jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhi Deng | Chi Chen | Xiangguo Li | Shyue Ping Ong
Source: npj Computational Materials (article, 2019-07-16)
Field: Materials Science — Materials Chemistry

An electrostatic spectral neighbor analysis potential for lithium nitride Abstract Machine-learned interatomic potentials based on local environment descriptors represent a transformative leap over traditional potentials based on rigid functional forms in terms of prediction accuracy. However, a challenge in their application to ionic systems is the treatment of long-ranged electrostatics. Here, we present a highly accurate electrostatic Spectral Neighbor Analysis Potential (eSNAP) for ionic α -Li 3 N, a prototypical lithium superionic conductor of interest as a solid electrolyte or coating for rechargeable lithium-ion batteries. We show that the optimized eSNAP model substantially outperforms traditional Coulomb–Buckingham potential in the prediction of energies and forces, as well as various properties, such as lattice constants, elastic constants, and phonon dispersion curves. We also demonstrate the application of eSNAP in long-time, large-scale Li diffusion studies in Li 3 N, providing atomistic insights into measures of concerted ionic motion (e.g., the Haven ratio) and grain boundary diffusion. This work aims at providing an approach to developing quantum-accurate force fields for multi-component ionic systems under the SNAP formalism, enabling large-scale atomistic simulations for such systems. Ionic bonding ; Chemical physics ; Coulomb ; Electrostatics ; Materials science ; Phonon ; Statistical physics ; Ion ; Chemistry ; Condensed matter physics ; Physics ; Physical chemistry ; Quantum mechanics ; Electron Machine Learning in Materials Science ; Fuel Cells and Related Materials ; X-ray Diffraction in Crystallography
Authors: Barry M. Trost | Zhijun Zuo
Source: Angewandte Chemie International Edition (article, 2019-10-14)
Field: Chemistry — Organic Chemistry

Highly Regio‐, Diastereo‐, and Enantioselective Synthesis of Tetrahydroazepines and Benzo[<i>b</i>]oxepines through Palladium‐Catalyzed [4+3] Cycloaddition Reactions A novel Pd<sup>0</sup> -catalyzed asymmetric [4+3] annulation reaction of two readily accessible starting materials has been developed for building seven-membered heterocyclic architectures. The potential [3+2] side pathway could be suppressed though fine tuning of the conditions. A broad scope of cycloaddition donors and acceptors participated in the transformation with excellent chemo-, regio-, diastereo-, and enantioselectivtities, leading to valuable tetrahydroazepines and benzo[b]oxepines. Cycloaddition ; Annulation ; Enantioselective synthesis ; Palladium ; Catalysis ; Chemistry ; Combinatorial chemistry ; Stereochemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Catalytic Alkyne Reactions ; Asymmetric Synthesis and Catalysis
Authors: Aayush R. Singh | Brian A. Rohr | Joseph A. Gauthier | Jens K. Nørskov
Source: Catalysis Letters (article, 2019-03-16)
Field: Materials Science — Materials Chemistry

Predicting Chemical Reaction Barriers with a Machine Learning Model Chemistry ; Organometallic chemistry ; Catalysis ; Biochemical engineering ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Zhenfeng Pang | Jun Zhang | Weicheng Cao | Xueqian Kong | Xiaogang Peng
Source: Nature Communications (article, 2019-06-05)
Field: Chemistry — Spectroscopy

Partitioning surface ligands on nanocrystals for maximal solubility A typical colloidal nanoparticle can be viewed as a nanocrystal-ligands complex with an inorganic single-crystalline core, the nanocrystal, bonded with a monolayer of organic ligands. The surface chemistry of nanocrystal-ligands complexes is crucial to their bulk properties. However, deciphering the molecular pictures of the nonperiodic and dynamic organic-inorganic interlayer is a grand technical challenge, and this hampers the quantitative perception of their macroscopic phenomena. Here we show that the atomic arrangement on nanocrystal surface and ligand-ligand interactions can be precisely quantified through comprehensive solid-state nuclear magnetic resonance (SSNMR) methodologies. The analyses reveal that the mixed ligands of n-alkanoates on a CdSe nanocrystal segregate in areal partitions and the unique arrangement unlocks their rotational freedom. The mathematical model based on the NMR-derived ligand partition and dynamics successfully predicts the unusual solubility of nanocrystal-ligands complexes with mixed ligands, which is several orders of magnitude higher than that of nanocrystal-ligands complexes with pure ligands. Nanocrystal ; Ligand (biochemistry) ; Solubility ; Colloid ; Materials science ; Nanoparticle ; Monolayer ; Chemical physics ; Nanostructure ; Nanotechnology ; Chemistry ; Crystallography ; Physical chemistry ; Receptor Advanced NMR Techniques and Applications ; Spectroscopy and Quantum Chemical Studies ; Quantum Dots Synthesis And Properties
Authors: Pravin Ambure | Amit Kumar Halder | Humberto González‐Díaz | M. Natália D. S. Cordeiro
Source: Journal of Chemical Information and Modeling (article, 2019-05-14)
Field: Chemistry — Analytical Chemistry

QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models Quantitative structure-activity relationships (QSAR) modeling is a well-known computational technique with wide applications in fields such as drug design, toxicity predictions, nanomaterials, etc. However, QSAR researchers still face certain problems to develop robust classification-based QSAR models, especially while handling response data pertaining to diverse experimental and/or theoretical conditions. In the present work, we have developed an open source standalone software "QSAR-Co" (available to download at https://sites.google.com/view/qsar-co ) to setup classification-based QSAR models that allow mining the response data coming from multiple conditions. The software comprises two modules: (1) the Model development module and (2) the Screen/Predict module. This user-friendly software provides several functionalities required for developing a robust multitasking or multitarget classification-based QSAR model using linear discriminant analysis or random forest techniques, with appropriate validation, following the principles set by the Organisation for Economic Co-operation and Development (OECD) for applying QSAR models in regulatory assessments. Quantitative structure–activity relationship ; Human multitasking ; Computer science ; Software ; Data mining ; Machine learning ; Biology ; Operating system Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fault Detection and Control Systems
Authors: Wei Chen | Chenxing Guo | Qing He | Xiaodong Chi | Vincent M. Lynch | Zhiyun Zhang | Jianhua Su | He Tian | Jonathan L. Sessler
Source: Journal of the American Chemical Society (article, 2019-08-22)
Field: Chemistry — Spectroscopy

Molecular Cursor Caliper: A Fluorescent Sensor for Dicarboxylate Dianions We report here the fluorescent sensing of both aromatic and linear saturated dicarboxylate anions (<b>DC<sup>2-</sup></b>) (as their tetrabutylammonium salts) with different lengths and shapes in acetonitrile using a single fluorescent probe, i.e., the bis-calix[4]pyrrole-appended 9,14-diphenyl-9,14-dihydrodibenzo[<i>a</i>,<i>c</i>]phenazine (<b>DPAC-bisC4P</b>) incorporating a vibration-induced emission (VIE) phenazine core. Fluorescence titration studies revealed that treating <b>DPAC-bisC4P</b> with dicarboxylate guests capable of forming pseudomacrocyclic host-guest complexes via multiple hydrogen-bonding interactions between the dicarboxylates and calix[4]pyrrole moieties led to a blue-shift in the emission of the phenazine core. The binding-based fluorescence-tuning features of <b>DPAC-bisC4P</b> allow the underlying binding events and inferred structural changes to be monitored in the form of different chromaticity outputs. The analyte-induced differences in the fluorescence response to <b>DC<sup>2-</sup></b> cover a wide range within the chromaticity diagram and can be visualized readily. The present system thus functions as a rudimentary dicarboxylate anion sensor. It highlights the potential benefits associated with combining a tunable VIE core with noncovalent binding interactions and thus sets the stage for the development of new fluorescent chemosensors where a single chemical entity responds to different analytes with a high level of tunability. Phenazine ; Fluorescence ; Chemistry ; Cursor (databases) ; Chromaticity ; Acetonitrile ; Titration ; Pyrrole ; Hydrogen bond ; Analyte ; Photochemistry ; Molecule ; Inorganic chemistry ; Organic chemistry ; Physical chemistry Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Porphyrin and Phthalocyanine Chemistry
Authors: Gareth S. Parkinson
Source: Catalysis Letters (article, 2019-02-25)
Field: Materials Science — Materials Chemistry

Single-Atom Catalysis: How Structure Influences Catalytic Performance It now seems clear that supported metal adatoms can be effective catalysts for some reactions, but how to make best use of this phenomenon remains an open question. Most studies to date have focussed on synthesizing stable “single-atom” variants of functioning supported nanoparticle systems, but there is mounting evidence that the properties of supported adatoms do not scale from those of larger nanoparticles in a simple way. The sensitivity of the adsorption properties to the charge state and coordination environment of the adatom has led researchers to dream that single atom catalysis (SAC) can bridge the gap between heterogeneous and homogeneous catalysis, opening the door to a new generation of highly-selective catalysts for difficult reactions. To make this dream a reality, a fundamental understanding of how the structure of the active site affects the adsorption properties of adatoms is essential. Since the active site geometry cannot be unambiguously determined from realistic SAC systems at present, experiments on precisely-defined model systems must work hand-in-hand with theory to provide a fundamental basis for this rapidly growing field. Catalysis ; Chemistry ; Organometallic chemistry ; Atom (system on chip) ; Heterogeneous catalysis ; Nanotechnology ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Steven K. Kauwe | Jake Graser | Ryan Murdock | Taylor D. Sparks
Source: Computational Materials Science (article, 2019-12-31)
Field: Materials Science — Materials Chemistry

Can machine learning find extraordinary materials? Extrapolation ; Machine learning ; Debye model ; Set (abstract data type) ; Computer science ; Artificial intelligence ; Support vector machine ; Shear modulus ; Regression ; Statistical physics ; Physics ; Mathematics ; Thermodynamics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Shuaihua Lu | Qionghua Zhou | Liang Ma | Yilv Guo | Jinlan Wang
Source: Small Methods (article, 2019-06-26)
Field: Materials Science — Materials Chemistry

Rapid Discovery of Ferroelectric Photovoltaic Perovskites and Material Descriptors via Machine Learning Rapid discovery of novel functional materials is urgent but a tremendous challenge using trial‐and‐error methods in vast chemical space. Here, a multistep screening scheme is developed by combining high‐throughput calculations and machine learning (ML) techniques. Successfully, 151 promising stable ferroelectric photovoltaic (FPV) perovskites with proper bandgap are screened out from 19 841 candidate compositions. Two new descriptors are proposed to describe mixed inorganic perovskites' formability through ML feature engineering. Additionally, phase‐transition energy difference is used as a criterion for directly judging whether the compound can expose spontaneous polarization. The ML prediction accuracy of both energy difference and bandgap regressions is over 90% and ML produces comparable results to density functional theory calculations. Moreover, bandgaps of eight selected FPV perovskites are all close to the optimal value of single‐junction solar cells. This scheme not only realizes the ML acceleration for targeted multiproperty materials' design and expansion of materials database, but also opens a way for descriptor development. Ferroelectricity ; Band gap ; Materials science ; Photovoltaic system ; Computer science ; Formability ; Polarization (electrochemistry) ; Optoelectronics ; Artificial intelligence ; Machine learning ; Nanotechnology ; Chemistry ; Dielectric ; Engineering ; Electrical engineering ; Composite material Machine Learning in Materials Science ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Huziel E. Sauceda | Stefan Chmiela | Igor Poltavsky | Klaus-Robert Müller | Alexandre Tkatchenko
Source: The Journal of Chemical Physics (article, 2019-03-18)
Field: Materials Science — Materials Chemistry

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) approach [Chmiela et al., Nat. Commun. 9, 3887 (2018) and Chmiela et al., Sci. Adv. 3, e1603015 (2017)]. This approach is able to accurately reconstruct complex high-dimensional potential-energy surfaces from just a few 100s of molecular conformations extracted from ab initio molecular dynamics trajectories. The data efficiency of the sGDML approach implies that atomic forces for these conformations can be computed with high-level wavefunction-based approaches, such as the "gold standard" coupled-cluster theory with single, double and perturbative triple excitations [CCSD(T)]. We demonstrate that the flexible nature of the sGDML model recovers local and non-local electronic interactions (e.g., H-bonding, proton transfer, lone pairs, changes in hybridization states, steric repulsion, and n → π<sup>*</sup> interactions) without imposing any restriction on the nature of interatomic potentials. The analysis of sGDML molecular dynamics trajectories yields new qualitative insights into dynamics and spectroscopy of small molecules close to spectroscopic accuracy. Molecular dynamics ; Cluster (spacecraft) ; Small molecule ; Ab initio ; Force field (fiction) ; Work (physics) ; Molecule ; Dynamics (music) ; Coupled cluster Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Suresh Bishnoi | Sourabh Singh | Ravinder Bhattoo | Mathieu Bauchy | Nitya Nand Gosvami | Hariprasad Kodamana | N. M. Anoop Krishnan
Source: Journal of Non-Crystalline Solids (article, 2019-09-05)
Field: Materials Science — Materials Chemistry

Predicting Young's modulus of oxide glasses with sparse datasets using machine learning Overfitting ; Extrapolation ; Computer science ; Kriging ; Machine learning ; Artificial intelligence ; Artificial neural network ; Ground-penetrating radar ; Regression ; Pattern recognition (psychology) ; Gaussian process ; Generalization ; Process (computing) ; Data mining ; Gaussian ; Mathematics ; Statistics ; Radar Machine Learning in Materials Science ; Material Dynamics and Properties ; Geochemistry and Geologic Mapping
Authors: Dongwon Shin | Yukinori Yamamoto | Michael P. Brady | Shinbuhm Lee | J. Allen Haynes
Source: Acta Materialia (article, 2019-02-18)
Field: Materials Science — Materials Chemistry

Modern data analytics approach to predict creep of high-temperature alloys Workflow ; Creep ; Ranking (information retrieval) ; Materials science ; Component (thermodynamics) ; Domain (mathematical analysis) ; Computer science ; Analytics ; Alloy ; Data mining ; Machine learning ; Thermodynamics ; Database ; Metallurgy ; Physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; High Temperature Alloys and Creep
Authors: Liang Wu | Zachary Armstrong | Sybrin P. Schröder | Casper de Boer | Marta Artola | Johannes M. F. G. Aerts | Herman S. Overkleeft | G.J. Davies
Source: Current Opinion in Chemical Biology (review, 2019-08-13)
Field: Chemistry — Organic Chemistry

An overview of activity-based probes for glycosidases Glycoside hydrolase ; Chemical biology ; Computational biology ; Biochemistry ; Biology ; Drug discovery ; High-throughput screening ; Chemistry ; Enzyme Carbohydrate Chemistry and Synthesis ; Click Chemistry and Applications ; Enzyme Catalysis and Immobilization
Authors: N. Scott Bobbitt | Randall Q. Snurr
Source: Molecular Simulation (article, 2019-04-04)
Field: Chemistry — Inorganic Chemistry

Molecular modelling and machine learning for high-throughput screening of metal-organic frameworks for hydrogen storage Hydrogen is an appealing energy storage solution for electric vehicles due to its low environmental impact and faster recharge times compared to batteries. However, there are many engineering challenges involved in safely storing a sufficient amount of hydrogen onboard a vehicle with a reasonable volumetric density. Nanoporous materials such as metal–organic frameworks (MOFs) have the potential to store hydrogen at high density and only moderate pressure. Considerable research has been devoted to finding new MOFs for hydrogen storage in recent years; however, a MOF that provides sufficient hydrogen density and is suitable to commercial applications has not yet been found. Much of this research makes use of molecular modelling to screen thousands of materials in a high-throughput way. Computational screening can be an effective tool for gaining insight into structure-performance relationships as well as finding specific candidates for an application. Recently, some research groups have also used machine learning to analyze data more effectively and accelerate the screening process. In this review, we discuss some recent advances in using molecular modelling and machine learning to find materials for hydrogen storage. We also discuss and compare some popular models for the hydrogen molecule and the accuracy of different equations of state, which are important considerations for accurate molecular simulations. Hydrogen storage ; Nanoporous ; Computer science ; Throughput ; Hydrogen ; Process (computing) ; Process engineering ; Energy storage ; Nanotechnology ; Materials science ; Chemistry ; Engineering ; Physics ; Power (physics) ; Telecommunications Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Regina Palkovits | Stefan Palkovits
Source: ACS Catalysis (article, 2019-08-12)
Field: Materials Science — Materials Chemistry

Using Artificial Intelligence To Forecast Water Oxidation Catalysts Artificial intelligence and various types of machine learning are of increasing interest not only in the natural sciences but also in a wide range of applied and engineering sciences. In this study, we rethink the view on combinatorial heterogeneous catalysis and combine machine learning methods with combinatorial approaches in electrocatalysis. Several machine learning methods were used to forecast water oxidation catalysts on the basis of literature published data sets and data from our own work. The machine learning models exhibit a decent prediction precision based on the data sets available and confirm that even simple models are suitable for good forecasts. Artificial intelligence ; Machine learning ; Computer science ; Electrocatalyst ; Range (aeronautics) ; Catalysis ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning and ELM
Authors: Muratahan Aykol | Vinay I. Hegde | Linda Hung | Santosh K. Suram | Patrick K. Herring | Chris Wolverton | Jens S. Hummelshøj
Source: Nature Communications (article, 2019-05-01)
Field: Materials Science — Materials Chemistry

Network analysis of synthesizable materials discovery Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Synthesis of a material is a complex process that depends not only on its thermodynamic stability with respect to others, but also on factors from kinetics, to advances in synthesis techniques, to the availability of precursors. This complexity makes the development of a general theory or first-principles approach to synthesizability currently impractical. Here we show how an alternative pathway to predicting synthesizability emerges from the dynamics of the materials stability network: a scale-free network constructed by combining the convex free-energy surface of inorganic materials computed by high-throughput density functional theory and their experimental discovery timelines extracted from citations. The time-evolution of the underlying network properties allows us to use machine-learning to predict the likelihood that hypothetical, computer-generated materials will be amenable to successful experimental synthesis. Computer science ; Timeline ; Stability (learning theory) ; Process (computing) ; Throughput ; Scale (ratio) ; Theoretical computer science ; Machine learning ; Mathematics ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Xin Li | Mahshid Ahmadi | Liam Collins | Sergei V. Kalinin
Source: Electrochimica Acta (article, 2019-05-10)
Field: Chemistry — Electrochemistry

Deconvolving distribution of relaxation times, resistances and inductance from electrochemical impedance spectroscopy via statistical model selection: Exploiting structural-sparsity regularization and data-driven parameter tuning Regularization (linguistics) ; Overfitting ; Deconvolution ; Computer science ; Model selection ; Dielectric spectroscopy ; Equivalent circuit ; Algorithm ; Lasso (programming language) ; Biological system ; Artificial neural network ; Chemistry ; Artificial intelligence ; Physics Electrochemical Analysis and Applications ; Conducting polymers and applications ; Advanced Battery Technologies Research