jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gauri A. Mahadik | J. F. Hernández-Sánchez | Sankara Arunachalam | Adair Gallo | Lanna Cheng | Andreia S.F. Farinha | S. T. Thoroddsen | Himanshu Mishra | Carlos M. Duarte
Source: Scientific Reports (article, 2020-05-08)
Field: Materials Science — Surfaces, Coatings and Films

Superhydrophobicity and size reduction enabled Halobates (Insecta: Heteroptera, Gerridae) to colonize the open ocean Despite the remarkable evolutionary success of insects at colonizing every conceivable terrestrial and aquatic habitat, only five Halobates (Heteroptera: Gerridae) species (~0.0001% of all known insect species) have succeeded at colonizing the open ocean - the largest biome on Earth. This remarkable evolutionary achievement likely required unique adaptations for them to survive and thrive in the challenging oceanic environment. For the first time, we explore the morphology and behavior of an open-ocean Halobates germanus and a related coastal species H. hayanus to understand mechanisms of these adaptations. We provide direct experimental evidence based on high-speed videos which reveal that Halobates exploit their specialized and self-groomed body hair to achieve extreme water repellence, which facilitates rapid skating and plastron respiration under water. Moreover, the grooming behavior and presence of cuticular wax aids in the maintenance of superhydrophobicity. Further, reductions of their body mass and size enable them to achieve impressive accelerations (~400 ms<sup>-2</sup>) and reaction times (~12 ms) to escape approaching predators or environmental threats and are crucial to their survival under harsh marine conditions. These findings might also inspire rational strategies for developing liquid-repellent surfaces for drag reduction, water desalination, and preventing bio-fouling. Gerridae ; Heteroptera ; Biology ; Ecology ; Insect ; Predation ; Habitat Surface Modification and Superhydrophobicity ; Hemiptera Insect Studies ; Insect and Arachnid Ecology and Behavior
Authors: Asif Equbal | Kan Tagami | Songi Han
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Chemistry — Spectroscopy

Balancing dipolar and exchange coupling in biradicals to maximize cross effect dynamic nuclear polarization Dynamic nuclear polarization (DNP) by the cross effect (CE) has become a game changer for solid-state nuclear magnetic resonance (NMR) spectroscopy. The efficiency of CE-DNP depends on the strength of the electron-electron coupling in biradical polarizing agents. Hence, the focus lately has been on designing biradicals with a large net exchange (J) and dipolar (D) coupling. In this study, we reveal that the crucial factor for CE-DNP is not the large sum, J + D, but rather the relative magnitude of J and D, expressed as the J/D ratio. We show that the mechanistic basis of this interference lies in the isotropic vs. the anisotropic nature of the J and D couplings, respectively. This interference can lead to a small (effective) electron-electron coupling for many orientations even when J + D is large, resulting in non-adiabatic rotor-events. We find that when 0 < |J/D| < 1 the CE-DNP efficiency is attenuated for the majority of orientations, with greater attenuation observed at higher magnetic fields and faster magic-angle spinning (MAS) frequency. The interference effect of J and D coupling introduced in this study can explain why many biradicals with high or comparable J + D still show significantly divergent DNP performances. We debut J/D as a consequential criteria for designing efficient biradicals to robustly perform across a large range of B0 fields and MAS frequencies. Polarization (electrochemistry) ; Dipole ; Coupling (piping) ; Chemical physics ; Magnetic dipole–dipole interaction ; Chemistry ; Physics ; Materials science ; Quantum mechanics ; Physical chemistry Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; Atomic and Subatomic Physics Research
Authors: Haotong Liang | Valentin Stanev | A. Gilad Kusne | Ichiro Takeuchi
Source: Physical Review Materials (article, 2020-12-18)
Field: Materials Science — Materials Chemistry

CRYSPNet: Crystal structure predictions via neural networks Structure is the most basic and important property of crystalline solids; it determines directly or indirectly most materials characteristics. However, predicting crystal structure of solids remains a formidable and not fully solved problem. Standard theoretical tools for this task are computationally expensive and at times inaccurate. Here we present an alternative approach utilizing machine learning for crystal structure prediction. We developed a tool called Crystal Structure Prediction Network (CRYSPNet) that can predict the Bravais lattice, space group, and lattice parameters of an inorganic material based only on its chemical composition. CRYSPNet consists of a series of neural network models, using as inputs predictors aggregating the properties of the elements constituting the compound. It was trained and validated on more than 100 000 entries from the Inorganic Crystal Structure Database. The tool demonstrates robust predictive capability and outperforms alternative strategies by a large margin. It can be used both as an independent prediction engine and as a method to generate candidate structures for further computational and/or experimental validation. Crystal structure prediction ; Bravais lattice ; Artificial neural network ; Crystal structure ; Property (philosophy) ; Network structure ; Crystal (programming language) ; Series (stratigraphy) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Erick J. Braham | Rachel D. Davidson | Mohammed Al‐Hashimi | Raymundo Arróyave | Sarbajit Banerjee
Source: Dalton Transactions (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Navigating the design space of inorganic materials synthesis using statistical methods and machine learning Data-driven approaches have brought about a revolution in manufacturing; however, challenges persist in their applications to synthetic strategies. Their application to the deterministic navigation of reaction trajectories to stabilize crystalline solids with precise composition, atomic connectivity, microstructural dimensionality, and surface structure remains a frontier in inorganic materials research. The design of synthetic methodologies for the preparation of inorganic materials is often inefficient in terms of exploration of potentially vast design spaces spanning multiple process variables, reaction sequences, as well as structural parameters and reactivities of precursors and structure-directing agents. Reported synthetic methods are further limited in terms of the insight they provide into underlying chemical and physical principles. The recent surge in interest in accelerating the discovery of new materials can be considered as an opportunity to re-evaluate our approach to materials synthesis, and for considering new frameworks for exploration that are systematic and strategic in approach. Herein, we outline with the help of several illustrative examples, the challenges, opportunities, and limitations of data-driven synthesis design. The account collates discussion of design-of-experiments sampling methods, machine learning modeling, and active learning to develop experimental workflows that accelerate the experimental navigation of synthetic landscapes. Workflow ; Computer science ; Chemical space ; Curse of dimensionality ; Process (computing) ; Nanotechnology ; Biochemical engineering ; Artificial intelligence ; Data science ; Machine learning ; Systems engineering ; Management science ; Engineering ; Materials science ; Chemistry ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Erin Antono | Nobuyuki Matsuzawa | Julia Ling | James E. Saal | Hideyuki Arai | Masaru Sasago | Eiji Fujii
Source: The Journal of Physical Chemistry A (article, 2020-09-17)
Field: Materials Science — Materials Chemistry

Machine-Learning Guided Quantum Chemical and Molecular Dynamics Calculations to Design Novel Hole-Conducting Organic Materials Materials exhibiting higher mobilities than conventional organic semiconducting materials such as fullerenes and fused thiophenes are in high demand for applications such as printed electronics, organic solar cells, and image sensors. In order to discover new molecules that might show improved charge mobility, combined density functional theory (DFT) and molecular dynamics (MD) calculations were performed, guided by predictions from machine learning (ML). A ML model was constructed based on 32 values of theoretically calculated hole mobilities for thiophene derivatives, benzodifuran derivatives, a carbazole derivative and a perylene diimide derivative with the maximum value of 10<sup>-1.96</sup> cm<sup>2</sup>/(V s). Sequential learning, also known as active learning, was applied to select compounds on which to perform DFT/MD calculation of hole mobility to simultaneously improve the mobility surrogate model and identify high mobility compounds. By performing 60 cycles of sequential learning with 165 DFT/MD calculations, a molecule having a fused thioacene structure with its calculated hole mobility of 10<sup>-1.86</sup> cm<sup>2</sup>/(V s) was identified. This values is higher than the maximum value of mobility in the initial training data set, showing that an extrapolative discovery could be made with the sequential learning. Quantum chemical ; Molecular dynamics ; Dynamics (music) ; Quantum ; Chemical physics ; Nanotechnology ; Materials science ; Computer science ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Derek P. Metcalf | Andy Jiang | Steven A. Spronk | Daniel L. Cheney | C. David Sherrill
Source: Journal of Chemical Information and Modeling (article, 2020-12-16)
Field: Materials Science — Materials Chemistry

Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge Atomic charges are critical quantities in molecular mechanics and molecular dynamics, but obtaining these quantities requires heuristic choices based on atom typing or relatively expensive quantum mechanical computations to generate a density to be partitioned. Most machine learning efforts in this domain ignore total molecular charges, relying on overfitting and arbitrary rescaling in order to match the total system charge. Here, we introduce the electron-passing neural network (EPNN), a fast, accurate neural network atomic charge partitioning model that conserves total molecular charge by construction. EPNNs predict atomic charges very similar to those obtained by partitioning quantum mechanical densities but at such a small fraction of the cost that they can be easily computed for large biomolecules. Charges from this method may be used directly for molecular mechanics, as features for cheminformatics, or as input to any neural network potential. Artificial neural network ; Charge (physics) ; Overfitting ; Molecular dynamics ; Computation ; Quantum ; Statistical physics ; Computer science ; Heuristic ; Atomic charge ; Electron ; Atom (system on chip) ; Algorithm ; Physics ; Biological system ; Quantum mechanics ; Artificial intelligence ; Molecule ; Parallel computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Maciej E. Marchwiany | Magdalena Birowska | Mariusz Popielski | Jacek A. Majewski | Agnieszka Jastrzębska
Source: Materials (article, 2020-07-10)
Field: Materials Science — Materials Chemistry

Surface-Related Features Responsible for Cytotoxic Behavior of MXenes Layered Materials Predicted with Machine Learning Approach To speed up the implementation of the two-dimensional materials in the development of potential biomedical applications, the toxicological aspects toward human health need to be addressed. Due to time-consuming and expensive analysis, only part of the continuously expanding family of 2D materials can be tested in vitro. The machine learning methods can be used-by extracting new insights from available biological data sets, and provide further guidance for experimental studies. This study identifies the most relevant highly surface-specific features that might be responsible for cytotoxic behavior of 2D materials, especially MXenes. In particular, two factors, namely, the presence of transition metal oxides and lithium atoms on the surface, are identified as cytotoxicity-generating features. The developed machine learning model succeeds in predicting toxicity for other 2D MXenes, previously not tested in vitro, and hence, is able to complement the existing knowledge coming from in vitro studies. Thus, we claim that it might be one of the solutions for reducing the number of toxicological studies needed, and allows for minimizing failures in future biological applications. MXenes ; Artificial intelligence ; Computer science ; Biochemical engineering ; Machine learning ; Nanotechnology ; Materials science ; Engineering MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene and Nanomaterials Applications
Authors: Akeem Adeyemi Oladipo
Source: Materials Chemistry and Physics (article, 2020-12-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

N,S co–doped biocarbon for supercapacitor application: Effect of electrolytes concentration and modelling with artificial neural network Supercapacitor ; Electrolyte ; Materials science ; Aqueous solution ; Dopant ; Capacitance ; Acetonitrile ; Specific energy ; Chemical engineering ; Analytical Chemistry (journal) ; Doping ; Electrode ; Chemistry ; Chromatography ; Organic chemistry ; Physical chemistry ; Optoelectronics ; Thermodynamics Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced battery technologies research
Authors: Rongzhi Dong | Yabo Dan | Li Xiang | Jianjun Hu
Source: Computational Materials Science (article, 2020-11-20)
Field: Materials Science — Materials Chemistry

Inverse design of composite metal oxide optical materials based on deep transfer learning and global optimization Bayesian optimization ; Computer science ; Artificial neural network ; Characterization (materials science) ; Absorption (acoustics) ; Inverse ; Transfer of learning ; Genetic algorithm ; Inverse problem ; Deep learning ; Materials science ; Artificial intelligence ; Algorithm ; Machine learning ; Nanotechnology ; Mathematics Machine Learning in Materials Science ; Transition Metal Oxide Nanomaterials ; Industrial Vision Systems and Defect Detection
Authors: Daniil A. Boiko | Evgeniy O. Pentsak | Vera A. Cherepanova | Valentine P. Ananikov
Source: Scientific Data (article, 2020-03-25)
Field: Materials Science — Materials Chemistry

Electron microscopy dataset for the recognition of nanoscale ordering effects and location of nanoparticles A unique ordering effect has been observed in functional catalytic nanoscale materials. Instead of randomly arranged binding to the catalyst surface, metal nanoparticles show spatially ordered behavior resulting in formation of geometrical patterns. Understanding of such nanoscale materials and analysis of corresponding microscopy images will never be comprehensive without appropriate reference datasets. Here we describe the first dataset of electron microscopy images comprising individual nanoparticles which undergo ordering on a surface towards the formation of geometrical patterns. The dataset developed in this study spans three levels of nanoscale organization: (i) individual nanoparticles (1-5 nm) and arrays of nanoparticles (5-20 nm), (ii) ordering effects (20-200 nm) and (iii) complex patterns (from nm to μm scales). The described dataset for the first time provides a possibility for the development of machine learning algorithms to study the unique phenomena of nanoparticles ordering and hierarchical organization. Nanoscopic scale ; Nanoparticle ; Materials science ; Nanotechnology ; Electron microscope ; Scale (ratio) ; Physics ; Optics Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Justin S. Smith | R.I. Zubatyuk | Benjamin Nebgen | Nicholas Lubbers | Kipton Barros | Adrián E. Roitberg | Olexandr Isayev | Sergei Tretiak
Source: ChemRxiv (preprint, 2020-04-28)
Field: Materials Science — Materials Chemistry

The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based eneral-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data set diversity, we visualize them with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5M density functional theory calculations, while the ANI-1ccx data set contains 500k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM properties from density functional theory and coupled cluster are provided: energies, atomic forces, multipole moments, atomic charges, and more. We provide this data to the community to aid research and development of ML models for chemistry. Extrapolation ; Density functional theory ; Coupled cluster ; Multipole expansion ; Data set ; Computer science ; Quantum chemistry ; Dimensionality reduction ; Chemistry ; Time-dependent density functional theory ; Statistical physics ; Molecule ; Physics ; Computational chemistry ; Quantum mechanics ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yaolong Zhang | Ce Hu | Bin Jiang
Source: Physical Chemistry Chemical Physics (article, 2020-11-16)
Field: Materials Science — Materials Chemistry

Accelerating atomistic simulations with piecewise machine-learned <i>ab Initio</i> potentials at a classical force field-like cost Recently, machine learning methods have become easy-to-use tools for constructing high-dimensional interatomic potentials with ab initio accuracy. Although machine-learned interatomic potentials are generally orders of magnitude faster than first-principles calculations, they remain much slower than classical force fields, at the price of using more complex structural descriptors. To bridge this efficiency gap, we propose an embedded atom neural network approach with simple piecewise switching function-based descriptors, resulting in a favorable linear scaling with the number of neighbor atoms. Numerical examples validate that this piecewise machine-learning model can be over an order of magnitude faster than various popular machine-learned potentials with comparable accuracy for both metallic and covalent materials, approaching the speed of the fastest embedded atom method (i.e. several μs per atom per CPU core). The extreme efficiency of this approach promises its potential in first-principles atomistic simulations of very large systems and/or in a long timescale. Piecewise ; Scaling ; Piecewise linear function ; Atom (system on chip) ; Interatomic potential ; Ab initio ; Molecular dynamics Machine Learning in Materials Science ; Quantum many-body systems ; Electrocatalysts for Energy Conversion
Authors: Kezheng Zhu | Erich A. Müller
Source: The Journal of Physical Chemistry B (article, 2020-09-01)
Field: Materials Science — Materials Chemistry

Generating a Machine-Learned Equation of State for Fluid Properties Equations of state (EoS) for fluids have been a staple of engineering design and practice for over a century. Available EoS are based on the fitting of a closed-form analytical expression to suitable experimental data. The mathematical structure and the underlying physical model significantly restrain the applicability and accuracy of the resulting EoS. This contribution explores the issues surrounding the substitution of machine-learned models for analytical EoS. In particular, we describe, as a proof of concept, the effectiveness of a machine-learned model to replicate the statistical associating fluid theory (SAFT-VR Mie) EoS for pure fluids. To quantify the effectiveness of machine-learning techniques, a large set of pseudodata is obtained from the EoS and used to train the machine-learning models. We employ artificial neural networks and Gaussian process regression to correlate and predict thermodynamic properties such as critical pressure and temperature, vapor pressures, and densities of pure model fluids; these are performed on the basis of molecular descriptors. The comparisons between the machine-learned EoS and the surrogate data set suggest that the proposed approach shows promise as a viable technique for the correlation and prediction of thermophysical properties of fluids. Kriging ; Equation of state ; Computer science ; Machine learning ; Set (abstract data type) ; Artificial neural network ; Experimental data ; Artificial intelligence ; Gaussian process ; Process (computing) ; Basis (linear algebra) ; Gaussian ; Thermodynamics ; Mathematics ; Chemistry ; Physics ; Statistics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Process Optimization and Integration