jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gerald Platzer | Moriz Mayer | Andreas Beier | Sven Brüschweiler | Julian E. Fuchs | Harald Engelhardt | Leonhard Geist | Gerd Bader | Julia Schörghuber | Roman J. Lichtenecker | B. Wolkerstorfer | Dirk Kessler | Darryl B. McConnell | Robert Konrat
Source: Angewandte Chemie International Edition (article, 2020-05-18)
Field: Chemistry — Physical and Theoretical Chemistry

PI by NMR: Probing CH–π Interactions in Protein–Ligand Complexes by NMR Spectroscopy While CH-π interactions with target proteins are crucial determinants for the affinity of arguably every drug molecule, no method exists to directly measure the strength of individual CH-π interactions in drug-protein complexes. Herein, we present a fast and reliable methodology called PI (π interactions) by NMR, which can differentiate the strength of protein-ligand CH-π interactions in solution. By combining selective amino-acid side-chain labeling with <sup>1</sup> H-<sup>13</sup> C NMR, we are able to identify specific protein protons of side-chains engaged in CH-π interactions with aromatic ring systems of a ligand, based solely on <sup>1</sup> H chemical-shift values of the interacting protein aromatic ring protons. The information encoded in the chemical shifts induced by such interactions serves as a proxy for the strength of each individual CH-π interaction. PI by NMR changes the paradigm by which chemists can optimize the potency of drug candidates: direct determination of individual π interactions rather than averaged measures of all interactions. Nuclear magnetic resonance spectroscopy ; Transverse relaxation-optimized spectroscopy ; Chemistry ; Ligand (biochemistry) ; Fluorine-19 NMR ; Nuclear magnetic resonance spectroscopy of nucleic acids ; Nuclear magnetic resonance ; Crystallography ; Stereochemistry ; Physics ; Biochemistry ; Receptor Crystallography and molecular interactions ; Protein Structure and Dynamics ; Molecular spectroscopy and chirality
Authors: WooSeok Jeong | Samuel J. Stoneburner | Daniel S. King | Ruye Li | Andrew Walker | Roland Lindh | Laura Gagliardi
Source: Journal of Chemical Theory and Computation (article, 2020-03-02)
Field: Materials Science — Materials Chemistry

Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn-Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structures and chemical bonds of such systems. In the complete active space self-consistent field (CASSCF) method, one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. We present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a "black-box" mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied. Chemical space ; Computer science ; Complete active space ; Atomic orbital ; Density functional theory ; Space (punctuation) ; Chemical bond ; Dissociation (chemistry) ; Configuration space ; Chemistry ; Computational chemistry ; Quantum mechanics ; Physics ; Electron ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Zhujun Huang | Abdullah Alharbi | William Mayer | Edoardo Cuniberto | Takashi Taniguchi | Kenji Watanabe | Javad Shabani | Davood Shahrjerdi
Source: Nature Communications (article, 2020-06-15)
Field: Materials Science — Materials Chemistry

Versatile construction of van der Waals heterostructures using a dual-function polymeric film The proliferation of van der Waals (vdW) heterostructures formed by stacking layered materials can accelerate scientific and technological advances. Here, we report a strategy for constructing vdW heterostructures through the interface engineering of the exfoliation substrate using a sub-5 nm polymeric film. Our construction method has two main features that distinguish it from existing techniques. First is the consistency of its exfoliation process in increasing the yield and in producing large (>10,000 μm<sup>2</sup>) monolayer graphene. Second is the applicability of its layer transfer process to different layered materials without requiring a specialized stamp-a feature useful for generalizing the assembly process. We demonstrate vdW graphene devices with peak carrier mobility of 200,000 and 800,000 cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup> at room temperature and 9 K, respectively. The simplicity of our construction method and its versatility to different layered materials may open doors for automating the fabrication process of vdW heterostructures. Graphene ; van der Waals force ; Heterojunction ; Exfoliation joint ; Stacking ; Materials science ; Monolayer ; Fabrication ; Nanotechnology ; Substrate (aquarium) ; Yield (engineering) ; Layer (electronics) ; Optoelectronics ; Chemistry ; Molecule ; Composite material Graphene research and applications ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Muhammad Wasim | M.R. Khan | Muhammad Mushtaq | Muhammad Awais Naeem | Mengchen Han | Qufu Weı
Source: Coatings (article, 2020-04-07)
Field: Materials Science — Materials Chemistry

Surface Modification of Bacterial Cellulose by Copper and Zinc Oxide Sputter Coating for UV-Resistance/Antistatic/Antibacterial Characteristics In our study, the surface of bacterial cellulose was successively modified by copper and zinc oxide nanoparticles using direct current (DC) magnetron sputtering and radio frequency (RF) reactive sputter coating techniques. The target materials, copper and zinc, were 99.99% pure and used in the presence of argon (Ar) gas, while zinc nanoparticles were sputtered in the presence of oxygen gas to make zinc oxide nanoparticles. The as-prepared bacterial cellulose/copper/zinc oxide nanocomposite has good ultraviolet resistance, anti-static and antibacterial characteristics. The surface morphology and chemical composition of the nanocomposite were examined by X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD) and energy-dispersive X-ray spectroscopic (EDS) techniques. The prepared bacterial cellulose/copper/zinc oxide nanocomposite illustrates excellent ultraviolet resistance (T.UVA%; 0.16 ± 0.02, T.UVB%; 0.07 ± 0.01, ultraviolet protection factor (UPF); 1850.33 ± 2.12), antistatic behavior (S.H.P; 51.50 ± 4.10, I.E. V; 349.33 ± 6.02) and antibacterial behavior (Escherichia coli; 98.45%, Staphylococcus aureus; 98.11%). Our nanocomposite prepared by sputter coating method could be a promising and effective candidate for ultraviolet resistance, antistatic and antibacterial in term of functional, technical, medical and in many daily life applications. Materials science ; Nanocomposite ; Zinc ; Antistatic agent ; Sputtering ; X-ray photoelectron spectroscopy ; Copper ; Cellulose ; Coating ; Sputter deposition ; Chemical engineering ; Nanoparticle ; Nuclear chemistry ; Nanotechnology ; Layer (electronics) ; Chemistry ; Metallurgy ; Thin film Nanoparticles: synthesis and applications ; Advanced Cellulose Research Studies ; TiO2 Photocatalysis and Solar Cells
Authors: Philipp M. Pflüger | Frank Glorius
Source: Angewandte Chemie International Edition (article, 2020-09-15)
Field: Materials Science — Materials Chemistry

Molecular Machine Learning: The Future of Synthetic Chemistry? During the last decade, modern machine learning has found its way into synthetic chemistry. Some long-standing challenges, such as computer-aided synthesis planning (CASP), have been successfully addressed, while other issues have barely been touched. This Viewpoint poses the question of whether current trends can persist in the long term and identifies factors that may lead to an (un)productive development. Thereby, specific risks of molecular machine learning (MML) are discussed. Furthermore, possible sustainable developments are suggested, such as explainable artificial intelligence (exAI) for synthetic chemistry. This Viewpoint will illuminate chances for possible newcomers and aims to guide the community into a discussion about current as well as future trends. CASP ; Computer science ; Artificial intelligence ; Data science ; Chemistry ; Management science ; Engineering ; Protein structure prediction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Weibin Chu | Wissam A. Saidi | Oleg V. Prezhdo
Source: ACS Nano (article, 2020-07-29)
Field: Materials Science — Materials Chemistry

Long-Lived Hot Electron in a Metallic Particle for Plasmonics and Catalysis: <i>Ab Initio</i> Nonadiabatic Molecular Dynamics with Machine Learning Multiple experiments provide evidence for photovoltaic, catalytic, optoelectronic, and plasmonic processes involving hot, <i>i</i>.<i>e</i>., high energy, electrons in nanoscale materials. However, the mechanisms of such processes remain elusive, because electrons rapidly lose energy by relaxation through dense manifolds of states. We demonstrate a long-lived hot electron state in a Pt nanocluster adsorbed on the MoS<sub>2</sub> substrate. For this purpose, we develop a simulation technique, combining classical molecular dynamics based on machine learning potentials with <i>ab initio</i> nonadiabatic molecular dynamics and real-time time-dependent density functional theory. Choosing Pt<sub>20</sub>/MoS<sub>2</sub> as a prototypical system, we find frequent shifting of a top atom in the Pt particle occurring on a 50 ps time scale. The distortion breaks particle symmetry and creates unsaturated chemical bonds. The lifetime of the localized state associated with the broken bonds is enhanced by a factor of 3. Hot electrons aggregate near the shifted atom and form a catalytic reaction center. Our findings prove that distortion of even a single atom can have important implications for nanoscale catalysis and plasmonics and provide insights for utilizing machine learning potentials to accelerate <i>ab initio</i> investigations of excited state dynamics in condensed matter systems. Plasmon ; Ab initio ; Atom (system on chip) ; Excited state ; Chemical physics ; Electron ; Molecular dynamics ; Relaxation (psychology) ; Ab initio quantum chemistry methods ; Atomic units ; Materials science ; Atomic physics ; Nanotechnology ; Chemistry ; Physics ; Computational chemistry ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Spectroscopy and Quantum Chemical Studies
Authors: Yong Zhao | Yuxin Cui | Zheng Xiong | Jing Jin | Zhonghao Liu | Rongzhi Dong | Jianjun Hu
Source: ACS Omega (article, 2020-02-13)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction of Crystal Systems and Space Groups from Inorganic Materials Compositions Structural information of materials such as the crystal systems and space groups are highly useful for analyzing their physical properties. However, the enormous composition space of materials makes experimental X-ray diffraction (XRD) or first-principle-based structure determination methods infeasible for large-scale material screening in the composition space. Herein, we propose and evaluate machine-learning algorithms for determining the structure type of materials, given only their compositions. We couple random forest (RF) and multiple layer perceptron (MLP) neural network models with three types of features: Magpie, atom vector, and one-hot encoding (atom frequency) for the crystal system and space group prediction of materials. Four types of models for predicting crystal systems and space groups are proposed, trained, and evaluated including one-versus-all binary classifiers, multiclass classifiers, polymorphism predictors, and multilabel classifiers. The synthetic minority over-sampling technique (SMOTE) is conducted to mitigate the effects of imbalanced data sets. Our results demonstrate that RF with Magpie features generally outperforms other algorithms for binary and multiclass prediction of crystal systems and space groups, while MLP with atom frequency features is the best one for structural polymorphism prediction. For multilabel prediction, MLP with atom frequency and binary relevance with Magpie models are the best for predicting crystal systems and space groups, respectively. Our analysis of the related descriptors identifies a few key contributing features for structural-type prediction such as electronegativity, covalent radius, and Mendeleev number. Our work thus paves a way for fast composition-based structural screening of inorganic materials via predicted material structural properties. Artificial intelligence ; Binary number ; Support vector machine ; Machine learning ; Computer science ; Perceptron ; Multilayer perceptron ; Atom (system on chip) ; Crystal structure prediction ; Random forest ; Chemical space ; Artificial neural network ; Algorithm ; Pattern recognition (psychology) ; Crystal structure ; Mathematics ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Ryan Jacobs | Tam Mayeshiba | Ben Afflerbach | Luke Harold Miles | Max Williams | Matthew D Turner | Raphael A. Finkel | Dane Morgan
Source: Computational Materials Science (article, 2020-01-28)
Field: Materials Science — Materials Chemistry

The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research Workflow ; Computer science ; Machine learning ; Python (programming language) ; Artificial intelligence ; Cyberinfrastructure ; Software ; Software engineering ; Data science ; Operating system ; Database Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: J. Westermayr | P. Marquetand
Source: The Journal of Chemical Physics (article, 2020-10-19)
Field: Materials Science — Materials Chemistry

Deep learning for UV absorption spectra with SchNarc: First steps toward transferability in chemical compound space Machine learning (ML) has shown to advance the research field of quantum chemistry in almost any possible direction and has also recently been applied to investigate the multifaceted photochemistry of molecules. In this paper, we pursue two goals: (i) We show how ML can be used to model permanent dipole moments for excited states and transition dipole moments by adapting the charge model of Gastegger et al. [Chem. Sci. 8, 6924-6935 (2017)], which was originally proposed for the permanent dipole moment vector of the electronic ground state. (ii) We investigate the transferability of our excited-state ML models in chemical space, i.e., whether an ML model can predict the properties of molecules that it has never been trained on and whether it can learn the different excited states of two molecules simultaneously. To this aim, we employ and extend our previously reported SchNarc approach for excited-state ML. We calculate UV absorption spectra from excited-state energies and transition dipole moments as well as electrostatic potentials from latent charges inferred by the ML model of the permanent dipole moment vectors. We train our ML models on CH<sub>2</sub>NH<sub>2</sub> <sup>+</sup> and C<sub>2</sub>H<sub>4</sub>, while predictions are carried out for these molecules and additionally for CHNH<sub>2</sub>, CH<sub>2</sub>NH, and C<sub>2</sub>H<sub>5</sub> <sup>+</sup>. The results indicate that transferability is possible for the excited states. Dipole ; Excited state ; Transferability ; Moment (physics) ; Transition dipole moment ; Spectral line ; Charge (physics) ; Molecule ; Bond dipole moment Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular spectroscopy and chirality
Authors: Rudolf Jason Kwaria | Evan Angelo Quimada Mondarte | Hiroyuki Tahara | Ryongsok Chang | T. Hayashi
Source: ACS Biomaterials Science & Engineering (article, 2020-08-17)
Field: Materials Science — Surfaces, Coatings and Films

Data-Driven Prediction of Protein Adsorption on Self-Assembled Monolayers toward Material Screening and Design We attempt to predict the water contact angle (WCA) of self-assembled monolayers (SAMs) and protein adsorption on the SAMs from the chemical structures of molecules constituting the SAMs using machine learning with an artificial neural network (ANN) model. After training the ANN with data of 145 SAMs, the ANN became capable of predicting the WCA and protein adsorption accurately. The analysis of the trained ANN quantitatively revealed the importance of each structural parameter for the WCA and protein adsorption, providing essential and quantitative information for material design. We found that the degree of importance agrees well with our general perception on the physicochemical properties of SAMs. We also present the prediction of the WCA and protein adsorption of hypothetical SAMs and discuss the possibility of our approach for the material screening and design of SAMs with desired functions. On the basis of these results, we also discuss the limitation of this approach and prospects. Adsorption ; Monolayer ; Self-assembled monolayer ; Contact angle ; Artificial neural network ; Protein adsorption ; Biological system ; Molecule ; Materials science ; Chemistry ; Nanotechnology ; Computer science ; Artificial intelligence ; Organic chemistry Polymer Surface Interaction Studies ; Molecular Junctions and Nanostructures ; Advanced Sensor and Energy Harvesting Materials
Authors: Nicolas Bertin | Ryan B. Sills | Wei Cai
Source: Annual Review of Materials Research (article, 2020-06-03)
Field: Materials Science — Materials Chemistry

Frontiers in the Simulation of Dislocations Dislocations play a vital role in the mechanical behavior of crystalline materials during deformation. To capture dislocation phenomena across all relevant scales, a multiscale modeling framework of plasticity has emerged, with the goal of reaching a quantitative understanding of microstructure–property relations, for instance, to predict the strength and toughness of metals and alloys for engineering applications. This review describes the state of the art of the major dislocation modeling techniques, and then discusses how recent progress can be leveraged to advance the frontiers in simulations of dislocations. The frontiers of dislocation modeling include opportunities to establish quantitative connections between the scales, validate models against experiments, and use data science methods (e.g., machine learning) to gain an understanding of and enhance the current predictive capabilities. Dislocation ; Multiscale modeling ; Materials science ; Deformation (meteorology) ; Plasticity ; Toughness ; Statistical physics ; Computer science ; Physics ; Metallurgy ; Composite material Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Materials Characterization Techniques
Authors: Kamal Choudhary | Kevin F. Garrity | Francesca Tavazza
Source: Journal of Physics Condensed Matter (article, 2020-06-26)
Field: Materials Science — Materials Chemistry

Data-driven discovery of 3D and 2D thermoelectric materials In this work, we first perform a systematic search for high-efficiency three-dimensional (3D) and two-dimensional (2D) thermoelectric materials by combining semiclassical transport techniques with density functional theory (DFT) calculations and then train machine-learning models on the thermoelectric data. Out of 36 000 three-dimensional and 900 two-dimensional materials currently in the publicly available JARVIS-DFT database, we identify 2932 3D and 148 2D promising thermoelectric materials using a multi-steps screening procedure, where specific thresholds are chosen for key quantities like bandgaps, Seebeck coefficients and power factors. We compute the Seebeck coefficients for all the materials currently in the database and validate our calculations by comparing our results, for a subset of materials, to experimental and existing computational datasets. We also investigate the effect of chemical, structural, crystallographic and dimensionality trends on thermoelectric performance. We predict several classes of efficient 3D and 2D materials such as Ba(MgX)<sub>2</sub>(X = P, As, Bi), X<sub>2</sub>YZ<sub>6</sub>(X = K, Rb, Y=Pd, Pt, Z = Cl, Br), K<sub>2</sub>PtX<sub>2</sub>(X = S, Se), NbCu<sub>3</sub>X<sub>4</sub>(X = S, Se, Te), Sr<sub>2</sub>XYO<sub>6</sub>(X = Ta, Zn, Y=Ga, Mo), TaCu<sub>3</sub>X<sub>4</sub>(X = S, Se, Te), and XYN (X = Ti, Zr, Y=Cl, Br). Finally, as high-throughput DFT is computationally expensive, we train machine learning models using gradient boosting decision trees and classical force-field inspired descriptors for n-and p-type Seebeck coefficients and power factors, to quickly pre-screen materials for guiding the next set of DFT calculations. The dataset and tools are made publicly available at the websites:https://www.ctcms.nist.gov/~knc6/JVASP.html,https://www.ctcms.nist.gov/jarvisml/andhttps://jarvis.nist.gov/. Thermoelectric effect ; Thermoelectric materials ; Curse of dimensionality ; Density functional theory ; Semiclassical physics ; Materials science ; Seebeck coefficient ; Computer science ; Boosting (machine learning) ; Algorithm ; Computational science ; Machine learning ; Computational chemistry ; Physics ; Chemistry ; Thermodynamics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials