jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoxi Mi | Lili Dai | Xuerui Jing | Jia She | Bjørn Holmedal | Aitao Tang | Fusheng Pan
Source: Journal of Magnesium and Alloys (article, 2024-01-21)
Field: Materials Science — Materials Chemistry

Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization Magnesium (Mg), being the lightest structural metal, holds immense potential for widespread applications in various fields. The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization. With the rapid advancement of machine learning (ML) technology in recent years, the ``data-driven'' approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys. This paper introduces a novel regression-based Bayesian optimization active learning model (RBOALM) for the development of high-performance Mg-Mn-based wrought alloys. RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges, facilitating the discovery of superior alloy combinations. This model further integrates pre-established regression models as surrogate functions in Bayesian optimization, significantly enhancing the precision of the design process. Leveraging RBOALM, several new high-performance alloys have been successfully designed and prepared. Notably, after mechanical property testing of the designed alloys, the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties, including an ultimate tensile strength of 406 MPa, a yield strength of 287 MPa, and a 23% fracture elongation. Furthermore, the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa, coupled with a remarkable 41% fracture elongation. Bayesian optimization ; Ultimate tensile strength ; Materials science ; Alloy ; Elongation ; Magnesium alloy ; Magnesium ; Fracture (geology) ; Process optimization ; Process (computing) ; Bayesian probability ; Computer science ; Machine learning ; Composite material ; Metallurgy ; Artificial intelligence ; Chemical engineering ; Engineering Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Machine Learning in Materials Science
Authors: Zhiming Zhang | Fusheng Pan | Saad Aldin Mohamed | Chengxin Ji | Kang Zhang | Jianwen Jiang | Zhongyi Jiang
Source: Small (article, 2024-08-18)
Field: Chemistry — Inorganic Chemistry

Accelerating Discovery of Water Stable Metal−Organic Frameworks by Machine Learning Metal-organic frameworks (MOFs) provide an extensive design landscape for nanoporous materials that drive innovation across energy and environmental fields. However, their practical applications are often hindered by water stability challenges. In this study, a machine learning (ML) approach is proposed to accelerate the discovery of water stable MOFs and validated through experimental test. First, the largest database currently available that contains water stability information of 1133 synthesized MOFs is constructed and categorized according to experimental stability. Then, structural and chemical descriptors are applied at various fragmental levels to develop ML classifiers for predicting the water stability of MOFs. The ML classifiers achieve high prediction accuracy and excellent transferability on out-of-sample validation. Next, two MOFs are experimentally synthesized with their water stability tested to validate ML predictions. Finally, the ML classifiers are applied to discover water stable MOFs in the ab initio REPEAT charge MOF (ARC-MOF) database. Among ≈280 000 candidates, ≈130 000 (47%) MOFs are predicted to be water stable; furthermore, through multi-stability analysis, 461 (0.16%) MOFs are identified as not only water stable but also thermal and activation stable. The ML approach is anticipated to serve as a prerequisite filtering tool to streamline the exploration of water stable MOFs for important practical applications. Nanoporous ; Stability (learning theory) ; Transferability ; Computer science ; Metal-organic framework ; Thermal stability ; Machine learning ; Artificial intelligence ; Materials science ; Nanotechnology ; Chemistry ; Chemical engineering ; Engineering ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Guoqing Wang | Zongmin Luo | Halefom G. Desta | Chen Mu | Yingchao Dong | Bin Lin
Source: Energy Reviews (article, 2024-08-10)
Field: Materials Science — Materials Chemistry

AI-driven development of high-performance solid-state hydrogen storage Energy drives the development of human civilization, and hydrogen energy is an inevitable choice under the goal of “global energy transition”. As hydrogen technology continues to advance, solid-state hydrogen storage materials have attracted significant attention as an efficient solution for hydrogen energy storage. However, existing research methods, such as experimental preparation and theoretical calculations, are inefficient and costly. Here, we summarize the latest advancements of high-throughput screening (HTS) and machine learning (ML) solid-state hydrogen storage materials. It elaborates on the advantages of HTS and ML in rapid material screening, performance assessment and prediction, and so on. We place particular emphasis on the exploration and analysis of research progress involving the application of HTS and ML in various types of solid-state hydrogen storage materials. Additionally, we discuss the advantages of integrating HTS and ML, emphasizing the application of this comprehensive strategy in the field of solid-state hydrogen storage. In the realm of hydrogen storage, artificial intelligence plays a dual role. It not only enhances the efficiency of material screening but also offers novel research tools future material design and development. This will aid in the discovery of new-type high-performance solid-state hydrogen storage materials, and facilitate their rapid commercialization and practical application. Hydrogen storage ; Solid-state ; Materials science ; Computer science ; Process engineering ; Engineering physics ; Engineering ; Metallurgy Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Advanced Battery Materials and Technologies
Authors: Yangxi Zhang | Sheng Zhang | Hao Wu | J. Y. Wang | Guang Lin | A. Ping Zhang
Source: Nature Communications (article, 2024-05-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Miniature computational spectrometer with a plasmonic nanoparticles-in-cavity microfilter array Optical spectrometers are essential tools for analysing light‒matter interactions, but conventional spectrometers can be complicated and bulky. Recently, efforts have been made to develop miniaturized spectrometers. However, it is challenging to overcome the trade-off between miniaturizing size and retaining performance. Here, we present a complementary metal oxide semiconductor image sensor-based miniature computational spectrometer using a plasmonic nanoparticles-in-cavity microfilter array. Size-controlled silver nanoparticles are directly printed into cavity-length-varying Fabry‒Pérot microcavities, which leverage strong coupling between the localized surface plasmon resonance of the silver nanoparticles and the Fabry‒Pérot microcavity to regulate the transmission spectra and realize large-scale arrayed spectrum-disparate microfilters. Supported by a machine learning-based training process, the miniature computational spectrometer uses artificial intelligence and was demonstrated to measure visible-light spectra at subnanometre resolution. The high scalability of the technological approaches shown here may facilitate the development of high-performance miniature optical spectrometers for extensive applications. Spectrometer ; Plasmon ; Surface plasmon resonance ; Materials science ; Nanoparticle ; Optoelectronics ; Nanotechnology ; Silver nanoparticle ; Leverage (statistics) ; Optics ; Computer science ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Photonic and Optical Devices
Authors: Lei Zhang | Gábor Cśanyi | E. van der Giessen | Francesco Maresca
Source: Acta Materialia (article, 2024-02-29)
Field: Materials Science — Materials Chemistry

Efficiency, accuracy, and transferability of machine learning potentials: Application to dislocations and cracks in iron Machine learning interatomic potentials (ML-IAPs) enable quantum-accurate, classical molecular dynamics simulations of large systems, beyond reach of density functional theory (DFT). Yet, their efficiency and ability to predict systems larger than DFT supercells are not fully explored, posing a question regarding transferability to large-scale simulations with defects (e.g. dislocations, cracks). Here, we apply a three-step validation approach to body-centered-cubic iron. First, accuracy and efficiency are assessed by optimizing ML-IAPs based on four state-of-the-art ML packages. The Pareto front of computational speed versus testing root-mean-square-error (RMSE) is computed. Second, benchmark properties relevant to plasticity and fracture are evaluated. Their relative root-mean-square-error (Q) with respect to DFT is found to correlate with RMSE. Third, transferability of ML-IAPs to dislocations and cracks is investigated by using per-atom model uncertainty quantification. The core structures and Peierls barriers of screw, M111 and three edge dislocations are compared with DFT. Traction-separation curve and critical stress intensity factor (KIc) are also predicted. Cleavage on the pre-existing crack plane is found to be the zero-temperature atomistic fracture mechanism of pure body-centered-cubic iron under mode-I loading, independent of ML package and training database. Quantitative predictions of dislocation glide paths and KIc can be sensitive to database, ML package, cutoff radius, and are limited by DFT accuracy. Our results highlight the importance of validating ML-IAPs by using indicators beyond RMSE. Moreover, significant computational speed-ups can be achieved by using the most efficient ML-IAP package, yet the assessment of the accuracy and transferability should be performed with care. Materials science ; Density functional theory ; Mean squared error ; Dislocation ; Atom (system on chip) ; Computer science ; Computational chemistry ; Statistics ; Mathematics ; Composite material Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties
Authors: Zhengxi Guo | Zhaoming Liu | Ruikang Tang
Source: Materials Chemistry Frontiers (article, 2024-01-01)
Field: Materials Science — Ceramics and Composites

Applications of amorphous inorganics as novel functional materials Amorphous inorganics have attracted much attention because of their long-range disordered structure with advantageous functional properties. Amorphous solid ; Materials science ; Nanotechnology ; Chemistry ; Crystallography Glass properties and applications ; Material Dynamics and Properties ; Ferroelectric and Piezoelectric Materials
Authors: S. Varadharajan | Kirthanashri S. Vasanthan | Vidhi Mathur | N. Hariperumal | Nirmal Mazumder
Source: Discover Nano (review, 2024-12-17)
Field: Materials Science — Materials Chemistry

Green synthesis and multifaceted applications: challenges and innovations in carbon dot nanocomposites This paper describes the potential of carbon dot nanocomposites (CDs) synthesized from waste materials by top-down and bottom-up state-of-the-art approaches. Through sustainable practices, wastes are converted into valuable nanomaterials, solving environmental problems and pioneering advances in nanotechnology. In this paper, an overview of the synthesis aspects of CDs is presented with the formation of their versatile nanocomposites and metal/metal oxide elements. The phase of this paper has been devoted to elaborate study of the multifaceted applications of CDs in various sectors, ranging from electronics and biomedicine to environmental remediation. Although having huge potential, CDs application is presently hampered due to limitations on scalability, stability, and reproducibility. In this review paper, most profound insights have been drawn into overcoming these barriers for clear routes toward future innovations. The present research being undertaken in this area has, therefore, underscored sustainable nanotechnology to resolve global problems and achieving technological development through green synthesis. Necessitating the efficient sewage disposal systems ensuring minimum toxin generation. Nanotechnology ; Nanocomposite ; Engineering ; Computer science ; Materials science Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Advanced Photocatalysis Techniques
Authors: Yi Liu | Sophie G. Bender | Damien Sorigue | Daniel J. Diaz | Andrew D. Ellington | Greg Mann | Simon Allmendinger | Todd K. Hyster
Source: Journal of the American Chemical Society (article, 2024-03-05)
Field: Chemistry — Organic Chemistry

Asymmetric Synthesis of α-Chloroamides via Photoenzymatic Hydroalkylation of Olefins Photoenzymatic intermolecular hydroalkylations of olefins are highly enantioselective for chiral centers formed during radical termination but poorly selective for centers set in the C-C bond-forming event. Here, we report the evolution of a flavin-dependent "ene"-reductase to catalyze the coupling of α,α-dichloroamides with alkenes to afford α-chloroamides in good yield with excellent chemo- and stereoselectivity. These products can serve as linchpins in the synthesis of pharmaceutically valuable motifs. Mechanistic studies indicate that radical formation occurs by exciting a charge-transfer complex templated by the protein. Precise control over the orientation of molecules within the charge-transfer complex potentially accounts for the observed stereoselectivity. The work expands the types of motifs that can be prepared using photoenzymatic catalysis. Chemistry ; Stereoselectivity ; Enantioselective synthesis ; Intermolecular force ; Yield (engineering) ; Combinatorial chemistry ; Stereochemistry ; Flavin group ; Molecule ; Catalysis ; Organic chemistry ; Enzyme Radical Photochemical Reactions ; Fluorine in Organic Chemistry ; Catalytic C–H Functionalization Methods
Authors: Felix Hemauer | Hans‐Peter Steinrück | Christian Papp
Source: ChemPhysChem (article, 2024-02-20)
Field: Chemistry — Physical and Theoretical Chemistry

The Norbornadiene/Quadricyclane Pair as Molecular Solar Thermal Energy Storage System: Surface Science Investigations For the transition to renewable energy sources, novel energy storage materials are more important than ever. This review addresses so-called molecular solar thermal (MOST) systems, which appear very promising since they combine light harvesting and energy storing in one-photon one-molecule processes. The focus is on norbornadiene (NBD), a particularly interesting candidate, which is converted to the strained valence isomer quadricyclane (QC) upon irradiation. The stored energy can be released on demand. The energy-releasing cycloreversion from QC to NBD can be initiated by a thermal, catalytic, or electrochemical trigger. The reversibility of the energy storage and release cycles determines the general practicality of a MOST system. In the search for derivatives, which enable large-scale applications, fundamental surface science studies help to assess the feasibility of potential substituted NBD/QC couples. We include investigations under well-defined ultra-high vacuum (UHV) conditions as well as experiments in liquid phase. Next to the influence of the catalytically active surfaces on the isomerization between the two valence isomers, information on adsorption geometries, thermal stability limits, and reaction pathways of the respective molecules are discussed. Moreover, laboratory-scaled test devices demonstrate the proof of concept in various areas of application. Quadricyclane ; Norbornadiene ; Materials science ; Thermal energy storage ; Thermal ; Asteroid ; Solar energy ; Chemistry ; Photochemistry ; Astrobiology ; Physics ; Organic chemistry ; Thermodynamics ; Engineering Photochemistry and Electron Transfer Studies ; Photochromic and Fluorescence Chemistry ; Fullerene Chemistry and Applications
Authors: Andrew Nosakhare Amenaghawon | Joshua Efosa Ayere | Ubani Oluwaseun Amune | Ifechukwude Christopher Otuya | Emmanuel Christopher Abuga | Chinedu L. Anyalewechi | Oseweuba Valentine Okoro | Jude A. Okolie | Peter Kayode Oyefolu | Steve Oshiokhai Eshiemogie | Blessing Esohe Osahon | Melissa Osagbemwenorhue Omede | Stanley Aimhanesi Eshiemogie | Shedrach Igemhokhai | Maxwell Ogaga Okedi | Heri Septya Kusuma | Obiora Ebuka Muojama | Amin Shavandi | Handoko Darmokoesoemo
Source: Environmental Research (review, 2024-03-20)
Field: Materials Science — Biomaterials

A comprehensive review of recent advances in the applications and biosynthesis of oxalic acid from bio-derived substrates Oxalic acid ; Fermentation ; Biochemical engineering ; Chemical industry ; Sustainability ; Biotechnology ; Chemistry ; Biochemistry ; Organic chemistry ; Biology ; Engineering ; Ecology Clay minerals and soil interactions ; Microbial Metabolites in Food Biotechnology ; Fluoride Effects and Removal
Authors: Zhen Li | Patrizio Graziosi | Neophytos Neophytou
Source: npj Computational Materials (article, 2024-01-06)
Field: Materials Science — Materials Chemistry

Efficient first-principles electronic transport approach to complex band structure materials: the case of n-type Mg3Sb2 Abstract We present an efficient method for accurately computing electronic scattering rates and transport properties in materials with complex band structures. Using ab initio simulations, we calculate a limited number of electron–phonon matrix elements, and extract scattering rates for acoustic and optical processes based on deformation potential theory. Polar optical phonon scattering rates are determined using the Fröhlich model, and ionized impurity scattering rates are derived from the Brooks-Herring theory. Subsequently, electronic transport coefficients are computed within the Boltzmann transport theory. We exemplify our approach with n -type Mg 3 Sb 2 , a promising thermoelectric material with a challenging large unit cell and low symmetry. Notably, our method attains competitive accuracy, requiring less than 10% of the computational cost compared to state-of-the-art ab initio methods, dropping to 1% for simpler materials. Additionally, our approach provides explicit information on individual scattering processes, offering an alternative that combines efficiency, robustness, and flexibility beyond the commonly employed constant relaxation time approximation with the accuracy of fully first-principles calculations. Scattering ; Ab initio ; Boltzmann equation ; Phonon ; Electronic structure ; Boltzmann constant ; Computational physics ; Translational symmetry ; Physics ; Statistical physics ; Robustness (evolution) ; Condensed matter physics ; Quantum mechanics ; Chemistry Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Yiyi Shen | Haiqin Zhou | Xiao‐Tong He | Feng Shen | Zhi-Xiang Xu | Bo Yang | Lingzhao Kong | Lichun Dai
Source: Green Chemistry (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Emerging applications of deep eutectic solvents in the preparation and functionalization of biomass-derived carbonaceous materials: challenges and prospects Deep eutectic solvents can be used as the pretreatment solvents, carbon precursors, soft templates, carbonization media, and post-modification agents for the preparation and functionalization of biomass-derived carbonaceous materials. Surface modification ; Eutectic system ; Biomass (ecology) ; Materials science ; Nanotechnology ; Chemical engineering ; Metallurgy ; Engineering ; Microstructure Catalytic Processes in Materials Science ; Catalysis for Biomass Conversion ; Mesoporous Materials and Catalysis
Authors: Yilun Zhou | Yinghui Xie | Xiaolu Liu | Mengjie Hao | Zhongshan Chen | Hui Yang | Geoffrey I. N. Waterhouse | Shengqian Ma | Xiangke Wang
Source: Research (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Single-Molecule Traps in Covalent Organic Frameworks for Selective Capture of C <sub>2</sub> H <sub>2</sub> from C <sub>2</sub> H <sub>4</sub> -Rich Gas Mixtures Removing trace amounts of acetylene (C<sub>2</sub>H<sub>2</sub>) from ethylene (C<sub>2</sub>H<sub>4</sub>)-rich gas mixtures is vital for the supply of high-purity C<sub>2</sub>H<sub>4</sub> to the chemical industry and plastics sector. However, selective removal of C<sub>2</sub>H<sub>2</sub> is challenging due to the similar physical and chemical properties of C<sub>2</sub>H<sub>2</sub> and C<sub>2</sub>H<sub>4</sub>. Here, we report a "single-molecule trap" strategy that utilizes electrostatic interactions between the one-dimensional (1D) channel of a covalent organic framework (denoted as COF-1) and C<sub>2</sub>H<sub>2</sub> molecules to massively enhance the adsorption selectivity toward C<sub>2</sub>H<sub>2</sub> over C<sub>2</sub>H<sub>4</sub>. C<sub>2</sub>H<sub>2</sub> molecules are immobilized via interactions with the O atom of C=O groups, the N atom of C≡N groups, and the H atom of phenyl groups in 1D channels of COF-1. Due to its exceptionally high affinity for C<sub>2</sub>H<sub>2</sub>, COF-1 delivered a remarkable C<sub>2</sub>H<sub>2</sub> uptake of 7.97 cm<sup>3</sup>/g at 298 K and 0.01 bar, surpassing all reported COFs and many other state-of-the-art adsorbents under similar conditions. Further, COF-1 demonstrated outstanding performance for the separation of C<sub>2</sub>H<sub>2</sub> and C<sub>2</sub>H<sub>4</sub> in breakthrough experiments under dynamic conditions. COF-1 adsorbed C<sub>2</sub>H<sub>2</sub> at a capacity of 0.17 cm<sup>3</sup>/g at 2,000 s/g when exposed to 0.5 ml/min C<sub>2</sub>H<sub>4</sub>-rich gas mixture (99% C<sub>2</sub>H<sub>4</sub>) at 298 K, directly producing high-purity C<sub>2</sub>H<sub>4</sub> gas at a rate of 3.95 cm<sup>3</sup>/g. Computational simulations showed that the strong affinity between C<sub>2</sub>H<sub>2</sub> and the single-molecule traps of COF-1 were responsible for the excellent separation performance. COF-1 is also robust, providing a promising new strategy for the efficient removal of trace amounts of C<sub>2</sub>H<sub>2</sub> in practical C<sub>2</sub>H<sub>4</sub> purification. Molecule ; Covalent organic framework ; Acetylene ; Covalent bond ; Adsorption ; Ethylene ; Chemistry ; Selectivity ; Atom (system on chip) ; Crystallography ; Analytical Chemistry (journal) ; Materials science ; Physical chemistry ; Organic chemistry ; Catalysis Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques