jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziqi Hu | Yuanyuan Wang | Aman Ullah | Gerliz M. Gutiérrez-Finol | Amilcar Bedoya‐Pinto | Pierluigi Gargiani | Dier Shi | Shangfeng Yang | Zujin Shi | Alejandro Gaita‐Ariño | Eugenio Coronado
Source: Chem (article, 2023-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-temperature magnetic blocking in a monometallic dysprosium azafullerene single-molecule magnet Single-molecule magnets (SMMs) showing magnetic blocking near or above liquid nitrogen temperature have recently been achieved by inducing exceptionally strong and axial crystal fields (CFs) via double decker ligands. However, further enhancing the performance at higher temperatures becomes a formidable task. Here, we provide an alternative strategy to advance toward this goal by entrapping a single dysprosium(III) ion within a nitrogen-substituted carbon cage. In this structure of Dy@C81N, DyIII is asymmetrically coordinated by one side to a hexagonal carbon ring of the azafullerene while lacking any coordination ligand on the other side. Despite the very weak CF resulting from this very unusual low-coordination environment, this compound exhibits a high blocking temperature (TB, defined as T(τ100 s)) of 45 K. Its extraordinary magnetic behavior is attributed to the minimal number of vibrations that couple to its spin states and is also responsible for the unusual slow Raman relaxation mechanism observed at high temperatures. Dysprosium ; Crystallography ; Ligand (biochemistry) ; Magnet ; Relaxation (psychology) ; Molecule ; Raman spectroscopy ; Liquid nitrogen ; Magnetic relaxation ; Chemistry ; Single-molecule magnet ; Blocking (statistics) ; Ion ; Lanthanide ; Materials science ; Inorganic chemistry ; Magnetization ; Receptor ; Magnetic field ; Organic chemistry ; Physics Magnetism in coordination complexes ; Organic and Molecular Conductors Research ; Advanced NMR Techniques and Applications
Authors: Ni Huo | Wyatt E. Tenhaeff
Source: Macromolecules (article, 2023-03-03)
Field: Materials Science — Polymers and Plastics

High Refractive Index Polymer Thin Films by Charge-Transfer Complexation High refractive index polymers are essential in next-generation flexible optical and optoelectronic devices. This paper describes a simple synthetic method to prepare polymeric optical coatings possessing high refractive indexes. Poly(4-vinylpyridine) (P4VP) thin films prepared using initiated chemical vapor deposition are exposed to highly polarizable halogen molecules to form stable charge-transfer complexes: P4VP-IX (X = I, Br, and Cl). Fourier transform infrared spectroscopy was used to confirm the formation of charge-transfer complexes. Characterized by spectroscopic ellipsometry, the maximum refractive index of 2.08 at 587.6 nm is obtained for P4VP-I<sub>2</sub>. For P4VP-IBr and P4VP-ICl, the maximum refractive indexes are 1.849 and 1.774, respectively. By controlling the concentration of charge-transfer complexes, either through the halogen incorporation step or polymer composition through copolymerization with ethylene glycol dimethacrylate, the refractive indexes of the polymer thin films can be precisely controlled. The feasibility of P4VP-IX materials as optical coatings is also explored. The refractive index and thickness uniformity of a P4VP-I<sub>2</sub> film over a 10 mm diameter circular area were characterized, showing standard deviations of 0.0769 and 1.91%, respectively. Refractive index ; Polymer ; Copolymer ; Materials science ; Thin film ; Ellipsometry ; Ethylene glycol ; Polarizability ; Fourier transform infrared spectroscopy ; Polymer chemistry ; Analytical Chemistry (journal) ; Halogen ; Chemical engineering ; Chemistry ; Optoelectronics ; Molecule ; Organic chemistry ; Nanotechnology ; Composite material Synthesis and properties of polymers ; Semiconductor Lasers and Optical Devices ; Photonic and Optical Devices
Authors: Chenwei Lu | Benjamin Clayville | Ji Yong Choi | Jihye Park
Source: Chem (article, 2023-07-14)
Field: Chemistry — Inorganic Chemistry

2D metal-organic frameworks as an emerging platform with tunable electronic structures Metal-organic framework ; Nanotechnology ; Materials science ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Luminescence and Fluorescent Materials
Authors: Dongjin Xie | Mei Zu | Mingyang Li | Dongqing Liu | Zi Wang | Qingwen Li | Haifeng Cheng
Source: Advanced Materials (article, 2023-07-31)
Field: Materials Science — Materials Chemistry

A Hyperspectral Camouflage Colorant Inspired by Natural Leaves The unmet spectral mimicry of foliar green in camouflage materials is hampered by the lack of colorants with similar spectral properties to chlorophyll, resulting in substantial risks of exposure from hyperspectral target detection. By drawing inspiration from leaf chromogenesis, a microcapsule colorant with a chloroplast-like structure and chlorophyll-like absorption is developed, and a generic bilayer coating is designed to provide high spectral similarity to leaves with different growth stages, seasons, and species. Specifically, the microcapsule colorant preserves the monomeric absorption of the internal phthalocyanine and features the manufacturability of conventional pigments, such as amenability to painting and patterning, and compatibility to different substrates. The pigmented artificial leaves successfully deceive the hyperspectral classification algorithm in a foliar background, and outperforming the state-of-art spectral simulation materials. This coloration strategy expands the knowledge base of the spectral fine tuning of composite colorants, which are essential for their application in spectral-resolved optical materials. Camouflage ; Hyperspectral imaging ; Materials science ; Spectral properties ; Coating ; Nanotechnology ; Computer science ; Artificial intelligence ; Chemistry Photochromic and Fluorescence Chemistry ; Polydiacetylene-based materials and applications ; Light effects on plants
Authors: Yuqing Bai | Xinwei Wang | Bo Wu | Xiaoqing Wang | Rong‐Zhen Liao | Man Li | Yong‐Gui Zhou
Source: ACS Catalysis (article, 2023-07-11)
Field: Chemistry — Organic Chemistry

Design and Synthesis of Planar-Chiral Oxazole–Pyridine <i>N</i>,<i>N</i>-Ligands: Application in Palladium-Catalyzed Asymmetric Acetoxylative Cyclization The development of chiral ligands to fine-tune the stereocontrol has been recognized as a crucial pillar of asymmetric catalysis. In contrast to the well-developed chiral pyridine–pyridine-type and pyridine–oxazoline-type ligands, chiral oxazole–pyridine-type ligands have rarely been exploited. In this study, a class of [2.2]paracyclophane-based planar-chiral oxazole–pyridine N,N-ligands have been designed and synthesized. These ligands presented a superior performance in the enantioselective palladium-catalyzed asymmetric acetoxylative cyclization of alkyne-tethered cyclohexadienones, providing the chiral cis-hydrobenzofurans that belong to bioactive molecules with potent NF-κB inhibition in broad substrate scope. These results demonstrated the promising potential of the chiral oxazole–pyridine ligands as an efficient type of N,N-ligand scaffold. Oxazole ; Pyridine ; Oxazoline ; Enantioselective synthesis ; Chemistry ; Combinatorial chemistry ; Catalysis ; Stereochemistry ; Alkyne ; Palladium ; Ligand (biochemistry) ; Chiral ligand ; Medicinal chemistry ; Organic chemistry Axial and Atropisomeric Chirality Synthesis ; Catalytic C–H Functionalization Methods ; Asymmetric Synthesis and Catalysis
Authors: Rui Huang | Cristina‐Maria Hirschbiegel | Victor Lehot | Liang Liu | Yağız Anıl Çiçek | Vincent M. Rotello
Source: Advanced Materials (review, 2023-04-12)
Field: Materials Science — Materials Chemistry

Modular Fabrication of Bioorthogonal Nanozymes for Biomedical Applications The incorporation of transition metal catalysts (TMCs) into nanoscaffolds generates nanocatalysts that replicate key aspects of enzymatic behavior. The TMCs can access bioorthogonal chemistry unavailable to living systems. These bioorthogonal nanozymes can be employed as in situ "factories" for generating bioactive molecules where needed. The generation of effective bioorthogonal nanozymes requires co-engineering of the TMC and the nanometric scaffold. This review presents an overview of recent advances in the field of bioorthogonal nanozymes, focusing on modular design aspects of both nanomaterial and catalyst and how they synergistically work together for in situ uncaging of imaging and therapeutic agents. Bioorthogonal chemistry ; Nanotechnology ; Modular design ; Nanomaterial-based catalyst ; Materials science ; Combinatorial chemistry ; Computer science ; Chemistry ; Nanoparticle ; Click chemistry Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Nanoplatforms for cancer theranostics
Authors: Shasha Ge | Yufen Chen | Xuan Tang | Yali Shen | Yang Lou | Li Wang | Yun Guo | Jordi Llorca
Source: ACS Applied Nano Materials (article, 2023-08-15)
Field: Materials Science — Materials Chemistry

Preformed Pt Nanoparticles Supported on Nanoshaped CeO<sub>2</sub> for Total Propane Oxidation Pt-based catalysts have been widely used for the removal of short-chain volatile organic compounds (VOCs), such as propane. In this study, we synthesized Pt nanoparticles with a size of ca. 2.4 nm and loaded them on various fine-shaped CeO<sub>2</sub> with different facets to investigate the effect of CeO<sub>2</sub> morphology on the complete oxidation of propane. The Pt/CeO<sub>2</sub>-o catalyst with {111} facets exhibited superior catalytic activity compared to the Pt/CeO<sub>2</sub>-r catalyst with {110} and {100} facets. Specifically, the turnover frequency (TOF) value of Pt/CeO<sub>2</sub>-o was 1.8 times higher than that of Pt/CeO<sub>2</sub>-r. Moreover, Pt/CeO<sub>2</sub>-o showed outstanding long-term stability during 50 h. X-ray photoelectron spectroscopy (XPS) and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) revealed that the excellent performance of Pt/CeO<sub>2</sub>-o is due to the prevalence of metallic Pt species, which promotes C-C bond cleavage and facilitates the rapid removal of surface formate species. In contrast, a stronger metal-support interaction in Pt/CeO<sub>2</sub>-r leads to easier oxidation of Pt species and the accumulation of intermediates, which is detrimental to the catalytic activity. Our work provides insight into the oxidation of propane on different nanoshaped Pt/CeO<sub>2</sub> catalysts. Propane ; Nanoparticle ; Chemical engineering ; Materials science ; Nanotechnology ; Chemistry ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Thomas Dippong | Dana Toloman | Mihaela D. Lazăr | Ioan Petean
Source: Nanomaterials (article, 2023-12-07)
Field: Materials Science — Materials Chemistry

Effects of Lanthanum Substitution and Annealing on Structural, Morphologic, and Photocatalytic Properties of Nickel Ferrite Nanoparticles of NiLaxFe2−xO4 ferrite spinel incorporated in a SiO2 matrix were synthesized via a sol-gel method, followed by annealing at 200, 500, and 800 °C. The resulting materials were characterized via XRD, AFM, and BET techniques and evaluated for photocatalytic activity. The XRD diffractograms validate the formation of a single-phase cubic spinel structure at all temperatures, without any evidence of secondary peaks. The size of crystallites exhibited a decrease from 37 to 26 nm with the substitution of Fe3+ with La3+ ions. The lattice parameters and crystallite sizes were found to increase with the rise in La3+ content and annealing temperature. Isotherms were employed to calculate the rate constants for the decomposition of malonate precursors to ferrites and the activation energy for each ferrite. All nanocomposites have pores within the mesoporous range, with a narrow dispersion of pore sizes. The impact of La content on sonophotocatalytic activity was evaluated by studying Rhodamine B degradation under visible light irradiation. The results indicate that the introduction of La enhances nanocomposite performance. The prepared Ni-La ferrites may have potential application for water decontamination. Materials science ; Crystallite ; Spinel ; Annealing (glass) ; Photocatalysis ; Nanocomposite ; Chemical engineering ; Lanthanum ; Mesoporous material ; Lattice constant ; Ferrite (magnet) ; Metallurgy ; Inorganic chemistry ; Composite material ; Chemistry Magnetic Properties and Synthesis of Ferrites ; Advanced Photocatalysis Techniques ; Copper-based nanomaterials and applications
Authors: Yuanyuan Chong | Yaoyuan Huo | Shuang Jiang | Xijun Wang | Baichen Zhang | Tian‐Fu Liu | Xin Chen | TianTian Han | Pieter E. S. Smith | Song Wang | Jun Jiang
Source: Proceedings of the National Academy of Sciences (article, 2023-05-08)
Field: Materials Science — Materials Chemistry

Machine learning of spectra-property relationship for imperfect and small chemistry data Machine learning (ML) is causing profound changes to chemical research through its powerful statistical and mathematical methodological capabilities. However, the nature of chemistry experiments often sets very high hurdles to collect high-quality data that are deficiency free, contradicting the need of ML to learn from big data. Even worse, the black-box nature of most ML methods requires more abundant data to ensure good transferability. Herein, we combine physics-based spectral descriptors with a symbolic regression method to establish interpretable spectra-property relationship. Using the machine-learned mathematical formulas, we have predicted the adsorption energy and charge transfer of the CO-adsorbed Cu-based MOF systems from their infrared and Raman spectra. The explicit prediction models are robust, allowing them to be transferrable to small and low-quality dataset containing partial errors. Surprisingly, they can be used to identify and clean error data, which are common data scenarios in real experiments. Such robust learning protocol will significantly enhance the applicability of machine-learned spectroscopy for chemical science. Transferability ; Computer science ; Property (philosophy) ; Machine learning ; Imperfect ; Artificial intelligence ; Quality (philosophy) ; Data mining ; Chemistry ; Physics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Adnan Eghtesad | Qixiang Luo | Shun‐Li Shang | Ricardo A. Lebensohn | Marko Knežević | Zi‐Kui Liu | Allison M. Beese
Source: International Journal of Plasticity (article, 2023-05-13)
Field: Materials Science — Materials Chemistry

Machine learning-enabled identification of micromechanical stress and strain hotspots predicted via dislocation density-based crystal plasticity simulations Materials science ; Dislocation ; Plasticity ; Grain boundary ; Microscale chemistry ; Crystallite ; Work hardening ; Slip (aerodynamics) ; Microstructure ; Crystal plasticity ; Intergranular corrosion ; Composite material ; Metallurgy ; Thermodynamics ; Physics Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Metal and Thin Film Mechanics
Authors: A. Nolan Wilson | Peter C. St. John | Daniela H. Marin | Caroline B. Hoyt | Erik G. Rognerud | Mark R. Nimlos | Robin M. Cywar | Nicholas A. Rorrer | Kevin M. Shebek | Linda J. Broadbelt | Gregg T. Beckham | Michael F. Crowley
Source: Macromolecules (article, 2023-10-19)
Field: Materials Science — Materials Chemistry

PolyID: Artificial Intelligence for Discovering Performance-Advantaged and Sustainable Polymers A necessary transformation for a sustainable economy is the transition from fossil-derived plastics to polymers derived from biomass and waste resources. While renewable feedstocks can enhance material performance through unique chemical moieties, probing the vast material design space by experiment alone is not practically feasible. Here, we develop a machine-learning-based tool, PolyID, to reduce the design space of renewable feedstocks to enable efficient discovery of performance-advantaged, biobased polymers. PolyID is a multioutput, graph neural network specifically designed to increase accuracy and to enable quantitative structure-property relationship (QSPR) analysis for polymers. It includes a novel domain-of-validity method that was developed and applied to demonstrate how gaps in training data can be filled to improve accuracy. The model was benchmarked with both a 20% held-out subset of the original training data and 22 experimentally synthesized polymers. A mean absolute error for the glass transition temperatures of 19.8 and 26.4 °C was achieved for the test and experimental data sets, respectively. Predictions were made on polymers composed of monomers from four databases that contain biologically accessible small molecules: MetaCyc, MINEs, KEGG, and BiGG. From 1.4 × 10<sup>6</sup> accessible biobased polymers, we identified five poly(ethylene terephthalate) (PET) analogues with predicted improvements to thermal and transport performance. Experimental validation for one of the PET analogues demonstrated a glass transition temperature between 85 and 112 °C, which is higher than PET and within the predicted range of the PolyID tool. In addition to accurate predictions, we show how the model's predictions are explainable through analysis of individual bond importance for a biobased nylon. Overall, PolyID can aid the biobased polymer practitioner to navigate the vast number of renewable polymers to discover sustainable materials with enhanced performance. Polymer ; Glass transition ; Quantitative structure–activity relationship ; Chemical space ; Monomer ; Computer science ; Artificial neural network ; Biochemical engineering ; Materials science ; Biological system ; Machine learning ; Chemistry ; Engineering ; Composite material Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Carbon dioxide utilization in catalysis
Authors: Pranav Roy | Lavie Rekhi | See Wee Koh | Hong Li | Tej S. Choksi
Source: Journal of Physics Energy (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

Predicting the work function of 2D MXenes using machine-learning methods Abstract MXenes, which are graphene-like two-dimensional transition metal carbides and nitrides, have tunable compositions and exhibit rich surface chemistry. This compositional flexibility has resulted in exquisitely tunable electronic, optical, and mechanical properties leading to the applications of MXenes in catalysis, electronics, and energy storage. The work function of MXenes is an important fundamental property that dictates the suitability of MXenes for these applications. We present a series of machine learning models to predict the work function of MXenes having generic compositions and containing surfaces terminated by O*, OH*, F*, and bare metal atoms. Our model uses the basic chemical properties of the elements constituting the MXene as features, and is trained on 275 data points from the Computational 2D Materials Database. Using 15 different features of the MXene as inputs, the neural network model predicts the work function of MXenes with a mean absolute error of 0.12 eV on the training data and 0.25 eV on the testing data. Our feature importance analysis indicates that properties of atoms terminating the MXene surface like their electronegativity, most strongly influence the work function. This sensitivity of the work function to the surface termination is also elucidated through experimental measurements on Ti 3 C 2 . We introduce reduced-order models comprising of ten-, eight-, and five-features to predict the work function. These reduced-order models exhibit easier transferability to new materials, while exhibiting a marginal increased mean average error. We demonstrate the transferability of these reduced order models to new materials, by predicting the work function of MXenes having surface terminations beyond the original training set, like Br*, Cl*, S*, N*, and NH*. Predicting electronic properties like the work function from the basic chemical properties of elements, paves the way towards rapidly identifying tailored MXenes having a targeted range of properties that are required for a specific application. MXenes ; Work function ; Electronegativity ; Function (biology) ; Machine learning ; Materials science ; Work (physics) ; Artificial intelligence ; Computer science ; Nanotechnology ; Chemistry ; Layer (electronics) ; Physics ; Thermodynamics MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Marco Eckhoff | Markus Reiher
Source: Journal of Chemical Theory and Computation (article, 2023-06-08)
Field: Materials Science — Materials Chemistry

Lifelong Machine Learning Potentials Machine learning potentials (MLPs) trained on accurate quantum chemical data can retain the high accuracy, while inflicting little computational demands. On the downside, they need to be trained for each individual system. In recent years, a vast number of MLPs have been trained from scratch because learning additional data typically requires retraining on all data to not forget previously acquired knowledge. Additionally, most common structural descriptors of MLPs cannot represent efficiently a large number of different chemical elements. In this work, we tackle these problems by introducing element-embracing atom-centered symmetry functions (eeACSFs), which combine structural properties and element information from the periodic table. These eeACSFs are key for our development of a lifelong machine learning potential (lMLP). Uncertainty quantification can be exploited to transgress a fixed, pretrained MLP to arrive at a continuously adapting lMLP, because a predefined level of accuracy can be ensured. To extend the applicability of an lMLP to new systems, we apply continual learning strategies to enable autonomous and on-the-fly training on a continuous stream of new data. For the training of deep neural networks, we propose the continual resilient (CoRe) optimizer and incremental learning strategies relying on rehearsal of data, regularization of parameters, and the architecture of the model. Computer science ; Artificial intelligence ; Retraining ; Machine learning ; Lifelong learning ; Key (lock) ; Artificial neural network ; Table (database) ; Regularization (linguistics) ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Shiru Wu | Xiaowei Yang | Xun Zhao | Zhipu Li | Min Lü | Xiaoji Xie | Jiaxu Yan
Source: Journal of Chemical Information and Modeling (review, 2023-09-26)
Field: Materials Science — Materials Chemistry

Applications and Advances in Machine Learning Force Fields Force fields (FFs) form the basis of molecular simulations and have significant implications in diverse fields such as materials science, chemistry, physics, and biology. A suitable FF is required to accurately describe system properties. However, an off-the-shelf FF may not be suitable for certain specialized systems, and researchers often need to tailor the FF that fits specific requirements. Before applying machine learning (ML) techniques to construct FFs, the mainstream FFs were primarily based on first-principles force fields (FPFF) and empirical FFs. However, the drawbacks of FPFF and empirical FFs are high cost and low accuracy, respectively, so there is a growing interest in using ML as an effective and precise tool for reconciling this trade-off in developing FFs. In this review, we introduce the fundamental principles of ML and FFs in the context of machine learning force fields (MLFF). We also discuss the advantages and applications of MLFF compared to traditional FFs, as well as the MLFF toolkits widely employed in numerous applications. Computer science ; Construct (python library) ; Context (archaeology) ; Machine learning ; Field (mathematics) ; Artificial intelligence ; Force field (fiction) ; Mathematics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics