jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhuoying Zhao | Ziling Tan | Pinghui Mo | Xiaonan Wang | Dan Zhao | Xin Zhang | Ming Tao | Jie Liu
Source: IEEE Transactions on Circuits and Systems I Regular Papers (article, 2023-03-14)
Field: Materials Science — Materials Chemistry

A Heterogeneous Parallel Non-von Neumann Architecture System for Accurate and Efficient Machine Learning Molecular Dynamics This paper proposes a special-purpose system to achieve high-accuracy and high-efficiency machine learning (ML) molecular dynamics (MD) calculations. The system consists of field programmable gate array (FPGA) and application specific integrated circuit (ASIC) working in heterogeneous parallelization. To be specific, a multiplication-less neural network (NN) is deployed on the non-von Neumann (NvN)-based ASIC (SilTerra 180 nm process) to evaluate atomic forces, which is the most computationally expensive part of MD. All other calculations of MD are done using FPGA (Xilinx XC7Z100). It is shown that, to achieve similar-level accuracy, the proposed NvN-based system based on low-end fabrication technologies (180 nm) is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.6\times $ </tex-math></inline-formula> faster and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{2}$ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{3}\times $ </tex-math></inline-formula> more energy efficiency than state-of-the-art vN-based MLMD using graphics processing units (GPUs) based on much more advanced technologies (12 nm), indicating superiority of the proposed NvN-based heterogeneous parallel architecture. Von Neumann architecture ; Application-specific integrated circuit ; Field-programmable gate array ; Notation ; Computer science ; Parallel computing ; Mathematics ; Arithmetic ; Computer hardware ; Programming language Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Bruno G. daFonseca | Sapanbir S. Thind | Ian R. Booth | Alexandre G. Brolo
Source: Journal of Raman Spectroscopy (article, 2023-10-18)
Field: Materials Science — Materials Chemistry

A machine learning approach for the automated classification of bulk sp<sup>2</sup> and sp<sup>3</sup> carbon materials Abstract Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to classify different types of carbon material based on their Raman spectra. The selected reference materials were highly oriented pyrolytic graphite (HOPG), diamond‐like carbon (DLC), glassy carbon (GC), hydrogenated graphite‐like carbon (GLCH), and hydrogenated polymer‐like carbon (PLCH). These materials vary in crystallinity, predominant carbon hybridization, and hydrogen content. The training dataset was Raman spectra collected from commercial samples (HOPG, DLC, GC) and samples synthesized in our laboratory (GLCH, PLCH). The Raman spectra were collected using 532 nm laser excitation. The classification model revealed that the first principal component (PC1) was the determinant source of information to separate the crystalline from the amorphous carbon samples. PC2 allowed the separation of amorphous material with different levels of hybridization (sp 2 and sp 3 ). Finally, both PC2 and PC3 contributed to separate materials with different levels of hydrogenation. The classification model was tested using a library of Raman spectra of carbon materials reported in the literature, and the results showed a high accuracy prediction (97%). The model presented here provides an avenue for automated classification of carbon materials using Raman spectroscopy and machine learning. Raman spectroscopy ; Glassy carbon ; Carbon fibers ; Amorphous carbon ; Materials science ; Crystallinity ; Highly oriented pyrolytic graphite ; Graphite ; Analytical Chemistry (journal) ; Principal component analysis ; Amorphous solid ; Pyrolytic carbon ; Pyrolysis ; Chemistry ; Artificial intelligence ; Crystallography ; Computer science ; Composite material ; Physical chemistry ; Organic chemistry ; Physics ; Optics Diamond and Carbon-based Materials Research ; Carbon Nanotubes in Composites ; Glass properties and applications
Authors: Xiaoyu Luo | Sheng Zheng | Zhibo Jiang | Zhiwei Chen | Yao Huang | Shuguang Zeng | Xiangyun Zeng
Source: Astronomy and Astrophysics (article, 2023-12-20)
Field: Chemistry — Spectroscopy

Semi-supervised deep learning for molecular clump verification Context. A reliable molecular clump detection algorithm is essential for studying these clumps. Existing detection algorithms for molecular clumps still require that detected candidates be verified manually, which is impractical for large-scale data. Semi-supervised learning methods, especially those based on deep features, have the potential to accomplish the task of molecular clump verification thanks to the powerful feature extraction capability of deep networks. Aims. Our main objective is to develop an automated method for the verification of molecular clump candidates. This method utilises a 3D convolutional neural network (3D CNN) to extract features of molecular clumps and employs semi-supervised learning to train the model, with the aim being to improve its generalisation ability and data utilisation. It addresses the issue of insufficient labelled samples in traditional supervised learning and enables the model to better adapt to new, unlabelled samples, achieving high accuracy in the verification of molecular clumps. Methods. We propose SS-3D-Clump, a semi-supervised deep clustering method that jointly learns the parameters of a 3D CNN and the cluster assignments of the generated features for automatic verification of molecular clumps. SS-3D-Clump iteratively classifies the features with the Constrained-KMeans and uses these class labels as supervision to update the weights of the entire network. Results. We used CO data from the Milky Way Imaging Scroll Painting project covering 350 square degrees in the Milky Way’s first, second, and third quadrants. The ClumpFind algorithm was applied to extract molecular clump candidates in these regions, which were subsequently verified using SS-3D-Clump. The SS-3D-Clump model, trained on a dataset comprising three different density regions, achieved an accuracy of 0.933, a recall rate of 0.955, a precision rate of 0.945, and an F1 score of 0.950 on the corresponding test dataset. These results closely align with those obtained through manual verification. Conclusions. Our experiments demonstrate that the SS-3D-Clump model achieves high accuracy in the automated verification of molecular clumps. It effectively captures the essential features of the molecular clumps and overcomes the challenge of limited labelled samples in supervised learning by using unlabelled samples through semi-supervised learning. This enhancement significantly improves the generalisation capability of the SS-3D-Clump model, allowing it to adapt effectively to new and unlabelled samples. Consequently, SS-3D-Clump can be integrated with any detection algorithm to create a comprehensive framework for the automated detection and verification of molecular clumps. Artificial intelligence ; Deep learning ; Computer science ; Psychology Mass Spectrometry Techniques and Applications ; Laser-Matter Interactions and Applications ; Quantum, superfluid, helium dynamics
Authors: Soni Shukla | Poonam Rawat | Pulkit Sharma | Prince Trivedi | Faraz Ghous | Abha Bishnoi
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spectroscopic characterization, molecular docking and machine learning studies of sulphur containing hydrazide derivatives Machine learning applied in chemistry is a growing field of research. For assessing structure-property variations, this paper describes <i>in silico</i> studies of the hydrazide derivatives of thiosemicarbazide (TSCZ) and thiocarbohydrazide (TCHZ). The structures of TSCZ and TCHZ have been elucidated using modern spectroscopic techniques. The UV-vis spectra showed strong charge transfer transitions (π-π*) for TSCZ and TCHZ with high extinction coefficients. The NBO analysis showed orbital overlap between lp1 (N2) and σ* (C3-S4) in TSCZ and TCHZ due to intramolecular charge transfer. The first hyperpolarizabilities (<i>β</i><sub>0</sub>) for TSCZ and TCHZ were found to be 0.7155 and 2.1615 × 10<sup>-30</sup> esu, respectively, indicating their greater suitability for NLO applications as compared to standard reference urea. The strong electrophilic behaviour of TSCZ and TCHZ has been indicated by their global elecrophilicity index. The electrophilic reactivity descriptor analysis indicated that the investigated molecules could serve as precursors for the targeted synthesis of new heterocyclic derivatives. The docking studies showed appreciable binding energies with target proteins having PDB IDs 2WJE and 6CLU of Gram-positive bacteria, namely, <i>Streptococcus pneumoniae</i> phosphatase (PTP-CPS4B) and <i>Staphylococcus aureus</i> dihydropteroate synthase (saDHPS), respectively, for TSCZ and TCHZ, predicting good antimicrobial activity. Natural bond orbital ; Chemistry ; Docking (animal) ; Hydrazide ; Electrophile ; Protein Data Bank (RCSB PDB) ; Computational chemistry ; DHPS ; AutoDock ; Dihydropteroate synthase ; Stereochemistry ; In silico ; Organic chemistry ; Density functional theory ; Biochemistry Nonlinear Optical Materials Research ; Synthesis and biological activity ; Structural and Chemical Analysis of Organic and Inorganic Compounds
Authors: Ayana Ghosh | Sergei V. Kalinin | Maxim Ziatdinov
Source: APL Machine Learning (article, 2023-10-20)
Field: Materials Science — Materials Chemistry

Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space The discovery of the molecular candidates for application in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries necessitates the development of machine learning algorithms capable of rapid exploration of chemical spaces targeting the desired functionalities. Here, we introduce a novel approach for active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data followed by introducing them as (probabilistic) mean functions for the Gaussian process. This approach combines the elements from the symbolic regression methods, such as SISSO and active learning, into a single framework. The primary focus of constructing this framework is to approximate physical laws in an active learning regime toward a more robust predictive performance, as traditional evaluation on hold-out sets in machine learning does not account for out-of-distribution effects which may lead to a complete failure on unseen chemical space. Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly to datasets from both domains of molecular and solid-state materials sciences. Chemical space ; Computer science ; Artificial intelligence ; Probabilistic logic ; Active learning (machine learning) ; Machine learning ; Property (philosophy) ; Focus (optics) ; Construct (python library) ; Space (punctuation) ; Process (computing) ; Gaussian process ; Drug discovery ; Gaussian ; Chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Maicon Pierre Lourenço | Alain Tchagang | Karthik Shankar | Venkataraman Thangadurai | Dennis R. Salahub
Source: Canadian Journal of Chemistry (article, 2023-04-06)
Field: Materials Science — Materials Chemistry

Active learning for optimum experimental design—insight into perovskite oxides Finding the optimum material with improved properties for a given application is challenging because data acquisition in materials science and chemistry is time consuming and expensive. Therefore, dealing with small datasets is a reality in chemistry, whether the data are obtained from synthesis or computational experiments. In this work, we propose a new artificial intelligence method based on active learning (AL) to guide new experiments with as little data as possible, for optimum experimental design. The AL method is applied to ABO 3 perovskites, where a descriptor based on atomic properties was developed. Several regressor algorithms were employed: artificial neural network, Gaussian process, and support vector regressor. The developed AL method was applied in the experimental design of two important materials: non-stoichiometric perovskites (Ba (1− x) A x Ti (1− y) B y O 3 ) due to substituting ionic sites with different concentrations and elements (A = Ca, Sr, Cd; B = Zr, Sn, Hf), aiming at the maximization of the energy storage density, and stoichiometric ABO 3 perovskites where different elements are changed in the A and B sites for the minimization of the formation energy. AL for experimental design is implemented in the machine learning agent for chemistry and design (MLChem4D) software, which has the potential to be applied in inorganic and organic synthesis (e.g., search for the optimum concentrations, catalysts, reactants, temperatures, and pH to improve the yield) and materials science (e.g., search the periodic table for the proper elements and their concentrations to improve the materials properties). The latter marks the first MLChem4D application for the design of perovskites. Periodic table ; Chemistry ; Stoichiometry ; Yield (engineering) ; Artificial neural network ; Energy minimization ; Perovskite (structure) ; Ionic bonding ; Maximization ; Minification ; Machine learning ; Computer science ; Thermodynamics ; Computational chemistry ; Physical chemistry ; Physics ; Mathematics ; Ion ; Mathematical optimization ; Crystallography ; Organic chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Jehad Saleh | Sajjad Haider | Muhammad Saeed Akhtar | Muhammad Saqib | Muqadas Javed | Sayed Elshahat | Ghulam Mustafa Kamal
Source: Molecules (article, 2023-01-27)
Field: Materials Science — Polymers and Plastics

Energy Level Prediction of Organic Semiconductors for Photodetectors and Mining of a Photovoltaic Database to Search for New Building Units Due to the large versatility in organic semiconductors, selecting a suitable (organic semiconductor) material for photodetectors is a challenging task. Integrating computer science and artificial intelligence with conventional methods in optimization and material synthesis can guide experimental researchers to develop, design, predict and discover high-performance materials for photodetectors. To find high-performance organic semiconductor materials for photodetectors, it is crucial to establish a relationship between photovoltaic properties and chemical structures before performing synthetic procedures in laboratories. Moreover, the fast prediction of energy levels is desirable for designing better organic semiconductor photodetectors. Herein, we first collected large sets of data containing photovoltaic properties of organic semiconductor photodetectors reported in the literature. In addition, molecular descriptors that make it easy and fast to predict the required properties were used to train machine learning models. Power conversion efficiency and energy levels were also predicted. Multiple models were trained using experimental data. The light gradient boosting machine (LGBM) regression model and Hist gradient booting regression model are the best models. The best models were further tuned to achieve better prediction ability. The reliability of our designed approach was further verified by mining the photovoltaic database to search for new building units. The results revealed that good consistency is obtained between experimental outcomes and model predictions, indicating that machine learning is a powerful approach to predict the properties of photodetectors, which can facilitate their rapid development in various fields. Photodetector ; Photovoltaic system ; Computer science ; Organic semiconductor ; Artificial intelligence ; Semiconductor ; Organic solar cell ; Machine learning ; Materials science ; Optoelectronics ; Engineering ; Electrical engineering Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: Yujia Liu | Zhenwei Liang | Jiazhun Huang | Biqi Zhong | Xiaojun Yang | Tiejun Wang
Source: Catalysis Science & Technology (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Screening of steam-reforming catalysts using unsupervised machine learning In this article, a bidirectional clustering model proposed for methanol-reforming catalysts demonstrates excellent mathematical performance and is of significance for the discovery of methanol-reforming catalysts. Catalysis ; Methanol ; Steam reforming ; Cluster analysis ; Process engineering ; Chemistry ; Computer science ; Artificial intelligence ; Organic chemistry ; Engineering ; Hydrogen production Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Yannik Köster | Julian Kimmig | Stefan Zechel | Ulrich S. Schubert
Source: Cell Reports Physical Science (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Fingerprint applicable for machine learning tested on LCST behavior of polymers Since the 1990s, there have been many attempts to efficiently decode molecules; however, for polymers, some descriptions have always been neglected. Details like position and occurrence of molecular structural patterns are hardly possible with such techniques. However, consideration of these details can lead to the ability to predict the properties of polymeric materials in a highly efficient manner. In this study, we have succeeded in taking a further step toward a more concise incorporation of this information. We introduce the polymer fingerprint, a way of representing polymers from common experimental characterization data such as size-exclusion chromatography. In addition, we demonstrate the functionality of the representation for the prediction of cloud-point temperatures. The newly created database for this proof consists of various polymers with different architectures and properties, which were extracted from scientific publications. Finally, we evaluate the limitations and potentialities for such representation-prediction models and their future importance. Representation (politics) ; Polymer ; Fingerprint (computing) ; Computer science ; Characterization (materials science) ; Lower critical solution temperature ; Position (finance) ; Artificial intelligence ; Machine learning ; Materials science ; Nanotechnology ; Chemistry ; Copolymer ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Fang Wu | Nicolas Courty | Shuting Jin | Stan Z. Li
Source: Patterns (article, 2023-04-01)
Field: Materials Science — Materials Chemistry

Improving molecular representation learning with metric learning-enhanced optimal transport Training data are usually limited or heterogeneous in many chemical and biological applications. Existing machine learning models for chemistry and materials science fail to consider generalizing beyond training domains. In this article, we develop a novel optimal transport-based algorithm termed MROT to enhance their generalization capability for molecular regression problems. MROT learns a continuous label of the data by measuring a new metric of domain distances and a posterior variance regularization over the transport plan to bridge the chemical domain gap. Among downstream tasks, we consider basic chemical regression tasks in unsupervised and semi-supervised settings, including chemical property prediction and materials adsorption selection. Extensive experiments show that MROT significantly outperforms state-of-the-art models, showing promising potential in accelerating the discovery of new substances with desired properties. Computer science ; Machine learning ; Metric (unit) ; Generalization ; Artificial intelligence ; Representation (politics) ; Regularization (linguistics) ; Regression ; Bridge (graph theory) ; Kriging ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Pier Paolo Prosini
Source: Batteries (article, 2023-02-05)
Field: Materials Science — Materials Chemistry

Crystal Group Prediction for Lithiated Manganese Oxides Using Machine Learning This work aimed to predict the crystal structure of a compound starting only from the knowledge of its chemical composition. The method was developed to select new materials in the field of lithium-ion batteries and tested on Li-Fe-O compounds. For each testing compound, the correspondence with respect to the training compounds was evaluated simply by calculating the Euclidean distance existing between the stoichiometric coefficients of the elements constituting the two compounds. At the compound under test was assigned the crystal structure of the training compound for which the distance value was minimum. The results showed that the model can predict the crystalline group of the test compound with an accuracy higher than 80% and a precision higher than 90%, for a cut-off distance higher than four. The method was then used to predict the crystalline group of manganese-based compounds (Li-Mn-O). The analysis conducted on twenty randomly selected compounds showed an accuracy of 70%. Out of ten valid predictions, nine were true positives, with a precision of 90%. Manganese ; Crystal (programming language) ; Crystal structure ; Stoichiometry ; Euclidean distance ; Group (periodic table) ; Materials science ; Euclidean geometry ; Work (physics) ; Analytical Chemistry (journal) ; Computer science ; Mathematics ; Chemistry ; Crystallography ; Artificial intelligence ; Physics ; Metallurgy ; Geometry ; Chromatography ; Physical chemistry ; Thermodynamics ; Organic chemistry Machine Learning in Materials Science ; Big Data and Digital Economy