jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Milton Chai | Sina Moradi | Eila Erfani | Mohsen Asadnia | Vicki Chen | Amir Razmjou
Source: Chemistry of Materials (article, 2021-11-01)
Field: Chemistry — Inorganic Chemistry

Application of Machine Learning Algorithms to Estimate Enzyme Loading, Immobilization Yield, Activity Retention, and Reusability of Enzyme–Metal–Organic Framework Biocatalysts The ability to predict enzyme–metal–organic framework (MOF) properties such as enzyme loading, immobilization yield, activity retention, and reusability can maximize product yield and extend the operational life of enzyme–MOF biocatalysts. However, this is challenging due to the vast combinations of available metal and ligand building blocks for MOF and enzymes. Therefore, several machine learning (ML) algorithms are applied in this study using data collected from 127 journal articles to estimate these biocatalyst parameters. Twelve input variables, including the metal and ligand properties of MOF, as well as the enzyme properties, were integrated and fed into two ML algorithms─random forest and Gaussian process regression (GPR)─to predict model outputs. A 10-fold cross-validation approach with grid search was applied to obtain the optimal hyperparameter values. The random forest model (RFM) provided more accurate estimates of the enzyme loading, immobilization yield, and reusability of the biocatalyst than the GPR model, with relatively high R2 values of 0.85, 0.77, and 0.91, respectively. Both models are less effective in predicting the enzyme activity retention, however, with R2 values of 0.63 or lower. Sensitivity analysis of the input variables revealed the most significant variables for each corresponding output parameter, allowing further optimization of the RFM. The final RFM was then tested with a second unseen dataset collected from experiments. Findings confirmed the validity of the predictive model, including a relative error of less than 25%. Our model can aid in the synthesis of enzyme–MOF biocatalysts by providing valuable estimates of these output parameters for different MOF precursors and enzymes, saving experimental time and cost. Reusability ; Hyperparameter ; Random forest ; Algorithm ; Yield (engineering) ; Immobilized enzyme ; Kriging ; Computer science ; Biocatalysis ; Biological system ; Machine learning ; Mathematics ; Chemistry ; Materials science ; Enzyme ; Catalysis ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors
Authors: Leila Rasuli | Mohammad Hadi Dehghani | Mahmood Alimohammadi | Kamyar Yaghmaeian | Noushin Rastkari | Mehdi Salari
Source: Journal of Molecular Liquids (article, 2021-06-24)
Field: Chemistry — Inorganic Chemistry

Mesoporous metal organic frameworks functionalized with the amino acids as advanced sorbents for the removal of bacterial endotoxins from water: Optimization, regression and kinetic models Adsorption ; Response surface methodology ; Langmuir adsorption model ; Mesoporous material ; Central composite design ; Materials science ; Chemistry ; Chemical engineering ; Chromatography ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Chemical Synthesis and Reactions
Authors: Fan-Shun Meng | Jun-Ping Du | Shuhei Shinzato | Hideki Mori | Peijun Yu | Kazuki Matsubara | Nobuyuki Ishikawa | Shigenobu Ogata
Source: Physical Review Materials (article, 2021-11-22)
Field: Materials Science — Metals and Alloys

General-purpose neural network interatomic potential for the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math>-iron and hydrogen binary system: Toward atomic-scale understanding of hydrogen embrittlement To understand the physics of hydrogen embrittlement at the atomic scale, a general-purpose neural network interatomic potential (NNIP) for the $\ensuremath{\alpha}$-iron and hydrogen binary system is presented. It is trained using an extensive reference database produced by density functional theory (DFT) calculations. The NNIP can properly describe the interactions of hydrogen with various defects in $\ensuremath{\alpha}$-iron, such as vacancies, surfaces, grain boundaries, and dislocations; in addition to the basic properties of $\ensuremath{\alpha}$-iron itself, the NNIP also handles the defect properties in $\ensuremath{\alpha}$-iron, hydrogen behavior in $\ensuremath{\alpha}$-iron, and hydrogen-hydrogen interactions in $\ensuremath{\alpha}$-iron and in vacuum, including the hydrogen molecule formation and dissociation at the $\ensuremath{\alpha}$-iron surface. These are superb challenges for the existing empirical interatomic potentials, like the embedded-atom method based potentials, for the $\ensuremath{\alpha}$-iron and hydrogen binary system. In this study, the NNIP was applied to several key phenomena necessary for understanding hydrogen embrittlement, such as hydrogen charging and discharging to $\ensuremath{\alpha}$-iron, hydrogen transportation in defective $\ensuremath{\alpha}$-iron, hydrogen trapping and desorption at the defects, and hydrogen-assisted cracking at the grain boundary. Unlike the existing interatomic potentials, the NNIP simulations quantitatively described the atomistic details of hydrogen behavior in the defective $\ensuremath{\alpha}$-iron system with DFT accuracy. Hydrogen ; Hydrogen embrittlement ; Materials science ; Interatomic potential ; Grain boundary ; Hydrogen atom ; Crystallography ; Atomic units ; Density functional theory ; Atomic physics ; Molecular dynamics ; Physics ; Computational chemistry ; Metallurgy ; Chemistry ; Quantum mechanics Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Sàisài Yuán | Qitao Zhang
Source: Frontiers in Chemistry (review, 2021-12-17)
Field: Materials Science — Materials Chemistry

Application of One-Dimensional Nanomaterials in Catalysis at the Single-Molecule and Single-Particle Scale The morphology of nanomaterials has a great influence on the catalytic performance. One-dimensional (1D) nanomaterials have been widely used in the field of catalysis due to their unique linear morphology with large specific surface area, high electron-hole separation efficiency, strong light absorption capacity, plentiful exposed active sites, and so on. In this review, we summarized the recent progress of 1D nanomaterials by focusing on the applications in photocatalysis and electrocatalysis. We highlighted the advanced characterization techniques, such as scanning tunneling microscopy (STM), atomic force microscopy (AFM), surface photovoltage microscopy (SPVM), single-molecule fluorescence microscopy (SMFM), and a variety of combined characterization methods, which have been used to identify the catalytic action of active sites and reveal the mechanism of 1D nanomaterials. Finally, the challenges and future directions of the research on the catalytic mechanism of single-particle 1D nanomaterials are prospected. To our best knowledge, there is no review on the application of single-molecule or single-particle characterization technology to 1D nanomaterial catalysis at present. This review provides a systematic introduction to the frontier field and opens the way for the 1D nanomaterial catalysis. Nanomaterials ; Nanotechnology ; Characterization (materials science) ; Materials science ; Scanning tunneling microscope ; Catalysis ; Particle (ecology) ; Chemistry ; Organic chemistry Quantum Dots Synthesis And Properties ; Molecular Junctions and Nanostructures ; Electronic and Structural Properties of Oxides
Authors: Ahmet Dok | Thibaut Legat | Yovan de Coene | Monique A. Van Der Veen | Thierry Verbiest | Stijn Van Cleuvenbergen
Source: Journal of Materials Chemistry C (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nonlinear optical probes of nucleation and crystal growth: recent progress and future prospects Nonlinear optical techniques are emerging as powerful in-situ probes of crystallization processes due to their inherent sensitivity to crystal structure and morphology. Materials science ; Nucleation ; Nonlinear optical ; Crystallization ; Crystal growth ; Nanotechnology ; Nonlinear system ; Crystal (programming language) ; Sensitivity (control systems) ; Nonlinear optics ; Chemical physics ; Optoelectronics ; Engineering physics ; Chemical engineering ; Crystallography ; Electronic engineering ; Computer science ; Thermodynamics ; Chemistry ; Physics Nonlinear Optical Materials Research ; Nonlinear Optical Materials Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Kan Hatakeyama‐Sato | Takahiro Kashikawa | Kôichi Kimura | Kenichi Oyaizu
Source: Advanced Intelligent Systems (article, 2021-01-08)
Field: Materials Science — Materials Chemistry

Tackling the Challenge of a Huge Materials Science Search Space with Quantum‐Inspired Annealing Efficient screening of chemicals is essential for exploring new materials. However, the search space is astronomically large, making calculations with conventional computers infeasible. For example, an N ‐component system of organic molecules generates &gt;10 60 N candidates. Here, a quantum‐inspired annealing machine is used to tackle the challenge of the large search space. The prototype system extracts candidate chemicals and their composites with desirable parameters, such as melting temperature and ionic conductivity. The system can be at least 10 4 –10 8 times faster than conventional approaches. Such dramatic acceleration is critical for exploring the enormous search space in virtual screening of materials. Computer science ; Simulated annealing ; Space (punctuation) ; Annealing (glass) ; Space exploration ; Quantum ; Chemical space ; Ionic bonding ; Nanotechnology ; Materials science ; Aerospace engineering ; Chemistry ; Physics ; Engineering ; Algorithm ; Ion ; Quantum mechanics ; Drug discovery Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Dylan M. Anstine | Dai Tang | David S. Sholl | Coray M. Colina
Source: npj Computational Materials (article, 2021-04-16)
Field: Materials Science — Materials Chemistry

Adsorption space for microporous polymers with diverse adsorbate species Abstract The enormous number of combinations of adsorbing molecules and porous materials that exist is known as adsorption space. The adsorption space for microporous polymers has not yet been systematically explored, especially when compared with efforts for crystalline adsorbents. We report molecular simulation data for the adsorptive and structural properties of polymers of intrinsic microporosity with a diverse set of adsorbate species with 345 distinct adsorption isotherms and over 240,000 fresh and swollen structures. These structures and isotherms were obtained using a sorption-relaxation technique that accounts for the critical role of flexibility of the polymeric adsorbents. This enables us to introduce a set of correlations that can estimate adsorbent swelling and fractional free volume dilation as a function of adsorbate uptake based on readily characterized properties. The separation selectivity of the 276 distinct binary molecular pairs in our data is reported and high-performing adsorbent systems are identified. Adsorption ; Microporous material ; Sorption ; Polymer ; Materials science ; Porous medium ; Molecule ; Chemical engineering ; Relaxation (psychology) ; Thermodynamics ; Porosity ; Chemistry ; Organic chemistry ; Composite material ; Physics Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Anusha Mairpady | Abdel‐Hamid I. Mourad | Mohammad Sayem Mozumder
Source: Polymers (article, 2021-09-15)
Field: Materials Science — Polymers and Plastics

Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young's modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO<sub>2</sub> and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO<sub>2</sub> and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%. Response surface methodology ; High-density polyethylene ; Materials science ; Artificial neural network ; Design of experiments ; Central composite design ; Genetic algorithm ; Durability ; Machine learning ; Computer science ; Composite material ; Mathematics ; Statistics ; Polyethylene Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Advanced Cellulose Research Studies
Authors: Hamza Ahmad Isiyaka | Khairulazhar Jumbri | Nonni Soraya Sambudi | Jun Wei Lim | Bahruddin Saad | Anita Ramli | Zakariyya Uba Zango
Source: Processes (article, 2021-02-26)
Field: Chemistry — Inorganic Chemistry

Experimental and Modeling of Dicamba Adsorption in Aqueous Medium Using MIL-101(Cr) Metal-Organic Framework Drift deposition of emerging and carcinogenic contaminant dicamba (3,6-dichloro-2-methoxy benzoic acid) has become a major health and environmental concern. Effective removal of dicamba in aqueous medium becomes imperative. This study investigates the adsorption of a promising adsorbent, MIL-101(Cr) metal-organic framework (MOF), for the removal of dicamba in aqueous solution. The adsorbent was hydrothermally synthesized and characterized using N2 adsorption-desorption isotherms, Brunauer, Emmett and Teller (BET), powdered X-ray diffraction (XRD), Fourier Transformed Infrared (FTIR) and field emission scanning electron microscopy (FESEM). Adsorption models such as kinetics, isotherms and thermodynamics were studied to understand details of the adsorption process. The significance and optimization of the data matrix, as well as the multivariate interaction of the adsorption parameters, were determined using response surface methodology (RSM). RSM and artificial neural network (ANN) were used to predict the adsorption capacity. In each of the experimental adsorption conditions used, the ANN gave a better prediction with minimal error than the RSM model. The MIL-101(Cr) adsorbent was recycled six times to determine the possibility of reuse. The results show that MIL-101(Cr) is a very promising adsorbent, in particular due to the high surface area (1439 m2 g−1), rapid equilibration (~25 min), high adsorption capacity (237.384 mg g−1) and high removal efficiency of 99.432%. Adsorption ; Response surface methodology ; Aqueous solution ; Metal-organic framework ; Desorption ; Materials science ; Fourier transform infrared spectroscopy ; BET theory ; Chemical engineering ; Chemistry ; Nuclear chemistry ; Chromatography ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Arsenic contamination and mitigation
Authors: Yongtao Liu | Maxim Ziatdinov | Sergei V. Kalinin
Source: ACS Nano (article, 2021-12-29)
Field: Materials Science — Materials Chemistry

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures Rapid emergence of multimodal imaging in scanning probe, electron, and optical microscopies has brought forth the challenge of understanding the information contained in these complex data sets, targeting the intrinsic correlations between different channels, and further exploring the underpinning causal physical mechanisms. Here, we develop such an analysis framework for Piezoresponse Force Microscopy. We argue that under certain conditions, we can bootstrap experimental observations with the prior knowledge of materials structure to get information on certain nonobserved properties, and demonstrate linear causal analysis for PFM observables. We further demonstrate that the strength of individual causal links between complex descriptors can be ascertained using the deep kernel learning (DKL) model. In this DKL analysis, we use the prior information on domain structure within the image to predict the physical properties. This analysis demonstrates the correlative relationships between morphology, piezoresponse, elastic property, etc., at nanoscale. The prediction of morphology and other physical parameters illustrates a mutual interaction between surface condition and physical properties in ferroelectric materials. This analysis is universal and can be extended to explore the correlative relationships of other multichannel data sets, and allow for high-fidelity reconstruction of underpinning functionalities and physical mechanisms. Correlative ; Kernel (algebra) ; Underpinning ; Domain (mathematical analysis) ; Causal structure ; Physical system ; Causal model ; Ferroelectricity Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Advanced Electron Microscopy Techniques and Applications
Authors: Lin Hu | Bing Huang | Feng Liu
Source: Physical Review Letters (article, 2021-04-30)
Field: Materials Science — Materials Chemistry

Atomistic Mechanism Underlying the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>Si</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mn>111</mml:mn><mml:mo stretchy="false">)</mml:mo><mml:mtext>−</mml:mtext><mml:mo stretchy="false">(</mml:mo><mml:mn>7</mml:mn><mml:mo>×</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:math> Surface Reconstruction Revealed by Artificial Neural-Network Potential The 7×7 reconstruction of the Si(111) surface represents arguably the most fascinating surface reconstruction so far observed in nature. Yet, the atomistic mechanism underpinning its formation remains unclear after it was discovered sixty years ago. Experimentally, it is observed post priori so that analysis of its formation mechanism can only be carried out in analogy with archaeology. Theoretically, density-functional theory (DFT) correctly predicts the Si(111)-(7×7) ground state but is impractical to simulate its formation process; while empirical potentials failed to produce it as the ground state. Developing an artificial neural-network potential of DFT quality, we carried out accurate large-scale simulations to unravel the formation of the Si(111)-(7×7) surface. We reveal a possible step-mediated atom-pop rate-limiting process that triggers massive nonconserved atomic rearrangements, most remarkably, a critical process of collective vacancy diffusion that mediates a sequence of selective dimer, corner-hole, stacking-fault, and dimer-line pattern formation, to fulfill the 7×7 reconstruction. Our findings may not only solve the long-standing mystery of this famous surface reconstruction but they also illustrate the power of machine learning in studying complex structures. Physics ; Density functional theory ; Stacking fault ; Artificial intelligence ; Algorithm ; Machine learning ; Computer science ; Crystallography ; Stacking ; Chemistry ; Quantum mechanics ; Nuclear magnetic resonance Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Ion-surface interactions and analysis
Authors: Kyunghoon Jung | M. H. Abobeih | Jiwon Yun | Gyeonghun Kim | Hyunseok Oh | Ang Henry | T. H. Taminiau | Dohun Kim
Source: npj Quantum Information (article, 2021-02-23)
Field: Materials Science — Materials Chemistry

Deep learning enhanced individual nuclear-spin detection Abstract The detection of nuclear spins using individual electron spins has enabled diverse opportunities in quantum sensing and quantum information processing. Proof-of-principle experiments have demonstrated atomic-scale imaging of nuclear-spin samples and controlled multi-qubit registers. However, to image more complex samples and to realize larger-scale quantum processors, computerized methods that efficiently and automatically characterize spin systems are required. Here, we realize a deep learning model for automatic identification of nuclear spins using the electron spin of single nitrogen-vacancy (NV) centers in diamond as a sensor. Based on neural network algorithms, we develop noise recovery procedures and training sequences for highly non-linear spectra. We apply these methods to experimentally demonstrate the fast identification of 31 nuclear spins around a single NV center and accurately determine the hyperfine parameters. Our methods can be extended to larger spin systems and are applicable to a wide range of electron-nuclear interaction strengths. These results pave the way towards efficient imaging of complex spin samples and automatic characterization of large spin-qubit registers. Spins ; Deep learning ; Spin (aerodynamics) ; Artificial neural network ; Hyperfine structure ; Quantum ; Quantum computer ; Identification (biology) ; Noise (video) Diamond and Carbon-based Materials Research ; Quantum and electron transport phenomena ; Graphene research and applications