jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yurui Liu | Duoteng Zhang | Yunwei Qu | Fang Tang | Hui Wang | Aixiang Ding | Lin Li
Source: Chemical & Biomedical Imaging (review, 2023-08-02)
Field: Chemistry — Spectroscopy

Advances in Small-Molecule Fluorescent pH Probes for Monitoring Mitophagy Mitochondria play a crucial role in regulating cellular energy homeostasis and cell death, making them essential organelles. Maintaining proper cellular functions relies on the removal of damaged mitochondria through a process called mitophagy. Mitophagy is associated with changes in the pH value and has implications for numerous diseases. To effectively monitor mitophagy, fluorescent probes that exhibit high selectivity and sensitivity based on pH detection have emerged as powerful tools. In this review, we present recent advancements in the monitoring of mitophagy using small-molecule fluorescence pH probes. We focus on various sensing mechanisms employed by these probes, including intramolecular charge transfer (ICT), fluorescence resonance energy transfer (FRET), through bond energy transfer (TBET), and photoelectron transfer (PET). Additionally, we discuss disease models used for studying mitophagy and summarize the design requirements for small-molecule fluorescent pH probes suitable for monitoring the mitophagy process. Lastly, we highlight the remaining challenges in this field and propose potential directions for the future development of mitophagy probes. Mitophagy ; Förster resonance energy transfer ; Fluorescence ; Mitochondrion ; Nanotechnology ; Organelle ; Biophysics ; Chemistry ; Cell biology ; Biology ; Autophagy ; Materials science ; Biochemistry ; Physics Molecular Sensors and Ion Detection ; Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors
Authors: Yuhan Bai | Tao Ouyang | Qing Guo | Yating Ning | Jiaqi Liu | Hansong Wei | Kang Du | Hongmei Jing | Ye Tian | Y. Pu
Source: Applied Physics Letters (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

Achieving ultrahigh energy storage density in super relaxor BCZT-based lead-free capacitors through multiphase coexistence Dielectric capacitors own great potential in next-generation energy storage devices for their fast charge-discharge time, while low energy storage capacity limits their commercialization. Enormous lead-free ferroelectric ceramic capacitor systems have been reported in recent decades, and energy storage density has increased rapidly. By comparing with some ceramic systems with fashioned materials or techniques, which lacks repeatability, as reported latterly, we proposed a unique but straightforward way to boost the energy storage capacity in a modified conventional ferroelectric system. Through stoichiometric ratio regulation, the coexistence of the C-phase and T-phase was obtained in 0.85(Ba1-xCax)(ZryTi1-y)O3-0.15BiSmO3-2 wt. % MnO ceramics with x = 0.1 and y = 0.15 under the proof of the combination of Rietveld XRD refinement and transmission electron microscope measurement. The Wrec of 3.90 J/cm3, an excellent value for BCZT-based ceramic at the present stage, was obtained because of the co-contribution of the optimization of electric field distribution and the additional interfacial polarization triggered at the higher electric fields. The finite element simulation and physical deduction, which fits very well with our experimental result, were also performed. As to the practical application, stable performance in a long-time cycle and frequency stability was obtained, and excellent discharge behaviors were also achieved. Materials science ; Capacitor ; Energy storage ; Ceramic ; Dielectric ; Ferroelectricity ; Ferroelectric ceramics ; Rietveld refinement ; Voltage ; Optoelectronics ; Electrical engineering ; Composite material ; Thermodynamics ; Optics ; Diffraction ; Physics Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Multiferroics and related materials
Authors: Chao Yun | Haoran Guo | Zhongchong Lin | Licong Peng | Zhongyu Liang | Miao Meng | Biao Zhang | Zijing Zhao | Leran Wang | Yifei Ma | Yajing Liu | Weiwei Li | Shuai Ning | Yanglong Hou | Jinbo Yang | Zhaochu Luo
Source: Science Advances (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Efficient current-induced spin torques and field-free magnetization switching in a room-temperature van der Waals magnet The discovery of magnetism in van der Waals (vdW) materials has established unique building blocks for the research of emergent spintronic phenomena. In particular, owing to their intrinsically clean surface without dangling bonds, the vdW magnets hold the potential to construct a superior interface that allows for efficient electrical manipulation of magnetism. Despite several attempts in this direction, it usually requires a cryogenic condition and the assistance of external magnetic fields, which is detrimental to the real application. Here, we fabricate heterostructures based on Fe<sub>3</sub>GaTe<sub>2</sub> flakes that have room-temperature ferromagnetism with excellent perpendicular magnetic anisotropy. The current-driven nonreciprocal modulation of coercive fields reveals a high spin-torque efficiency in the Fe<sub>3</sub>GaTe<sub>2</sub>/Pt heterostructures, which further leads to a full magnetization switching by current. Moreover, we demonstrate the field-free magnetization switching resulting from out-of-plane polarized spin currents by asymmetric geometry design. Our work could expedite the development of efficient vdW spintronic logic, memory, and neuromorphic computing devices. Spintronics ; Condensed matter physics ; Magnetism ; Magnetization ; Ferromagnetism ; van der Waals force ; Magnet ; Materials science ; Magnetic anisotropy ; Magnetic field ; Physics 2D Materials and Applications ; Advanced Memory and Neural Computing ; Multiferroics and related materials
Authors: Yanhong Su | Zhao Zhang | Leo Tsz On Lee | Ling Peng | Ligong Lu | Xu He | Xuanjun Zhang
Source: Advanced Healthcare Materials (article, 2023-01-18)
Field: Materials Science — Polymers and Plastics

Amphiphilic Dendrimer Doping Enhanced pH‐Sensitivity of Liposomal Vesicle for Effective Co‐delivery toward Synergistic Ferroptosis–Apoptosis Therapy of Hepatocellular Carcinoma Ferroptosis, characterized by the accumulation of reactive oxygen species and lipid peroxides, has emerged as an attractive strategy to reverse drug resistance. Of particular interest is the ferroptosis-apoptosis combination therapy for cancer treatment. Herein, a nanoplatform is reported for effective co-delivery of the anticancer drug sorafenib (S) and the ferroptosis inducer hemin (H), toward synergistic ferroptosis-apoptosis therapy of advanced hepatocellular carcinoma (HCC) as a proof-of-concept study. Liposome is an excellent delivery system; however, it is not sufficiently responsive to the acidic tumor microenvironment (TME) for tumor-targeted drug delivery. The pH-sensitive vesicles are therefore developed (SH-AD-L) by incorporating amphiphilic dendrimers (AD) into liposomes for controlled and pH-stimulated release of sorafenib and hemin in the acidic TME, thanks to the protonation of numerous amine functionalities in AD. Importantly, SH-AD-L not only blocked glutathione synthesis to disrupt the antioxidant system, but also increased intracellular Fe<sup>2+</sup> and ·OH concentrations to amplify oxidative stress, both of which contribute to enhanced ferroptosis. Remarkably, high levels of ·OH also augmented sorafenib-mediated apoptosis in tumor cells. This study demonstrates the efficacy of ferroptosis-apoptosis combination therapy, as well as the promise of the AD-doped TME-responsive vesicles for drug delivery in combination therapy to treat advanced HCC. Dendrimer ; Liposome ; Hepatocellular carcinoma ; Apoptosis ; Amphiphile ; Materials science ; Cancer research ; Vesicle ; Nanotechnology ; Biophysics ; Chemistry ; Cell biology ; Medicine ; Copolymer ; Biochemistry ; Polymer chemistry ; Membrane ; Biology Dendrimers and Hyperbranched Polymers ; MicroRNA in disease regulation ; RNA Interference and Gene Delivery
Authors: Chao Zhang | Jun Cheng | Yiming Chen | Maria K. Y. Chan | Qiong Cai | Rodrigo P. Carvalho | Cleber F. N. Marchiori | Daniel Brandell | C. Moysés Araújo | Ming Chen | Xiangyu Ji | Guang Feng | Kateryna Goloviznina | Alessandra Serva | Mathieu Salanne | Toshihiko Mandai | Tomooki Hosaka | Mirna Alhanash | Patrik Johansson | Yunze Qiu | Hai Xiao | Michael Eikerling | Ryosuke Jinnouchi | Marko Melander | Georg Kastlunger | Assil Bouzid | Alfredo Pasquarello | Seung‐Jae Shin | Minho M. Kim | Hyungjun Kim | Kathleen Schwarz | Ravishankar Sundararaman
Source: Journal of Physics Energy (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

2023 Roadmap on molecular modelling of electrochemical energy materials Abstract New materials for electrochemical energy storage and conversion are the key to the electrification and sustainable development of our modern societies. Molecular modelling based on the principles of quantum mechanics and statistical mechanics as well as empowered by machine learning techniques can help us to understand, control and design electrochemical energy materials at atomistic precision. Therefore, this roadmap, which is a collection of authoritative opinions, serves as a gateway for both the experts and the beginners to have a quick overview of the current status and corresponding challenges in molecular modelling of electrochemical energy materials for batteries, supercapacitors, CO 2 reduction reaction, and fuel cell applications. Electrochemistry ; Environmental science ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Physical chemistry ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Valentina Di Francesco | Daniela P. Boso | Thomas L. Moore | Bernhard A. Schrefler | Paolo Decuzzi
Source: Biomedical Microdevices (article, 2023-08-05)
Field: Materials Science — Biomaterials

Machine learning instructed microfluidic synthesis of curcumin-loaded liposomes The association of machine learning (ML) tools with the synthesis of nanoparticles has the potential to streamline the development of more efficient and effective nanomedicines. The continuous-flow synthesis of nanoparticles via microfluidics represents an ideal playground for ML tools, where multiple engineering parameters - flow rates and mixing configurations, type and concentrations of the reagents - contribute in a non-trivial fashion to determine the resultant morphological and pharmacological attributes of nanomedicines. Here we present the application of ML models towards the microfluidic-based synthesis of liposomes loaded with a model hydrophobic therapeutic agent, curcumin. After generating over 200 different liposome configurations by systematically modulating flow rates, lipid concentrations, organic:water mixing volume ratios, support-vector machine models and feed-forward artificial neural networks were trained to predict, respectively, the liposome dispersity/stability and size. This work presents an initial step towards the application and cultivation of ML models to instruct the microfluidic formulation of nanoparticles. Dispersity ; Microfluidics ; Liposome ; Materials science ; Nanoparticle ; Curcumin ; Nanotechnology ; Mixing (physics) ; Biological system ; Chemistry Nanoparticle-Based Drug Delivery ; Lipid Membrane Structure and Behavior ; Microfluidic and Bio-sensing Technologies
Authors: Sina Stocker | Hyunwook Jung | Gábor Cśanyi | C. Franklin Goldsmith | Karsten Reuter | Johannes T. Margraf
Source: Journal of Chemical Theory and Computation (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration Predicting the rate constants of elementary reaction steps is key for the computational modeling of catalytic processes. Within transition state theory (TST), this requires an accurate estimation of the corresponding free energy barriers. While sophisticated methods for estimating free energy differences exist, these typically require extensive (biased) molecular dynamics simulations that are computationally prohibitive with the first-principles electronic structure methods that are typically used in catalysis research. In this contribution, we show that machine-learning (ML) interatomic potentials can be trained in an automated iterative workflow to perform such free energy calculations at a much reduced computational cost as compared to a direct density functional theory (DFT) based evaluation. For the decomposition of CHO on Rh(111), we find that thermal effects are substantial and lead to a decrease in the free energy barrier, which can be vanishingly small, depending on the DFT functional used. This is in stark contrast to previously reported estimates based on a harmonic TST approximation, which predicted an increase in the barrier at elevated temperatures. Since CHO is the reactant of the putative rate limiting reaction step in syngas conversion on Rh(111) and essential for the selectivity toward oxygenates containing multiple carbon atoms (C<sub>2+</sub> oxygenates), our results call into question the reported mechanism established by microkinetic models. Oxygenate ; Density functional theory ; Molecular dynamics ; Chemistry ; Transition state theory ; Catalysis ; Transition state ; Chemical physics ; Reaction rate constant ; Computational chemistry ; Statistical physics ; Computer science ; Physics ; Quantum mechanics ; Kinetics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalytic Processes in Materials Science
Authors: Adil Kabylda | Valentín Vassilev-Galindo | Stefan Chmiela | Igor Poltavsky | Alexandre Tkatchenko
Source: Nature Communications (article, 2023-06-15)
Field: Materials Science — Materials Chemistry

Efficient interatomic descriptors for accurate machine learning force fields of extended molecules Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations of realistic molecules, including: (1) developing efficient descriptors for non-local interatomic interactions, which are essential to capture long-range molecular fluctuations, and (2) reducing the dimensionality of the descriptors to enhance the applicability and interpretability of MLFFs. Here we propose an automatized approach to substantially reduce the number of interatomic descriptor features while preserving the accuracy and increasing the efficiency of MLFFs. To simultaneously address the two stated challenges, we illustrate our approach on the example of the global GDML MLFF. We found that non-local features (atoms separated by as far as 15 Å in studied systems) are crucial to retain the overall accuracy of the MLFF for peptides, DNA base pairs, fatty acids, and supramolecular complexes. Interestingly, the number of required non-local features in the reduced descriptors becomes comparable to the number of local interatomic features (those below 5 Å). These results pave the way to constructing global molecular MLFFs whose cost increases linearly, instead of quadratically, with system size. Interpretability ; Computer science ; Curse of dimensionality ; Molecular dynamics ; Interatomic potential ; Quadratic growth ; Machine learning ; Artificial intelligence ; Algorithm ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Christopher K. H. Borg | Eric S. Muckley | Clara Nyby | James E. Saal | Logan Ward | Apurva Mehta | Bryce Meredig
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Quantifying the performance of machine learning models in materials discovery In this work, we simulate a sequential learning (SL)-guided materials discovery process and demonstrate a decoupling between traditional model error metrics and model performance in guiding materials discoveries. Machine learning ; Computer science ; Artificial intelligence ; Data science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: James P. Darby | Dávid Péter Kovács | Ilyes Batatia | A. Miguel | Gus L. W. Hart | Christoph Ortner | Gábor Csányi
Source: Physical Review Letters (article, 2023-07-13)
Field: Materials Science — Materials Chemistry

Tensor-Reduced Atomic Density Representations Density-based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modeling, and the visualization and analysis of material datasets. The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them. This leads to a steep scaling in the size of the representation as the number of elements increases. Graph neural networks, which do not explicitly use density representations, escape this scaling by mapping the chemical element information into a fixed dimensional space in a learnable way. By exploiting symmetry, we recast this approach as tensor factorization of the standard neighbour-density-based descriptors and, using a new notation, identify connections to existing compression algorithms. In doing so, we form compact tensor-reduced representation of the local atomic environment whose size does not depend on the number of chemical elements, is systematically convergable, and therefore remains applicable to a wide range of data analysis and regression tasks. Scaling ; Tensor (intrinsic definition) ; Invariant (physics) ; Computer science ; Representation (politics) ; Homogeneous space ; Visualization ; Theoretical computer science ; Physics ; Mathematics ; Artificial intelligence ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Songyang Zhang | Xiaojie He | Xuejian Xia | Peng Xiao | Qi Wu | Feng Zheng | Qinghua Lu
Source: ACS Applied Materials & Interfaces (article, 2023-07-25)
Field: Materials Science — Materials Chemistry

Machine-Learning-Enabled Framework in Engineering Plastics Discovery: A Case Study of Designing Polyimides with Desired Glass-Transition Temperature Great and continuous efforts have been made to discover high-performance engineering plastics with specific properties to replace traditional engineering materials in many fields. The utilization of machine learning (ML) has brought more opportunities for the discovery of high-performing engineering plastics. However, hindered by either the relatively small database or a lack of accurate structure descriptors with clear physical and chemical meanings relating to polymer properties, the current ML studies show some flaws in the accuracy and efficiency in polymer development. Herein, we collected a dataset of 878 polyimides (PI), one of the best engineering plastics, with experimentally measured glass-transition temperature (<i>T</i><sub>g</sub>) values, and developed a rapid and accurate ML approach to design PI candidates with the desired <i>T</i><sub>g</sub> value. After the conversion from PI structures into "mechanically identifiable" SMILES (Simplified molecular input line entry system) language, the eight most critical descriptors were ultimately obtained by multiple analysis methods. The physiochemical meaning of the key descriptors was further analyzed carefully to translate the implicit "machine language" to chemical knowledge. The artificial neural network (ANN)-based model gave the most accurate results with a root-mean-square error of ∼11 K among the studied ML methods. More importantly, three potential PI candidates with desired <i>T</i><sub>g</sub> (DPIs) were designed according to the chemical insight of the key descriptors, which were then verified by experiments. The experimental and predicted <i>T</i><sub>g</sub> values of DPIs have an acceptable average deviation of ca. 3.66%. This accuracy has reached the level of the traditional molecular simulation, but the time consumption and hold-up computing resource are tremendously reduced. Furthermore, the current ML approach could offer a scalable and adaptable framework in future engineer plastics innovation. Materials science ; Glass transition ; Nanotechnology ; Mechanical engineering ; Composite material ; Polymer ; Engineering physics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Gianluca Armeli | Jan-Hendrik Peters | Thomas Koop
Source: ACS Omega (article, 2023-03-22)
Field: Materials Science — Ceramics and Composites

Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data Knowledge of the glass transition temperature of molecular compounds that occur in atmospheric aerosol particles is important for estimating their viscosity, as it directly influences the kinetics of chemical reactions and particle phase state. While there is a great diversity of organic compounds present in aerosol particles, for only a minor fraction of them experimental glass transition temperatures are known. Therefore, we have developed a machine learning model designed to predict the glass transition temperature of organic molecular compounds based on molecule-derived input variables. The <i>extremely randomized trees (extra trees)</i> procedure was chosen for this purpose. Two approaches using different sets of input variables were followed. The first one uses the number of selected functional groups present in the compound, while the second one generates descriptors from a SMILES (Simplified Molecular Input Line Entry System) string. Organic compounds containing carbon, hydrogen, oxygen, nitrogen, and halogen atoms are included. For improved results, both approaches can be combined with the melting temperature of the compound as an additional input variable. The results show that the predictions of both approaches show a similar mean absolute error of about 12-13 K, with the SMILES-based predictions performing slightly better. In general, the model shows good predictive power considering the diversity of the experimental input data. Furthermore, we also show that its performance exceeds that of previous parameterizations developed for this purpose and also performs better than existing machine learning models. In order to provide user-friendly versions of the model for applications, we have developed a web site where the model can be run by interested scientists via a web-based interface without prior technical knowledge. We also provide Python code of the model. Additionally, all experimental input data are provided in form of the Bielefeld Molecular Organic Glasses (BIMOG) database. We believe that this model is a powerful tool for many applications in atmospheric aerosol science and material science. Glass transition ; Materials science ; Experimental data ; Thermodynamics ; Machine learning ; Artificial intelligence ; Computer science ; Composite material ; Physics ; Mathematics ; Statistics ; Polymer Glass properties and applications ; Material Dynamics and Properties ; Phase Equilibria and Thermodynamics