jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peter Blümler | Helmut Soltner
Source: Applied Magnetic Resonance (article, 2023-10-10)
Field: Chemistry — Spectroscopy

Practical Concepts for Design, Construction and Application of Halbach Magnets in Magnetic Resonance Abstract This review is a compilation of relevant concepts in designing Halbach multipoles for magnetic resonance applications. The main focus is on providing practical guidelines to plan, design and build such magnets. Therefore, analytical equations are presented for estimating the magnetic field from ideal to realistic systems. Various strategies of homogenizing magnetic fields are discussed together with concepts of opening such magnets without force or combining them for variable fields. Temperature compensation and other practical aspects are also reviewed. For magnetic resonance two polarities (di- and quadrupole) are of main interest, but higher polarities are also included. Magnet ; Halbach array ; Focus (optics) ; Magnetic field ; Computer science ; Quadrupole magnet ; Mechanical engineering ; Ideal (ethics) ; Nuclear magnetic resonance ; Physics ; Engineering ; Optics Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Muon and positron interactions and applications
Authors: Milad Abolhasani | Keith A. Brown | Guest Editors
Source: MRS Bulletin (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

Role of AI in experimental materials science Computer science ; Flexibility (engineering) ; Data science ; Field (mathematics) ; Interface (matter) ; Key (lock) ; Artificial intelligence ; Human–computer interaction Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Yigitcan Comlek | Thang Duc Pham | Randall Q. Snurr | Wei Chen
Source: npj Computational Materials (article, 2023-09-21)
Field: Chemistry — Inorganic Chemistry

Rapid design of top-performing metal-organic frameworks with qualitative representations of building blocks Abstract Data-driven materials design often encounters challenges where systems possess qualitative (categorical) information. Specifically, representing Metal-organic frameworks (MOFs) through different building blocks poses a challenge for designers to incorporate qualitative information into design optimization, and leads to a combinatorial challenge, with large number of MOFs that could be explored. In this work, we integrated Latent Variable Gaussian Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to identify top-performing MOFs adaptively, autonomously, and efficiently. We showcased that our method (i) requires no specific physical descriptors and only uses building blocks that construct the MOFs for global optimization through qualitative representations, (ii) is application and property independent, and (iii) provides an interpretable model of building blocks with physical justification. By searching only ~1% of the design space, LVGP-MOBBO identified all MOFs on the Pareto front and 97% of the 50 top-performing designs for the CO 2 working capacity and CO 2 /N 2 selectivity properties. Categorical variable ; Bayesian optimization ; Computer science ; Multi-objective optimization ; Construct (python library) ; Process (computing) ; Artificial intelligence ; Theoretical computer science ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Nathaniel H. Park | Matteo Manica | Jannis Born | James L. Hedrick | Tim Erdmann | Dmitry Yu. Zubarev | Nil Adell-Mill | Pedro L. Arrechea
Source: Nature Communications (article, 2023-06-21)
Field: Materials Science — Materials Chemistry

Artificial intelligence driven design of catalysts and materials for ring opening polymerization using a domain-specific language Advances in machine learning (ML) and automated experimentation are poised to vastly accelerate research in polymer science. Data representation is a critical aspect for enabling ML integration in research workflows, yet many data models impose significant rigidity making it difficult to accommodate a broad array of experiment and data types found in polymer science. This inflexibility presents a significant barrier for researchers to leverage their historical data in ML development. Here we show that a domain specific language, termed Chemical Markdown Language (CMDL), provides flexible, extensible, and consistent representation of disparate experiment types and polymer structures. CMDL enables seamless use of historical experimental data to fine-tune regression transformer (RT) models for generative molecular design tasks. We demonstrate the utility of this approach through the generation and the experimental validation of catalysts and polymers in the context of ring-opening polymerization-although we provide examples of how CMDL can be more broadly applied to other polymer classes. Critically, we show how the CMDL tuned model preserves key functional groups within the polymer structure, allowing for experimental validation. These results reveal the versatility of CMDL and how it facilitates translation of historical data into meaningful predictive and generative models to produce experimentally actionable output. Computer science ; Leverage (statistics) ; Workflow ; Generative grammar ; Representation (politics) ; Artificial intelligence ; Database Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Ziquan Qin | Dapeng Li | Yapeng Ou | Sijia Du | Qingjie Jiao | Jiwu Peng | Ping Liu
Source: Crystals (article, 2023-06-19)
Field: Materials Science — Surfaces, Coatings and Films

Recent Advances in Polydopamine for Surface Modification and Enhancement of Energetic Materials: A Mini-Review Polydopamine (PDA), inspired by the adhesive mussel foot proteins, is widely applied in chemical, biological, medical, and material science due to its unique surface coating capability and abundant active sites. Energetic materials (EMs) play an essential role in both military and civilian fields as a chemical energy source. Recently, PDA was introduced into EMs for the modification of crystal phase stability and the interfacial bonding effect, and, as a result, to enhance the mechanical, thermal, and safety performances. This mini-review summarizes the representative works in PDA modified EMs from three perspectives. Before that, the self-polymerization mechanisms of dopamine and the methods accelerating this process are briefly presented for consideration of researchers in this field. The future directions and remaining issues of PDA in this field are also discussed at last in this mini-review. Nanotechnology ; Surface modification ; Coating ; Materials science ; Mechanical engineering ; Engineering Polymer Surface Interaction Studies ; Advanced Sensor and Energy Harvesting Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Hosam Aboul Anean | L. O. Mallasiy | Dina M. D. Bader | Heba Ali Yousef Shaat
Source: Materials (article, 2023-02-13)
Field: Materials Science — Biomaterials

Nano Edible Coatings and Films Combined with Zinc Oxide and Pomegranate Peel Active Phenol Compounds Has Been to Extend the Shelf Life of Minimally Processed Pomegranates Edible coating and film from chitosan and incorporating it with the action of ZnONPs on active phenol compounds from extracts of pomegranate peel (PPE) The physical and chemical properties of edible films composed of zinc oxide ZnONPs and active phenol compounds extracted from pomegranate peel (PPE) were investigated. Adding ZnONPs with active phenol compounds from extracted pomegranate peel(PPE) to chitosan films can provide safe edible films, decrease microbial growth and consequently prolong the shelf life of pomegranates, as well as improve the physiochemical stability of the pomegranate. The substances used in this experiment were film with a (A) extract of pomegranate peels (PPE), 5% (0.1%), (B)ZnONPs 1% (0.02%), (C) ZnONPs 2% (0.04%), (D) ZnONPs 3% (0.06%), (E) ZnONPs 1%/PPE1% (0.02%), (F) ZnONPs 2%/PPE2% (0.04%), (G) ZnONPs 3%/PPE3% (0.06%) wt% of chitosan on quality attributes and prolonging the shelf life of pomegranates were stored in plastic containers at 2 °C and 90-95% relative humidity for 20 days. The treatments of (G) ZnONPs 3%/PPE3% (0.06%) loaded on chitosan as well as chitosan and (D) ZnONPs 3% (0.06%) reduced the weight loss, had excellent microbial count until 20 days of storage, and recorded the lowest microbial count and mould & yeast colonies. Other chemical properties, such as total soluble solids content, acidity, anthocyanin content, firmness, and ascorbic acid, were investigated. Results indicated that ZnONPs 3%/PPE3% (0.06%) loaded on chitosan or ZnONPs 3% (0.06%) are the best treatments for preserving pomegranate arils. It was found that the best measurements were that the film-forming nan emulsion solutions decreased by E% 110 nm and B% 134 nm. Nano followed treatment, F% 188.7 nm, compared to nano edible films, which were A 0% 1312 nm. Chitosan ; Shelf life ; Ascorbic acid ; Chemistry ; Phenol ; Food science ; Aril ; Zinc ; Nuclear chemistry ; Organic chemistry Nanocomposite Films for Food Packaging ; Nanoparticles: synthesis and applications ; Pomegranate: compositions and health benefits
Authors: Dayana Gulevich | Igor Nabiev | Pavel Samokhvalov
Source: Materials Today Chemistry (review, 2023-12-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning–assisted colloidal synthesis: A review Nanomaterials ; Nanotechnology ; Colloid ; Computer science ; Reagent ; Materials science ; Artificial intelligence ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Advanced Nanomaterials in Catalysis
Authors: Qiaofeng Li | Huaibo Chen | Benjamin C. Koenig | Sili Deng
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification We develop Bayesian Chemical Reaction Neural Network (B-CRNN), a method to infer chemical reaction models and provide the associated uncertainty purely from data without prior knowledge of reaction templates. Computer science ; Uncertainty quantification ; Bayesian probability ; Interpretability ; Robustness (evolution) ; Markov chain Monte Carlo ; Bayesian inference ; Artificial intelligence ; Machine learning ; Chemistry Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Fault Detection and Control Systems
Authors: Xi He | Jinde Liu | Yang Chen | Gang Jiang
Source: Computational Materials Science (article, 2023-03-27)
Field: Materials Science — Materials Chemistry

Predicting thermodynamic stability of magnesium alloys in machine learning Magnesium ; Stability (learning theory) ; Density functional theory ; Atom (system on chip) ; Ridge ; Generalization ; Computer science ; Molecular dynamics ; Work (physics) ; Chemical stability ; Kernel (algebra) ; Materials science ; Algorithm ; Thermodynamics ; Machine learning ; Chemistry ; Computational chemistry ; Mathematics ; Metallurgy ; Physics Machine Learning in Materials Science ; Nuclear Materials and Properties ; X-ray Diffraction in Crystallography
Authors: Yankai Dai | Zhiwei Zhang | Dong Wang | Tianliang Li | Yuze Ren | Jingqi Chen | Lingyan Feng
Source: Advanced Materials (article, 2023-11-21)
Field: Chemistry — Organic Chemistry

Machine‐Learning‐Driven G‐Quartet‐Based Circularly Polarized Luminescence Materials Circularly polarized luminescence (CPL) materials have garnered significant interest due to their potential applications in chiral functional devices. Synthesizing CPL materials with a high dissymmetry factor (g<sub>lum</sub> ) remains a significant challenge. Inspired by efficient machine learning (ML) applications in scientific research, this work demonstrates ML-based techniques for the first time to guide the synthesis of G-quartet-based CPL gels with high g<sub>lum</sub> values and multiple chiral regulation strategies. Employing an "experiment-prediction-verification" approach, this work devises a ML classification and regression model for the solvothermal synthesis of G-quartet gels in deep eutectic solvents. This process illustrates the relationship between various synthesis parameters and the g<sub>lum</sub> value. The decision tree algorithm demonstrates superior performance across six ML models, with model accuracy and determination coefficients amounting to 0.97 and 0.96, respectively. The screened CPL gels exhibiting a g<sub>lum</sub> value up to 0.15 are obtained through combined ML guidance and experimental verification, among the highest ones reported till now for biomolecule-based CPL systems. These findings indicate that ML can streamline the rational design of chiral nanomaterials, thereby expediting their further development. Luminescence ; Materials science ; Eutectic system ; Biomolecule ; Nanotechnology ; Nanomaterials ; Expediting ; Artificial intelligence ; Computer science ; Optoelectronics ; Systems engineering ; Composite material ; Engineering Synthesis and Properties of Aromatic Compounds ; Supramolecular Self-Assembly in Materials ; Luminescence and Fluorescent Materials
Authors: Minyi Dai | Mehmet F. Demirel | Xuanhan Liu | Yingyu Liang | Jia‐Mian Hu
Source: Computational Materials Science (article, 2023-09-07)
Field: Materials Science — Materials Chemistry

Graph neural network for predicting the effective properties of polycrystalline materials: A comprehensive analysis Artificial neural network ; Crystallite ; Materials science ; Graph ; Computer science ; Artificial intelligence ; Metallurgy ; Theoretical computer science Machine Learning in Materials Science ; Advanced machining processes and optimization ; Force Microscopy Techniques and Applications
Authors: Vipul Mann | Mauricio Sales‐Cruz | Rafiqul Gani | Venkat Venkatasubramanian
Source: Computers & Chemical Engineering (article, 2023-11-20)
Field: Materials Science — Materials Chemistry

eSFILES: Intelligent process flowsheet synthesis using process knowledge, symbolic AI, and machine learning Hypergraph ; Computer science ; Process (computing) ; Representation (politics) ; Domain (mathematical analysis) ; Ontology ; Data flow diagram ; Theoretical computer science ; Data mining ; Programming language ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Injection Molding Process and Properties
Authors: Gokhan Onder Aksu | Seda Keskın
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Advancing CH<sub>4</sub>/H<sub>2</sub> separation with covalent organic frameworks by combining molecular simulations and machine learning A high-throughput computational screening approach combined with machine learning (ML) was introduced to unlock the potential of both synthesized and hypothetical COFs (hypoCOFs) for adsorption-based CH<sub>4</sub>/H<sub>2</sub> separation. We studied 597 synthesized COFs for adsorption of a CH<sub>4</sub>/H<sub>2</sub> mixture using Grand Canonical Monte Carlo (GCMC) simulations under pressure-swing adsorption (PSA) and vacuum-swing adsorption (VSA) conditions. Based on the simulation results, the CH<sub>4</sub>/H<sub>2</sub> selectivities, CH<sub>4</sub> working capacities, adsorbent performance scores, and regenerabilities of the synthesized COFs were assessed and the structural properties of the top-performing COFs were identified. The hypoCOF database composed of 69 840 materials was then filtered to identify 7737 hypothetical materials having similar structural properties to the top synthesized COFs. These hypothetical COFs were then examined for CH<sub>4</sub>/H<sub>2</sub> separation using molecular simulations and the results showed that the top hypoCOFs have CH<sub>4</sub> selectivities and working capacities in the ranges of 21.9-28.7 (64.7-128.6) and 5.8-7.6 (1.3-3.1) mol kg<sup>-1</sup> under PSA (VSA) conditions, respectively, outperforming the synthesized COFs and metal-organic frameworks (MOFs). ML models were then developed based on the hypoCOF simulation results to accurately predict the CH<sub>4</sub>/H<sub>2</sub> mixture adsorption properties of all remaining hypothetical materials when their structural and chemical properties are fed into the models. These models accurately assessed the CH<sub>4</sub>/H<sub>2</sub> mixture separation performances of any hypoCOF within seconds without performing computationally demanding molecular simulations. The computational approach that we have proposed in this study will provide an accurate and efficient assessment of COF materials for CH<sub>4</sub>/H<sub>2</sub> separation and significantly accelerate the experimental efforts towards the design and discovery of new high-performing COF adsorbents. Covalent bond ; Metal-organic framework ; Nanotechnology ; Separation (statistics) ; Materials science ; Computer science ; Artificial intelligence ; Chemistry ; Machine learning ; Organic chemistry ; Adsorption Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Alejandro Rodriguez | Changpeng Lin | Hong-Ao Yang | Mohammed Al‐Fahdi | Chen Shen | Kamal Choudhary | Yong Zhao | Jianjun Hu | Bing Cao | Hongbin Zhang | Ming Hu
Source: npj Computational Materials (article, 2023-02-06)
Field: Materials Science — Materials Chemistry

Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table Abstract Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneck with the developed Elemental Spatial Density Neural Network Force Field, namely Elemental-SDNNFF. The effectiveness and precision of our Elemental-SDNNFF approach are demonstrated on 11,866 full, half, and quaternary Heusler structures spanning 55 elements in the periodic table by prediction of complete phonon properties. Self-improvement schemes including active learning and data augmentation techniques provide an abundant 9.4 million atomic data for training. Deep insight into predicted ultralow lattice thermal conductivity (&lt;1 Wm −1 K −1 ) of 774 Heusler structures is gained by p–d orbital hybridization analysis. Additionally, a class of two-band charge-2 Weyl points, referred to as “double Weyl points”, are found in 68% and 87% of 1662 half and 1550 quaternary Heuslers, respectively. Phonon ; Bottleneck ; Artificial neural network ; Scaling ; Materials science ; Atomic units ; Periodic table ; Condensed matter physics ; Computer science ; Artificial intelligence ; Statistical physics ; Mathematics ; Physics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Inorganic Chemistry and Materials
Authors: Akash Singh | Yumeng Li
Source: Computational Materials Science (article, 2023-06-07)
Field: Materials Science — Materials Chemistry

Reliable machine learning potentials based on artificial neural network for graphene Graphene ; Materials science ; Molecular dynamics ; Interatomic potential ; Nanotechnology ; Computer science ; Computational chemistry ; Chemistry Graphene research and applications ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: You Hu | Junhua Chen | Zheng Wei | Qiu He | Yan Zhao
Source: Journal of Materials Informatics (article, 2023-08-31)
Field: Materials Science — Materials Chemistry

Recent advances and applications of machine learning in electrocatalysis Electrocatalysis plays an important role in the production of clean energy and pollution control. Researchers have made great efforts to explore efficient, stable, and inexpensive electrocatalysts. However, traditional trial and error experiments and theoretical calculations require a significant amount of time and resources, which limits the development speed of electrocatalysts. Fortunately, the rapid development of machine learning (ML) has brought new solutions to scientific problems and new paradigms to the development of electrocatalysts. The combination of ML with experimental and theoretical calculations has propelled significant advancements in electrocatalysis research, particularly in the areas of materials screening, performance prediction, and catalysis theory development. In this review, we present a comprehensive overview of the workflow and cutting-edge techniques of ML in the field of electrocatalysis. In addition, we discuss the diverse applications of ML in predicting performance, guiding synthesis, and exploring the theory of catalysis. Finally, we conclude the review with the challenges of ML in electrocatalysis. Electrocatalyst ; Workflow ; Computer science ; Biochemical engineering ; Nanotechnology ; Field (mathematics) ; Materials science ; Chemistry ; Engineering ; Electrochemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Dulyawat Doonyapisut | Padmanathan-Karthick Kannan | Byeongkyu Kim | Jung Kyu Kim | Eunseok Lee | Chan‐Hwa Chung
Source: Advanced Intelligent Systems (article, 2023-04-21)
Field: Chemistry — Electrochemistry

Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks Technology advancements in energy storage, photocatalysis, and sensors have generated enormous impedimetric data. Electrochemical impedance spectroscopy (EIS) results play an essential role in analyzing the interfacial properties of materials. Nonetheless, in many situations, the data is misinterpreted due to the complexity of the electrochemical system or the compromise between the experimental result and the theoretical model, resulting in partiality in the interpretation process, especially for the impedimetric results. Typically, the experimenter interprets impedimetric results using a searching approach based on a theoretical model until the best‐fitting model is obtained, which is a time‐consuming process, and errors can occur. To reduce misinterpretation by the experimenter, herein, the machine‐learning strategy is demonstrated for the classification of an EIS circuit model and parameter prediction using a deep neural network (DNN). The DNN model shows a highly accurate classifier for the commonly used EIS circuit with an average area under the receiver operating characteristic curve of more than 0.95. Additionally, the model demonstrates high accuracy in the prediction of EIS parameters on a complex EIS system, with a maximum R 2 of 0.999. These reveal that the machine‐learning strategy may open a new room for studying electrochemical systems. Dielectric spectroscopy ; Artificial neural network ; Electrical impedance ; Computer science ; Artificial intelligence ; Process (computing) ; Classifier (UML) ; Machine learning ; Equivalent circuit ; Biological system ; Pattern recognition (psychology) ; Electrochemistry ; Engineering ; Voltage ; Electrical engineering ; Chemistry Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Fuel Cells and Related Materials