jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arnaldo F. Silva | Leonardo J. Duarte | Paul L. A. Popelier
Source: Structural Chemistry (article, 2020-02-08)
Field: Chemistry — Physical and Theoretical Chemistry

Contributions of IQA electron correlation in understanding the chemical bond and non-covalent interactions Abstract The quantum topological energy partitioning method Interacting Quantum Atoms (IQA) has been applied for over a decade resulting in an enlightening analysis of a variety of systems. In the last three years we have enriched this analysis by incorporating into IQA the two-particle density matrix obtained from Møller–Plesset (MP) perturbation theory. This work led to a new computational and interpretational tool to generate atomistic electron correlation and thus topologically based dispersion energies. Such an analysis determines the effects of electron correlation within atoms and between atoms, which covers both bonded and non-bonded “through -space” atom–atom interactions within a molecule or molecular complex. A series of papers published by us and other groups shows that the behavior of electron correlation is deeply ingrained in structural chemistry. Some concepts that were shown to be connected to bond correlation are bond order, multiplicity, aromaticity, and hydrogen bonding. Moreover, the concepts of covalency and ionicity were shown not to be mutually excluding but to both contribute to the stability of polar bonds. The correlation energy is considerably easier to predict by machine learning (kriging) than other IQA terms. Regarding the nature of the hydrogen bond, correlation energy presents itself in an almost contradicting way: there is much localized correlation energy in a hydrogen bond system, but its overall effect is null due to internal cancelation. Furthermore, the QTAIM delocalization index has a connection with correlation energy. We also explore the role of electron correlation in protobranching, which provides an explanation for the extra stabilization present in branched alkanes compared to their linear counterparts. We hope to show the importance of understanding the true nature of the correlation energy as the foundation of a modern representation of dispersion forces for ab initio, DFT, and force field calculations. Chemistry ; Electronic correlation ; Atoms in molecules ; Electron localization function ; Delocalized electron ; Covalent bond ; Multiplicity (mathematics) ; Chemical physics ; Hydrogen bond ; Chemical bond ; Molecule ; Perturbation theory (quantum mechanics) ; Aromaticity ; Atom (system on chip) ; Electron ; Computational chemistry ; Quantum mechanics ; Physics Crystallography and molecular interactions ; Thermal and Kinetic Analysis ; Computational Drug Discovery Methods
Authors: Troy D. Loeffler | Tarak K. Patra | Henry Chan | Mathew J. Cherukara | Subramanian K. R. S. Sankaranarayanan
Source: The Journal of Physical Chemistry C (article, 2020-01-31)
Field: Materials Science — Materials Chemistry

Active Learning the Potential Energy Landscape for Water Clusters from Sparse Training Data Molecular dynamics with predefined functional forms is a popular technique for understanding dynamical evolution of systems. The predefined functional forms impose limits on the physics that can be captured. Artificial neural network (ANN) models have emerged as an attractive flexible alternative to the expensive quantum calculations (e.g., density functional theory) in the area of molecular force-fields. Ideally, if one is able to train a ANN to accurately predict the correct DFT energy and forces for any given structure, they gain the ability to perform molecular dynamics with high accuracy while simultaneously reducing the computation cost in a dramatic fashion. While this goal is very lucrative, neural networks are interpolative and therefore, it is not always clear how one should go about training a neural network to exhaustively fit the entire phase space of a given system. Currently, ANNs are trained by generating large quantities (on the order of 104 or greater) of training data in hopes that the ANN has adequately sampled the energy landscape both near and far-from-equilibrium. This can, however, be a bit prohibitive when it comes to more accurate levels of quantum theory. As such, it is desirable to train a model using the absolute minimal data set possible, especially when costs of high-fidelity calculations such as CCSD and QMC are high. Here, we present an active learning approach that iteratively trains an ANN model to faithfully replicate the coarse-grained energy surface of water clusters using only 426 total structures in its training data. Our active learning workflow starts with a sparse training data set which is continually updated via a Nested Ensemble Monte Carlo scheme that sparsely queries the energy landscape and tests the network performance. Next, the network is retrained with an updated training set that includes failed configurations/energies from previous iteration until convergence is attained. Once trained, we generate an extensive test set of 100 000 configurations sampled across clusters ranging from 1 to 200 molecules and demonstrate that the trained network adequately reproduces the energies (within mean absolute error (MAE) of 2 meV/molecule) and forces (MAE 40 meV/Å) compared to the reference model. More importantly, the trained ANN model also accurately captures both the structure as well as the free energy as a function of the various cluster sizes. Overall, this study reports a new active learning scheme with promising strategy to develop accurate force-fields for molecular simulations using extremely sparse training data sets. Computer science ; Artificial neural network ; Artificial intelligence ; Set (abstract data type) ; Machine learning ; Fidelity Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Dingchen Wang | Songrui Wei | Anran Yuan | Fanghua Tian | Kaiyan Cao | Qizhong Zhao | Yin Zhang | Chao Zhou | Xiaoping Song | Dezhen Xue | Sen Yang
Source: Advanced Science (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Machine Learning Magnetic Parameters from Spin Configurations Hamiltonian parameters estimation is crucial in condensed matter physics, but is time- and cost-consuming. High-resolution images provide detailed information of underlying physics, but extracting Hamiltonian parameters from them is difficult due to the huge Hilbert space. Here, a protocol for Hamiltonian parameters estimation from images based on a machine learning (ML) architecture is provided. It consists in learning a mapping between spin configurations and Hamiltonian parameters from a small amount of simulated images, applying the trained ML model to a single unexplored experimental image to estimate its key parameters, and predicting the corresponding materials properties by a physical model. The efficiency of the approach is demonstrated by reproducing the same spin configuration as the experimental one and predicting the coercive field, the saturation field, and even the volume of the experiment specimen accurately. The proposed approach paves a way to achieve a stable and efficient parameters estimation. Hamiltonian (control theory) ; Computer science ; Parameter space ; Hilbert space ; Classifier (UML) ; Artificial intelligence ; Algorithm ; Physics ; Statistical physics ; Mathematics ; Mathematical optimization ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Magnetic properties of thin films ; Physics of Superconductivity and Magnetism
Authors: Chiara Panosetti | Artur Engelmann | Lydia Nemec | Karsten Reuter | Johannes T. Margraf
Source: Journal of Chemical Theory and Computation (article, 2020-03-10)
Field: Materials Science — Materials Chemistry

Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression The Density-Functional Tight Binding (DFTB) method is a popular semiempirical approximation to Density Functional Theory (DFT). In many cases, DFTB can provide comparable accuracy to DFT at a fraction of the cost, enabling simulations on length and time scales that are unfeasible with first-principles DFT. At the same time (and in contrast to empirical interatomic potentials and force fields), DFTB still offers direct access to electronic properties such as the band structure. These advantages come at the cost of introducing empirical parameters to the method, leading to a reduced transferability compared to true first-principle approaches. Consequently, it would be very useful if the parameter sets could be routinely adjusted for a given project. While fairly robust and transferable parametrization workflows exist for the electronic structure part of DFTB, the so-called repulsive potential <i>V</i><sub>rep</sub> poses a major challenge. In this paper, we propose a machine-learning (ML) approach to fitting <i>V</i><sub>rep</sub>, using Gaussian Process Regression (GPR) to reconstruct <i>V</i><sub>rep</sub> with DFT-DFTB force residues as training data. The use of GPR circumvents the need for nonlinear or global parameter optimization, while at the same time offering arbitrary flexibility in terms of the functional form. We also show that the proposed method can be applied to multiple elements at once, by fitting repulsive potentials for organic molecules containing carbon, hydrogen, and oxygen. Overall, the new approach removes focus from the choice of functional form and parametrization procedure, in favor of a data-driven philosophy. Kriging ; Tight binding ; Density functional theory ; Parametrization (atmospheric modeling) ; Computer science ; Gaussian process ; Flexibility (engineering) ; Gaussian ; Statistical physics ; Hybrid functional ; Electronic structure ; Computational chemistry ; Machine learning ; Chemistry ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Simin Huang | Xue-Rong Shi | Chunyan Sun | Zhichang Duan | Pan Ma | Shusheng Xu
Source: Nanomaterials (review, 2020-11-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Application of Metal–Organic Frameworks and Their Derivatives for Supercapacitors Supercapacitors (SCs), one of the most popular types of energy-storage devices, present lots of advantages, such as large power density and fast charge/discharge capability. Being the promising SCs electrode materials, metal-organic frameworks (MOFs) and their derivatives have gained ever-increasing attention due to their large specific surface area, controllable porous structure and rich diversity. Herein, the recent development of MOFs-based materials and their application in SCs as the electrode are reviewed and summarized. The preparation method, the morphology of the materials and the electrical performance of various MOFs and their derivatives (such as carbon, metal oxide/hydroxide and metal sulfide) are briefly discussed. Most of recent works concentrate on Ni-, Co- and Mn-MOFs and their composites/derivatives. Conclusions and our outlook for the researches are also given, which would be a valuable guideline for the rational design of MOFs materials for SCs in the near future. Supercapacitor ; Metal-organic framework ; Environmental science ; Business ; Materials science ; Nanotechnology ; Chemistry ; Organic chemistry ; Electrochemistry ; Electrode Supercapacitor Materials and Fabrication ; Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications
Authors: Philippe B. Green | Pournima Narayanan | Ziqi Li | Philip Sohn | Christian J. Imperiale | Mark W. B. Wilson
Source: Chemistry of Materials (article, 2020-04-30)
Field: Materials Science — Materials Chemistry

Controlling Cluster Intermediates Enables the Synthesis of Small PbS Nanocrystals with Narrow Ensemble Line Widths PbS nanocrystals are critical materials for infrared optoelectronics, but the persistent challenge in synthesizing small nanocrystals with narrow line widths demands improved mechanistic understanding. Here, we show that the conventional hot-injection synthesis of PbS nanocrystals per Hines exhibits two-step kinetics involving an intermediate species. The intermediate is small, lead-rich, and has characteristic, reproducible, visible-wavelength emission—all consistent. with a PbS prenucleation cluster (PNC). We then demonstrate that high-pKa amines disrupt the PNC, accelerating nanocrystal nucleation and enabling the synthesis of PbS nanocrystals with diameters as small as ⌀ ∼ 1.7 nm and distinct ensemble absorption peaks (hν = 2.2 eV, λ = 560 nm) in reactions allowed to run to completion. We show that the basicity of the amine additive controls the average size of nanocrystals at reaction completion, which we understand by incorporating metastable PNCs into reaction models that partition monomers between nanocrystal nucleation and nanocrystal growth. This conceptual advance permits the routine synthesis of ultrasmall PbS NCs with excitonic absorption line widths that are up to 25% narrower than previously reported for comparable sizes (⌀: 1.7–3 nm, λpeak,abs: 560–885 nm, hνpeak,abs: 2.2–1.4 eV). This reduced electronic dispersity will enhance device performance, and the underlying insight is further evidence of the exquisite ability of metal-complexing additives to direct the bottom-up syntheses of nanostructured materials. Nanocrystal ; Nucleation ; Materials science ; Dispersity ; Absorption (acoustics) ; Metastability ; Nanotechnology ; Absorption spectroscopy ; Cluster (spacecraft) ; Chemical engineering ; Chemical physics ; Chemistry ; Optics ; Organic chemistry ; Physics Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Nanocluster Synthesis and Applications
Authors: Matthew D. Wodrich | Alberto Fabrizio | Benjamin Meyer | Clémence Corminbœuf
Source: Chemical Science (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Data-powered augmented volcano plots for homogeneous catalysis Given the computational resources available today, data-driven approaches can propel the next leap forward in catalyst design. Using a data-driven inspired workflow consisting of data generation, statistical analysis, and dimensionality reduction algorithms we explore trends surrounding the thermodynamics of a model hydroformylation reaction catalyzed by group 9 metals bearing phosphine ligands. Specifically, we introduce "augmented volcano plots" as a means to easily visualize the similarity of each catalyst's complete catalytic cycle energy profile to that of a hypothetical ideal reference profile without relying upon linear scaling relationships. In addition to quickly identifying catalysts that most closely match the ideal thermodynamic catalytic cycle energy profile, these maps also enable a more refined comparison of closely lying species in standard volcano plots. For the reaction studied here, they inherently uncover the presence of multiple sets of scaling relationships differentiated by metal type, where iridium catalysts follow distinct relationships from cobalt/rhodium catalysts and have profiles that more closely match the ideal thermodynamic profile. Reconstituted molecular volcano plots confirm the findings of the augmented volcanoes by showing that hydroformylation thermodynamics are governed by two distinct volcano shapes, one for iridium catalysts and a second for cobalt/rhodium species. Homogeneous ; Volcano ; Similarity (geometry) ; Scaling ; Ideal (ethics) ; Energy (signal processing) ; Catalysis ; Computer science ; Biological system ; Mathematics ; Geology ; Chemistry ; Artificial intelligence ; Statistics ; Geometry ; Image (mathematics) ; Biology ; Combinatorics ; Seismology Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Francoise M. Amombo Noa | Erik Svensson Grape | Steffen M. Brülls | Ocean Cheung | Per Malmberg | A. Ken Inge | Christine J. McKenzie | Jerker Mårtensson | Lars Öhrström
Source: Journal of the American Chemical Society (article, 2020-04-21)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Frameworks with Hexakis(4-carboxyphenyl)benzene: Extensions to Reticular Chemistry and Introducing Foldable Nets Nine metal-organic frameworks have been prepared with the hexagon-shaped linker 1,2,3,4,5,6-hexakis(4-carboxyphenyl)-benzene (H(6)cpb) by solvothermal reactions in dimethylformamide (dmf) or dimethylacetamide (dmac) with acetic acid or formic acid as modulators: [Bi-2(cpb)(acetato)(2)(dmf)(2)]center dot 2dmf CTH-6 forms a rtl-net; 2(H2NMe2)[Cu-2(cpb)] CTH-7 forms a kgd-net; [Fe-4(cpb)-(acetato)(2)(dmf)(4)] CTH-8 and [Co-4(cpb)(acetato)(2)(dmf)(4)] CTH-9 are isostructural and form yav-nets; 2(HNEt3)[Fe-2(cpb)] CTH-10 and the two polymorphs of 2(H2NMe2)[Zn-2(cpb)]center dot 1.5dmac, Zn-MOF-888 and CTH-11, show kgd-nets; [Cu-2(cpb)-(acetato)(2)(dmf)(2)]center dot 2dmf, CTH-12, forms a mixed coordination and hydrogen-bonded sql-net; and 2(H2NMe2)[Zn-2(cpb)] CTH-13, a similarly mixed yav-net. Surface area values (Brunauer-Emmett-Teller, BET) range from 34 m(2) g(-1) for CTH-12 to 303 m(2) g(-1) for CTH-9 for samples activated at 120 degrees C in dynamic vacuum. All compounds show normal (10-fold higher) molar CO2 versus N-2 uptake at 298 K, except the 19-fold CO2 uptake for CTH-12 containing Cu(II) dinuclear paddle-wheels. We also show how perfect hexagons and triangles can combine to a new 3D topology laf, a model of which gave us the idea of foldable network topologies, as the laf-net can fold into a 2D form while retaining the local geometry around each vertex. Other foldable nets identified are cds, cds-a, ths, sqc163, clh, jem, and tfc covering the basic polygons and their combinations. The impact of this concept on "breathing" MOFs is discussed. I-2 sorption, both from gas phase and from MeOH solution, into CTH-7 were studied by time of flight secondary ion mass spectrometry (ToF-SIMS) on dried crystals. I-2 was shown to have penetrated the crystals, as layers were consecutively peeled off by the ion beam. We suggest ToF-SIMS to be a method for studying sorption depth profiles of MOFs. Chemistry ; Benzene ; Reticular connective tissue ; Organic chemistry ; Metal Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Covalent Organic Framework Applications
Authors: Guillaume Fraux | Rose K. Cersonsky | Michele Ceriotti
Source: The Journal of Open Source Software (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Chemiscope: interactive structure-property explorer for materials and molecules The number of materials or molecules that can be created by combining different chemical elements in various proportions and spatial arrangements is enormous. Computational chemistry can be used to generate databases containing billions of potential structures (Ruddigkeit, Deursen, Blum, & Reymond, 2012), and predict some of the associated properties Unfortunately, the very large number of structures makes exploring such database -to understand structureproperty relations or find the best structure for a given application -a daunting task. In recent years, multiple molecular representations Property (philosophy) ; Computer science ; Astrobiology ; Physics ; Epistemology ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Sh. Akhondzadeh | Ryan B. Sills | Nicolas Bertin | Wei Cai
Source: Journal of the Mechanics and Physics of Solids (article, 2020-09-19)
Field: Materials Science — Materials Chemistry

Dislocation density-based plasticity model from massive discrete dislocation dynamics database Materials science ; Dislocation ; Critical resolved shear stress ; Plasticity ; Slip line field ; Flow stress ; Slip (aerodynamics) ; Strain rate ; Shear (geology) ; Strain hardening exponent ; Peierls stress ; Shear stress ; Mechanics ; Hardening (computing) ; Dislocation creep ; Constitutive equation ; Crystallography ; Composite material ; Shear rate ; Thermodynamics ; Physics ; Rheology Microstructure and mechanical properties ; Metallurgy and Material Forming ; Fusion materials and technologies
Authors: Fenghua Yu | Shuai Feng | Wen Du | Dingkang Wang | Zhonghui Guo | Simin Xing | Zhongyu Jin | Yingli Cao | Tongyu Xu
Source: Frontiers in Plant Science (article, 2020-12-02)
Field: Chemistry — Analytical Chemistry

A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential To achieve rapid, accurate, and non-destructive diagnoses of nitrogen deficiency in cold land japonica rice, hyperspectral data were collected from field experiments to investigate the relationship between the nitrogen (N) content and the difference in the spectral reflectance relationship and to establish the hyperspectral reflectance difference inversion model of differences in the N content of rice. In this study, the hyperspectral reflectance difference was used to invert the nitrogen deficiency of rice and provide a method for the implementation of precision fertilization without reducing the yield of chemical fertilizer. For the purpose of constructing the standard N content and standard spectral reflectance the principle of minimum fertilizer application at maximum yield was used as a reference standard, and the acquired rice leaf nitrogen content and leaf spectral reflectance were differenced from the standard N content and standard spectral reflectance to obtain N content. The difference and spectral reflectance differential were then subjected to discrete wavelet multiscale decomposition, successive projections algorithm, principal component analysis, and iteratively retaining informative variables (IRIVs); the results were treated as partial least squares (PLSR), extreme learning machine (ELM), and genetic algorithm-extreme learning machine (GA-ELM). The results of hyperspectral dimensionality reduction were used as input to establish the inverse model of N content differential in japonica rice. The results showed that the GA-ELM inversion model established by discrete wavelet multi-scale decomposition obtained the optimal results in data set modeling and training. Both the R<sup>2</sup> of the training data set and the validation data set were above 0.68, and the root mean square errors (RMSEs) were <0.6 mg/g and were more predictive, stable, and generalizable than the PLSR and ELM predictive models. Hyperspectral imaging ; Mathematics ; Principal component analysis ; Wavelet ; Extreme learning machine ; Nitrogen deficiency ; Remote sensing ; Reflectivity ; Nitrogen ; Biological system ; Computer science ; Artificial intelligence ; Chemistry ; Statistics ; Biology ; Optics ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Rice Cultivation and Yield Improvement
Authors: Farhad Salek | Mohammad Zamen | Seyed Vahid Hosseini | Meisam Babaie
Source: International Journal of Hydrogen Energy (article, 2020-07-08)
Field: Materials Science — Materials Chemistry

Novel hybrid system of pulsed HHO generator/TEG waste heat recovery for CO reduction of a gasoline engine Automotive engineering ; Gasoline ; Fuel efficiency ; Brake specific fuel consumption ; Inlet manifold ; Diesel fuel ; Energy consumption ; Automotive industry ; Environmental science ; Waste management ; Engineering ; Electrical engineering ; Internal combustion engine Hydrogen Storage and Materials ; Refrigeration and Air Conditioning Technologies ; Hybrid Renewable Energy Systems