jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Efi Hadjixenophontos | Erika Michela Dematteis | Nicola Berti | Anna Wołczyk | Priscilla Huen | Matteo Brighi | Thi Thu Le | Antonio Santoru | SeyedHosein Payandeh | Filippo Peru | Anh Ha Dao | Yinzhe Liu | Michael Heere
Source: Inorganics (review, 2020-03-02)
Field: Materials Science — Materials Chemistry

A Review of the MSCA ITN ECOSTORE—Novel Complex Metal Hydrides for Efficient and Compact Storage of Renewable Energy as Hydrogen and Electricity Hydrogen as an energy carrier is very versatile in energy storage applications. Developments in novel, sustainable technologies towards a CO2-free society are needed and the exploration of all-solid-state batteries (ASSBs) as well as solid-state hydrogen storage applications based on metal hydrides can provide solutions for such technologies. However, there are still many technical challenges for both hydrogen storage material and ASSBs related to designing low-cost materials with low-environmental impact. The current materials considered for all-solid-state batteries should have high conductivities for Na+, Mg2+ and Ca2+, while Al3+-based compounds are often marginalised due to the lack of suitable electrode and electrolyte materials. In hydrogen storage materials, the sluggish kinetic behaviour of solid-state hydride materials is one of the key constraints that limit their practical uses. Therefore, it is necessary to overcome the kinetic issues of hydride materials before discussing and considering them on the system level. This review summarizes the achievements of the Marie Skłodowska-Curie Actions (MSCA) innovative training network (ITN) ECOSTORE, the aim of which was the investigation of different aspects of (complex) metal hydride materials. Advances in battery and hydrogen storage materials for the efficient and compact storage of renewable energy production are discussed. Renewable energy ; Electricity ; Energy storage ; Hydrogen storage ; Environmental science ; Hydrogen ; Chemistry ; Engineering ; Electrical engineering ; Power (physics) ; Physics Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Hybrid Renewable Energy Systems
Authors: Jun Zhang | Yao-Kun Lei | Zhen Zhang | Junhan Chang | Maodong Li | Xu Han | Lijiang Yang | Yi Yang | Yi Qin Gao
Source: The Journal of Physical Chemistry A (article, 2020-07-14)
Field: Materials Science — Materials Chemistry

A Perspective on Deep Learning for Molecular Modeling and Simulations Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. However, unlike the mature deployment of deep learning in computer vision and natural language processing, its development in molecular modeling and simulations is still at an early stage, largely because the inductive biases of molecules are completely different from those of images or texts. Footed on these differences, we first reviewed the limitations of traditional deep learning models from the perspective of molecular physics and wrapped up some relevant technical advancement at the interface between molecular modeling and deep learning. We do not focus merely on the ever more complex neural network models; instead, we introduce various useful concepts and ideas brought by modern deep learning. We hope that transacting these ideas into molecular modeling will create new opportunities. For this purpose, we summarized several representative applications, ranging from supervised to unsupervised and reinforcement learning, and discussed their connections with the emerging trends in deep learning. Finally, we give an outlook for promising directions which may help address the existing issues in the current framework of deep molecular modeling. Deep learning ; Artificial intelligence ; Computer science ; Perspective (graphical) ; Cognitive science ; Focus (optics) ; Software deployment ; Data science ; Artificial neural network ; Psychology ; Software engineering ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Alexander L. Bowler | Serafim Bakalis | Nicholas J. Watson
Source: Sensors (article, 2020-03-25)
Field: Chemistry — Analytical Chemistry

Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning Mixing is one of the most common processes across food, chemical, and pharmaceutical manufacturing. Real-time, in-line sensors are required for monitoring, and subsequently optimising, essential processes such as mixing. Ultrasonic sensors are low-cost, real-time, in-line, and applicable to characterise opaque systems. In this study, a non-invasive, reflection-mode ultrasonic measurement technique was used to monitor two model mixing systems. The two systems studied were honey-water blending and flour-water batter mixing. Classification machine learning models were developed to predict if materials were mixed or not mixed. Regression machine learning models were developed to predict the time remaining until mixing completion. Artificial neural networks, support vector machines, long short-term memory neural networks, and convolutional neural networks were tested, along with different methods for engineering features from ultrasonic waveforms in both the time and frequency domain. Comparisons between using a single sensor and performing multisensor data fusion between two sensors were made. Classification accuracies of up to 96.3% for honey-water blending and 92.5% for flour-water batter mixing were achieved, along with R<sup>2</sup> values for the regression models of up to 0.977 for honey-water blending and 0.968 for flour-water batter mixing. Each prediction task produced optimal performance with different algorithms and feature engineering methods, vindicating the extensive comparison between different machine learning approaches. Mixing (physics) ; Ultrasonic sensor ; Support vector machine ; Artificial neural network ; Convolutional neural network ; Artificial intelligence ; Machine learning ; Computer science ; Pattern recognition (psychology) ; Acoustics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xiaotian Li | Lin Chen | Guangfeng Wei | Cheng Shang | Zhi‐Pan Liu
Source: ACS Catalysis (article, 2020-08-03)
Field: Materials Science — Materials Chemistry

Sharp Increase in Catalytic Selectivity in Acetylene Semihydrogenation on Pd Achieved by a Machine Learning Simulation-Guided Experiment Pd (metal) is of key value as a heterogeneous hydrogenation catalyst for its high activity and stability. It, however, fails in selective acetylene hydrogenation: at high H2 pressures and 100% conversion, the dominant product is ethane, not the desirable ethene. Despite decades’ efforts, even the structure of the catalyst remains to be veiled by the in situ formation of unknown PdHx and, controversially, PdCx phases. Here, by combining our recently developed machine learning potential global optimization, microkinetic simulation, and catalysis experiment, we resolve a Pd4H3 phase formed under hydrogenation reaction conditions (e.g., 298 K and p(H2) > 0.1 atm), in which the exposed Pd4H3(100) open surface is the most responsible for catalyzing the deep hydrogenation to ethane at high H2 pressures. This finding is rooted in the thermodynamics phase diagram for the Pd–H bulk and surfaces from millions of structure candidates explored by stochastic surface walking (SSW) global optimization and the lowest-energy pathways for the hydrogenation on different surfaces. Guided by the theoretical prediction, Pd catalysts with a large particle size (26 nm) dominated by the close-packed (111) surface are synthesized and tested for selective acetylene hydrogenation in comparison with that of the commercial Pd/C catalyst (particle size ∼2 nm). We show that simple nanostructure engineering improves markedly the selectivity by 16 times, from 4.5% for the commercial Pd catalyst to 76% for our designed Pd catalysts at 100% acetylene conversion and 293 K, showing great promise for machine learning-guided catalyst design. General guidelines to further improve catalyst selectivity are proposed. Acetylene ; Catalysis ; Selectivity ; Surface engineering ; Chemical engineering ; Particle size ; Chemistry ; Nanotechnology ; Nanostructure ; Particle (ecology) ; Materials science ; Chemical physics ; Photochemistry ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Asymmetric Hydrogenation and Catalysis
Authors: Yuxuan Chen | Bailin Tian | Zheng Cheng | Xiaoshan Li | Min Huang | Yuxia Sun | Shuai Liu | Xu Cheng | Shuhua Li | Mengning Ding
Source: Angewandte Chemie International Edition (article, 2020-11-12)
Field: Chemistry — Organic Chemistry

Electro‐Descriptors for the Performance Prediction of Electro‐Organic Synthesis Electrochemical organic synthesis has attracted increasing attentions as a sustainable and versatile synthetic platform. Quantitative assessment of the electro-organic reactions, including reaction thermodynamics, electro-kinetics, and coupled chemical processes, can lead to effective analytical tool to guide their future design. Herein, we demonstrate that electrochemical parameters such as onset potential, Tafel slope, and effective voltage can be utilized as electro-descriptors for the evaluation of reaction conditions and prediction of reactivities (yields). An "electro-descriptor-diagram" is generated, where reactive and non-reactive conditions/substances show distinct boundary. Successful predictions of reaction outcomes have been demonstrated using electro-descriptor diagram, or from machine learning algorithms with experimentally-derived electro-descriptors. This method represents a promising tool for data-acquisition, reaction prediction, mechanistic investigation, and high-throughput screening for general organic electro-synthesis. Tafel equation ; Electrochemistry ; Biological system ; Organic synthesis ; Throughput ; Reaction conditions ; Chemistry ; Computer science ; Biochemical engineering ; Nanotechnology ; Combinatorial chemistry ; Materials science ; Electrode ; Organic chemistry ; Physical chemistry ; Catalysis ; Engineering Radical Photochemical Reactions ; Electrochemical Analysis and Applications ; CO2 Reduction Techniques and Catalysts
Authors: Ibrahim Olanrewaju Alade | Ismail Adewale Olumegbon | Aliyu Bagudu
Source: Journal of Applied Physics (article, 2020-01-02)
Field: Materials Science — Materials Chemistry

Lattice constant prediction of A2XY6 cubic crystals (A = K, Cs, Rb, TI; X = tetravalent cation; Y = F, Cl, Br, I) using computational intelligence approach Lattice constant mismatch between materials affects the quality of thin film fabrication. For this reason, lattice constants information is vital in the design of materials for technological applications. The determination of lattice constants via experimental analysis is relatively expensive and laborious. As a result, several linear empirical models have been proposed to predict the lattice constant of crystal structures. However, the accuracies of these models are limited partly due to their failure to account for nonlinearity in the atomic parameters-lattice constant relationship. Machine learning techniques have shown excellent ability to deal with nonlinear problems in many areas of materials science; hence, they are considered suitable computation tools to study the crystal structure of materials. In this contribution, we developed a support vector regression (SVR) model to predict the lattice constant of cubic crystals of the form A2XY6 (A = K, Cs, Rb, TI; X = tetravalent cation; and Y = F, Cl, Br, I). The SVR algorithm uses the ionic radii and electronegativities data of the constituent elements of A2XY6 cubic crystals as model inputs. The robustness of the proposed model was demonstrated by comparing our result with an existing linear model based on 26 cubic crystal samples. The result revealed a total relative deviation of 1.757 and 2.704 for the SVR model and the existing linear equation, respectively. This result proves that the SVR model has a huge potential in the search for new materials for different applications. Lattice constant ; Electronegativity ; Ionic radius ; Crystal structure ; Lattice (music) ; Nonlinear system ; Crystal (programming language) ; Computation ; Materials science ; Mathematics ; Thermodynamics ; Statistical physics ; Condensed matter physics ; Algorithm ; Chemistry ; Computer science ; Ion ; Physics ; Diffraction ; Crystallography ; Quantum mechanics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Rasmus Fjordbak Nielsen | Nima Nazemzadeh | Laura Wind Sillesen | Martin Andersson | Krist V. Gernaey | Seyed Soheil Mansouri
Source: Computers & Chemical Engineering (article, 2020-05-23)
Field: Materials Science — Materials Chemistry

Hybrid machine learning assisted modelling framework for particle processes Flexibility (engineering) ; Process (computing) ; Computer science ; Particle (ecology) ; Population ; Scale (ratio) ; Soft sensor ; Process control ; Machine learning ; Process engineering ; Artificial intelligence ; Control engineering ; Engineering ; Mathematics Crystallization and Solubility Studies ; Fluid Dynamics and Mixing ; Mineral Processing and Grinding
Authors: Joshua P. Allers | Jacob Harvey | Fernando H. Garzón | Todd M. Alam
Source: The Journal of Chemical Physics (article, 2020-07-15)
Field: Materials Science — Materials Chemistry

Machine learning prediction of self-diffusion in Lennard-Jones fluids Different machine learning (ML) methods were explored for the prediction of self-diffusion in Lennard-Jones (LJ) fluids. Using a database of diffusion constants obtained from the molecular dynamics simulation literature, multiple Random Forest (RF) and Artificial Neural Net (ANN) regression models were developed and characterized. The role and improved performance of feature engineering coupled to the RF model development was also addressed. The performance of these different ML models was evaluated by comparing the prediction error to an existing empirical relationship used to describe LJ fluid diffusion. It was found that the ANN regression models provided superior prediction of diffusion in comparison to the existing empirical relationships. Diffusion ; Random forest ; Regression ; Computer science ; Artificial neural network ; Artificial intelligence ; Regression analysis ; Machine learning ; Feature (linguistics) ; Statistical physics ; Thermodynamics ; Mathematics ; Statistics ; Physics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Fuel Cells and Related Materials
Authors: Andrey A. Buglak | Mikhail A. Filatov | Mushraf Hussain | Manabu Sugimoto
Source: Journal of Photochemistry and Photobiology A Chemistry (article, 2020-08-07)
Field: Materials Science — Materials Chemistry

Singlet oxygen generation by porphyrins and metalloporphyrins revisited: A quantitative structure-property relationship (QSPR) study Quantitative structure–activity relationship ; Singlet oxygen ; Chemistry ; Computational chemistry ; Applicability domain ; Porphyrin ; Quantum chemical ; Density functional theory ; Singlet state ; Linear regression ; Excited state ; Photochemistry ; Oxygen ; Molecule ; Computer science ; Machine learning ; Organic chemistry ; Stereochemistry Porphyrin and Phthalocyanine Chemistry ; Photodynamic Therapy Research Studies ; Electrochemical Analysis and Applications
Authors: Jiaqi Yue | Heng-Yu Huang | Yuanzhong Wang
Source: Microchemical Journal (article, 2020-11-12)
Field: Chemistry — Analytical Chemistry

A practical method superior to traditional spectral identification: Two-dimensional correlation spectroscopy combined with deep learning to identify Paris species Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Identification (biology) ; Two-dimensional nuclear magnetic resonance spectroscopy ; Residual ; Feature (linguistics) ; Biological system ; Chemistry ; Algorithm ; Botany ; Stereochemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Zhe Shi | Ming Dao | Evgenii Tsymbalov | Alexander V. Shapeev | Ju Li | Subra Suresh
Source: Proceedings of the National Academy of Sciences (article, 2020-10-05)
Field: Materials Science — Materials Chemistry

Metallization of diamond Experimental discovery of ultralarge elastic deformation in nanoscale diamond and machine learning of its electronic and phonon structures have created opportunities to address new scientific questions. Can diamond, with an ultrawide bandgap of 5.6 eV, be completely metallized, solely under mechanical strain without phonon instability, so that its electronic bandgap fully vanishes? Through first-principles calculations, finite-element simulations validated by experiments, and neural network learning, we show here that metallization/demetallization as well as indirect-to-direct bandgap transitions can be achieved reversibly in diamond below threshold strain levels for phonon instability. We identify the pathway to metallization within six-dimensional strain space for different sample geometries. We also explore phonon-instability conditions that promote phase transition to graphite. These findings offer opportunities for tailoring properties of diamond via strain engineering for electronic, photonic, and quantum applications. Diamond ; Phonon ; Materials science ; Condensed matter physics ; Instability ; Deformation (meteorology) ; Strain engineering ; Band gap ; Nanotechnology ; Optoelectronics ; Strain (injury) ; Chemical physics ; Composite material ; Physics ; Mechanics Diamond and Carbon-based Materials Research ; Electronic and Structural Properties of Oxides ; Force Microscopy Techniques and Applications
Authors: Erik Fransson | Fredrik Eriksson | Paul Erhart
Source: npj Computational Materials (article, 2020-09-07)
Field: Materials Science — Materials Chemistry

Efficient construction of linear models in materials modeling and applications to force constant expansions Abstract Linear models, such as force constant (FC) and cluster expansions, play a key role in physics and materials science. While they can in principle be parametrized using regression and feature selection approaches, the convergence behavior of these techniques, in particular with respect to thermodynamic properties is not well understood. Here, we therefore analyze the efficacy and efficiency of several state-of-the-art regression and feature selection methods, in particular in the context of FC extraction and the prediction of different thermodynamic properties. Generic feature selection algorithms such as recursive feature elimination with ordinary least-squares (OLS), automatic relevance determination regression, and the adaptive least absolute shrinkage and selection operator can yield physically sound models for systems with a modest number of degrees of freedom. For large unit cells with low symmetry and/or high-order expansions they come, however, with a non-negligible computational cost that can be more than two orders of magnitude higher than that of OLS. In such cases, OLS with cutoff selection provides a viable route as demonstrated here for both second-order FCs in large low-symmetry unit cells and high-order FCs in low-symmetry systems. While regression techniques are thus very powerful, they require well-tuned protocols. Here, the present work establishes guidelines for the design of protocols that are readily usable, e.g., in high-throughput and materials discovery schemes. Since the underlying algorithms are not specific to FC construction, the general conclusions drawn here also have a bearing on the construction of other linear models in physics and materials science. Context (archaeology) ; Feature selection ; Linear regression ; Convergence (economics) ; Constant (computer programming) ; Feature (linguistics) ; Model selection ; Regression ; Selection (genetic algorithm) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Austin M. Ferrenti | Nathalie P. de Leon | Jeff D. Thompson | R. J. Cava
Source: npj Computational Materials (article, 2020-08-18)
Field: Materials Science — Materials Chemistry

Identifying candidate hosts for quantum defects via data mining Abstract Atom-like defects in solid-state hosts are promising candidates for the development of quantum information systems, but despite their importance, the host substrate/defect combinations currently under study have almost exclusively been found serendipitously. Here we systematically evaluate the suitability of host materials by applying a combined four-stage data mining and manual screening process to all entries in the Materials Project database, with literature-based experimental confirmation of band gap values. We identify a total of 541 viable hosts (16 unary and 74 binary) for quantum defect introduction and potential use in quantum information systems. This represents a significant (99.57%) reduction from the total number of known inorganic phases, and the application of additional selection criteria for specific applications will reduce their number even further. The screening principles outlined may easily be applied to previously unrealized phases and other technologically important materials systems. Computer science ; Quantum ; Unary operation ; Data mining ; Reduction (mathematics) ; Process (computing) ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Diamond and Carbon-based Materials Research
Authors: Maria Sorokina | Peter Merseburger | Kohulan Rajan | Mehmet Aziz Yirik | Christoph Steinbeck
Source: Research Square (preprint, 2020-09-16)
Field: Chemistry — Inorganic Chemistry

Coconut Online: Collection of Open Natural Products Database Database ; Natural (archaeology) ; World Wide Web ; Computer science ; Online database ; Business ; Geography ; Archaeology Coconut Research and Applications ; Identification and Quantification in Food ; Plant-Derived Bioactive Compounds
Authors: Sebastiaan Van Nuffel | Marceau Quatredeniers | Alexander Pirkl | Julia Zakel | Jean‐Pierre Le Caër | Nicolas Elie | Quentin Vanbellingen | Sébastien J. Dumas | Morad K. Nakhleh | Maria‐Rosa Ghigna | Élie Fadel | Marc Humbert | Pierre Chaurand | David Touboul | Sylvia Cohen‐Kaminsky | Alain Brunelle
Source: Analytical Chemistry (article, 2020-07-28)
Field: Chemistry — Spectroscopy

Multimodal Imaging Mass Spectrometry to Identify Markers of Pulmonary Arterial Hypertension in Human Lung Tissue Using MALDI-ToF, ToF-SIMS, and Hybrid SIMS Pulmonary arterial hypertension (PAH) is a rare and deadly disease affecting roughly 15-60 people per million in Europe with a poorly understood pathology. There are currently no diagnostic tools for early detection nor does a curative treatment exist. The lipid composition of arteries in lung tissue samples from human PAH and control patients were investigated using matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) combined with time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging. Using random forests as an IMS data analysis technique, it was possible to identify the ion at <i>m</i>/<i>z</i> 885.6 as a marker of PAH in human lung tissue. The <i>m</i>/<i>z</i> 885.6 ion intensity was shown to be significantly higher around diseased arteries and was confirmed to be a diacylglycerophosphoinositol PI(C18:0/C20:4) via MS/MS using a novel hybrid SIMS instrument. The discovery of a potential biomarker opens up new research avenues which may finally lead to a better understanding of the PAH pathology and highlights the vital role IMS can play in modern biomedical research. Chemistry ; Mass spectrometry imaging ; Mass spectrometry ; Matrix-assisted laser desorption/ionization ; Lung ; Chromatography ; Internal medicine Mass Spectrometry Techniques and Applications ; Metal and Thin Film Mechanics ; Nuclear Materials and Properties