jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Susana I.L. Gomes | Mónica J.B. Amorim | Suman Pokhrel | Lutz Mädler | Matteo Fasano | Eliodoro Chiavazzo | Pietro Asinari | Jaak Jänes | Kaido Tämm | Jaanus Burk | Janeck J. Scott‐Fordsmand
Source: Nanoscale (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine learning and materials modelling interpretation of <i>in vivo</i> toxicological response to TiO<sub>2</sub> nanoparticles library (UV and non-UV exposure) Assessing the risks of nanomaterials/nanoparticles (NMs/NPs) under various environmental conditions requires a more systematic approach, including the comparison of effects across many NMs with identified different but related characters/descriptors. Hence, there is an urgent need to provide coherent (eco)toxicological datasets containing comprehensive toxicity information relating to a diverse spectra of NPs characters. These datasets are test benches for developing holistic methodologies with broader applicability. In the present study we assessed the effects of a custom design Fe-doped TiO<sub>2</sub> NPs library, using the soil invertebrate <i>Enchytraeus crypticus</i> (Oligochaeta), <i>via</i> a 5-day pulse <i>via</i> aqueous exposure followed by a 21-days recovery period in soil (survival, reproduction assessment). Obviously, when testing TiO<sub>2</sub>, realistic conditions should include UV exposure. The 11 Fe-TiO<sub>2</sub> library contains NPs of size range between 5-27 nm with varying %Fe (enabling the photoactivation of TiO<sub>2</sub> at energy wavelengths in the visible-light range). The NPs were each described by 122 descriptors, being a mixture of measured and atomistic model descriptors. The data were explored using single and univariate statistical methods, combined with machine learning and multiscale modelling techniques. An iterative pruning process was adopted for identifying automatically the most significant descriptors. TiO<sub>2</sub> NPs toxicity decreased when combined with UV. Notably, the short-term water exposure induced lasting biological responses even after longer-term recovery in clean exposure. The correspondence with Fe-content correlated with the band-gap hence the reduction of UV oxidative stress. The inclusion of both measured and modelled materials data benefitted the explanation of the results, when combined with machine learning. Univariate ; Nanoparticle ; Materials science ; Computer science ; Nanotechnology ; Key (lock) ; Biological system ; Machine learning ; Multivariate statistics ; Biology Nanoparticles: synthesis and applications ; Computational Drug Discovery Methods ; Advanced Nanomaterials in Catalysis
Authors: Max Hodapp | Alexander V. Shapeev
Source: Physical Review Materials (article, 2021-11-22)
Field: Materials Science — Materials Chemistry

Machine-learning potentials enable predictive and tractable high-throughput screening of random alloys We present an automated procedure for computing stacking fault energies in random alloys from large-scale simulations using moment tensor potentials (MTPs) with the accuracy of density functional theory (DFT). To that end, we develop an algorithm for training MTPs on random alloys. In the first step, our algorithm constructs a set of $\ensuremath{\sim}10$ 000 or more training candidate configurations with 50--100 atoms that are representative for the atomic neighborhoods occurring in the large-scale simulation. In the second step, we use active learning to reduce this set to $\ensuremath{\sim}100$ most distinct configurations---for which DFT energies and forces are computed and on which the potential is ultimately trained. We validate our algorithm for the MoNbTa medium-entropy alloy by showing that the MTP reproduces the DFT $\frac{1}{4}[111]$ unstable stacking fault energy over the entire compositional space up to a few percent. Contrary to state-of-the-art methods, e.g., the coherent potential approximation (CPA) or special quasi-random structures (SQSs), our algorithm naturally accounts for relaxation, is not limited by DFT cell sizes, and opens opportunities to efficiently investigate follow-up problems, such as chemical ordering. In a broader sense, our algorithm can be easily modified to compute related properties of random alloys, for instance, misfit volumes, or grain boundary energies. Moreover, it forms the basis for an efficient construction of MTPs to be used in large-scale simulations of multicomponent systems. Materials science ; Density functional theory ; Statistical physics ; Stacking ; Relaxation (psychology) ; Stacking fault ; Scale (ratio) ; Gaussian ; Set (abstract data type) ; Algorithm ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties
Authors: Wei Wu | Qiang Sun
Source: ACS Materials Letters (article, 2021-12-17)
Field: Materials Science — Materials Chemistry

Screening Topological Quantum Materials for Na-Ion Battery Cathode Recent studies have suggested that more than 27% of existing materials are topological and exhibit intrinsically high electrical conductivity with high stability protected by topology, which can meet the requirement that the battery cathode needs to be conductive not just ionically but also electronically for the high rate performance. In this work, using machine learning and first-principle calculations, for the first time we have screened 7386 topological quantum materials with low diffusion energy barriers. Especially, topological semimetal Na4CoO3 has been identified to exhibit high reversible capacity of 269.5 mAh/g and high energy density of 746.5 Wh/kg. Furthermore, Na4CoO3 also exhibits good compatibility with many sodium metal oxides solid electrolyte such as Na3PO4 and NaAlO2. These findings suggest that Na4CoO3 can be a novel high performance Na-ion battery cathode material going beyond the conventional ones. Cathode ; Topology (electrical circuits) ; Electrolyte ; Battery (electricity) ; Materials science ; Ion ; Quantum ; Nanotechnology ; Engineering physics ; Optoelectronics ; Chemistry ; Physics ; Electrical engineering ; Electrode ; Physical chemistry ; Thermodynamics ; Quantum mechanics Graphene research and applications ; Topological Materials and Phenomena ; Advancements in Battery Materials
Authors: Uta Hejral | Janis Timoshenko | David Kordus | Mauricio López Luna | Núria J. Divins | Simon Widrinna | Ioannis Zegkinoglou | Lukas Pielsticker | Hemma Mistry | J. Anibal Boscoboinik | Stefanie Kuehl | Beatriz Roldán Cuenya
Source: Journal of Catalysis (article, 2021-11-23)
Field: Materials Science — Materials Chemistry

Tracking the phase changes in micelle-based NiGa nanocatalysts for methanol synthesis under activation and working conditions Nanomaterial-based catalyst ; Catalysis ; Chemical engineering ; X-ray photoelectron spectroscopy ; Phase (matter) ; Chemistry ; Methanol ; Alloy ; Nanoparticle ; Ambient pressure ; Materials science ; Nanotechnology ; Organic chemistry ; Thermodynamics Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Electrocatalysts for Energy Conversion
Authors: Eric M. Collins | Krishnan Raghavachari
Source: The Journal of Physical Chemistry A (article, 2021-08-03)
Field: Materials Science — Materials Chemistry

A Fragmentation-Based Graph Embedding Framework for QM/ML We introduce a new fragmentation-based molecular representation framework "FragGraph" for QM/ML methods involving embedding fragment-wise fingerprints onto molecular graphs. Our model is specifically designed for delta machine learning (Δ-ML) with the central goal of correcting the deficiencies of approximate methods such as DFT to achieve high accuracy. Our framework is based on a judicious combination of ideas from fragmentation, error cancellation, and a state-of-the-art deep learning architecture. Broadly, we develop a general graph-network framework for molecular machine learning by incorporating the inherent advantages prebuilt into error cancellation methods such as the generalized Connectivity-Based Hierarchy. More specifically, we develop a QM/ML representation through a fragmentation-based attributed graph representation encoded with fragment-wise molecular fingerprints. The utility of our representation is demonstrated through a graph network fingerprint encoder in which a global fingerprint is generated through message passing of local neighborhoods of fragment-wise fingerprints, effectively augmenting standard fingerprints to also include the inbuilt molecular graph structure. On the 130k-GDB9 dataset, our method predicts an out-of-sample mean absolute error significantly lower than 1 kJ/mol compared to target G4(MP2) calculated energies, rivaling current deep learning methods with reduced computational scaling. Embedding ; Computer science ; Fragmentation (computing) ; Graph ; Scaling ; Encoder ; Fingerprint (computing) ; Representation (politics) ; Fragment (logic) ; Theoretical computer science ; Graph embedding ; Feature learning ; Algorithm ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: In‐Ho Lee | K. J. Chang
Source: Computational Materials Science (article, 2021-04-13)
Field: Materials Science — Materials Chemistry

Crystal structure prediction in a continuous representative space Here we report a method of finding multiple crystal structures similar to the known crystal structures of materials on database through machine learning. The radial distribution function is used to represent the general characteristics of the known crystal structures, and then the variational autoencoder is employed to generate a set of representative crystal replicas defined in a two-dimensional optimal continuous space. For given chemical compositions and crystal volume, we generate random crystal structures using constraints for crystal symmetry and atomic positions and directly compare their radial distribution functions with those of the known and/or replicated crystals. For selected crystal structures, energy minimization is subsequently performed through first-principles electronic structure calculations. This approach enables us to predict a set of new low-energy crystal structures using only the information on the radial distribution functions of the known structures. Crystal (programming language) ; Crystal structure prediction ; Crystal structure ; Autoencoder ; Space (punctuation) ; Distribution (mathematics) ; Statistical physics ; Symmetry (geometry) ; Energy minimization ; Set (abstract data type) ; Materials science ; Physics ; Computer science ; Mathematics ; Crystallography ; Chemistry ; Artificial intelligence ; Geometry ; Mathematical analysis ; Quantum mechanics ; Artificial neural network Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Deniz İren | Marc Ackermann | Julian Gorfer | Gaurav Pujar | Sebastian Wesselmecking | Ulrich Krupp | Stefano Bromuri
Source: Scientific Data (article, 2021-05-26)
Field: Materials Science — Materials Chemistry

Aachen-Heerlen annotated steel microstructure dataset Studying steel microstructures yields important insights regarding its mechanical characteristics. Within steel, microstructures transform based on a multitude of factors including chemical composition, transformation temperatures, and cooling rates. Martensite-austenite (MA) islands in bainitic steel appear as blocky structures with abstract shapes that are difficult to identify and differentiate from other types of microstructures. In this regard, material science may benefit from machine learning models that are able to automatically and accurately detect these structures. However, the training process of the state-of-the-art machine learning models requires a large amount of high-quality data. In this dataset, we provide 1.705 scanning electron microscopy images along with a set of 8.909 expert-annotated polygons to describe the geometry of the MA islands that appear on the images. We envision that this dataset will be useful for material scientists to explore the relationship between the morphology of bainitic steel and mechanical characteristics. Moreover, computer vision researchers and practitioners may use this data for training state-of-the-art object segmentation models for abstract geometries such as MA islands. Microstructure ; Martensite ; Austenite ; Segmentation ; Set (abstract data type) ; Process (computing) ; Transformation (genetics) ; Computer science ; Materials science ; Artificial intelligence ; Metallurgy ; Chemistry Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Tejaswi Soori | Seyed Moein Rassoulinejad-Mousavi | Lige Zhang | Arif Rokoni | Ying Sun
Source: Fluid Phase Equilibria (article, 2021-03-13)
Field: Materials Science — Surfaces, Coatings and Films

A machine learning approach for estimating surface tension based on pendant drop images Surface tension ; Drop (telecommunication) ; Chemistry ; Generality ; Convolutional neural network ; Spinning drop method ; Artificial neural network ; Artificial intelligence ; Biological system ; Algorithm ; Mechanics ; Thermodynamics ; Computer science ; Mechanical engineering ; Physics ; Engineering Surface Modification and Superhydrophobicity ; Electrohydrodynamics and Fluid Dynamics ; Fluid Dynamics and Heat Transfer
Authors: Peng Gao | Jie Zhang | Hongbo Qiu | Shuaifei Zhao
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

A general QSPR protocol for the prediction of atomic/inter-atomic properties: a fragment based graph convolutional neural network (F-GCN) In this study, a general quantitative structure-property relationship (QSPR) protocol, fragment based graph convolutional neural network (F-GCN), was developed for the prediction of atomic/inter-atomic properties. We applied this novel artificial intelligence (AI) tool in predictions of NMR chemical shifts and bond dissociation energies (BDEs). The obtained results were comparable to experimental measurements, while the computational cost was substantially reduced, with respect to pure density functional theory (DFT) calculations. The two important features of F-GCN can be summarised as: first, it could utilise different levels of molecular fragments for atomic/inter-atomic information extraction; second, the designed architecture is also open to include additional descriptors for a more accurate solution of the local environment at atomic level, making itself more efficient for structural solutions. And during our test, the averaged prediction error of 1H NMR chemical shifts is as small as 0.32 ppm, and the error of C-H BDE estimation is 2.7 kcal mol-1. Moreover, we further demonstrated the applicability of this developed F-GCN model via several challenging structural assignments. The success of the F-GCN in atomic and inter-atomic predictions also indicates an essential improvement of computational chemistry with the assistance of AI tools. Convolutional neural network ; Fragment (logic) ; Quantitative structure–activity relationship ; Graph ; Artificial neural network ; Computer science ; Chemistry ; Computational chemistry ; Biological system ; Algorithm ; Artificial intelligence ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Giulio Imbalzano | Michele Ceriotti
Source: Physical Review Materials (article, 2021-06-22)
Field: Materials Science — Materials Chemistry

Modeling the Ga/As binary system across temperatures and compositions from first principles Materials composed of elements from the third and fifth columns of the periodic table display a very rich behavior, with the phase diagram usually containing a metallic liquid phase and a polar semiconducting solid. As a consequence, it is very hard to achieve transferable empirical models of interactions between the atoms that can reliably predict their behavior across the temperature and composition range that is relevant to the study of the synthesis and properties of III/V nanostructures and devices. We present a machine-learning potential trained on density functional theory reference data that provides a general-purpose model for the ${\mathrm{Ga}}_{x}{\text{As}}_{1\ensuremath{-}x}\phantom{\rule{3.33333pt}{0ex}}\text{system}$. We provide a series of stringent tests that showcase the accuracy of the potential, and its applicability across the whole binary phase space, computing with ab initio accuracy a large number of finite-temperature properties as well as the location of phase boundaries. We also show how a committee model can be used to reliably determine the uncertainty induced by the limitations of the machine-learning model on its predictions, to identify regions of phase space that are predicted with insufficient accuracy, and to iteratively refine the training set to achieve consistent, reliable modeling. Phase diagram ; Binary number ; Materials science ; Polar ; Phase (matter) ; Set (abstract data type) ; Parameter space ; Ab initio ; Ternary operation ; Range (aeronautics) ; Table (database) ; Statistical physics ; Series (stratigraphy) ; Space (punctuation) ; Lookup table ; Algorithm ; Thermodynamics ; Computer science ; Machine learning ; Data mining ; Physics ; Mathematics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; nanoparticles nucleation surface interactions
Authors: Alexander Winckelmann | Sascha Nowak | Silke Richter | Sebastian Recknagel | Jens Riedel | Jochen Vogl | Ulrich Panne | Carlos Abad
Source: Analytical Chemistry (article, 2021-07-07)
Field: Chemistry — Analytical Chemistry

High-Resolution Atomic Absorption Spectrometry Combined With Machine Learning Data Processing for Isotope Amount Ratio Analysis of Lithium An alternative method for lithium isotope amount ratio analysis based on a combination of high-resolution atomic absorption spectrometry and spectral data analysis by machine learning (ML) is proposed herein. It is based on the well-known isotope shift of approximately 15 pm for the electronic transition 2<sup>2</sup>P←2<sup>2</sup>S at around the wavelength of 670.8 nm, which can be measured by the state-of-the-art high-resolution continuum source graphite furnace atomic absorption spectrometry. For isotope amount ratio analysis, a scalable tree boosting ML algorithm (XGBoost) was employed and calibrated using a set of samples with <sup>6</sup>Li isotope amount fractions, ranging from 0.06 to 0.99 mol mol<sup>-1</sup>, previously determined by a multicollector inductively coupled plasma mass spectrometer (MC-ICP-MS). The calibration ML model was validated with two certified reference materials (LSVEC and IRMM-016). The procedure was applied toward the isotope amount ratio determination of a set of stock chemicals (Li<sub>2</sub>CO<sub>3</sub>, LiNO<sub>3</sub>, LiCl, and LiOH) and a BAM candidate reference material NMC111 (LiNi<sub>1/3</sub>Mn<sub>1/3</sub>Co<sub>1/3</sub>O<sub>2</sub>), a Li-battery cathode material. The results of these determinations were compared with those obtained by MC-ICP-MS and found to be metrologically comparable and compatible. The residual bias was -1.8‰, and the precision obtained ranged from 1.9 to 6.2‰. This precision was sufficient to resolve naturally occurring variations, as demonstrated for samples ranging from approximately -3 to +15‰. To assess its suitability to technical applications, the NMC111 cathode candidate reference material was analyzed using high-resolution continuum source atomic absorption spectrometry with and without matrix purification. The results obtained were metrologically compatible with each other. Chemistry ; Analytical Chemistry (journal) ; Isotope ; Mass spectrometry ; Certified reference materials ; Graphite furnace atomic absorption ; Detection limit ; Chromatography Analytical chemistry methods development ; Radioactive element chemistry and processing ; Extraction and Separation Processes
Authors: Jiwon Yeom | Tiberiu Stan | Seungbum Hong | Peter W. Voorhees
Source: Acta Materialia (article, 2021-05-25)
Field: Materials Science — Materials Chemistry

Segmentation of experimental datasets via convolutional neural networks trained on phase field simulations The ability to quickly analyze large imaging datasets is vital to the widespread adoption of modern materials characterization tools, and thus the development of new materials. Image segmentation can be the most subjective and time-consuming step in the data analysis workflow. A promising approach to segmentation of large materials datasets is the use of convolutional neural networks (CNNs). However, a major challenge is to obtain the images and segmentations needed for CNN training, since this requires segmentations performed by humans. We show that it is possible to segment experimental materials science data using a SegNet-based CNN that was trained only using simple phase field simulations. A test image from an in-situ solidification experiment of an Al-Zn alloy was used to parameterize the phase field simulations. The most important microstructural features required for the best CNN to “understand” the contents of the image are ranked as: (1) having training images with diffuse particle-background interfaces, (2) modifying the images by adding noise, (3) removing particles at the image edges, and (4) adding sub-images to the particles to account for the feint bands present on some dendrites. The CNN trained on phase field images segmented the experimental test image with 99.3% accuracy, comparable to CNNs trained on experimental data. This approach of using computationally generated images to train CNNs capable of segmenting experiments will accelerate the rate of materials design and discovery. Convolutional neural network ; Materials science ; Segmentation ; Field (mathematics) ; Artificial intelligence ; Phase (matter) ; Pattern recognition (psychology) ; Artificial neural network ; Machine learning ; Computer science ; Physics Solidification and crystal growth phenomena ; Non-Destructive Testing Techniques ; Magnetic Properties and Applications