jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jürgen Claesen | Alan L. Rockwood | Mikhail V. Gorshkov | Dirk Valkenborg
Source: Mass Spectrometry Reviews (review, 2023-02-06)
Field: Chemistry — Spectroscopy

The isotope distribution: A rose with thorns The isotope distribution, which reflects the number and probabilities of occurrence of different isotopologues of a molecule, can be theoretically calculated. With the current generation of (ultra)-high-resolution mass spectrometers, the isotope distribution of molecules can be measured with high sensitivity, resolution, and mass accuracy. However, the observed isotope distribution can differ substantially from the expected isotope distribution. Although differences between the observed and expected isotope distribution can complicate the analysis and interpretation of mass spectral data, they can be helpful in a number of specific applications. These applications include, yet are not limited to, the identification of peptides in proteomics, elucidation of the elemental composition of small organic molecules and metabolites, as well as wading through peaks in mass spectra of complex bioorganic mixtures such as petroleum and humus. In this review, we give a nonexhaustive overview of factors that have an impact on the observed isotope distribution, such as elemental isotope deviations, ion sampling, ion interactions, electronic noise and dephasing, centroiding, and apodization. These factors occur at different stages of obtaining the isotope distribution: during the collection of the sample, during the ionization and intake of a molecule in a mass spectrometer, during the mass separation and detection of ionized molecules, and during signal processing. Chemistry ; Isotopologue ; Isotope ; Mass spectrometry ; Monoisotopic mass ; Analytical Chemistry (journal) ; Mass spectrum ; Kinetic isotope effect ; Molecule ; Environmental chemistry ; Deuterium ; Chromatography ; Atomic physics ; Organic chemistry ; Nuclear physics ; Physics Mass Spectrometry Techniques and Applications ; Isotope Analysis in Ecology ; Metabolomics and Mass Spectrometry Studies
Authors: Bruno Camino | John Buckeridge | P. A. Warburton | Viv Kendon | Scott M. Woodley
Source: Journal of Applied Physics (article, 2023-06-13)
Field: Materials Science — Materials Chemistry

Quantum computing and materials science: A practical guide to applying quantum annealing to the configurational analysis of materials Using quantum computers for computational chemistry and materials science will enable us to tackle problems that are intractable on classical computers. In this paper, we show how the relative energy of defective graphene structures can be calculated by using a quantum annealer. This simple system is used to guide the reader through the steps needed to translate a chemical structure (a set of atoms) and energy model to a representation that can be implemented on quantum annealers (a set of qubits). We discuss in detail how different energy contributions can be included in the model and what their effect is on the final result. The code used to run the simulation on D-Wave quantum annealers is made available as a Jupyter Notebook. This Tutorial was designed to be a quick-start guide for the computational chemists interested in running their first quantum annealing simulations. The methodology outlined in this paper represents the foundation for simulating more complex systems, such as solid solutions and disordered systems. Quantum annealing ; Qubit ; Computer science ; Quantum ; Quantum computer ; Computational science ; Set (abstract data type) ; Simple (philosophy) ; Statistical physics ; Theoretical computer science ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Graphene research and applications
Authors: Andrew Wang | Carlota Bozal‐Ginesta | Sai Govind Hari Kumar | Alán Aspuru‐Guzik | Geoffrey A. Ozin
Source: Matter (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Designing materials acceleration platforms for heterogeneous CO2 photo(thermal)catalysis Acceleration ; Thermal ; Catalysis ; Materials science ; Nanotechnology ; Computer science ; Engineering physics ; Aerospace engineering ; Physics ; Chemistry ; Engineering ; Meteorology Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: James M. Rondinelli | Wei Chen
Source: Applied Physics Reviews (article, 2023-04-10)
Field: Materials Science — Materials Chemistry

ET-AL: Entropy-targeted active learning for bias mitigation in materials data Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. In this work, we focus on data bias arising from uneven coverage of materials families in existing knowledge. Observing different diversities among crystal systems in common materials databases, we propose an information entropy-based metric for measuring this bias. To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems. We demonstrate the capability of ET-AL for bias mitigation and the resulting improvement in downstream machine learning models. This approach is broadly applicable to data-driven materials discovery, including autonomous data acquisition and dataset trimming to reduce bias, as well as data-driven informatics in other scientific domains. Trimming ; Computer science ; Informatics ; Data science ; Entropy (arrow of time) ; Data mining ; Machine learning ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Haoyuan Du | Guorui Li | Jiyu Sun | Yanyan Zhang | Yunhe Bai | Qian Chen | Yiming Liang
Source: Smart Materials and Structures (review, 2023-08-18)
Field: Materials Science — Materials Chemistry

A review of shape memory alloy artificial muscles in bionic applications Abstract As an important part of soft robots, artificial muscles have received increasing attention. And since artificial muscles are developed by imitating the characteristics of biological muscles, they are naturally suitable for bionic applications. Shape memory alloys (SMAs) have been widely used in the field of artificial muscles due to their high energy density, biocompatibility, corrosion resistance, and self-sensing properties. In this review, the bionic applications of SMA artificial muscles are classified and summarized, and they are divided into two categories: bionic robotics (animal imitation) and biomedical (human imitation) applications. In the part of bionic robots, we summarize the applications of SMA artificial muscles in bionic robots such as flying, jumping, walking, crawling and swimming robots according to the motion characteristics. In the part of biomedical applications, we summarize the applications of SMA artificial muscles in various parts of the human body. In addition, this review also counts the proportion of SMA wires and springs used in applications, and provides a reference for the subsequent selection of SMA wires and springs. Finally, the challenges and opportunities of SMA artificial muscles are summarized and prospected. SMA* ; Artificial muscle ; Shape-memory alloy ; Bionics ; Artificial intelligence ; Crawling ; Robot ; Artificial life ; Pneumatic artificial muscles ; Computer science ; Robotics ; Imitation ; Engineering ; Actuator ; Biology ; Anatomy ; Neuroscience Shape Memory Alloy Transformations ; Advanced Sensor and Energy Harvesting Materials ; Soft Robotics and Applications
Authors: Jules Lee | Prajakatta Mulay | Matthew Tamasi | Jonathan Yeow | Molly M. Stevens | Adam J. Gormley
Source: Digital Discovery (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

A fully automated platform for photoinitiated RAFT polymerization Oxygen tolerant polymerizations including Photoinduced Electron/Energy Transfer-Reversible Addition-Fragmentation Chain-Transfer (PET-RAFT) polymerization allow for high-throughput synthesis of diverse polymer architectures on the benchtop in parallel. Recent developments have further increased throughput using liquid handling robotics to automate reagent handling and dispensing into well plates thus enabling the combinatorial synthesis of large polymer libraries. Although liquid handling robotics can enable automated polymer reagent dispensing in well plates, photoinitiation and reaction monitoring require automation to provide a platform that enables the reliable and robust synthesis of various polymer compositions in high-throughput where polymers with desired molecular weights and low dispersity are obtained. Here, we describe the development of a robotic platform to fully automate PETRAFT polymerizations and provide individual control of reactions performed in well plates. On our platform, reagents are automatically dispensed in well plates, photoinitiated in individual wells with a custom-designed lightbox until the polymerizations are complete, and monitored online in real-time by tracking fluorescence intensities on a fluorescence plate reader, with well plate transfers between instruments occurring <i>via</i> a robotic arm. We found that this platform enabled robust parallel polymer synthesis of both acrylate and acrylamide homopolymers and copolymers, with high monomer conversions and low dispersity. The successful polymerizations obtained on this platform make it an efficient tool for combinatorial polymer chemistry. In addition, with the inclusion of machine learning protocols to help navigate the polymer space towards specific properties of interest, this robotic platform can ultimately become a self-driving lab that can dispense, synthesize, and monitor large polymer libraries. Python (programming language) ; Scripting language ; Raft ; Reversible addition−fragmentation chain-transfer polymerization ; Computer science ; Polymerization ; Programming language ; Polymer ; Materials science ; Radical polymerization Advanced Polymer Synthesis and Characterization ; Innovative Microfluidic and Catalytic Techniques Innovation ; Photopolymerization techniques and applications
Authors: Ashraf Al Sharah | Hamza Abu Owida | Feras Alnaimat | Suhaila Abuowaida
Source: IAES International Journal of Artificial Intelligence (article, 2023-12-25)
Field: Materials Science — Materials Chemistry

Application of machine learning in chemical engineering: outlook and perspectives Chemical engineers' formulation, development, and stance processes all heavily rely on models. The physical and economic consequences of these decisions can have disastrous effects. Attempts to employ a hybrid form of artificial intelligence for modeling in various disciplines. However, they fell short of expectations. Due to a rise in the amount of data and computational resources during the previous five years. A lot of recent work has gone into developing new data sources, indexes, chemical interface designs, and machine learning algorithms in an effort to facilitate the adoption of these techniques in the research community. However, there are some important downsides to machine learning gains. The most promising uses for machine learning are in time-critical tasks like real-time optimization and planning that require extreme precision and can build on models that can self-learn to recognize patterns, draw conclusions from data, and become more intelligent over time. Due to their limited exposure to computer science and data analysis, the majority of chemical engineers are potentially vulnerable to the development of artificial intelligence. But in the not-too-distant future, chemical engineers' modeling toolbox will include a reliable machine learning component. Toolbox ; Computer science ; Machine learning ; Artificial intelligence ; Component (thermodynamics) ; Interface (matter) ; Data science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Noah Hoffmann | Jonathan Schmidt | Silvana Botti | Miguel A. L. Marques
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Transfer learning on large datasets for the accurate prediction of material properties Pretraining on large, lower-fidelity datasets enables extremely effective training of graph neural networks on smaller, high-fidelity datasets. Fidelity ; Transfer of learning ; Computer science ; Artificial intelligence ; Graph ; High fidelity ; Machine learning ; Artificial neural network ; Training set ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Çiğdem Karabacak Atay | Ömer Dilek | Tahir Tilki | Bülent Dede
Source: Journal of Molecular Modeling (article, 2023-07-05)
Field: Chemistry — Organic Chemistry

A novel imidazole-based azo molecule: synthesis, characterization, quantum chemical calculations, molecular docking, molecular dynamics simulations and ADMET properties Chemistry ; Molecule ; Molecular dynamics ; Computational chemistry ; HOMO/LUMO ; Molecular geometry ; Carbon-13 NMR ; Combinatorial chemistry ; Stereochemistry ; Organic chemistry Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Nonlinear Optical Materials Research
Authors: Sabrina Di Masi | Giuseppe Egidio De Benedetto | Cosimino Malitesta
Source: Analytical and Bioanalytical Chemistry (article, 2023-12-20)
Field: Chemistry — Analytical Chemistry

Optimisation of electrochemical sensors based on molecularly imprinted polymers: from OFAT to machine learning Molecularly imprinted polymer ; Computer science ; Chemometrics ; Analyte ; Sensitivity (control systems) ; Nanotechnology ; Machine learning ; Artificial intelligence ; Chemical sensor ; Biochemical engineering ; Materials science ; Chemistry ; Selectivity ; Engineering ; Electronic engineering Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies
Authors: Jeffrey Hu | David Liu | Nihang Fu | Rongzhi Dong
Source: Digital Discovery (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Realistic material property prediction using domain adaptation based machine learning Scientists aim to predict properties for a known subset of related out-of-distribution materials rather than universally distributed samples. Introducing target material information enhances machine learning models. Domain adaptation ; Property (philosophy) ; Adaptation (eye) ; Domain (mathematical analysis) ; Computer science ; Artificial intelligence ; Machine learning ; Mathematics ; Psychology ; Epistemology Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Non-Destructive Testing Techniques
Authors: Serpil Edebalı
Source: Applied Surface Science Advances (article, 2023-11-24)
Field: Chemistry — Inorganic Chemistry

Synthesis and characterization of MIL-101 (Fe) as efficient catalyst for tetracycline degradation by using NaBH4: Artificial neural network modeling In this work, Fe-based metal organic framework (MIL-101 (Fe) MOF) was synthesized to use it as a catalyst for the catalytic degradation of tetracycline antibiotic with NaBH4. The synthesized MOF were characterized by Fourier Transform Infrared Spectroscopy, X-ray Diffraction, Scanning Electron Microscopy/Electron Dispersive X-ray and point of zero charge pH analysis. The effects of parameters such as contact time, pH, NaBH4 concentration and the amount of catalyst on the removal process were investigated and optimum parameters were determined. According to this, tetracycline removal efficiency was determined as 0.180 in terms of Ct/C0 at the end of 60-min contact time under determined optimum conditions for 60 ppm tetracycline solution. Moreover, the process was modeled with Artificial Neural Networks approach by using empirical results obtained from where process parameters were investigated. Finally, it was revealed with HPLC analysis that tetracycline was converted into different products as a result of applied process. Tetracycline ; Fourier transform infrared spectroscopy ; Catalysis ; Scanning electron microscope ; Degradation (telecommunications) ; Materials science ; Tetracycline antibiotics ; Characterization (materials science) ; Nuclear chemistry ; Chemical engineering ; Chemistry ; Composite material ; Nanotechnology ; Organic chemistry ; Computer science ; Antibiotics Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Nanomaterials for catalytic reactions
Authors: Wang Jun-ya | Pengcheng Xu | Xiaobo Ji | Minjie Li | Wencong Lu
Source: Materials Today Communications (article, 2023-08-16)
Field: Materials Science — Materials Chemistry

MIC-SHAP: An ensemble feature selection method for materials machine learning Feature selection ; Interpretability ; Feature (linguistics) ; Computer science ; Workflow ; Selection (genetic algorithm) ; Dimensionality reduction ; Artificial intelligence ; Stability (learning theory) ; Machine learning ; k-nearest neighbors algorithm ; Data mining ; Pattern recognition (psychology) ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Neural Networks and Applications
Authors: Jiahao Ye | Qingguo Peng
Source: Energy (article, 2023-02-23)
Field: Materials Science — Materials Chemistry

Improved emissions conversion of diesel oxidation catalyst using multifactor impact analysis and neural network NOx ; Catalysis ; Diesel fuel ; Diesel engine ; Pollutant ; Chemistry ; Diesel exhaust ; Exhaust gas ; Diesel particulate filter ; Particulates ; Materials science ; Environmental chemistry ; Environmental engineering ; Chemical engineering ; Analytical Chemistry (journal) ; Environmental science ; Inorganic chemistry ; Automotive engineering ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Vehicle emissions and performance ; Advanced Combustion Engine Technologies
Authors: Hao‐Cheng Thong | Xiaoyang Wang | Jian Han | Linfeng Zhang | Bei Li | Ke Wang | Ben Xu
Source: Physical review. B./Physical review. B (article, 2023-01-05)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential for molecular dynamics simulation of the ferroelectric <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>KNb</mml:mi><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> perovskite Ferroelectric perovskites have been ubiquitously applied in piezoelectric devices for decades, among which ecofriendly lead-free $(\mathrm{K},\phantom{\rule{0.16em}{0ex}}\mathrm{Na})\mathrm{Nb}{\mathrm{O}}_{3}\text{\ensuremath{-}}\mathrm{based}$ materials have been recently demonstrated to be an excellent candidate for sustainable development. Molecular dynamics is a versatile theoretical calculation approach for the investigation of the dynamical properties of ferroelectric perovskites. However, molecular dynamics simulation of ferroelectric perovskites has been limited to simple systems, since the conventional construction of interatomic potential is rather difficult and inefficient. In the present study, we construct a machine-learning interatomic potential of $\mathrm{KNb}{\mathrm{O}}_{3}$ [as a representative system of $(\mathrm{K},\phantom{\rule{0.16em}{0ex}}\mathrm{Na})\mathrm{Nb}{\mathrm{O}}_{3}$] by using a deep neural network model. Including first-principles calculation data into the training data set ensures the quantum-mechanics accuracy of the interatomic potential. The molecular dynamics based on machine-learning interatomic potential shows good agreement with the first-principles calculations, which can accurately predict multiple fundamental properties, e.g., atomic force, energy, elastic properties, and phonon dispersion. In addition, the interatomic potential exhibits satisfactory performance in the simulation of domain wall and temperature-dependent phase transition. The construction of interatomic potential based on machine learning could potentially be transferred to other ferroelectric perovskites and consequently benefit the theoretical study of ferroelectrics. Interatomic potential ; Ferroelectricity ; Molecular dynamics ; Machine learning ; Phonon ; Algorithm ; Statistical physics ; Phase transition ; Materials science ; Physics ; Artificial intelligence ; Computer science ; Condensed matter physics ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Sergei Manzhos | Manabu Ihara
Source: The Journal of Physical Chemistry A (article, 2023-09-12)
Field: Materials Science — Materials Chemistry

Neural Network with Optimal Neuron Activation Functions Based on Additive Gaussian Process Regression Feed-forward neural networks (NNs) are a staple machine learning method widely used in many areas of science and technology, including physical chemistry, computational chemistry, and materials informatics. While even a single-hidden-layer NN is a universal approximator, its expressive power is limited by the use of simple neuron activation functions (such as sigmoid functions) that are typically the same for all neurons. More flexible neuron activation functions would allow the use of fewer neurons and layers and thereby save computational cost and improve expressive power. We show that additive Gaussian process regression (GPR) can be used to construct optimal neuron activation functions that are individual to each neuron. An approach is also introduced that avoids nonlinear fitting of neural network parameters by defining them with rules. The resulting method combines the advantage of robustness of a linear regression with the higher expressive power of an NN. We demonstrate the approach by fitting the potential energy surfaces of the water molecule and formaldehyde. Without requiring any nonlinear optimization, the additive-GPR-based approach outperforms a conventional NN in the high-accuracy regime, where a conventional NN suffers more from overfitting. Overfitting ; Sigmoid function ; Computer science ; Artificial neural network ; Kriging ; Activation function ; Artificial intelligence ; Robustness (evolution) ; Nonlinear system ; Gaussian process ; Gaussian ; Biological system ; Algorithm ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy Techniques in Biomedical and Chemical Research