materials Active learning has been increasingly applied to screening functional materials from existing materials databases with desired properties. However, the number of known materials deposited in the popular materials databases such as ICSD and Materials Project is extremely limited and consists of just a tiny portion of the vast chemical design space. Herein we present an active generative inverse design method that combines active learning with a deep variational autoencoder neural network and a generative adversarial deep neural network model to discover new materials with a target property in the whole chemical design space. The application of this method has allowed us to discover new thermodynamically stable materials with high band gap (SrYF$_5$) and semiconductors with specified band gap ranges (SrClF$_3$, CaClF$_5$, YCl$_3$, SrC$_2$F$_3$, AlSCl, As$_2$O$_3$), all of which are verified by the first principle DFT calculations. Our experiments show that while active learning itself may sample chemically infeasible candidates, these samples help to train effective screening models for filtering out materials with desired properties from the hypothetical materials created by the generative model. The experiments show the effectiveness of our active generative inverse design approach. Autoencoder ; Generative grammar ; Computer science ; Artificial neural network ; Chemical space ; Deep learning ; Artificial intelligence ; Space (punctuation) ; Generative model ; Generative Design ; Inverse ; Property (philosophy) ; Machine learning ; Materials science ; Mathematics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Xinqiao Wang | Chuansheng Yao | Yun Zhang | Jiahui Yu | Haoran Qiao | Chengyun Zhang | Yejian Wu | Renren Bai | Hongliang Duan
Source: ChemRxiv (preprint, 2021-08-10)
Field: Materials Science — Materials Chemistry

From Theory to Experiment: Transformer-Based Generation Enables Rapid Discovery of Novel Reactions Deep learning methods have been proven their potential roles in the chemical field, such as reaction prediction and retrosynthesis analysis. However, the de novo generation of unreported reactions using artificial intelligence technology remains not be completely explored. Inspired by molecular generation, we proposed the task of novel reaction generation. In this work, we applied the Heck reactions to train the transformer model, state-of-art natural language process model and obtained 4717 generated reactions after sampling and processing. We then confirmed that 2253 novel Heck reactions by organizing chemists to judge the generated reactions, and adopted organic synthesis experiment to verify the feasibility of unreported reactions. In this process, it only took 15 days from Heck reaction generation to experimental verification, proving that our model learns reaction rules in-depth and can make great contributions in the novel reaction discovery. Transformer ; Computer science ; Retrosynthetic analysis ; Artificial intelligence ; Heck reaction ; Process (computing) ; Chemistry ; Engineering ; Palladium ; Organic chemistry ; Programming language ; Electrical engineering ; Catalysis Machine Learning in Materials Science
Authors: R. Roopalakshmi
Source: Machine Graphics and Vision (article, 2021-12-19)
Field: Chemistry — Analytical Chemistry

Chemical ripening and contaminations detection using neural networks-based image features and spectrometric signatures In this pandemic-prone era, health is of utmost concern for everyone and hence eating good quality fruits is very much essential for sound health. Unfortunately, nowadays it is quite very difficult to obtain naturally ripened fruits, due to existence of chemically ripened fruits being ripened using hazardous chemicals such as calcium carbide. However, most of the state-of-the art techniques are primarily focusing on identification of chemically ripened fruits with the help of computer vision-based approaches, which are less effective towards quantification of chemical contaminations present in the sample fruits. To solve these issues, a new framework for chemical ripening and contamination detection is presented, which employs both visual and IR spectrometric signatures in two different stages. The experiments conducted on both the GUI tool as well as hardware-based setups, clearly demonstrate the efficiency of the proposed framework in terms of detection confidence levels followed by the percentage of presence of chemicals in the sample fruit. Ripening ; Sample (material) ; Computer science ; Identification (biology) ; Artificial intelligence ; Environmental science ; Process engineering ; Biochemical engineering ; Chemistry ; Chromatography ; Food science ; Engineering ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Mingguang Chen | Junzhu Li | Bo Tian | Yas Al‐Hadeethi | Bassim Arkook | Xiaojuan Tian | Xixiang Zhang
Source: Computation (article, 2021-08-02)
Field: Materials Science — Materials Chemistry

Predicting Interfacial Thermal Resistance by Ensemble Learning Interfacial thermal resistance (ITR) plays a critical role in the thermal properties of a variety of material systems. Accurate and reliable ITR prediction is vital in the structure design and thermal management of nanodevices, aircraft, buildings, etc. However, because ITR is affected by dozens of factors, traditional models have difficulty predicting it. To address this high-dimensional problem, we employ machine learning and deep learning algorithms in this work. First, exploratory data analysis and data visualization were performed on the raw data to obtain a comprehensive picture of the objects. Second, XGBoost was chosen to demonstrate the significance of various descriptors in ITR prediction. Following that, the top 20 descriptors with the highest importance scores were chosen except for fdensity, fmass, and smass, to build concise models based on XGBoost, Kernel Ridge Regression, and deep neural network algorithms. Finally, ensemble learning was used to combine all three models and predict high melting points, high ITR material systems for spacecraft, automotive, building insulation, etc. The predicted ITR of the Pb/diamond high melting point material system was consistent with the experimental value reported in the literature, while the other predicted material systems provide valuable guidelines for experimentalists and engineers searching for high melting point, high ITR material systems. Computer science ; Machine learning ; Artificial intelligence ; Artificial neural network ; Ensemble learning ; Variety (cybernetics) ; Deep learning ; Data mining Machine Learning in Materials Science ; Thermal properties of materials ; Phase Equilibria and Thermodynamics
Authors: Jules Leguy | Béatrice Duval | Benoit Da Mota | Thomas Cauchy
Source: 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

Surrogate-Based Black-Box Optimization Method for Costly Molecular Properties AI-assisted molecular optimization is a very active research field as it is expected to provide the next-generation drugs and molecular materials. An important difficulty is that the properties to be optimized rely on costly evaluations. Machine learning methods are investigated with success to predict these properties, but show generalization issues on less known areas of the chemical space. We propose here a surrogate-based black box optimization method, to tackle jointly the optimization and machine learning problems. It consists in optimizing the expected improvement of the surrogate of a molecular property using an evolutionary algorithm. The surrogate is defined as a Gaussian Process Regression (GPR) model, learned on a relevant area of the search space with respect to the property to be optimized. We show that our approach can successfully optimize a costly property of interest much faster than a purely metaheuristic approach. Surrogate model ; Black box ; Computer science ; Kriging ; Property (philosophy) ; Metaheuristic ; Mathematical optimization ; Gaussian process ; Artificial intelligence ; Generalization ; Process (computing) ; Machine learning ; Gaussian ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration