jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xueyao Zhou | Yaolong Zhang | Hua Guo | Bin Jiang
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Towards bridging the structure gap in heterogeneous catalysis: the impact of defects in dissociative chemisorption of methane on Ir surfaces A quantitative understanding of the role played by defect sites in heterogeneous catalysis is of great importance in designing new and more effective catalysts. In this work, we report a detailed dynamic study of a key step in methane steam reforming under experimentally relevant conditions on a new high-dimensional potential energy surface determined from first principles data with the aid of machine learning, with which the interactions of CH4 with both the flat Ir(111) and stepped Ir(332) surfaces are described. In particular, we argue based on our simulations that the experimentally observed "negatively activated" dissociative chemisorption of methane on Ir surfaces could be due to a combined effect of defects and high substrate temperature, which lowers the reaction barrier relative to that on terraces. Furthermore, a model based on dynamic information of trapping and reaction channels is proposed, which allows a quantitative prediction of the initial sticking probability for different defect densities, thus helping to close the so-called structure gap in heterogeneous catalysis. Bridging (networking) ; Chemisorption ; Methane ; Chemical physics ; Dissociative ; Catalysis ; Nanotechnology ; Chemistry ; Materials science ; Psychology ; Computer science ; Organic chemistry Catalytic Processes in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Sathya R. Chitturi | Daniel Ratner | Richard C. Walroth | Vivek Thampy | Evan J. Reed | Mike Dunne | Christopher J. Tassone | Kevin H. Stone
Source: Journal of Applied Crystallography (article, 2021-11-30)
Field: Materials Science — Materials Chemistry

Automated prediction of lattice parameters from X-ray powder diffraction patterns A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution. Lattice (music) ; Bottleneck ; Diffraction ; Crystal structure ; Crystal (programming language) ; Impurity ; Experimental data ; Convolutional neural network ; Powder diffraction X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Natalie Fey | Jason M. Lynam
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2021-11-23)
Field: Materials Science — Materials Chemistry

Computational mechanistic study in organometallic catalysis: Why prediction is still a challenge Abstract Although computational contributions to the understanding of organometallic homogeneous catalysts have become fairly routine, a step‐change in the application of computational methods would be to achieve efficient, robust, and reliable prediction of the outcome of catalytic transformations. While we concur that there have been a number of recent promising advances in the interactions between computational and experimental mechanistic studies, the mapping of reactivity space remains incomplete and large‐scale studies have to make limiting assumptions which restrict their transferability. Close synergies between characterization and analysis techniques which are integrated with computational data, along with data capture, curation, and exploitation, are vital and develop our understanding of all aspects of the catalytic pathways (including activation and deactivation) and allow the continual refinement of mechanistic understanding, challenged by testing predictions experimentally. Here we review recent examples to formulate a protocol for such interactions. This article is categorized under: Electronic Structure Theory &gt; Ab Initio Electronic Structure Methods Structure and Mechanism &gt; Reaction Mechanisms and Catalysis Data Science &gt; Artificial Intelligence/Machine Learning Electronic Structure Theory &gt; Density Functional Theory Computer science ; Transferability ; Homogeneous ; Density functional theory ; Ab initio ; Computational model ; Biochemical engineering ; Artificial intelligence ; Computational chemistry ; Chemistry ; Machine learning ; Statistical physics ; Physics Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Wenbo Sun | Yujie Zheng | Qi Zhang | Ke Yang | Haiyan Chen | Yongjoon Cho | Jiehao Fu | Omololu Odunmbaku | A.A. Shah | Zeyun Xiao | Shirong Lu | Shanshan Chen | Meng Li | Bo Qin | Changduk Yang | Thomas Frauenheim | Kuan Sun
Source: The Journal of Physical Chemistry Letters (article, 2021-09-08)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials Designing efficient organic photovoltaic (OPV) materials purposefully is still challenging and time-consuming. It is of paramount importance in material development to identify basic functional units that play the key roles in material performance and subsequently establish the substructure-property relationship. Herein, we describe an automatic design framework based on an in-house designed La FREMD Fingerprint and machine learning (ML) algorithms for highly efficient OPV donor molecules. The key building blocks are identified, and a library consisting of 18 960 new molecules is generated within this framework. Through investigating the chemical structures of materials with different performance, a guidance on designing efficient OPV materials is proposed. Furthermore, the most promising candidates exhibit a predicted power conversion efficiency (PCE) value of over 15% when combined with acceptor Y6. Density functional theory (DFT) studies show these candidate materials possess exceptional potential for efficient charge carrier transport. The proposed framework demonstrates the ability to design new materials based on the substructure-property relationship built by ML, which provides an alternative methodology for applying ML in new material discovery. Key (lock) ; Computer science ; Photovoltaic system ; Substructure ; Property (philosophy) ; Fingerprint (computing) ; Energy conversion efficiency ; Nanotechnology ; Biochemical engineering ; Materials science ; Artificial intelligence ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Conducting polymers and applications ; Perovskite Materials and Applications
Authors: Huan Liu | Xin Qian | Hua Bao | Changying Zhao | Xiaokun Gu
Source: Journal of Physics Condensed Matter (article, 2021-07-13)
Field: Materials Science — Materials Chemistry

High-temperature phonon transport properties of SnSe from machine-learning interatomic potential As a promising thermoelectric material, tin selenide (SnSe) is of relatively low thermal conductivity. However, the phonon transport mechanisms in SnSe are not fully understood due to the complex phase transition, dynamical instability, and strong anharmonicity. In this work, we perform molecular dynamics simulations with a machine-learning interatomic potential to explore the thermal transport properties of SnSe at different temperatures. The developed interatomic potential is parameterized using the framework of moment tensor potential, exhibiting satisfactory predictions on temperature-dependent lattice constants and phonon dispersion, as well as phase transition temperature. From equilibrium molecular dynamics simulations, we obtained the thermal conductivity tensor from 200 K to 900 K. The origins of temperature-dependent thermal conductivity anisotropy and the roles of four-phonon scatterings are identified. The obtained interatomic potential can be utilized to study the mechanical and thermal properties of SnSe and related nanostructures in a wide range of temperatures. Interatomic potential ; Phonon ; Thermal conductivity ; Condensed matter physics ; Anharmonicity ; Materials science ; Thermoelectric materials ; Molecular dynamics ; Phase transition ; Atmospheric temperature range ; Anisotropy ; Thermodynamics ; Chemistry ; Physics ; Computational chemistry ; Optics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Maxim Secor | Alexander V. Soudackov | Sharon Hammes‐Schiffer
Source: The Journal of Physical Chemistry Letters (article, 2021-10-27)
Field: Materials Science — Materials Chemistry

Artificial Neural Networks as Propagators in Quantum Dynamics The utilization of artificial neural networks (ANNs) provides strategies for accelerating molecular simulations. Herein, ANNs are implemented as propagators of the time-dependent Schrödinger equation to simulate the quantum dynamics of systems with time-dependent potentials. These ANN propagators are trained to map nonstationary wavepackets from a given time to a future time within the discrete variable representation. Each propagator is trained for a specified time step, and iterative application of the propagator enables the propagation of wavepackets over long time scales. Such ANN propagators are developed and applied to one- and two-dimensional proton transfer systems, which exhibit nuclear quantum effects such as hydrogen tunneling. These ANN propagators are trained for either a specific time-independent potential or general potentials that can be time-dependent. Hierarchical, multiple time step algorithms enable parallelization, and the extension to higher dimensions is straightforward. This strategy is applicable to quantum dynamical simulations of diverse chemical and biological processes. Propagator ; Quantum ; Artificial neural network ; Representation (politics) ; Statistical physics ; Quantum dynamics ; Time evolution ; Computer science ; Physics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Saeed Arabha | Ali Rajabpour
Source: International Journal of Heat and Mass Transfer (article, 2021-06-28)
Field: Materials Science — Materials Chemistry

Thermo-mechanical properties of nitrogenated holey graphene (C2N): A comparison of machine-learning-based and classical interatomic potentials Interatomic potential ; Thermal conductivity ; Molecular dynamics ; Materials science ; Graphene ; Phonon ; Density functional theory ; Elastic modulus ; Ab initio quantum chemistry methods ; Condensed matter physics ; Computational chemistry ; Nanotechnology ; Chemistry ; Composite material ; Physics ; Molecule Thermal properties of materials ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Siddarth K. Achar | Linfeng Zhang | J. Karl Johnson
Source: The Journal of Physical Chemistry C (article, 2021-07-01)
Field: Materials Science — Materials Chemistry

Efficiently Trained Deep Learning Potential for Graphane We have developed an accurate and efficient deep-learning potential (DP) for graphane, which is a fully hydrogenated version of graphene, using a very small training set consisting of 1000 snapshots from a 0.5 ps density functional theory (DFT) molecular dynamics simulation at 1000 K. We have assessed the ability of the DP to extrapolate to system sizes, temperatures, and lattice strains not included in the training set. The DP performs surprisingly well, outperforming an empirical many-body potential when compared with DFT data for the phonon density of states, thermodynamic properties, velocity autocorrelation function, and stress–strain curve up to the yield point. This indicates that our DP can reliably extrapolate beyond the limit of the training data. We have computed the thermal fluctuations as a function of system size for graphane. We found that graphane has larger thermal fluctuations compared with graphene, but having about the same out-of-plane stiffness. Graphane ; Graphene ; Density functional theory ; Materials science ; Molecular dynamics ; Autocorrelation ; Phonon ; Thermal ; Statistical physics ; Nanotechnology ; Physics ; Condensed matter physics ; Thermodynamics ; Mathematics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Graphene research and applications ; Thermal properties of materials
Authors: Alexander A. Ksenofontov | Michail M. Lukanov | Pavel S. Bocharov | М. Б. Березин | Igor V. Tetko
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2021-11-03)
Field: Materials Science — Materials Chemistry

Deep neural network model for highly accurate prediction of BODIPYs absorption Solvatochromism ; Mean squared error ; Absorption (acoustics) ; Computer science ; Artificial neural network ; Solvent ; Biological system ; Fluorescence ; Mean squared prediction error ; Artificial intelligence ; Chemistry ; Machine learning ; Materials science ; Statistics ; Mathematics ; Optics ; Physics ; Organic chemistry Luminescence and Fluorescent Materials ; Per- and polyfluoroalkyl substances research ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Jeff Kabel | Sambhawana Sharma | Amit Acharya | Dongyan Zhang | Yoke Khin Yap
Source: C – Journal of Carbon Research (article, 2021-05-08)
Field: Materials Science — Materials Chemistry

Molybdenum Disulfide Quantum Dots: Properties, Synthesis, and Applications Molybdenum disulfide quantum dots (MoS2 QDs) are a unique class of zero-dimensional (0D) van der Waals nanostructures. MoS2 QDs have attracted significant attention due to their unique optical, electronic, chemical, and biological properties due to the presence of edge states of these van der Waals QDs for various chemical functionalization. Their novel properties have enabled applications in many fields, including advanced electronics, electrocatalysis, and biomedicine. In this review, the various synthesis techniques, the novel properties, and the wide applications of MoS2 quantum dots are discussed in detail. Molybdenum disulfide ; Quantum dot ; van der Waals force ; Nanotechnology ; Materials science ; Surface modification ; Graphene ; Electrocatalyst ; Molybdenum ; Chemistry ; Molecule ; Electrochemistry ; Physical chemistry ; Electrode 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: Qiang Gao | Wan‐Yu Tsai | Nina Balke
Source: Electrochemical Science Advances (article, 2021-05-24)
Field: Materials Science — Polymers and Plastics

In situ and operando force‐based atomic force microscopy for probing local functionality in energy storage materials Abstract Electrochemical energy storage is the key enabling component of electric vehicles and solar‐/wind‐based energy technologies. The enhancement of energy stored requires the detailed understanding of charge storage mechanisms and local electrochemical and electromechanical phenomena over a variety of length scales from atoms to full cells. Classical electrochemical techniques, such as voltammetry, represent the macroscopic electrochemical properties, and consequently do not allow to extract important information about local electrochemical reactions, ions adsorption, intercalations, and transport. Understanding, controlling, and tuning the local electrochemical functionalities in functional energy materials require in situ/operando techniques which limit the use of structural and functional characterization techniques that provide local information. Here, force‐based atomic force microscopies (AFMs) have provided novel insights into locally probing electrochemical mechanisms on tens of nanometer and even molecular length scales and provide a viable pathway to probe electrochemical processes in situ/operando. In this review, we highlight the contributions in the development and application of force‐based AFM methods to elucidate the local charge storage mechanism in a variety of energy‐related materials. We will focus in particular on methods or AFM modalities in a liquid electrolyte environment. Nanotechnology ; Characterization (materials science) ; Energy storage ; Electrochemistry ; Materials science ; Electrochemical energy conversion ; Electrolyte ; Nanometre ; Chemical physics ; Electrode ; Chemistry ; Physics Conducting polymers and applications ; Supercapacitor Materials and Fabrication ; Advanced battery technologies research
Authors: Irene Vassalini | Ivano Alessandri | Domenico de Ceglia
Source: Materials (review, 2021-06-19)
Field: Materials Science — Polymers and Plastics

Stimuli-Responsive Phase Change Materials: Optical and Optoelectronic Applications Stimuli-responsive materials offer a large variety of possibilities in fabrication of solid- state devices. Phase change materials (PCMs) undergo rapid and drastic changes of their optical properties upon switching from one crystallographic phase to another one. This peculiarity makes PCMs ideal candidates for a number of applications including sensors, active displays, photonic volatile and non-volatile memories for information storage and computer science and optoelectronic devices. This review analyzes different examples of PCMs, in particular germanium-antimonium tellurides and vanadium dioxide (VO<sub>2</sub>) and their applications in the above-mentioned fields, with a detailed discussion on potential, limitations and challenges. Materials science ; Vanadium dioxide ; Fabrication ; Phase change ; Optoelectronics ; Photonics ; Vanadium ; Information storage ; Solid-state ; Nanotechnology ; 3D optical data storage ; Variety (cybernetics) ; Phase (matter) ; Germanium ; Engineering physics ; Computer science ; Thin film ; Engineering ; Chemistry Transition Metal Oxide Nanomaterials ; Phase-change materials and chalcogenides ; Perovskite Materials and Applications
Authors: Sebastian Dick | Mariví Fernández-Serra
Source: Physical review. B./Physical review. B (article, 2021-10-12)
Field: Materials Science — Materials Chemistry

Highly accurate and constrained density functional obtained with differentiable programming Using an end-to-end differentiable implementation of the Kohn-Sham self-consistent field equations, we obtain a highly accurate neural network--based exchange and correlation (XC) functional of the electronic density. The functional is optimized using information on both energy and density while exact constraints are enforced through an appropriate neural network architecture. We evaluate our model against different families of XC approximations and show that at the meta-GGA level our functional exhibits unprecedented accuracy for both energy and density predictions. For nonempirical functionals, there is a strong linear correlation between energy and density errors. We use this correlation to define an XC functional quality metric that includes both energy and density errors, leading to an improved way to rank different approximations. Differentiable function ; Metric (unit) ; Density functional theory ; Rank (graph theory) ; Energy functional ; Artificial neural network ; Energy (signal processing) ; Correlation ; Orbital-free density functional theory ; Hybrid functional ; Statistical physics ; Energy density ; Physics ; Applied mathematics ; Computer science ; Mathematics ; Mathematical analysis ; Quantum mechanics ; Theoretical physics ; Artificial intelligence ; Combinatorics Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Advanced Chemical Physics Studies
Authors: Soo-In Sohn | Subramani Pandian | Young‐Ju Oh | John‐Lewis Zinia Zaukuu | Hyeon‐Jung Kang | Tae‐Hun Ryu | Woosuk Cho | Youn-Sung Cho | Eun-Kyoung Shin | Byoung–Kwan Cho
Source: International Journal of Molecular Sciences (review, 2021-09-14)
Field: Chemistry — Analytical Chemistry

An Overview of Near Infrared Spectroscopy and Its Applications in the Detection of Genetically Modified Organisms Near-infrared spectroscopy (NIRS) has become a more popular approach for quantitative and qualitative analysis of feeds, foods and medicine in conjunction with an arsenal of chemometric tools. This was the foundation for the increased importance of NIRS in other fields, like genetics and transgenic monitoring. A considerable number of studies have utilized NIRS for the effective identification and discrimination of plants and foods, especially for the identification of genetically modified crops. Few previous reviews have elaborated on the applications of NIRS in agriculture and food, but there is no comprehensive review that compares the use of NIRS in the detection of genetically modified organisms (GMOs). This is particularly important because, in comparison to previous technologies such as PCR and ELISA, NIRS offers several advantages, such as speed (eliminating time-consuming procedures), non-destructive/non-invasive analysis, and is inexpensive in terms of cost and maintenance. More importantly, this technique has the potential to measure multiple quality components in GMOs with reliable accuracy. In this review, we brief about the fundamentals and versatile applications of NIRS for the effective identification of GMOs in the agricultural and food systems. Identification (biology) ; Genetically modified organism ; Biotechnology ; Biochemical engineering ; Computer science ; Genetically modified crops ; Risk analysis (engineering) ; Computational biology ; Biology ; Transgene ; Engineering ; Business ; Genetics ; Ecology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Listeria monocytogenes in Food Safety