jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ryosuke Jinnouchi | Ferenc Karsai | Georg Kresse
Source: npj Computational Materials (article, 2024-05-20)
Field: Materials Science — Materials Chemistry

Machine learning-aided first-principles calculations of redox potentials Abstract We present a method combining first-principles calculations and machine learning to predict the redox potentials of half-cell reactions on the absolute scale. By applying machine learning force fields for thermodynamic integration from the oxidized to the reduced state, we achieve efficient statistical sampling over a broad phase space. Furthermore, through thermodynamic integration from machine learning force fields to potentials of semi-local functionals, and from semi-local functionals to hybrid functionals using Δ-machine learning, we refine the free energy with high precision step-by-step. Utilizing a hybrid functional that includes 25% exact exchange (PBE0), this method predicts the redox potentials of the three redox couples, Fe 3+ /Fe 2+ , Cu 2+ /Cu + , and Ag 2+ /Ag + , to be 0.92, 0.26, and 1.99 V, respectively. These predictions are in good agreement with the best experimental estimates (0.77, 0.15, 1.98 V). This work demonstrates that machine-learned surrogate models provide a flexible framework for refining the accuracy of free energy from coarse approximation methods to precise electronic structure calculations, while also facilitating sufficient statistical sampling. Redox ; Computer science ; Artificial intelligence ; Chemistry ; Computational chemistry ; Inorganic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Leonardo Medrano Sandonas | Dries Van Rompaey | Alessio Fallani | Mathias Hilfiker | David F. Hahn | Laura Pérez‐Benito | Jonas Verhoeven | Gary Tresadern | Jörg K. Wegner | Hugo Ceulemans | Alexandre Tkatchenko
Source: Scientific Data (article, 2024-07-07)
Field: Materials Science — Materials Chemistry

Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global and local physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and de novo generation of large (solvated) molecules with pharmaceutical and biological relevance. Chemical physics ; Molecule ; Conformational isomerism ; Dispersion (optics) ; Quantum ; Chemistry ; Statistical physics ; Solvation ; Computational chemistry ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Yanbin Ren | Junya Du | Minghui Zhang | Jingsong Li
Source: Sensors and Actuators B Chemical (article, 2024-08-23)
Field: Chemistry — Spectroscopy

Laser absorption spectroscopy based on dual-convolutional neural network algorithms for multiple trace gases analysis Trace gas ; Convolutional neural network ; TRACE (psycholinguistics) ; Spectroscopy ; Dual (grammatical number) ; Computer science ; Laser ; Algorithm ; Absorption (acoustics) ; Absorption spectroscopy ; Materials science ; Chemistry ; Artificial intelligence ; Physics ; Optics Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Taotao Shi | Yuan Gao | Jingyan Song | Min Ao | Xin Hu | Wanneng Yang | Wei Chen | Yanyan Liu | Hui Feng
Source: Food Chemistry (article, 2024-07-29)
Field: Chemistry — Analytical Chemistry

Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains High-throughput and low-cost quantification of the nutrient content in crop grains is crucial for food processing and nutritional research. However, traditional methods are time-consuming and destructive. A high-throughput and low-cost method of quantification of wheat nutrients with VIS-NIR (400-1700 nm) hyperspectral imaging is proposed in this study. Stepwise linear regression (SLR) was used to predict hundreds of nutrients accurately (R<sup>2</sup> > 0.6); results improved when the hyperspectral data was processed with the first derivative. Knockout materials were also used to verify their practical application value. Various nutrients' characteristic wavelengths were mainly concentrated in the visible regions of 400-500 nm and 900-1000 nm. Finally, we proposed an improved pix2pix conditional generative network model to visualize the nutrients distribution and showed better results compared with the original. This research highlights the potential of hyperspectral technology in high-throughput and non-destructive determination and visualization of grain nutrients with deep learning. Hyperspectral imaging ; Nutrient ; Throughput ; Visualization ; Computer science ; Deep learning ; Environmental science ; Biological system ; Artificial intelligence ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Hanwen Ge | Yuekun Bai | Rui Zhou | Yaoze Liu | Jiahui Wei | Shenglin Wang | Bin Li | Huanfei Xu
Source: ACS Sustainable Chemistry & Engineering (article, 2024-04-29)
Field: Materials Science — Biomaterials

Explicable Machine Learning for Predicting High-Efficiency Lignocellulose Pretreatment Solvents Based on Kamlet–Taft and Polarity Parameters Incorporating density functional theory (DFT) and machine learning (ML) methodologies, an intrinsic relationship model was developed utilizing the Kamlet–Taft parameters and polarity values of 104 deep eutectic solvents (DES). DES with high lignocellulosic pretreatment efficiency were expected to be screened through the synergistic combination of hydrogen bond acidity (α), hydrogen bond basicity (β), polarization (Π*) and molecular polarity index (MPI). Partial least-squares (PLS) models and a variety of ML models were used to predict cellulose retention and delignification. The XGBoost model has the highest predictive performance with R2 of 0.97 and 0.91, respectively. Feature importance analysis and partial dependence analysis were used to explain the importance of variables based on the XGBoost model. Feature importance analysis showed that α, β, Π* of DES and MPI of hydrogen bond donor determined the pretreatment efficiency. The partial dependence analysis showed that the relationship among 4 parameters and the pretreatment efficiency is nonlinear, and there are multiple extreme values in different intervals. The model gave a parameter range corresponding to the high pretreatment efficiency. Based on the range of 4 parameters given in this study, the composition and ratio of DES can be selected to ensure that at least 80% of the cellulose is retained and 50% of the lignin is removed. Molecular simulation results showed that these highly efficient DES often contain a large number of hydrogen bonds and highly polar groups. Hydrogen bond ; Chemistry ; Partial least squares regression ; Quantitative structure–activity relationship ; Chemical polarity ; Cellulose ; Density functional theory ; Polarity (international relations) ; Computational chemistry ; Organic chemistry ; Molecule ; Stereochemistry ; Computer science ; Machine learning Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Catalysis for Biomass Conversion
Authors: Mengyao Chen | Chengyu Guo | Lubing Qin | Lei Wang | Liang Qiao | Kebin Chi | Zhenghua Tang
Source: Nano-Micro Letters (review, 2024-12-03)
Field: Materials Science — Materials Chemistry

Atomically Precise Cu Nanoclusters: Recent Advances, Challenges, and Perspectives in Synthesis and Catalytic Applications Atomically precise metal nanoclusters are an emerging type of nanomaterial which has diverse interfacial metal-ligand coordination motifs that can significantly affect their physicochemical properties and functionalities. Among that, Cu nanoclusters have been gaining continuous increasing research attentions, thanks to the low cost, diversified structures, and superior catalytic performance for various reactions. In this review, we first summarize the recent progress regarding the synthetic methods of atomically precise Cu nanoclusters and the coordination modes between Cu and several typical ligands and then discuss the catalytic applications of these Cu nanoclusters with some explicit examples to explain the atomical-level structure-performance relationship. Finally, the current challenges and future research perspectives with some critical thoughts are elaborated. We hope this review can not only provide a whole picture of the current advances regarding the synthesis and catalytic applications of atomically precise Cu nanoclusters, but also points out some future research visions in this rapidly booming field. Nanoclusters ; Nanotechnology ; Nanomaterials ; Catalysis ; Materials science ; Computer science ; Chemistry ; Organic chemistry Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Lun Lu | Liangzhong Li | Ming‐Wen Chu | Cheng Chen | Boya Wang | Jun Wang | Yi Shen | Ruixue Ma | Bisheng Li | Liguo Shen | Hongjun Lin | Banglin Chen
Source: Advanced Functional Materials (review, 2024-11-20)
Field: Chemistry — Inorganic Chemistry

Recent Advancement in 2D Metal–Organic Framework for Environmental Remediation: A Review Abstract In a time characterized by the increasing interest in metal–organic frameworks (MOFs) as widely researched crystalline porous substances geared toward enhancing device and system capabilities across diverse environmental contexts, 2D MOFs emerge as a noteworthy class of nanomaterials that integrate the benefits of 2D structures with the unique characteristics inherent to MOFs. These 2D MOFs possess ultrathin nanosheet configuration, abundant accessible active sites, and remarkable mechanical flexibility. Such distinctive properties differentiate them from bulk MOFs and other 2D materials, offering the potential to instigate novel environmental phenomena and applications. This review focuses on the latest progress in the application of 2D MOFs within essential water‐related ecological fields, including contaminant adsorption, photocatalytic degradation, membrane separation, environmental sensing, and disinfection. A variety of synthesis approaches for 2D MOFs are analyzed, accompanied by a discussion on their effectiveness across different environmental settings. The unique structure and features of 2D MOFs that grant outstanding environmental functionalities are compared with those of bulk MOFs. The environmental ramifications of 2D MOFs are highlighted while outlining future research needs to explore the environmental applications of these innovative materials. Metal-organic framework ; Nanotechnology ; Nanosheet ; Materials science ; Flexibility (engineering) ; Environmental remediation ; Nanomaterials ; Adsorption ; Ecology ; Chemistry ; Contamination Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques
Authors: Patrick W. V. Butler | Roohollah Hafizi | Graeme M. Day
Source: The Journal of Physical Chemistry A (article, 2024-01-26)
Field: Materials Science — Materials Chemistry

Machine-Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes A primary challenge in organic molecular crystal structure prediction (CSP) is accurately ranking the energies of potential structures. While high-level solid-state density functional theory (DFT) methods allow for mostly reliable discrimination of the low-energy structures, their high computational cost is problematic because of the need to evaluate tens to hundreds of thousands of trial crystal structures to fully explore typical crystal energy landscapes. Consequently, lower-cost but less accurate empirical force fields are often used, sometimes as the first stage of a hierarchical scheme involving multiple stages of increasingly accurate energy calculations. Machine-learned interatomic potentials (MLIPs), trained to reproduce the results of ab initio methods with computational costs close to those of force fields, can improve the efficiency of the CSP by reducing or eliminating the need for costly DFT calculations. Here, we investigate active learning methods for training MLIPs with CSP datasets. The combination of active learning with the well-developed sampling methods from CSP yields potentials in a highly automated workflow that are relevant over a wide range of the crystal packing space. To demonstrate these potentials, we illustrate efficiently reranking large, diverse crystal structure landscapes to near-DFT accuracy from force field-based CSP, improving the reliability of the final energy ranking. Furthermore, we demonstrate how these potentials can be extended to more accurately model structures far from lattice energy minima through additional on-the-fly training within Monte Carlo simulations. Crystal structure prediction ; Force field (fiction) ; Computer science ; Maxima and minima ; Ranking (information retrieval) ; Density functional theory ; Monte Carlo method ; Artificial intelligence ; Machine learning ; Energy landscape ; Ab initio ; Statistical physics ; Crystal structure ; Computational chemistry ; Physics ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Lamiaa Reda Ahmed | Johann Lüder | Cheng-Hsin Chuang | Ahmed F. M. EL‐Mahdy
Source: ACS Applied Materials & Interfaces (article, 2024-05-28)
Field: Materials Science — Materials Chemistry

Covalent-Organic-Framework-Modified Quartz Crystal Microbalance Sensor for Selective Detection of Hazardous Formic Acid Covalent organic frameworks (COFs) are a novel family of porous crystalline materials utilized in various advanced applications. However, applying COFs as a hazardous organic acid gas sensor is substantial but still challenging. Herein, a phenylenediamine-based covalent organic framework (TPDA-TPB COF) featuring excellent crystallinity, ultrastable thermal stability, and high surface area was successfully constructed. Then, the TPDA-TPB COF-modified quartz crystal microbalance (QCM) sensor is fabricated by immobilizing the TPDA-TPB COF thin film on the gold-QCM chip. The fabricated TPDA-TPB COF-modified QCM sensor demonstrates a rapid response, excellent reproducibility, high selectivity, and sensitivity to formic gas, arising from hydrogen-bonding interactions between formic acid and the outermost layer of the TPDA-TPB COF, as determined by extensive analysis and density functional theory calculations. The basic sites of the TPDA-TPB COF, which are numerous due to its high nitrogen content, and the carboxylic acid groups present in formic acid exhibit efficient interactions. The sensitivity of the TPDA-TPB COF-modified QCM sensor was found to be 7.75 Hz ppm<sup>-1</sup> at standard room temperature and pressure conditions, with a limit of detection (LOD) of formic acid down to 1.18 ppm, which is significantly below the workplace olfactory threshold limit of 5.0 ppm established by the Occupational Safety and Health Administration. The TPDA-TPB COF-modified QCM sensor exhibits remarkable detecting capabilities, making it highly attractive for detecting organic acid vapors in diverse applications that require superior performance. Formic acid ; Quartz crystal microbalance ; Materials science ; Detection limit ; Covalent organic framework ; Dimethyl methylphosphonate ; Chemical engineering ; Porosity ; Composite material ; Chromatography ; Inorganic chemistry ; Organic chemistry ; Chemistry Covalent Organic Framework Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Chemical Sensor Technologies
Authors: Kamil Reza Khondakar | Divya Tripathi | Hirak Mazumdar | Kirti Ahuja | Ajeet Kaushik
Source: Materials Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Tailored MXenes and graphene as efficient telemedicine platforms for personalized health wellness This comprehensive review paper provides an insightful exploration of the burgeoning field of 2D nanostructures and their development as telemedicine platforms for futuristic smart healthcare systems. Telemedicine ; MXenes ; Health care ; Field (mathematics) ; Computer science ; Nanotechnology ; Political science ; Materials science MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; 2D Materials and Applications
Authors: Yizhang Wu | Yuan Li | Yihan Liu | Dashuai Zhu | Sicheng Xing | Noah Lambert | Hannah Weisbecker | Siyuan Liu | Brayden Davis | Lin Zhang | Meixiang Wang | Gongkai Yuan | Chris Zhoufan You | Anran Zhang | Cate Duncan | Wanrong Xie | Yihang Wang | Yong Wang | Sreya Kanamurlapudi | Garcia-Guzman Evert | Arjun Putcha | Michael D. Dickey | Ke Huang | Wubin Bai
Source: Science Advances (article, 2024-10-02)
Field: Materials Science — Materials Chemistry

Orbit symmetry breaking in MXene implements enhanced soft bioelectronic implants Bioelectronic implants featuring soft mechanics, excellent biocompatibility, and outstanding electrical performance hold promising potential to revolutionize implantable technology. These biomedical implants can record electrophysiological signals and execute direct therapeutic interventions within internal organs, offering transformative potential in the diagnosis, monitoring, and treatment of various pathological conditions. However, challenges remain in improving excessive impedance at the bioelectronic-tissue interface and thus the efficacy of electrophysiological signaling and intervention. Here, we devise orbit symmetry breaking in MXene (a low-cost scalability, biocompatible, and conductive two dimensionally layered material, which we refer to as OBXene), which exhibits low bioelectronic-tissue impedance, originating from the out-of-plane charge transfer. Furthermore, the Schottky-induced piezoelectricity stemming from the asymmetric orbital configuration of OBXene facilitates interlayered charge transport in the device. We report an OBXene-based cardiac patch applied on the left ventricular epicardium of both rodent and porcine models to enable spatiotemporal epicardium mapping and pacing while coupling the wireless and battery-free operation for long-term real-time recording and closed-loop stimulation. Nanotechnology ; Biocompatible material ; Materials science ; Computer science ; Orbit (dynamics) ; Biomedical engineering ; Medicine ; Engineering ; Aerospace engineering MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Advanced Memory and Neural Computing
Authors: Chengyi Yu | Kun Lin | Qinghua Zhang | Huihui Zhu | Ke An | Yan Chen | Dunji Yu | Tianyi Li | Xiaoqian Fu | Qian Yu | Youyong Li | Xiaojun Kuang | Yili Cao | Qiang Li | Jinxia Deng | Xianran Xing
Source: Nature Communications (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

An isotropic zero thermal expansion alloy with super-high toughness Zero thermal expansion (ZTE) alloys with high mechanical response are crucial for their practical usage. Yet, unifying the ZTE behavior and mechanical response in one material is a grand obstacle, especially in multicomponent ZTE alloys. Herein, we report a near isotropic zero thermal expansion (α<sub>l</sub> = 1.10 × 10<sup>-6 </sup>K<sup>-1</sup>, 260-310 K) in the natural heterogeneous LaFe<sub>54</sub>Co<sub>3.5</sub>Si<sub>3.35</sub> alloy, which exhibits a super-high toughness of 277.8 ± 14.7 J cm<sup>-3</sup>. Chemical partition, in the dual-phase structure, assumes the role of not only modulating thermal expansion through magnetic interaction but also enhancing mechanical properties via interface bonding. The comprehensive analysis reveals that the hierarchically synergistic enhancement among lattice, phase interface, and heterogeneous structure is significant for strong toughness. Our findings pave the way to tailor thermal expansion and obtain prominent mechanical properties in multicomponent alloys, which is essential to ultra-stable functional materials. Thermal expansion ; Isotropy ; Materials science ; Toughness ; Alloy ; Thermal ; Composite material ; Lattice (music) ; Negative thermal expansion ; Phase (matter) ; Condensed matter physics ; Thermodynamics ; Chemistry ; Optics ; Physics Thermal Expansion and Ionic Conductivity ; Rare-earth and actinide compounds ; Magnetic and transport properties of perovskites and related materials
Authors: Alexandru Scafa‐Udriște | Alexandra Cristina Burdușel | Adelina-Gabriela Niculescu | Marius Rădulescu | Alexandru Mihai Grumezescu
Source: International Journal of Molecular Sciences (review, 2024-01-13)
Field: Materials Science — Materials Chemistry

Metal-Based Nanoparticles for Cardiovascular Diseases Globally, cardiovascular diseases (CVDs) are the leading cause of death and disability. While there are many therapeutic alternatives available for the management of CVDs, the majority of classic therapeutic strategies were found to be ineffective at stopping or significantly/additionally slowing the progression of these diseases, or they had unfavorable side effects. Numerous metal-based nanoparticles (NPs) have been created to overcome these limitations, demonstrating encouraging possibilities in the treatment of CVDs due to advancements in nanotechnology. Metallic nanomaterials, including gold, silver, and iron, come in various shapes, sizes, and geometries. Metallic NPs are generally smaller and have more specialized physical, chemical, and biological properties. Metal-based NPs may come in various forms, such as nanoshells, nanorods, and nanospheres, and they have been studied the most. Massive potential applications for these metal nanomaterial structures include supporting molecular imaging, serving as drug delivery systems, enhancing radiation-based anticancer therapy, supplying photothermal transforming effects for thermal therapy, and being compounds with bactericidal, fungicidal, and antiviral qualities that may be helpful for cardiovascular diseases. In this context, the present paper aims to review the applications of relevant metal and metal oxide nanoparticles in CVDs, creating an up-to-date framework that aids researchers in developing more efficient treatment strategies. Photothermal therapy ; Nanotechnology ; Context (archaeology) ; Nanoshell ; Nanomaterials ; Drug delivery ; Nanorod ; Nanoparticle ; Materials science ; Medicine Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Advanced Nanomaterials in Catalysis