jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Endri Stoja | Simon Konstandin | Dennis Philipp | Robin N. Wilke | Diego Betancourt | Thomas Bertuch | Jürgen Jenne | Reiner Umathum | Matthias Günther
Source: Scientific Reports (article, 2021-08-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Improving magnetic resonance imaging with smart and thin metasurfaces Over almost five decades of development and improvement, Magnetic Resonance Imaging (MRI) has become a rich and powerful, non-invasive technique in medical imaging, yet not reaching its physical limits. Technical and physiological restrictions constrain physically feasible developments. A common solution to improve imaging speed and resolution is to use higher field strengths, which also has subtle and potentially harmful implications. However, patient safety is to be considered utterly important at all stages of research and clinical routine. Here we show that dynamic metamaterials are a promising solution to expand the potential of MRI and to overcome some limitations. A thin, smart, non-linear metamaterial is presented that enhances the imaging performance and increases the signal-to-noise ratio in 3T MRI significantly (up to eightfold), whilst the transmit field is not affected due to self-detuning and, thus, patient safety is also assured. This self-detuning works without introducing any additional overhead related to MRI-compatible electronic control components or active (de-)tuning mechanisms. The design paradigm, simulation results, on-bench characterization, and MRI experiments using homogeneous and structural phantoms are described. The suggested single-layer metasurface paves the way for conformal and patient-specific manufacturing, which was not possible before due to typically bulky and rigid metamaterial structures. Metamaterial ; Magnetic resonance imaging ; Overhead (engineering) ; Homogeneous ; Conformal map ; Field (mathematics) ; Medical imaging ; High resolution Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Micro and Nano Robotics
Authors: Jingnan Zheng | Xiang Sun | Jiaxi Hu | Shibin Wang | Zihao Yao | Shengwei Deng | Xiang Pan | Zhiyan Pan | Jianguo Wang
Source: ACS Applied Materials & Interfaces (article, 2021-10-21)
Field: Materials Science — Materials Chemistry

Symbolic Transformer Accelerating Machine Learning Screening of Hydrogen and Deuterium Evolution Reaction Catalysts in MA<sub>2</sub>Z<sub>4</sub> Materials Two-dimensional (2D) materials have been developed into various catalysts with high performance, but employing them for developing highly stable and active nonprecious hydrogen evolution reaction (HER) catalysts still encounters many challenges. To this end, the machine learning (ML) screening of HER catalysts is accelerated by using genetic programming (GP) of symbolic transformers for various typical 2D MA<sub>2</sub>Z<sub>4</sub> materials. The values of the Gibbs free energy of hydrogen adsorption (Δ<i>G</i><sub>H*</sub>) are accurately and rapidly predicted via extreme gradient boosting regression by using only simple GP-processed elemental features, with a low predictive root-mean-square error of 0.14 eV. With the analysis of ML and density functional theory (DFT) methods, it is found that various electronic structural properties of metal atoms and the p-band center of surface atoms play a crucial role in regulating the HER performance. Based on these findings, NbSi<sub>2</sub>N<sub>4</sub> and VSi<sub>2</sub>N<sub>4</sub> are discovered to be active catalysts with thermodynamical and dynamical stability as Δ<i>G</i><sub>H*</sub> approaches to zero (-0.041 and 0.024 eV). In addition, DFT calculations reveal that these catalysts also exhibit good deuterium evolution reaction (DER) performance. Overall, a multistep workflow is developed through ML models combined with DFT calculations for efficiently screening the potential HER and DER catalysts from 2D materials with the same crystal prototype, which is believed to have significant contribution to catalyst design and fabrication. Density functional theory ; Materials science ; Catalysis ; Deuterium ; Hydrogen ; Gibbs free energy ; Machine learning ; Physical chemistry ; Thermodynamics ; Computational chemistry ; Computer science ; Atomic physics ; Quantum mechanics ; Physics ; Chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Machine Learning in Materials Science
Authors: Jan Weinreich | Nicholas J. Browning | O. Anatole von Lilienfeld
Source: The Journal of Chemical Physics (article, 2021-04-05)
Field: Materials Science — Materials Chemistry

Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation Free energies govern the behavior of soft and liquid matter, and improving their predictions could have a large impact on the development of drugs, electrolytes, or homogeneous catalysts. Unfortunately, it is challenging to devise an accurate description of effects governing solvation such as hydrogen-bonding, van der Waals interactions, or conformational sampling. We present a Free energy Machine Learning (FML) model applicable throughout chemical compound space and based on a representation that employs Boltzmann averages to account for an approximated sampling of configurational space. Using the FreeSolv database, FML's out-of-sample prediction errors of experimental hydration free energies decay systematically with training set size, and experimental uncertainty (0.6 kcal/mol) is reached after training on 490 molecules (80% of FreeSolv). Corresponding FML model errors are on par with state-of-the art physics based approaches. To generate the input representation for a new query compound, FML requires approximate and short molecular dynamics runs. We showcase its usefulness through analysis of solvation free energies for 116k organic molecules (all force-field compatible molecules in the QM9 database), identifying the most and least solvated systems and rediscovering quasi-linear structure-property relationships in terms of simple descriptors such as hydrogen-bond donors, number of NH or OH groups, number of oxygen atoms in hydrocarbons, and number of heavy atoms. FML's accuracy is maximal when the temperature used for the molecular dynamics simulation to generate averaged input representation samples in training is the same as for the query compounds. The sampling time for the representation converges rapidly with respect to the prediction error. Solvation ; Representation (politics) ; van der Waals force ; Molecular dynamics ; Statistical physics ; Chemistry ; Molecule ; Space (punctuation) ; Chemical space ; Sampling (signal processing) ; Computational chemistry ; Chemical physics ; Computer science ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Joung Sik Suh | Byeong‐Chan Suh | Sang Eun Lee | Jun Ho Bae | Byoung Gi Moon
Source: Journal of Material Science and Technology (article, 2021-10-07)
Field: Materials Science — Biomaterials

Quantitative analysis of mechanical properties associated with aging treatment and microstructure in Mg-Al-Zn alloys through machine learning Microstructure ; Ultimate tensile strength ; Materials science ; Rod ; Texture (cosmology) ; Precipitation ; Standard deviation ; Deformation (meteorology) ; Decision tree ; Linear regression ; Machine learning ; Metallurgy ; Artificial intelligence ; Computer science ; Composite material ; Mathematics ; Statistics ; Medicine Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Simon Batzner | Tess Smidt | Lixin Sun | Jonathan P. Mailoa | Mordechai Kornbluth | Nicola Molinari | Boris Kozinsky
Source: Research Square (preprint, 2021-03-01)
Field: Materials Science — Materials Chemistry

SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Equivariant map ; Computer science ; Invariant (physics) ; Artificial neural network ; Representation (politics) ; Ab initio ; Set (abstract data type) ; Statistical physics ; Theoretical computer science ; Algorithm ; Artificial intelligence ; Physics ; Quantum mechanics ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Nunzio Tuccitto | Luca Fichera | Roberta Ruffino | Valentina Cantaro | Gianfranco Sfuncia | Giuseppe Nicotra | Giuseppe Trusso Sfrazzetto | Giovanni Li‐Destri | Andrea Valenti | Antonino Licciardello | Alberto Torrisi
Source: ACS Applied Nano Materials (article, 2021-05-27)
Field: Materials Science — Materials Chemistry

Carbon Quantum Dots as Fluorescence Nanochemosensors for Selective Detection of Amino Acids Fluorescent carbon quantum dots (CDs) are synthesized and employed as fluorescent nanochemosensors for selective detection of amino acids. A detailed investigation of excitation–emission maps revealed that the fluorescence properties of CDs are intensely and strongly influenced by the interaction at the surface with different amino acids. The discrimination capability was demonstrated by tensor rank decomposition of the differences induced by the surface reaction in the excitation–emission maps and by means of a common machine learning approach based on artificial neural networks. Fluorescence ; Carbon quantum dots ; Quantum dot ; Amino acid ; Carbon fibers ; Excitation ; Tensor (intrinsic definition) ; Decomposition ; Chemistry ; Artificial neural network ; Photochemistry ; Materials science ; Nanotechnology ; Computer science ; Organic chemistry ; Physics ; Artificial intelligence ; Mathematics ; Optics ; Biochemistry ; Composite number ; Quantum mechanics Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Electrochemical sensors and biosensors
Authors: Nicolás M. Morato | MyPhuong T. Le | Dylan T. Holden | R. Graham Cooks
Source: SLAS TECHNOLOGY (review, 2021-10-26)
Field: Chemistry — Spectroscopy

Automated High-Throughput System Combining Small-Scale Synthesis with Bioassays and Reaction Screening Throughput ; Desorption electrospray ionization ; Analyte ; Computer science ; High-throughput screening ; Bioassay ; Electrospray ionization ; Mass spectrometry ; Nanotechnology ; Process engineering ; Combinatorial chemistry ; Biological system ; Computer hardware ; Chemistry ; Chromatography ; Materials science ; Ionization ; Chemical ionization ; Engineering Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Fayza Yulia | Intan Chairina | Agustino Zulys | Nasruddin Nasruddin
Source: Thermal Science and Engineering Progress (article, 2021-05-24)
Field: Chemistry — Inorganic Chemistry

Multi-objective genetic algorithm optimization with an artificial neural network for CO2/CH4 adsorption prediction in metal–organic framework Artificial neural network ; Genetic algorithm ; Adsorption ; Computer science ; Optimization algorithm ; Artificial intelligence ; Materials science ; Machine learning ; Mathematical optimization ; Chemistry ; Mathematics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Zhichao Liu | Xiaolong Yang | Bo Zhang | Wu Li
Source: ACS Applied Materials & Interfaces (article, 2021-08-20)
Field: Materials Science — Materials Chemistry

High Thermal Conductivity of Wurtzite Boron Arsenide Predicted by Including Four-Phonon Scattering with Machine Learning Potential Materials with high thermal conductivity are of great importance to the thermal management of modern electronic devices. Recently, it was found that cubic boron arsenide (c-BAs) is a high thermal conductivity (κ) material with a value of ∼1300 W/(m·K) at room temperature (RT), where four-phonon scattering plays a crucial role in limiting the κ. In this work, with four-phonon scattering included, we find that the κ of wurtzite BAs (w-BAs) reaches as high as 1036 W/(m·K) along the <i>a-b</i> plane at RT, decreasing by 43% compared to the calculation without considering four-phonon scattering. The similar phonon transport properties between c-BAs and w-BAs can be understood in terms of similar projected density of states and scattering rates, which have the origin in crystal structural resemblance. To accelerate the calculation, the moment tensor potential derived from machine learning is adopted and proven to be a reliable and efficient method to obtain high-order interatomic force constants. Thermal conductivity ; Wurtzite crystal structure ; Phonon ; Materials science ; Condensed matter physics ; Scattering ; Phonon scattering ; Thermal barrier coating ; Nanotechnology ; Optics ; Physics ; Composite material Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Yan Cao | Hayder A. Dhahad | Sara Ghaboulian Zare | Naem Farouk | Ali E. Anqi | Alibek Issakhov | Amir Raise
Source: International Journal of Hydrogen Energy (article, 2021-09-13)
Field: Chemistry — Inorganic Chemistry

Potential application of metal-organic frameworks (MOFs) for hydrogen storage: Simulation by artificial intelligent techniques Hydrogen storage ; Artificial neural network ; Metal-organic framework ; Support vector machine ; Computer science ; Mean squared error ; Estimator ; Adsorption ; Correlation coefficient ; Hydrogen ; Artificial intelligence ; Machine learning ; Mathematics ; Chemistry ; Statistics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Hydrogen Storage and Materials
Authors: Jigyasa Nigam | Michael J. Willatt | Michele Ceriotti
Source: The Journal of Chemical Physics (article, 2021-12-13)
Field: Materials Science — Materials Chemistry

Equivariant representations for molecular Hamiltonians and <i>N</i>-center atomic-scale properties Symmetry considerations are at the core of the major frameworks used to provide an effective mathematical representation of atomic configurations that is then used in machine-learning models to predict the properties associated with each structure. In most cases, the models rely on a description of atom-centered environments and are suitable to learn atomic properties or global observables that can be decomposed into atomic contributions. Many quantities that are relevant for quantum mechanical calculations, however-most notably the single-particle Hamiltonian matrix when written in an atomic orbital basis-are not associated with a single center, but with two (or more) atoms in the structure. We discuss a family of structural descriptors that generalize the very successful atom-centered density correlation features to the N-center case and show, in particular, how this construction can be applied to efficiently learn the matrix elements of the (effective) single-particle Hamiltonian written in an atom-centered orbital basis. These N-center features are fully equivariant-not only in terms of translations and rotations but also in terms of permutations of the indices associated with the atoms-and are suitable to construct symmetry-adapted machine-learning models of new classes of properties of molecules and materials. Hamiltonian (control theory) ; Physics ; Observable ; Atom (system on chip) ; Equivariant map ; Irreducible representation ; Theoretical physics ; Quantum mechanics ; Statistical physics ; Mathematics ; Computer science ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Heikki Muhli | Xi Chen | Albert P. Bartók | Patricia Hernández-León | Gábor Cśanyi | Tapio Ala-Nissilä | A. Miguel
Source: Physical review. B./Physical review. B (article, 2021-08-06)
Field: Materials Science — Materials Chemistry

Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi mathvariant="normal">C</mml:mi><mml:mn>60</mml:mn></mml:msub></mml:math> We present a comprehensive methodology to enable the addition of van der Waals (vdW) corrections to machine learning (ML) atomistic force fields. Using a Gaussian approximation potential (GAP) [Bart\'ok et al., Phys. Rev. Lett. 104, 136403 (2010)] as a baseline, we accurately machine learn a local model of atomic polarizabilities based on Hirshfeld volume partitioning of the charge density [Tkatchenko and Scheffler, Phys. Rev. Lett. 102, 073005 (2009)]. These environment-dependent polarizabilities are then used to parametrize a screened London-dispersion approximation to the vdW interactions. Our ML vdW model only needs to learn the charge density partitioning implicitly by learning the reference Hirshfeld volumes from density functional theory (DFT). In practice, we can predict accurate Hirshfeld volumes from the knowledge of the local atomic environment (atomic positions) alone, making the model highly computationally efficient. For additional efficiency, our ML model of atomic polarizabilities reuses the same many-body atomic descriptors used for the underlying GAP learning of bonded interatomic interactions. We also show how the method enables straightforward computation of gradients of the observables, even when these remain challenging for the reference method (e.g., calculating gradients of the Hirshfeld volumes in DFT). Finally, we demonstrate the approach by studying the phase diagram of ${\mathrm{C}}_{60}$, where vdW effects are important. The need for a highly accurate vdW-inclusive reactive force field is highlighted by modeling the decomposition of the ${\mathrm{C}}_{60}$ molecules taking place at high pressures and temperatures. van der Waals force ; Parametrization (atmospheric modeling) ; Phase diagram ; Density functional theory ; Force field (fiction) ; Physics ; Atomic charge ; Statistical physics ; Machine learning ; Phase (matter) ; Computer science ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; High-pressure geophysics and materials ; Advanced Chemical Physics Studies
Authors: Jasper Clarysse | A. Moser | Olesya Yarema | Vanessa Wood | Maksym Yarema
Source: Science Advances (article, 2021-07-28)
Field: Materials Science — Materials Chemistry

Size- and composition-controlled intermetallic nanocrystals via amalgamation seeded growth Intermetallic nanocrystals are a large family of emerging materials with extensive applications in many fields. Yet, a generalized synthetic method for intermetallic nanocrystals is lacking. Here, we report the development of a colloidal synthesis method based on amalgamation of monometallic nanocrystal seeds with low-melting point metals. We use this approach to achieve crystalline and compositionally uniform intermetallic nanocrystals of Au-Ga, Ag-Ga, Cu-Ga, Ni-Ga, Pd-Ga, Pd-In, and Pd-Zn compounds. We demonstrate both compositional tunability across the phase spaces (e.g., AuGa<sub>2</sub>, AuGa, Au<sub>7</sub>Ga<sub>2</sub>, and Ga-doped Au), size tunability (e.g., 14.0-, 7.6-, and 3.8-nm AuGa<sub>2</sub>), and size uniformity (e.g., 5.4% size deviations). This approach makes it possible to systematically achieve size- and composition-controlled intermetallic nanocrystals, opening up a multitude of possibilities for these materials. Intermetallic ; Nanocrystal ; Seeding ; Materials science ; Composition (language) ; Nanotechnology ; Chemical engineering ; Metallurgy ; Biology ; Agronomy ; Engineering Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Nanomaterials for catalytic reactions
Authors: Yu Xie | Jonathan Vandermause | Lixin Sun | Andrea Cepellotti | Boris Kozinsky
Source: npj Computational Materials (article, 2021-03-19)
Field: Materials Science — Materials Chemistry

Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene Abstract We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features. This allows for automated active learning of models combining near-quantum accuracy, built-in uncertainty, and constant cost of evaluation that is comparable to classical analytical models, capable of simulating millions of atoms. Using this approach, we perform large-scale molecular dynamics simulations of the stability of the stanene monolayer. We discover an unusual phase transformation mechanism of 2D stanene, where ripples lead to nucleation of bilayer defects, densification into a disordered multilayer structure, followed by formation of bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal at low temperature. The presented method opens possibilities for rapid development of fast accurate uncertainty-aware models for simulating long-time large-scale dynamics of complex materials. Nucleation ; Molecular dynamics ; Stability (learning theory) ; Transformation (genetics) ; Force field (fiction) ; Gaussian ; Gaussian process ; Phase (matter) Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Block Copolymer Self-Assembly
Authors: Umer Shahzad Malik | Muhammad Bilal Khan | Zaib Jahan | Mazhar Iqbal Zafar | Dai‐Viet N. Vo | Farooq Sher
Source: Environmental Chemistry Letters (review, 2021-10-31)
Field: Materials Science — Polymers and Plastics

Nano-structured dynamic Schiff base cues as robust self-healing polymers for biomedical and tissue engineering applications: a review Abstract Polymer materials are vulnerable to damages, failures, and degradations, making them economically unreliable. Self-healing polymers, on the other hand, are multifunctional materials with superior properties of autonomic recovery from physical damages. These materials are suitable for biomedical and tissue engineering in terms of cost and durability. Schiff base linkages-based polymer materials are one of the robust techniques owing to their simple self-healing mechanism. These are dynamic reversible covalent bonds, easy to fabricate at mild conditions, and can self-reintegrate after network disruption at physiological conditions making them distinguished. Here we review self-healing polymer materials based on Schiff base bonds. We discuss the Schiff base bond formation between polymeric networks, which explains the self-healing phenomenon. These bonds have induced 100% recovery in optimal cases. Polymer ; Schiff base ; Self-healing material ; Materials science ; Self-healing ; Durability ; Covalent bond ; Damages ; Tissue engineering ; Nanotechnology ; Composite material ; Biomedical engineering ; Polymer chemistry ; Chemistry ; Organic chemistry ; Engineering Polymer composites and self-healing ; Electrospun Nanofibers in Biomedical Applications ; Advanced Polymer Synthesis and Characterization
Authors: Robert T. Furbank | Viridiana Silva‐Pérez | John R. Evans | Anthony G. Condon | Gonzalo M. Estavillo | Wennan He | Saul Newman | Richard Poiré | Ashley Hall | Zhen He
Source: Plant Methods (article, 2021-10-19)
Field: Chemistry — Analytical Chemistry

Wheat physiology predictor: predicting physiological traits in wheat from hyperspectral reflectance measurements using deep learning The method reported provides an improved prediction of wheat leaf and photosynthetic traits from leaf hyperspectral reflectance and do not require a full range, high cost leaf spectrometer. We provide a web service for deploying these algorithms to predict physiological traits in wheat from a variety of spectral data sets, with important implications for wheat yield prediction and crop breeding. Hyperspectral imaging ; Reflectivity ; Environmental science ; Remote sensing ; Biology ; Geology ; Optics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI