jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bernard Mazouin | Alexandre Alain Schöpfer | O. Anatole von Lilienfeld
Source: Materials Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Selected machine learning of HOMO–LUMO gaps with improved data-efficiency Despite their relevance for organic electronics, quantum machine learning (QML) models of molecular electronic properties, such as HOMO-LUMO-gaps, often struggle to achieve satisfying data-efficiency as measured by decreasing prediction errors for increasing training set sizes. We demonstrate that partitioning training sets into different chemical classes prior to training results in independently trained QML models with overall reduced training data needs. For organic molecules drawn from previously published QM7 and QM9-data-sets we have identified and exploited three relevant classes corresponding to compounds containing either aromatic rings and carbonyl groups, or single unsaturated bonds, or saturated bonds The selected QML models of band-gaps (considered at GW and hybrid DFT levels of theory) reach mean absolute prediction errors of ∼0.1 eV for up to an order of magnitude fewer training molecules than for QML models trained on randomly selected molecules. Comparison to Δ-QML models of band-gaps indicates that selected QML exhibit superior data-efficiency. Our findings suggest that selected QML, <i>e.g.</i> based on simple classifications prior to training, could help to successfully tackle challenging quantum property screening tasks of large libraries with high fidelity and low computational burden. HOMO/LUMO ; Artificial intelligence ; Computer science ; Machine learning ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Machine Learning and ELM
Authors: Aeshah H. Alamri | N. Alhazmi
Source: Journal of Saudi Chemical Society (article, 2022-09-05)
Field: Materials Science — Materials Chemistry

Development of data driven machine learning models for the prediction and design of pyrimidine corrosion inhibitors Pyrimidines have been shown as promising nontoxic corrosion inhibitors for carbon steel in acid media that can replace toxic chemicals currently in use. However, the discovery of this important corrosion inhibitor has mainly be conducted by expensive trial and error experimental approaches. However, recent studies indicates that the use of machine learning can help in the speedy discovery of novel corrosion inhibitor molecules with minimal cost. In the present work, machine learning algorithms were utilized to develop predictive models for fifty-four (54) pyrimidines derivatives whose experimentally determined inhibition efficiencies data as corrosion inhibitors for carbon steel in hydrochloric acid medium are available in the literature utilizing the partial least square regression (PLS) and the random forest (RF). Seven descriptors were selected by PLS and used to develop the linear model. The variable importance results using the PLS indicates that molecular mass, molecular volume, electrophilicity, electronegativity, energy of the lowest unoccupied molecular orbital, electron affinity, and the logarithm of the partition coefficient were the main factors that determine the inhibition efficiencies. RF was used to capture the nonlinear nature of the data and to accurately predict the inhibition efficiency. Rigorous internal and external validation were performed using the PLS and RF to further verify the robustness and predictive ability of the models. The random forest yielded the best results with the mean standard error (MSE) of 32.602 compared to the PLS with MSE of 64.641. Both models were subsequently used for the prediction of five (5) new pyrimidines with very high inhibition efficiency. The result of this work can provide reference information and theoretical guidance for designing and synthesizing new and effective pyrimidines corrosion inhibitors. Random forest ; Electronegativity ; Quantitative structure–activity relationship ; Corrosion inhibitor ; Molecular descriptor ; Corrosion ; Mean squared error ; Biological system ; Coefficient of determination ; Linear regression ; Partial least squares regression ; HOMO/LUMO ; Chemistry ; Computer science ; Machine learning ; Mathematics ; Statistics ; Molecule ; Organic chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Paul L. Houston | Chen Qu | Apurba Nandi | Riccardo Conte | Qi Yu | Joel M. Bowman
Source: The Journal of Chemical Physics (article, 2022-01-03)
Field: Materials Science — Materials Chemistry

Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods Permutationally invariant polynomial (PIP) regression has been used to obtain machine-learned potential energy surfaces, including analytical gradients, for many molecules and chemical reactions. Recently, the approach has been extended to moderate size molecules with up to 15 atoms. The algorithm, including "purification of the basis," is computationally efficient for energies; however, we found that the recent extension to obtain analytical gradients, despite being a remarkable advance over previous methods, could be further improved. Here, we report developments to further compact a purified basis and, more significantly, to use the reverse differentiation approach to greatly speed up gradient evaluation. We demonstrate this for our recent four-body water interaction potential. Comparisons of training and testing precision on the MD17 database of energies and gradients (forces) for ethanol against numerous machine-learning methods, which were recently assessed by Dral and co-workers, are given. The PIP fits are as precise as those using these methods, but the PIP computation time for energy and force evaluation is shown to be 10-1000 times faster. Finally, a new PIP potential energy surface (PES) is reported for ethanol based on a more extensive dataset of energies and gradients than in the MD17 database. Diffusion Monte Carlo calculations that fail on MD17-based PESs are successful using the new PES. Computation ; Invariant (physics) ; Polynomial ; Computer science ; Regression ; Algorithm ; Monte Carlo method ; Basis (linear algebra) ; Statistical physics ; Artificial intelligence ; Applied mathematics ; Mathematics ; Physics ; Geometry ; Quantum mechanics ; Mathematical analysis ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ruoyu Huang | Dongliang Ding | Xiaoxiao Guo | Changjiang Liu | Xinhua Li | Gaoxiao Jiang | Yufeng Zhang | Yanhui Chen | Weiwei Cai | Xueao Zhang
Source: Composites Science and Technology (article, 2022-09-08)
Field: Materials Science — Materials Chemistry

Improving through-plane thermal conductivity of PDMS-based composites using highly oriented carbon fibers bridged by Al2O3 particles Materials science ; Composite material ; Thermal conductivity ; Polydimethylsiloxane ; Thermal conduction ; Thermal grease ; Thermal stability ; Electrical conductor ; Thermal ; Finite element method ; Modulus ; Carbon nanotube ; Structural engineering Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Heat Transfer and Optimization
Authors: M. Aminul Haque | Bing Chen | Muhammad Faisal Javed | Fazal E. Jalal
Source: Journal of Cleaner Production (article, 2022-04-18)
Field: Materials Science — Materials Chemistry

Evaluating the mechanical strength prediction performances of fly ash-based MPC mortar with artificial intelligence approaches Fly ash ; Artificial neural network ; Computer science ; Process (computing) ; Machine learning ; Process engineering ; Engineering ; Waste management Magnesium Oxide Properties and Applications ; Recycling and utilization of industrial and municipal waste in materials production ; Concrete and Cement Materials Research
Authors: Kevin M. Roccapriore | Ondrej Dyck | Mark P. Oxley | Maxim Ziatdinov | Sergei V. Kalinin
Source: ACS Nano (article, 2022-04-27)
Field: Materials Science — Materials Chemistry

Automated Experiment in 4D-STEM: Exploring Emergent Physics and Structural Behaviors Automated experiments in 4D scanning transmission electron microscopy (STEM) are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials. Deep kernel learning enables active learning of the relationship between local structure and 4D-STEM-based descriptors. With this, efficient and "intelligent" probing of dissimilar structural elements to discover desired physical functionality is made possible. This approach allows effective navigation of the sample in an automated fashion guided by either a predetermined physical phenomenon, such as strongest electric field magnitude, or in an exploratory fashion. We verify the approach first on preacquired 4D-STEM data and further implement it experimentally on an operational STEM. The experimental discovery workflow is demonstrated using graphene and subsequently extended toward a lesser-known layered 2D van der Waals material, MnPS<sub>3</sub>. This approach establishes a pathway for physics-driven automated 4D-STEM experiments that enable probing the physics of strongly correlated systems and quantum materials and devices, as well as exploration of beam-sensitive materials. Workflow ; van der Waals force ; Scanning transmission electron microscopy ; Physics ; Quantum ; Electric field ; Computer science ; Nanotechnology ; Materials science ; Transmission electron microscopy ; Quantum mechanics Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications ; Surface and Thin Film Phenomena
Authors: Christian T. Madsen | Jan C. Refsgaard | Felix Teufel | Sonny K. Kjærulff | Zhe Wang | Meng Guang-jun | Carsten Jessen | Petteri Heljo | Qunfeng Jiang | Xin Zhao | Bo Wu | Xueping Zhou | Yang Tang | Jacob Jeppesen | Christian D. Kelstrup | Stephen T. Buckley | Søren Tullin | Jan Nygaard-Jensen | Xiaoli Chen | Fang Zhang | Jesper V. Olsen | Dan Han | Mads Grønborg | Ulrik de Lichtenberg
Source: Nature Communications (article, 2022-10-20)
Field: Chemistry — Spectroscopy

Combining mass spectrometry and machine learning to discover bioactive peptides Peptides play important roles in regulating biological processes and form the basis of a multiplicity of therapeutic drugs. To date, only about 300 peptides in human have confirmed bioactivity, although tens of thousands have been reported in the literature. The majority of these are inactive degradation products of endogenous proteins and peptides, presenting a needle-in-a-haystack problem of identifying the most promising candidate peptides from large-scale peptidomics experiments to test for bioactivity. To address this challenge, we conducted a comprehensive analysis of the mammalian peptidome across seven tissues in four different mouse strains and used the data to train a machine learning model that predicts hundreds of peptide candidates based on patterns in the mass spectrometry data. We provide in silico validation examples and experimental confirmation of bioactivity for two peptides, demonstrating the utility of this resource for discovering lead peptides for further characterization and therapeutic development. Mass spectrometry ; Computer science ; Computational biology ; Chemistry ; Biology ; Chromatography Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; vaccines and immunoinformatics approaches
Authors: Zeinab Ahsani-Namin | Reza Norouzbeigi | Hadi Shayesteh
Source: Ceramics International (article, 2022-04-12)
Field: Materials Science — Materials Chemistry

Green mediated combustion synthesis of copper zinc oxide using Eryngium planum leaf extract as a natural green fuel: Excellent adsorption capacity towards Congo red dye Adsorption ; Materials science ; Thermogravimetric analysis ; Langmuir adsorption model ; Fourier transform infrared spectroscopy ; Chemical engineering ; Congo red ; Microporous material ; Nuclear chemistry ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Organic chemistry ; Composite material Copper-based nanomaterials and applications ; Laser-Ablation Synthesis of Nanoparticles ; Magnesium Oxide Properties and Applications
Authors: Junjie Si | Shuai Zhang | Xiuming Liu | Kuanjun Fang
Source: Langmuir (article, 2022-05-04)
Field: Chemistry — Inorganic Chemistry

Flower-Shaped Ni/Co MOF with the Highest Adsorption Capacity for Reactive Dyes Reactive dyes are widely used in textile industry, but their excessive use has caused several water pollution problems. In order to reasonably treat printing and dyeing wastewater, the highly efficient adsorbent for reactive dyes employed in this study is a new type metal-organic framework (MOF) material. Ni/Co MOF (NCM) was synthesized using the solvothermal method; then, the materials were analyzed by a series of characterization methods. This study mainly investigated the adsorption properties of NCM toward reactive dyes, and the adsorption capacities of NCM toward reactive red 218 were up to 200 mg·g<sup>-1</sup>. The results were found to conform to the Langmuir isotherm model, and the pseudo-second-order kinetic model by performing kinetic and isotherm studies on the adsorption process of reactive red 218 on NCM. The results of the intraparticle diffusion model suggest that the binding of reactive red 218 to NCM was mainly divided into three steps: adsorption, diffusion, and saturation. Moreover, it was concluded by thermodynamic fitting of the adsorption process that the adsorption of reactive red 218 by NCM proceeded spontaneously and was accompanied by an endothermic reaction, in which the adsorption of both occurred mainly by electrostatic attraction. The NCM has good reusability and still has good adsorption performance after being reused 5 times. Therefore, NCM is a very promising and excellent adsorbent for the treatment of dye wastewater because of its high efficiency and reusability. Adsorption ; Endothermic process ; Reusability ; Dyeing ; Chemistry ; Reactive dye ; Langmuir adsorption model ; Wastewater ; Chemical engineering ; Diffusion ; Inorganic chemistry ; Organic chemistry ; Waste management ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: Fatima A. Adam | Monira G. Ghoniem | Moussa Diawara | Seyfeddine Rahali | Babiker Y. Abdulkhair | Mohamed R. Elamin | Mohamed Ali Ben Aissa | Mahamadou Seydou
Source: RSC Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Enhanced adsorptive removal of indigo carmine dye by bismuth oxide doped MgO based adsorbents from aqueous solution: equilibrium, kinetic and computational studies Novel doped MgO nanoadsorbents were effectively fabricated at various Bi<sub>2</sub>O<sub>3</sub> doping concentrations (0, 2.5, 5 and 10%). DFT-D3 study showed that the doping is done by substitution of two magnesium atoms by two bismuth atoms with the creation of a vacancy of one Mg atom. TEM, SEM, EDX, BET, XRD, and FTIR were used to characterize the obtained nanostructures. The removal of indigo carmine (IC) dyes from wastewater by doped MgO nanoparticles is investigated. Experimental parameters such as the initial dye concentration, contact time, Bi<sub>2</sub>O<sub>3</sub> doping concentration, and pH were optimized to enhance the adsorption capacity. Bi<sub>2</sub>O<sub>3</sub> doped MgO prepared at 5% (MgOBi2) is the best adsorbent with a maximum IC adsorption capacity of 126 mg g<sup>-1</sup> at a solution pH equal to 7.00 and contact time of 74 min. The results indicated that the adsorption process followed pseudo-second-order (PSO) reaction kinetics, and the Freundlich isotherm model gave a better goodness-of-fit than the linear Langmuir model. The FTIR study established that IC molecules are successfully adsorbed onto the surface of MgOBi2 <i>via</i> a chemisorption process. Indigo carmine ; Adsorption ; Chemisorption ; Aqueous solution ; Bismuth ; Doping ; Langmuir adsorption model ; Fourier transform infrared spectroscopy ; Materials science ; Freundlich equation ; Langmuir ; Inorganic chemistry ; Chemistry ; Nuclear chemistry ; Chemical engineering ; Physical chemistry ; Organic chemistry Magnesium Oxide Properties and Applications ; Adsorption and biosorption for pollutant removal ; Graphene and Nanomaterials Applications
Authors: Jinyuan Hu | Junhui Li | Jennifer Jiang | Jinpeng Wang | Jonathan D. Roth | Kenneth N. McGuinness | Jean Baum | Wei Dai | Yao Sun | Vikas Nanda | Fei Xu
Source: Nature Communications (article, 2022-11-09)
Field: Materials Science — Biomaterials

Design of synthetic collagens that assemble into supramolecular banded fibers as a functional biomaterial testbed Collagens are the most abundant proteins of the extracellular matrix, and the hierarchical folding and supramolecular assembly of collagens into banded fibers is essential for mediating cell-matrix interactions and tissue mechanics. Collagen extracted from animal tissues is a valuable commodity, but suffers from safety and purity issues, limiting its biomaterials applications. Synthetic collagen biomaterials could address these issues, but their construction requires molecular-level control of folding and supramolecular assembly into ordered banded fibers, comparable to those of natural collagens. Here, we show an innovative class of banded fiber-forming synthetic collagens that recapitulate the morphology and some biological properties of natural collagens. The synthetic collagens comprise a functional-driver module that is flanked by adhesive modules that effectively promote their supramolecular assembly. Multiscale simulations support a plausible molecular-level mechanism of supramolecular assembly, allowing precise design of banded fiber morphology. We also experimentally demonstrate that synthetic fibers stimulate osteoblast differentiation at levels comparable to natural collagen. This work thus deepens understanding of collagen biology and disease by providing a ready source of safe, functional biomaterials that bridge the current gap between the simplicity of peptide biophysical models and the complexity of in vivo animal systems. Supramolecular chemistry ; Extracellular matrix ; Nanotechnology ; Folding (DSP implementation) ; Biomaterial ; Nanofiber ; Tissue engineering ; Synthetic biology ; Materials science ; Biophysics ; Chemistry ; Computational biology ; Biology ; Biochemistry ; Molecule Collagen: Extraction and Characterization ; Connective tissue disorders research ; Silk-based biomaterials and applications
Authors: Ricardo I. Rodríguez | Marina Sicignano | José Alemán
Source: Angewandte Chemie International Edition (article, 2022-01-04)
Field: Chemistry — Organic Chemistry

Fluorinated Sulfinates as Source of Alkyl Radicals in the Photo‐Enantiocontrolled β‐Functionalization of Enals The generation of sulfonyl radicals has long been known as a flexible strategy in a wide range of different sulfonylative transformations. Meanwhile their use in alkylation processes has been somehow limited due to their inherent difficulty in evolving to less-stable radicals after sulfur dioxide extrusion. Herein we report a convenient strategy that involves gem-difluorinated sulfinates as an "upgrading-mask", allowing these precursors to decompose into their corresponding alkyl radicals. The electron-donor character of sulfinates in the formation of an electron donor-acceptor (EDA) complex with transient iminium ions is displayed, achieving the first example of a stereocontrolled light-driven insertion of gem-difluoro derivatives into unsaturated aldehydes. This methodology is compatible with flow conditions, maintaining identical levels of enantiocontrol. Radical ; Chemistry ; Iminium ; Sulfonyl ; Alkyl ; Alkylation ; Combinatorial chemistry ; Photochemistry ; Surface modification ; Ion ; Organic chemistry ; Catalysis Sulfur-Based Synthesis Techniques ; Radical Photochemical Reactions ; Fluorine in Organic Chemistry
Authors: Shan Li | Yilin Wang | Mingdi Yang | Jun Miao | Kun Lin | Qiang Li | Xin Chen | Jinxia Deng | Xianran Xing
Source: Materials Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Ferroelectric thin films: performance modulation and application A review on performance optimization and application of ferroelectric thin film materials. Ferroelectricity ; Materials science ; Thin film ; Modulation (music) ; Optoelectronics ; Ferroelectric capacitor ; Nanotechnology ; Dielectric ; Physics ; Acoustics Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Ferroelectric and Negative Capacitance Devices
Authors: Maxim Ziatdinov | Ayana Ghosh | Sergei V. Kalinin
Source: Machine Learning Science and Technology (article, 2022-01-14)
Field: Materials Science — Materials Chemistry

Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process Abstract Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces to discover optimal experimental conditions and regions of interest in the image space or parameter space of computational models. The direct grid search of the parameter space tends to be extremely time-consuming, leading to the development of strategies balancing exploration of unknown parameter spaces and exploitation towards required performance metrics. However, classical Bayesian optimization (BO) strategies based on the Gaussian process (GP) do not readily allow for the incorporation of the known physical behaviors or past knowledge. Here we explore a hybrid optimization/exploration algorithm created by augmenting the standard GP with a structured probabilistic model of the expected system’s behavior. This approach balances the flexibility of the non-parametric GP approach with a rigid structure of physical knowledge encoded into the parametric model. The fully Bayesian treatment of the latter allows additional control over the optimization via the selection of priors for the model parameters. The method is demonstrated for a noisy version of a standard univariate test function used to evaluate optimization algorithms and further extended to physical lattice models. This methodology is expected to be universally suitable for injecting prior knowledge in the form of physical models and past data in the BO framework. Bayesian optimization ; Gaussian process ; Hyperparameter optimization ; Computer science ; Prior probability ; Parametric statistics ; Machine learning ; Parameter space ; Artificial intelligence ; Grid ; Mathematical optimization ; Flexibility (engineering) ; Univariate ; Optimization problem ; Bayesian probability ; Gaussian ; Algorithm ; Mathematics ; Multivariate statistics ; Support vector machine Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Multi-Objective Optimization Algorithms