jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jimmy‐Xuan Shen | Jason M. Munro | Matthew K. Horton | Patrick Huck | Shyam Dwaraknath | Kristin A. Persson
Source: Scientific Data (article, 2022-10-28)
Field: Materials Science — Materials Chemistry

A representation-independent electronic charge density database for crystalline materials In addition to being the core quantity in density-functional theory, the charge density can be used in many tertiary analyses in materials sciences from bonding to assigning charge to specific atoms. The charge density is data-rich since it contains information about all the electrons in the system. With the increasing prevalence of machine-learning tools in materials sciences, a data-rich object like the charge density can be utilized in a wide range of applications. The database presented here provides a modern and user-friendly interface for a large and continuously updated collection of charge densities as part of the Materials Project. In addition to the charge density data, we provide the theory and code for changing the representation of the charge density which should enable more advanced machine-learning studies for the broader community. Charge (physics) ; Charge density ; Representation (politics) ; Density functional theory ; Range (aeronautics) ; Computer science ; Electron density ; Object (grammar) ; Interface (matter) ; Electron ; Database ; Physics ; Materials science ; Information retrieval ; Chemical physics ; Artificial intelligence ; Particle physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Viktor C. Birschitzky | Florian Ellinger | Ulrike Diebold | Michele Reticcioli | Cesare Franchini
Source: npj Computational Materials (article, 2022-06-06)
Field: Materials Science — Materials Chemistry

Machine learning for exploring small polaron configurational space Abstract Polaron defects are ubiquitous in materials and play an important role in many processes involving carrier mobility, charge transfer and surface reactivity. Determining small polarons’ spatial distributions is essential to understand materials properties and functionalities. However, the required exploration of the configurational space is computationally demanding when using first principles methods. Here, we propose a machine-learning (ML) accelerated search that determines the ground state polaronic configuration. The ML model is trained on databases of polaron configurations generated by density functional theory (DFT) via molecular dynamics or random sampling. To establish a mapping between configurations and their stability, we designed descriptors modelling the interactions among polarons and charged point defects. We used the DFT+ML protocol to explore the polaron configurational space for two surface-systems, reduced rutile TiO 2 (110) and Nb-doped SrTiO 3 (001). The ML-aided search proposes additional polaronic configurations and can be utilized to determine optimal polaron distributions at any charge concentration. Polaron ; Density functional theory ; Space (punctuation) ; Charge (physics) ; Computer science ; Stability (learning theory) ; Chemical physics ; Materials science ; Statistical physics ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Machine learning Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Keita Kobayashi | A. Yamaguchi | Masahiko Okumura
Source: Applied Clay Science (article, 2022-06-23)
Field: Materials Science — Biomaterials

Machine learning potentials of kaolinite based on the potential energy surfaces of GGA and meta-GGA density functional theory Kaolinite ; Density functional theory ; Inelastic neutron scattering ; Molecular dynamics ; Materials science ; Neutron ; Physics ; Chemistry ; Statistical physics ; Computational chemistry ; Neutron scattering ; Quantum mechanics ; Mineralogy Clay minerals and soil interactions ; Soil and Unsaturated Flow ; Geological and Geochemical Analysis
Authors: Emad Al Ibrahim | Aamir Farooq
Source: The Journal of Physical Chemistry A (article, 2022-07-06)
Field: Materials Science — Materials Chemistry

Transfer Learning Approach to Multitarget Temperature-Dependent Reaction Rate Prediction Accurate prediction of temperature-dependent reaction rate constants of organic compounds is of great importance to both atmospheric chemistry and combustion science. Extensive work has been done on developing automated mechanism generation systems but the lack of quality reaction rate data remains a huge bottleneck in the application of highly detailed mechanisms. Machine learning prediction models have been recently adopted to alleviate the data gap in thermochemistry and have great potential to do the same for kinetic data with the recent release of quality reaction rate data compilations. The ultimate goal is to formulate easily accessible, general-purpose, temperature-dependent, and multitarget models for the prediction of reaction rates. To that end, we propose a model that depends on the well-known Morgan fingerprints as well as learned representations transferred from the QM9 data set. We propose the use of an Arrhenius-based loss where predictions of the three modified-Arrhenius parameters (<i>A</i>, <i>n</i>, and <i>B</i> = <i>E</i><sub>a</sub>/<i>R</i>) are given instead of the direct prediction of reaction rate constants. Our model is >35% more accurate compared to a baseline model of feed forward network (FFN) on Morgan fingerprints. Thermochemistry ; Arrhenius equation ; Bottleneck ; Computer science ; Reaction rate ; Reaction rate constant ; Work (physics) ; Chemistry ; Biological system ; Thermodynamics ; Activation energy ; Physical chemistry ; Kinetics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Yifan Zhao | Cheng Qian | Vladislav Gladkikh | Feng Ding
Source: Carbon (article, 2022-11-07)
Field: Chemistry — Organic Chemistry

Simulated Pressure-temperature Carbon Structure Map obtained through uniaxial compression of Bulk C60 Amorphous carbon ; Carbon fibers ; Materials science ; Fullerene ; Diamond ; Amorphous solid ; Phase diagram ; Chemical physics ; Carbide-derived carbon ; Compression (physics) ; Nanotechnology ; Phase (matter) ; Chemical engineering ; Composite material ; Composite number ; Carbon nanotube ; Crystallography ; Chemistry ; Carbon nanofiber ; Organic chemistry Fullerene Chemistry and Applications ; Diamond and Carbon-based Materials Research ; Boron and Carbon Nanomaterials Research
Authors: Bipeng Wang | Weibin Chu | Oleg V. Prezhdo
Source: The Journal of Physical Chemistry Letters (article, 2022-01-03)
Field: Materials Science — Materials Chemistry

Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Inverse Fast Fourier Transform Nonadiabatic (NA) molecular dynamics (MD) allows one to investigate far-from-equilibrium processes in nanoscale and molecular materials at the atomistic level and in the time domain, mimicking time-resolved spectroscopic experiments. Ab initio NAMD is limited to about 100 atoms and a few picoseconds, due to computational cost of excitation energies and NA couplings. We develop a straightforward methodology that can extend ab initio quality NAMD to nanoseconds and thousands of atoms. The ab initio NAMD Hamiltonian is sampled and interpolated along a trajectory using a Fourier transform, and then, it is used to perform NAMD with known algorithms. The methodology relies on the classical path approximation, which holds for many materials and processes. To achieve a complete ab initio quality description, the trajectory can be obtained using an ab initio trained machine learning force field. The method is demonstrated with charge carrier trapping and relaxation in hybrid organic-inorganic and all-inorganic metal halide perovskites that exhibit complex dynamics and are actively studied for optoelectronic applications. Ab initio ; Hamiltonian (control theory) ; Fourier transform ; Ab initio quantum chemistry methods ; Molecular dynamics ; Nanosecond ; Physics ; Chemistry ; Computational chemistry ; Statistical physics ; Quantum mechanics ; Molecule ; Mathematics ; Mathematical optimization ; Laser Machine Learning in Materials Science ; Perovskite Materials and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Mantas Sriubas | Kristina Bočkutė | Paulius Palevičius | Marius Kaminskas | Žilvinas Rinkevičius | Minvydas Ragulskis | Sandrita Šimonytė | Modestas Ružauskas | Giedrius Laukaitis
Source: Nanomaterials (article, 2022-04-02)
Field: Materials Science — Materials Chemistry

Antibacterial Activity of Silver and Gold Particles Formed on Titania Thin Films Metal-based nanoparticles with antimicrobial activity are gaining a lot of attention in recent years due to the increased antibiotics resistance. The development and the pathogenesis of oral diseases are usually associated with the formation of bacteria biofilms on the surfaces; therefore, it is crucial to investigate the materials and their properties that would reduce bacterial attachment and biofilm formation. This work provides a systematic investigation of the physical-chemical properties and the antibacterial activity of TiO<sub>2</sub> thin films decorated by Ag and Au nanoparticles (NP) against <i>Veillonella parvula</i> and <i>Neisseria sicca</i> species associated with oral diseases. TiO<sub>2</sub> thin films were formed using reactive magnetron sputtering by obtaining as-deposited amorphous and crystalline TiO<sub>2</sub> thin films after annealing. Au and Ag NP were formed using a two-step process: magnetron sputtering of thin metal films and solid-state dewetting. The surface properties and crystallographic nature of TiO<sub>2</sub>/NP structures were investigated by SEM, XPS, XRD, and optical microscopy. It was found that the higher thickness of Au and Ag thin films results in the formation of the enlarged NPs and increased distance between them, influencing the antibacterial activity of the formed structures. TiO<sub>2</sub> surface with AgNP exhibited higher antibacterial efficiency than Au nanostructured titania surfaces and effectively reduced the concentration of the bacteria. The process of the observation and identification of the presence of bacteria using the deep learning technique was realized. Dewetting ; Materials science ; Thin film ; Sputter deposition ; Amorphous solid ; Chemical engineering ; X-ray photoelectron spectroscopy ; Nanotechnology ; Nanoparticle ; Sputtering ; Antibacterial activity ; Annealing (glass) ; Biofilm ; Bacteria ; Metallurgy ; Chemistry ; Crystallography Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Laser-Ablation Synthesis of Nanoparticles
Authors: Yuki Kimura | Hiroyasu Katsuno | Tomoya Yamazaki
Source: Faraday Discussions (article, 2022-01-01)
Field: Materials Science — Biomaterials

Possible embryos and precursors of crystalline nuclei of calcium carbonate observed by liquid-cell transmission electron microscopy Several different building blocks or precursors play an important role in the early stages of the crystallization of calcium carbonate (CaCO<sub>3</sub>). Many studies have been conducted over a long period to elucidate the fundamental processes involved in this crystallization. Here, we report the role of an amorphous phase and embryo at the beginning of the nucleation of CaCO<sub>3</sub> from solutions of relatively low supersaturation. Prenucleation crystals formed in amorphous calcium carbonate (ACC) at a significantly large formation rate of 2 × 10<sup>22</sup> m<sup>-3</sup> s<sup>-1</sup>, suggesting that a low interfacial energy exists between the ACC and crystals. Only one calcite crystal exceeded the size for a critical nucleus (∼10<sup>4</sup> molecules) in 150 pre-nucleation crystals. Each pre-nucleation crystal might consist of a different polymorph, and ACCs have a similar composition and structure. A particle-detection algorithm, used in conjunction with machine learning, suggested that an embryo with a characteristic structure exists in solution and might play a crucial role in nucleation. No similar embryonic structure could be observed immediately after the dissolution of pre-nucleation particles, implying that their dissolution process is not simply the reverse process of their growth. This method should provide a new approach to understanding nucleation processes. Transmission electron microscopy ; Electron microscope ; Calcium carbonate ; Calcium ; Materials science ; Chemistry ; Nanotechnology ; Physics ; Metallurgy ; Optics ; Organic chemistry Calcium Carbonate Crystallization and Inhibition ; nanoparticles nucleation surface interactions ; Crystallization and Solubility Studies
Authors: Emil Annevelink | Rachel C. Kurchin | Eric S. Muckley | Lance Kavalsky | Vinay I. Hegde | Valentin Sulzer | Shang Zhu | Jiankun Pu | David Fariña | Matthew Johnson | Dhairya Gandhi | Adarsh Dave | Hongyi Lin | Alan Edelman | Bharath Ramsundar | James E. Saal | Christopher Rackauckas | Viral B. Shah | Bryce Meredig | Venkatasubramanian Viswanathan
Source: MRS Bulletin (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

AutoMat: Automated materials discovery for electrochemical systems Workflow ; Computer science ; Fidelity ; Pipeline (software) ; Systems engineering ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Ashish Soni | Mohammad Yusuf | Mukarram Beg | Abdul Wahab Hashmi
Source: Materials Today Proceedings (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

An application of Artificial Neural Network (ANN) to predict the friction coefficient of nuclear grade graphite Artificial neural network ; Backpropagation ; Computer science ; Sophistication ; Artificial intelligence ; Machine learning Graphite, nuclear technology, radiation studies ; Mineral Processing and Grinding ; Neural Networks and Applications
Authors: Zifeng Wang | Shizhuo Ye | Hao Wang | Qijun Huang | Jin He | Sheng Chang
Source: Science China Materials (article, 2022-06-28)
Field: Materials Science — Materials Chemistry

Graph representation-based machine learning framework for predicting electronic band structures of quantum-confined nanostructures Electronic structure ; Electronic band structure ; Hamiltonian (control theory) ; Nanostructure ; Graph ; Computer science ; Materials science ; Quantum ; Representation (politics) ; Artificial intelligence ; Nanotechnology ; Theoretical computer science ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Graphene research and applications ; Fuel Cells and Related Materials
Authors: Abiodun Kilani | Oluwatobi Adeleke | Christopher Fapohunda
Source: Asian Journal of Civil Engineering (article, 2022-02-01)
Field: Materials Science — Polymers and Plastics

Application of machine learning models to investigate the performance of concrete reinforced with oil palm empty fruit brunch (OPEFB) fibers Mean absolute percentage error ; Mean squared error ; Ultimate tensile strength ; Adaptive neuro fuzzy inference system ; Compressive strength ; Artificial neural network ; Curing (chemistry) ; Materials science ; Coefficient of determination ; Correlation coefficient ; Composite material ; Mathematics ; Machine learning ; Computer science ; Biological system ; Artificial intelligence ; Statistics ; Fuzzy logic ; Fuzzy control system Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Structural Behavior of Reinforced Concrete
Authors: Jeroen Koopman | Stefan Grimme
Source: Journal of the American Society for Mass Spectrometry (article, 2022-11-07)
Field: Chemistry — Spectroscopy

Calculation of Mass Spectra with the QCxMS Method for Negatively and Multiply Charged Molecules Analysis and validation of a mass spectrometry (MS) experiment are usually performed by comparison to reference spectra. However, if references are missing, measured spectra cannot be properly matched. To close this gap, the Quantum Chemical Mass Spectrometry (QCxMS) program has been developed. It enables fully automatic calculations of electron ionization (EI) and positive ion collision-induced dissociation (CID) mass spectra of singly charged molecular ions. In this work, the extension to negative and multiple ion charge for the CID run mode is presented. QCxMS is now capable of calculating structures carrying any charge, without the need for pretabulated fragmentation pathways or machine learning of database spectra. Mass spectra of four single negatively charged and two multiple positively charged organic ions with molecular sizes from 12 to 92 atoms were computed and compared to reference spectra. The underlying Born-Oppenheimer molecular dynamics (MD) calculations were conducted using the semiempirical quantum mechanical GFN2-xTB method, while for some small molecules, ab initio DFT-based MD simulations were performed. Detailed insights into the fragmentation pathways were gained, and the effects of the computed charge assignments on the resulting spectrum are discussed. Especially for the negative ion mode, the influence of the deprotonation site to create the anion was found to be substantial. Doubly charged fragments could successfully be calculated fully automatically for the first time, while higher charged structures introduced severe assignment problems. Overall, this extension of the QCxMS program further enhances its applicability and underlines its value as a sophisticated toolkit for CID-based tandem MS structure elucidation. Chemistry ; Fragmentation (computing) ; Mass spectrum ; Ion ; Mass spectrometry ; Collision-induced dissociation ; Spectral line ; Electron ionization ; Ab initio ; Dissociation (chemistry) ; Tandem mass spectrometry ; Molecule ; Ionization ; Ab initio quantum chemistry methods ; Atomic physics ; Analytical Chemistry (journal) ; Physical chemistry ; Physics Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Zefeng Yu | Xinyuan Xu | Wei‐Ying Chen | Yogesh Sharma | Xing Wang | Aiping Chen | Christopher Ulmer | Arthur T. Motta
Source: Acta Materialia (article, 2022-03-22)
Field: Materials Science — Materials Chemistry

In-situ irradiation-induced studies of grain growth kinetics of nanocrystalline UO2 Materials science ; Nanocrystalline material ; Kinetics ; In situ ; Irradiation ; Grain growth ; Grain size ; Metallurgy ; Chemical engineering ; Nanotechnology ; Nuclear physics Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear reactor physics and engineering
Authors: Zoltán Varga | Yinan Shu | Jiaxin Ning | Donald G. Truhlar
Source: Electronic Structure (article, 2022-09-23)
Field: Chemistry — Spectroscopy

Diabatic potential energy surfaces and semiclassical multi-state dynamics for fourteen coupled <sup>3</sup> A′ states of O<sub>3</sub> Abstract Dissociation and energy transfer in high-energy collisions of O 2 play important roles in simulating thermal energy content and heat flux in flows around hypersonic vehicles. Furthermore, atomic oxygen reactions on the vehicle surface are an important contributor to heat shield erosion. Molecular dynamics modeling is needed to better understand the relevant rate processes. Because it is necessary to model the gas flows in high-temperature shock waves, electronically excited states of O 2 and O can be populated, and molecular dynamics simulations should include collisions of electronically excited species and electronically nonadiabatic collisions. This requires potential energy surfaces and state couplings for many energetically accessible electronic states. Here we report a systematic strategy to calculate such surfaces and couplings. We have applied this method to the fourteen lowest-energy potential energy surfaces in the 3 A ′ manifold of O 3 , and we report a neural-network fit to diabatic potential energy matrix (DPEM). We illustrate the use of the resulting DPEM by carrying out semiclassical dynamics calculations of cross sections for excitation of O 2 in 3 A ′ collisions with O at two collision energies; these dynamics calculations are carried out by the curvature-driven coherent switching with decay of mixing method. Diabatic ; Excited state ; Potential energy ; Semiclassical physics ; Molecular dynamics ; Atomic physics ; Physics ; Potential energy surface ; Adiabatic process ; Surface hopping ; Chemistry ; Quantum mechanics ; Quantum ; Molecule Spectroscopy and Laser Applications ; Quantum, superfluid, helium dynamics ; Advanced Chemical Physics Studies
Authors: Andrew Smith | Vinayak Bhat | Qianxiang Ai | Chad Risko
Source: Chemistry of Materials (article, 2022-05-24)
Field: Materials Science — Materials Chemistry

Challenges in Information-Mining the Materials Literature: A Case Study and Perspective The rapid development and application of machine learning (ML) techniques in materials science have led to new tools for machine-enabled and autonomous/high-throughput materials design and discovery. Alongside, efforts to extract data from traditional experiments in the published literature with natural language processing (NLP) algorithms provide opportunities to develop tremendous data troves for these in silico design and discovery endeavors. While NLP is used in all aspects of society, its application in materials science is still in the very early stages. This perspective provides a case study on the application of NLP to extract information related to the preparation of organic materials. We present the case study at a basic level with the aim to discuss these technologies and processes with researchers from diverse scientific backgrounds. We also discuss the challenges faced in the case study and provide an assessment to improve the accuracy of NLP techniques for materials science with the aid of community contributions. Perspective (graphical) ; Computer science ; Data science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Sara Mantach | Puneet Gill | Derek R. Oliver | Ahmed Ashraf | Behzad Kordi
Source: Neural Computing and Applications (article, 2022-03-08)
Field: Materials Science — Materials Chemistry

An interpretable CNN model for classification of partial discharge waveforms in 3D-printed dielectric samples with different void sizes Partial discharge ; Waveform ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Convolutional neural network ; Artificial neural network ; Void (composites) ; Cluster analysis ; Fourier transform ; Voltage ; Materials science ; Mathematics ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical and Bioimpedance Tomography