jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gabriel A. Pinheiro | Juarez L. F. Da Silva | Marcos G. Quiles
Source: Journal of Chemical Information and Modeling (article, 2022-08-31)
Field: Materials Science — Materials Chemistry

SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning Machine learning as a tool for chemical space exploration broadens horizons to work with known and unknown molecules. At its core lies molecular representation, an essential key to improve learning about structure-property relationships. Recently, contrastive frameworks have been showing impressive results for representation learning in diverse domains. Therefore, this paper proposes a contrastive framework that embraces multimodal molecular data. Specifically, our approach jointly trains a graph encoder and an encoder for the simplified molecular-input line-entry system (SMILES) string to perform the contrastive learning objective. Since SMILES is the basis of our method, i.e., we built the molecular graph from the SMILES, we call our framework as SMILES Contrastive Learning (SMICLR). When stacking a nonlinear regressor on the SMICLR's pretrained encoder and fine-tuning the entire model, we reduced the prediction error by, on average, 44% and 25% for the energetic and electronic properties of the QM9 data set, respectively, over the supervised baseline. We further improved our framework's performance when applying data augmentations in each molecular-input representation. Moreover, SMICLR demonstrated competitive representation learning results in an unsupervised setting. Computer science ; Artificial intelligence ; Feature learning ; Representation (politics) ; Encoder ; Graph ; Machine learning ; Natural language processing ; Set (abstract data type) ; Embedding ; Stacking ; Unsupervised learning ; Pattern recognition (psychology) ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Joseph Musielewicz | Xiaoxiao Wang | Tian Tian | Zachary W. Ulissi
Source: Machine Learning Science and Technology (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

FINETUNA: fine-tuning accelerated molecular simulations Abstract Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomistic systems. However, simulation techniques based on first principles, such as density functional theory (DFT), are limited in their practical use due to their high computational expense. Machine learning approaches have the potential to approximate DFT in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world problems. However, they are limited by their accuracy and the cost of generating labeled data. Here, we present an online active learning framework for accelerating the simulation of atomic systems efficiently and accurately by incorporating prior physical information learned by large-scale pre-trained graph neural network models from the Open Catalyst Project. Accelerating these simulations enables useful data to be generated more cheaply, allowing better models to be trained and more atomistic systems to be screened. We also present a method of comparing local optimization techniques on the basis of both their speed and accuracy. Experiments on 30 benchmark adsorbate-catalyst systems show that our method of transfer learning to incorporate prior information from pre-trained models accelerates simulations by reducing the number of DFT calculations by 91%, while meeting an accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a technique for leveraging the interactive functionality built in to Vienna ab initio Simulation Package (VASP) to efficiently compute single point calculations within our online active learning framework without the significant startup costs. This allows VASP to work in tandem with our framework while requiring 75% fewer self-consistent cycles than conventional single point calculations. The online active learning implementation, and examples using the VASP interactive code, are available in the open source FINETUNA package on Github. Computer science ; Benchmark (surveying) ; Artificial neural network ; Density functional theory ; Complex system ; Artificial intelligence ; Machine learning ; Computational science ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; Ferroelectric and Negative Capacitance Devices
Authors: Taihao Han | Jie Huang | Gaurav Sant | Narayanan Neithalath | Aditya Kumar
Source: Journal of the American Ceramic Society (article, 2022-07-02)
Field: Materials Science — Ceramics and Composites

Predicting mechanical properties of ultrahigh temperature ceramics using machine learning Abstract Ultrahigh temperature ceramics (UHTCs) have melting points above 3000°C and outstanding strength at high temperatures, thus making them apposite structural materials for high‐temperature applications. Di‐borides, nitride, and carbide compounds—processed via various techniques—have been extensively studied and used in the manufacture of UHTCs. Current analytical models, based on our current but incomplete understanding of the theory, are unable to produce a priori predictions of mechanical properties of UHTCs based on their mixture designs and processing parameters. As a result, researchers have to rely on experiments—which are often costly and time‐consuming—to understand composition–structure–performance links in UHTCs. This study employs machine learning (ML) models (i.e., random forest and artificial neural network models) to predict Young's modulus, flexural strength, and fracture toughness of UHTCs in relation to a wide range of mixture designs, processing parameters, and testing conditions. Outcomes demonstrate that adequately trained ML models can yield reliable predictions, a priori, of the three aforesaid mechanical properties. The prediction performance on Young's modulus is superior to flexural strength and fracture toughness. Next, the ML model with the best prediction performance is utilized to evaluate and rank the impacts of input variables on Young's modulus. Finally, on the basis of such classification of consequential and inconsequential input variables, this study develops an easy‐to‐use, closed‐form analytical model to predict Young's modulus of UHTCs. Overall, this study highlights the ability of data‐driven numerical models to complement, or even replace, time‐consuming experiments, thereby accelerating the development of UHTCs. Flexural strength ; Materials science ; Modulus ; Toughness ; Fracture toughness ; Ceramic ; Artificial neural network ; A priori and a posteriori ; Machine learning ; Computer science ; Composite material Advanced ceramic materials synthesis ; Advanced materials and composites ; Thermal properties of materials
Authors: Wassim Gabriel | Matthew The | Daniel P. Zolg | Florian Bayer | Omar Shouman | Ludwig Lautenbacher | Karsten Schnatbaum | Johannes Zerweck | Tobias Knaute | Bernard Delanghe | Andreas F. R. Hühmer | Holger Wenschuh | Ulf Reimer | Guillaume Médard | Bernhard Küster | Mathias Wilhelm
Source: Analytical Chemistry (article, 2022-05-12)
Field: Chemistry — Spectroscopy

Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides The prediction of fragment ion intensities and retention time of peptides has gained significant attention over the past few years. However, the progress shown in the accurate prediction of such properties focused primarily on unlabeled peptides. Tandem mass tags (TMT) are chemical peptide labels that are coupled to free amine groups usually after protein digestion to enable the multiplexed analysis of multiple samples in bottom-up mass spectrometry. It is a standard workflow in proteomics ranging from single-cell to high-throughput proteomics. Particularly for TMT, increasing the number of confidently identified spectra is highly desirable as it provides identification and quantification information with every spectrum. Here, we report on the generation of an extensive resource of synthetic TMT-labeled peptides as part of the ProteomeTools project and present the extension of the deep learning model Prosit to accurately predict the retention time and fragment ion intensities of TMT-labeled peptides with high accuracy. Prosit-TMT supports CID and HCD fragmentation and ion trap and Orbitrap mass analyzers in a single model. Reanalysis of published TMT data sets show that this single model extracts substantial additional information. Applying Prosit-TMT, we discovered that the expression of many proteins in human breast milk follows a distinct daily cycle which may prime the newborn for nutritional or environmental cues. Chemistry ; Orbitrap ; Tandem mass tag ; Tandem mass spectrometry ; Mass spectrometry ; Proteomics ; Fragmentation (computing) ; Isobaric labeling ; Computational biology ; Mass spectrum ; Peptide ; Chromatography ; Quantitative proteomics ; Protein mass spectrometry ; Biochemistry ; Computer science Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Yilin Mao | He Li | Yu Wang | Kai Fan | Yujie Song | Xiao Han | Jie Zhang | Shibo Ding | Dapeng Song | Hui Wang | Zhaotang Ding
Source: Foods (article, 2022-08-22)
Field: Chemistry — Analytical Chemistry

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing. Hyperspectral imaging ; Fermentation ; Polyphenol ; Partial least squares regression ; Support vector machine ; Artificial intelligence ; Mathematics ; Principal component analysis ; Food science ; Biological system ; Chemistry ; Computer science ; Statistics ; Biology ; Biochemistry Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Advanced Chemical Sensor Technologies
Authors: M. Yu. Alyapyshev | В. А. Бабаин | Dmitry Kirsanov
Source: Energies (article, 2022-10-08)
Field: Chemistry — Inorganic Chemistry

Isolation and Purification of Actinides Using N,O-Hybrid Donor Ligands for Closing the Nuclear Fuel Cycle Despite the fact that in the mass consciousness nuclear power is associated with increased environmental risks, this type of energy today remains one of the cleanest, most efficient and carbon neutral. Further development of nuclear energy is hampered by the problem of appropriate handling of spent nuclear fuel (SNF). A very attractive concept of the closed nuclear cycle has been developed to solve it. A real-life implementation of this concept requires the development of technological processes for the efficient separation of minor actinides from the rest of the SNF components. Recent progress in this area has been closely associated with N,O-hybrid donor ligands for liquid–liquid extraction. This work systematically reviews the available literature on the topic and provides detailed explanations on the behavior of the most perspective ligands. Notably, another important aspect of the application of N,O-hybrid donor ligands in nuclear cycle analytics through their incorporation in chemical sensors is also discussed. Nuclear fuel cycle ; Spent nuclear fuel ; Nuclear power ; Actinide ; Closing (real estate) ; Fuel cycle ; Nuclear fuel ; Chemistry ; Work (physics) ; Isolation (microbiology) ; Process engineering ; Biochemical engineering ; Environmental science ; Nuclear engineering ; Engineering ; Computer science ; Physics ; Nuclear chemistry ; Nuclear physics ; Bioinformatics ; Political science ; Mechanical engineering Radioactive element chemistry and processing ; Analytical chemistry methods development ; Radioactive contamination and transfer
Authors: Migara Kavishka Jayasinghe | Chang Yu Lee | Trinh Tran | Rachel Tan | Sarah Min Chew | Brendon Zhi Jie Yeo | Wen Xiu Loh | Marco Pirisinu | Minh T. N. Le
Source: Frontiers in Digital Health (review, 2022-03-16)
Field: Materials Science — Biomaterials

The Role of in silico Research in Developing Nanoparticle-Based Therapeutics Nanoparticles (NPs) hold great potential as therapeutics, particularly in the realm of drug delivery. They are effective at functional cargo delivery and offer a great degree of amenability that can be used to offset toxic side effects or to target drugs to specific regions in the body. However, there are many challenges associated with the development of NP-based drug formulations that hamper their successful clinical translation. Arguably, the most significant barrier in the way of efficacious NP-based drug delivery systems is the tedious and time-consuming nature of NP formulation-a process that needs to account for downstream effects, such as the onset of potential toxicity or immunogenicity, <i>in vivo</i> biodistribution and overall pharmacokinetic profiles, all while maintaining desirable therapeutic outcomes. Computational and AI-based approaches have shown promise in alleviating some of these restrictions. <i>Via</i> predictive modeling and deep learning, <i>in silico</i> approaches have shown the ability to accurately model NP-membrane interactions and cellular uptake based on minimal data, such as the physicochemical characteristics of a given NP. More importantly, machine learning allows computational models to predict how specific changes could be made to the physicochemical characteristics of a NP to improve functional aspects, such as drug retention or endocytosis. On a larger scale, they are also able to predict the <i>in vivo</i> pharmacokinetics of NP-encapsulated drugs, predicting aspects such as circulatory half-life, toxicity, and biodistribution. However, the convergence of nanomedicine and computational approaches is still in its infancy and limited in its applicability. The interactions between NPs, the encapsulated drug and the body form an intricate network of interactions that cannot be modeled with absolute certainty. Despite this, rapid advancements in the area promise to deliver increasingly powerful tools capable of accelerating the development of advanced nanoscale therapeutics. Here, we describe computational approaches that have been utilized in the field of nanomedicine, focusing on approaches for NP design and engineering. Biodistribution ; In silico ; Nanomedicine ; Drug delivery ; Computer science ; Drug ; Endocytosis ; Pharmacokinetics ; Computational biology ; Biochemical engineering ; Nanotechnology ; In vivo ; Pharmacology ; Chemistry ; Nanoparticle ; Medicine ; Materials science ; Biology ; Engineering ; Biotechnology Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications ; Drug Solubulity and Delivery Systems
Authors: Baohua Tan | Wenhao You | Shihao Tian | Tengfei Xiao | Mengchen Wang | Beitian Zheng | Lina Luo
Source: Sensors (article, 2022-10-20)
Field: Chemistry — Analytical Chemistry

Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy Traditional soil nitrogen detection methods have the characteristics of being time-consuming and having an environmental pollution effect. We urgently need a rapid, easy-to-operate, and non-polluting soil nitrogen detection technology. In order to quickly measure the nitrogen content in soil, a new method for detecting the nitrogen content in soil is presented by using a near-infrared spectrum technique and random forest regression (RF). Firstly, the experiment took the soil by the Xunsi River in the area of Hubei University of Technology as the research object, and a total of 143 soil samples were collected. Secondly, NIR spectral data from 143 soil samples were acquired, and chemical and physical methods were used to determine the content of nitrogen in the soil. Thirdly, the raw spectral data of soil samples were denoised by preprocessing. Finally, a forecast model for the soil nitrogen content was developed by using the measured values of components and modeling algorithms. The model was optimized by adjusting the changes in the model parameters and Gini coefficient (∆Gini), and the model was compared with the back propagation (BP) and support vector machine (SVM) models. The results show that: the RF model modeling set prediction R<sup>2</sup><sub>C</sub> is 0.921, the RMSEC is 0.115, the test set R<sup>2</sup><sub>P</sub> is 0.83, and the RMSEP is 0.141; the detection of the soil nitrogen content can be realized by using a near-infrared spectrum technique and random forest algorithm, and its prediction accuracy is better than that of the BP and SVM models; using ∆ Gini to optimize the RF modeling data, the spectral information of the soil nitrogen content can be extracted, and the data redundancy can be reduced effectively. Random forest ; Soil test ; Nitrogen ; Environmental science ; Support vector machine ; Soil science ; Content (measure theory) ; Remote sensing ; Mathematics ; Computer science ; Soil water ; Artificial intelligence ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Ana Jurinjak Tušek | Dunja Šamec | Anita Šalić
Source: Applied Sciences (article, 2022-11-21)
Field: Chemistry — Spectroscopy

Modern Techniques for Flavonoid Extraction—To Optimize or Not to Optimize? Flavonoids, specialized metabolites found in plants, have a number of beneficial properties and are important for maintaining good health. Efficient extraction methods are required to extract the most bioactive compounds from plant material. Modern techniques are replacing conventional methods of flavonoids extraction in order to reduce energy and solvent consumption, increase extraction efficiency, and satisfy growing market demand as well as environmental legislation. The extraction of bioactive molecules compounds is affected by a number of variables. To determine the conditions that ensure the highest extraction yield, it is advisable to analyze the interactions between the above in parallel. In this work, an overview of the advantages and performance of modern methods (microwave-assisted extraction, ultrasound-assisted extraction, pressurized liquids-assisted extraction, and supercritical fluids extraction) for the extraction of flavonoids is presented. This work also presents the application of extraction process optimization and extraction kinetics for flavonoid extraction, using different types of experimental designs for different flavonoid sources and different extraction methods. The general conclusion of all the studies listed is that an experimental design combined with RSM modeling reduces the number of experiments that should be performed to achieve maximum extraction yield. Extraction (chemistry) ; Yield (engineering) ; Process engineering ; Flavonoid ; Supercritical fluid extraction ; Supercritical fluid ; Chromatography ; Biochemical engineering ; Computer science ; Chemistry ; Materials science ; Organic chemistry ; Engineering Analytical Chemistry and Chromatography ; Essential Oils and Antimicrobial Activity ; Chromatography in Natural Products
Authors: Ali Ahmed Salem | Kwan Yiew Lau | Wan Rahiman | Zulkurnain Abdul‐Malek | Samir A. Al-Gailani | Nabil Mohammed | Rahisham Abd Rahman | Salem Mgammal Al-Ameri
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Pollution Flashover Voltage of Transmission Line Insulators: Systematic Review of Experimental Works Over the past decades, extensive experimental-based research works have been carried out to investigate the flashover phenomenon on the performance of polluted transmission line insulators. The critical focus has been on developing methods that can determine the safety, reliability, and sustainability of the overall power transmission network based on experimental results obtained from polluted insulators’ flashover voltage tests. In this paper, a systematic review of available scientific works, published as early as the 1990s, for the analysis of pollution flashover voltage, is undertaken. The review mainly focuses on factors influencing the efficiency of transmission line insulators under polluted conditions. Specifically, publication databases utilizing various synonyms and keywords associated with the terms “contaminated insulators” and “flashover voltage test” have been scrutinized. The search has resulted in 1364 articles, from which 97 articles have satisfied the review requirements and have been subsequently analyzed to determine the parameters associated with polluted insulators. Major factors that affect the performance of insulators, including electrical and environmental impacts, are discussed. Variations in factors affecting flashover test development and insulator efficiency are also considered. Overall, the current analysis provides an important insight toward successful evaluations of the health of transmission line insulators and research advancements of electric power transmission line insulators. Arc flash ; Insulator (electricity) ; Electric power transmission ; Transmission line ; Power transmission ; Environmental science ; Transmission (telecommunications) ; Electric power system ; Voltage ; Computer science ; Engineering ; Electrical engineering ; Power (physics) ; Physics High voltage insulation and dielectric phenomena ; Elevator Systems and Control ; Power Transformer Diagnostics and Insulation