jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Panqin Ma | Xiyu Lai | Zheng Luo | Ying Chen | Xian Jun Loh | Enyi Ye | Zibiao Li | Caisheng Wu | Yun‐Long Wu
Source: Nanoscale Advances (review, 2022-01-01)
Field: Materials Science — Biomaterials

Recent advances in mechanical force-responsive drug delivery systems Mechanical force responsive drug delivery systems (in terms of mechanical force induced chemical bond breakage or physical structure destabilization) have been recently explored to exhibit a controllable pharmaceutical release behaviour at a molecular level. In comparison with chemical or biological stimulus triggers, mechanical force is not only an external but also an internal stimulus which is closely related to the physiological status of patients. However, although this mechanical force stimulus might be one of the most promising and feasible sources to achieve on-demand pharmaceutical release, current research in this field is still limited. Hence, this tutorial review aims to comprehensively evaluate the recent advances in mechanical force-responsive drug delivery systems based on different types of mechanical force, in terms of direct stimulation by compressive, tensile, and shear force, or indirect/remote stimulation by ultrasound and a magnetic field. Furthermore, the exciting developments and current challenges in this field will also be discussed to provide a blueprint for potential clinical translational research of mechanical force-responsive drug delivery systems. Mechanical system ; Drug delivery ; Shear force ; Mechanical compression ; Materials science ; Mechanical engineering ; Nanotechnology ; Engineering ; Biomedical engineering ; Composite material Nanoparticle-Based Drug Delivery ; Hydrogels: synthesis, properties, applications ; Electrohydrodynamics and Fluid Dynamics
Authors: Lei Zhang | Shaofeng Shao
Source: Journal of Applied Physics (article, 2022-09-08)
Field: Materials Science — Materials Chemistry

Image-based machine learning for materials science Materials research studies are dealing with a large number of images, which can now be facilitated via image-based machine learning techniques. In this article, we review recent progress of machine learning-driven image recognition and analysis for the materials and chemical domains. First, the image-based machine learning that facilitates the property prediction of chemicals or materials is discussed. Second, the analysis of nanoscale images including those from a scanning electron microscope and a transmission electron microscope is discussed, which is followed by the discussion about the identification of molecular structures via image recognition. Subsequently, the image-based machine learning works to identify and classify various practical materials such as metal, ceramics, and polymers are provided, and the image recognition for a range of real-scenario device applications such as solar cells is provided in detail. Finally, suggestions and future outlook for image-based machine learning for classification and prediction tasks in the materials and chemical science are presented. This article highlights the importance of the integration of the image-based machine learning method into materials and chemical science and calls for a large-scale deployment of image-based machine learning methods for prediction and classification of images in materials and chemical science. Artificial intelligence ; Computer science ; Machine learning ; Image processing ; Image (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Guoqing Zhou | Nicholas Lubbers | Kipton Barros | Sergei Tretiak | Benjamin Nebgen
Source: Proceedings of the National Academy of Sciences (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics Conventional machine-learning (ML) models in computational chemistry learn to directly predict molecular properties using quantum chemistry only for reference data. While these heuristic ML methods show quantum-level accuracy with speeds several orders of magnitude faster than traditional quantum chemistry methods, they suffer from poor extensibility and transferability; i.e., their accuracy degrades on large or new chemical systems. Incorporating quantum chemistry frameworks into the ML models directly solves this problem. Here we take the structure of semiempirical quantum mechanics (SEQM) methods to construct dynamically responsive Hamiltonians. SEQM methods use empirical parameters fitted to experimental properties to construct reduced-order Hamiltonians, facilitating much faster calculations than ab initio methods but with compromised accuracy. By replacing these static parameters with machine-learned dynamic values inferred from the local environment, we greatly improve the accuracy of the SEQM methods. Trained on molecular energies and atomic forces, these dynamically generated Hamiltonian parameters show a strong correlation with atomic hybridization and bonding. Trained with only about 60,000 small organic molecular conformers, the resulting model retains interpretability, extensibility, and transferability when testing on much larger chemical systems and predicting various molecular properties. Overall, this work demonstrates the virtues of incorporating physics-based descriptions with ML to develop models that are simultaneously accurate, transferable, and interpretable. Interpretability ; Transferability ; Hamiltonian (control theory) ; Quantum chemistry ; Ab initio ; Quantum ; Computer science ; Heuristic ; Construct (python library) ; Molecular dynamics ; Statistical physics ; Experimental data ; Theoretical chemistry ; Computational chemistry ; Chemistry ; Quantum mechanics ; Artificial intelligence ; Machine learning ; Physics ; Mathematics ; Molecule ; Mathematical optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ziliang Lu | Jianwei Wang | Yuanfang Wu | Xiumei Guo | Wei Xiao
Source: International Journal of Hydrogen Energy (article, 2022-08-28)
Field: Materials Science — Materials Chemistry

Predicting hydrogen storage capacity of V–Ti–Cr–Fe alloy via ensemble machine learning Hydrogen storage ; Alloy ; Materials science ; Valence (chemistry) ; Ranking (information retrieval) ; Crystal structure ; Lattice constant ; Work (physics) ; Hydrogen ; Computer science ; Thermodynamics ; Machine learning ; Chemistry ; Metallurgy ; Crystallography ; Physics ; Diffraction Hydrogen Storage and Materials ; Catalysis and Hydrodesulfurization Studies ; Hybrid Renewable Energy Systems
Authors: Shahzad Ahmad Qureshi | Wesley Wei‐Wen Hsiao | Lal Hussain | Haroon Aman | Trong‐Nghia Le | Muhammad Rafique
Source: Biosensors (review, 2022-12-19)
Field: Materials Science — Materials Chemistry

Recent Development of Fluorescent Nanodiamonds for Optical Biosensing and Disease Diagnosis The ability to precisely monitor the intracellular temperature directly contributes to the essential understanding of biological metabolism, intracellular signaling, thermogenesis, and respiration. The intracellular heat generation and its measurement can also assist in the prediction of the pathogenesis of chronic diseases. However, intracellular thermometry without altering the biochemical reactions and cellular membrane damage is challenging, requiring appropriately biocompatible, nontoxic, and efficient biosensors. Bright, photostable, and functionalized fluorescent nanodiamonds (FNDs) have emerged as excellent probes for intracellular thermometry and magnetometry with the spatial resolution on a nanometer scale. The temperature and magnetic field-dependent luminescence of naturally occurring defects in diamonds are key to high-sensitivity biosensing applications. Alterations in the surface chemistry of FNDs and conjugation with polymer, metallic, and magnetic nanoparticles have opened vast possibilities for drug delivery, diagnosis, nanomedicine, and magnetic hyperthermia. This study covers some recently reported research focusing on intracellular thermometry, magnetic sensing, and emerging applications of artificial intelligence (AI) in biomedical imaging. We extend the application of FNDs as biosensors toward disease diagnosis by using intracellular, stationary, and time-dependent information. Furthermore, the potential of machine learning (ML) and AI algorithms for developing biosensors can revolutionize any future outbreak. Biosensor ; Intracellular ; Nanotechnology ; Nanomedicine ; Materials science ; Biophysics ; Chemistry ; Nanoparticle ; Biology ; Biochemistry Diamond and Carbon-based Materials Research ; Fullerene Chemistry and Applications ; Advanced Fiber Laser Technologies
Authors: Sicong Ma | Zhi‐Pan Liu
Source: Chemical Science (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning potential era of zeolite simulation Zeolites, owing to their great variety and complexity in structure and wide applications in chemistry, have long been the hot topic in chemical research. This perspective first presents a short retrospect of theoretical investigations on zeolites using the tools from classical force fields to quantum mechanics calculations and to the latest machine learning (ML) potential simulations. ML potentials as the next-generation technique for atomic simulation open new avenues to simulate and interpret zeolite systems and thus hold great promise for finally predicting the structure-functionality relation of zeolites. Recent advances using ML potentials are then summarized from two main aspects: the origin of zeolite stability and the mechanism of zeolite-related catalytic reactions. We also discussed the possible scenarios of ML potential application aiming to provide instantaneous and easy access of zeolite properties. These advanced applications could now be accomplished by combining cloud-computing-based techniques with ML potential-based atomic simulations. The future development of ML potentials for zeolites in the respects of improving the calculation accuracy, expanding the application scope and constructing the zeolite-related datasets is finally outlooked. Zeolite ; Computer science ; Nanotechnology ; Artificial intelligence ; Materials science ; Chemistry ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography
Authors: Bohayra Mortazavi | Masoud Shahrokhi | Fazel Shojaei | Timon Rabczuk | Xiaoying Zhuang | Alexander V. Shapeev
Source: Nanoscale (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

A first-principles and machine-learning investigation on the electronic, photocatalytic, mechanical and heat conduction properties of nanoporous C<sub>5</sub>N monolayers Carbon nitride nanomembranes are currently among the most appealing two-dimensional (2D) materials. As a nonstop endeavor in this field, a novel 2D fused aromatic nanoporous network with a C<sub>5</sub>N stoichiometry has been most recently synthesized. Inspired by this experimental advance and exciting physics of nanoporous carbon nitrides, herein we conduct extensive density functional theory calculations to explore the electronic, optical and photocatalytic properties of the C<sub>5</sub>N monolayer. In order to examine the dynamic stability and evaluate the mechanical and heat transport properties under ambient conditions, we employ state of the art methods on the basis of machine-learning interatomic potentials. The C<sub>5</sub>N monolayer is found to be a direct band gap semiconductor, with a band-gap of 2.63 eV according to the HSE06 method. The obtained results confirm the dynamic stability, remarkable tensile strengths over 10 GPa and a low lattice thermal conductivity of ∼9.5 W m<sup>-1</sup> K<sup>-1</sup> for the C<sub>5</sub>N monolayer at room temperature. The first absorption peak of the single-layer C<sub>5</sub>N along the in-plane polarization is predicted to appear in the visible range of light. With a combination of high carrier mobility, appropriate band edge positions and strong absorption of visible light, the C<sub>5</sub>N monolayer might be an appealing candidate for photocatalytic water splitting reactions. The presented results provide an extensive understanding concerning the critical physical properties of the C<sub>5</sub>N nanosheets and also highlight the robustness of machine-learning interatomic potentials in the exploration of complex physical behaviors. Nanoporous ; Photocatalysis ; Thermal conduction ; Materials science ; Monolayer ; Nanotechnology ; Composite material ; Chemistry ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; Graphene research and applications ; Advanced Memory and Neural Computing
Authors: Jingzi Zhang | Zhuoxuan Zhu | X.‐D. Xiang | Ke Zhang | Shangchao Huang | Chengquan Zhong | Hua‐Jun Qiu | Kailong Hu | Xi Lin
Source: The Journal of Physical Chemistry C (article, 2022-05-12)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Superconducting Critical Temperature through the Structural Descriptor Superconductivity allows electric conductance with no energy losses when the ambient temperature drops below a critical value (Tc). Currently, the machine learning (ML)-based prediction of potential superconductors has been limited to chemical formulas without explicit treatment of material structures. Herein, we implement an efficient structural descriptor, the smooth overlap of atomic position (SOAP), into the ML models to predict the Tc values with explicit atomic structural information. Using a data set containing 5713 compounds, our ML models with the SOAP descriptor achieved a 92.9% prediction accuracy of coefficient of determination (R2) score via rigorous multialgorithm cross-verification procedures, exceeding the 86.3% accuracy record without atomic structure information. Several new high-temperature superconductors with Tc values over 90 K were predicted using the SOAP-assisted ML model. This study provides insights into the structure–property relationship of high-temperature superconductors. Superconductivity ; Position (finance) ; Materials science ; High-temperature superconductivity ; Conductance ; Artificial intelligence ; Machine learning ; Computer science ; Algorithm ; Thermodynamics ; Condensed matter physics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Jonathan Willman | Kien Nguyen-Cong | Ashley Williams | A. B. Belonoshko | Stan Moore | Aidan P. Thompson | Mitchell Wood | Ivan Oleynik
Source: Physical review. B./Physical review. B (article, 2022-11-30)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential for simulations of carbon at extreme conditions A spectral neighbor analysis (SNAP) machine learning interatomic potential (MLIP) has been developed for simulations of carbon at extreme pressures (up to $5\phantom{\rule{0.16em}{0ex}}\mathrm{TPa}$) and temperatures (up to 20 000 K). This was achieved using a large database of experimentally relevant quantum molecular dynamics (QMD) data, training the SNAP potential using a robust machine learning methodology, and performing extensive validation against QMD and experimental data. The resultant carbon MLIP demonstrates unprecedented accuracy and transferability in predicting the carbon phase diagram, melting curves of crystalline phases, and the shock Hugoniot, all within 3% of QMD. By achieving quantum accuracy and efficient implementation on leadership-class high-performance computing systems, SNAP advances frontiers of classical MD simulations by enabling atomic-scale insights at experimental time and length scales. Transferability ; Phase diagram ; Molecular dynamics ; Computer science ; Interatomic potential ; Carbon fibers ; Scale (ratio) ; Statistical physics ; Phase (matter) ; Materials science ; Computational science ; Algorithm ; Physics ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Stéfano Frizzo Stefenon | Rafael Bruns | Andreza Sartori | Luiz Henrique Meyer | Raúl García Ovejero | Valderi Reis Quietinho Leithardt
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods Outdoor insulators may experience stress due to severe environmental conditions, such as pollution and contamination. Through the identification of partial discharges by ultrasonic noise, it is possible to assess the possibility of a power grid failure occurring. In this paper, ensemble models are used to analyze an ultrasonic signal from an ultrasonic microphone Pettersson M500. As the insulators are susceptible to developing irreversible failures, it will be evaluated whether the ultrasonic signal will remain over time, so that it is possible to assess whether the discharges being captured can result in a failure in contaminated polymeric insulators, evaluated in a high voltage laboratory under controlled conditions. The ensemble models were used in this paper because they typically require less computational effort than techniques based on deep learning and have acceptable performance for the problem at hand. The bagging, boosting, random subspace, bagging plus random subspace, and stacked generalization ensemble models are evaluated, and the best result of each model is used to compare the differences between the models. The bagging ensemble learning model proved to be faster and have lower error than other ensemble models, long short-term memory (LSTM), and nonlinear autoregressive (NAR). Ensemble learning ; Computer science ; Ultrasonic sensor ; Subspace topology ; Random forest ; Autoregressive model ; Artificial intelligence ; Pressboard ; Boosting (machine learning) ; Machine learning ; Acoustics ; Voltage ; Mathematics ; Statistics ; Transformer ; Engineering High voltage insulation and dielectric phenomena ; Water Systems and Optimization ; Non-Destructive Testing Techniques
Authors: Garret D. Bland | Matthew Battifarano | A. Real | Géraldine Sarret | Gregory V. Lowry
Source: Environmental Science & Technology (article, 2022-02-08)
Field: Materials Science — Materials Chemistry

Distinguishing Engineered TiO<sub>2</sub> Nanomaterials from Natural Ti Nanomaterials in Soil Using spICP-TOFMS and Machine Learning Identifying engineered nanomaterials (ENMs) made from earth-abundant elements in soils is difficult because soil also contains natural nanomaterials (NNMs) containing similar elements. Here, machine learning models using elemental fingerprints and mass distributions of three TiO<sub>2</sub> ENMs and Ti-based NNMs recovered from three natural soils measured by single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOFMS) was used to identify TiO<sub>2</sub> ENMs in soil. Synthesized TiO<sub>2</sub> ENMs were unassociated with other elements (>98%), while 40% of Ti-based ENM particles recovered from wastewater sludge had distinguishable elemental associations. All Ti-based NNMs extracted from soil had a similar chemical fingerprint despite the soils being from different regions, and >60% of Ti-containing NNMs had no measurable associated elements. A machine learning model best distinguished NNMs and ENMs when differences in Ti-mass distribution existed between them. A trained LR model could classify 100 nm TiO<sub>2</sub> ENMs at concentrations of 150 mg kg<sup>-1</sup> or greater. The presence of TiO<sub>2</sub> ENMs in soil could be confirmed using this approach for most ENM-soil combinations, but the absence of a unique chemical fingerprint in a large fraction of both TiO<sub>2</sub> ENMs and Ti-NNMs increases model uncertainty and hinders accurate quantification. Soil water ; Environmental chemistry ; Chemistry ; Particle (ecology) ; Soil test ; Nanomaterials ; Environmental science ; Soil science ; Materials science ; Nanotechnology ; Geology Nanoparticles: synthesis and applications ; Heavy metals in environment ; Geochemistry and Elemental Analysis
Authors: Laura E. Ratcliff | Takayoshi Oshima | Felix Nippert | Benjamin M. Janzen | Elias Kluth | R. Goldhahn | Martin Feneberg | Piero Mazzolini | Oliver Bierwagen | Charlotte Wouters | Musbah Nofal | M. Albrecht | Jack E. N. Swallow | Leanne A. H. Jones | P. Thakur | Tien‐Lin Lee | Curran Kalha | Christoph Schlueter | T. D. Veal | Joel B. Varley | Markus R. Wagner | Anna Regoutz
Source: Advanced Materials (article, 2022-07-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tackling Disorder in γ‐Ga<sub>2</sub>O<sub>3</sub> Ga<sub>2</sub> O<sub>3</sub> and its polymorphs are attracting increasing attention. The rich structural space of polymorphic oxide systems such as Ga<sub>2</sub> O<sub>3</sub> offers potential for electronic structure engineering, which is of particular interest for a range of applications, such as power electronics. γ-Ga<sub>2</sub> O<sub>3</sub> presents a particular challenge across synthesis, characterization, and theory due to its inherent disorder and resulting complex structure-electronic-structure relationship. Here, density functional theory is used in combination with a machine-learning approach to screen nearly one million potential structures, thereby developing a robust atomistic model of the γ-phase. Theoretical results are compared with surface and bulk sensitive soft and hard X-ray photoelectron spectroscopy, X-ray absorption spectroscopy, spectroscopic ellipsometry, and photoluminescence excitation spectroscopy experiments representative of the occupied and unoccupied states of γ-Ga<sub>2</sub> O<sub>3</sub> . The first onset of strong absorption at room temperature is found at 5.1 eV from spectroscopic ellipsometry, which agrees well with the excitation maximum at 5.17 eV obtained by photoluminescence excitation spectroscopy, where the latter shifts to 5.33 eV at 5 K. This work presents a leap forward in the treatment of complex, disordered oxides and is a crucial step toward exploring how their electronic structure can be understood in terms of local coordination and overall structure. Materials science ; Nanotechnology Ga2O3 and related materials ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: Dayang Zheng | Min Wu | Eryang Zheng | Yayi Wang | Cang Feng | Jiali Zou | Maoling Juan | Xinxing Bai | Teng Wang | Shi Yu-xiang
Source: Journal of environmental chemical engineering (article, 2022-08-02)
Field: Materials Science — Materials Chemistry

Parallel adsorption of low concentrated ciprofloxacin by a CoFe-LDH modified sludge biochar Biochar ; Adsorption ; Fourier transform infrared spectroscopy ; Pyrolysis ; Chemical engineering ; Zeta potential ; X-ray photoelectron spectroscopy ; Exothermic reaction ; Composite number ; Materials science ; Nuclear chemistry ; Chemistry ; Composite material ; Organic chemistry ; Nanoparticle ; Nanotechnology Layered Double Hydroxides Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Covalent Organic Framework Applications