jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Apoorv Kushwaha | T. J. Dhilip Kumar
Source: International Journal of Quantum Chemistry (article, 2022-09-06)
Field: Materials Science — Materials Chemistry

Benchmarking PES‐Learn's machine learning models predicting accurate potential energy surface for quantum scattering Abstract Machine learning (ML) models, neural networks, and Gaussian processes have been used to predict the potential energy surface taking C 2 ‐He (both static and dynamic scenario) and NCCN‐He collision systems. The surface is restricted to ∽125 points where traditional spline becomes inefficacious. Quantum dynamics is performed by solving close‐coupling equation to compute cross sections benchmarking the performance of the ML models. The current study forms a basis for any future investigation of larger molecules where conventional fitting fails due to sparser ab initio points and cuts down the computational time without compromising on the quality of the surface. Benchmarking ; Potential energy surface ; Ab initio ; Quantum ; Computer science ; Surface (topology) ; Artificial neural network ; Coupling (piping) ; Scattering ; Quantum dynamics ; Gaussian ; Statistical physics ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics ; Mathematics ; Materials science ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Egor S. Rodionov | Victor G. Lupanov | Natalya A. Gracheva | Polina N. Mayer | Alexander E. Mayer
Source: Metals (article, 2022-01-30)
Field: Materials Science — Materials Chemistry

Taylor Impact Tests with Copper Cylinders: Experiments, Microstructural Analysis and 3D SPH Modeling with Dislocation Plasticity and MD-Informed Artificial Neural Network as Equation of State Taylor impact tests involving the collision of a cylindrical sample with an anvil are widely used to study the dynamic properties of materials and to test numerical methods. We apply a combined experimental-numerical approach to study the dynamic plasticity of cold-rolled oxygen-free high thermal conductivity OFHC copper. In the experimental part, impact velocities up to 113.6 m/s provide a strain up to 0.3 and strain rates up to 1.7 × 104 s−1 at the edge of the sample. Microstructural analysis allows us to find out pore-like structures with a size of about 15–30 µm and significant refinement of the grain structure in the deformed parts of the sample. In terms of modeling, the dislocation plasticity model, which was previously tested for the problem of a shock wave upon impact of a plate, is implemented in the 3D case using the numerical scheme of smoothed particle hydrodynamics (SPH). The model includes an equation of state implemented in the form of an artificial neural network (ANN) and trained according to molecular dynamics (MD) simulations of uniform isothermal stretching/compression of representative volumes of copper. The dislocation friction coefficient is taken from previous MD simulations. These two efforts are aimed at building a fully MD-based material model. Comparison of the final shape of the projectile, the reduction of the sample length and increase in the diameter of the impacted edge of the sample confirm the applicability of the developed model and allow us to optimize the model parameters for the case of cold-rolled OFHC copper. Dislocation ; Plasticity ; Materials science ; Mechanics ; Isothermal process ; Shock (circulatory) ; Equation of state ; Computer simulation ; Composite material ; Thermodynamics ; Physics High-Velocity Impact and Material Behavior ; Fluid Dynamics Simulations and Interactions ; High-pressure geophysics and materials
Authors: Rongen Yan | Xue Jiang | Weiren Wang | Depeng Dang | Yanjing Su
Source: Scientific Data (article, 2022-07-13)
Field: Materials Science — Materials Chemistry

Materials information extraction via automatically generated corpus Information Extraction (IE) in Natural Language Processing (NLP) aims to extract structured information from unstructured text to assist a computer in understanding natural language. Machine learning-based IE methods bring more intelligence and possibilities but require an extensive and accurate labeled corpus. In the materials science domain, giving reliable labels is a laborious task that requires the efforts of many professionals. To reduce manual intervention and automatically generate materials corpus during IE, in this work, we propose a semi-supervised IE framework for materials via automatically generated corpus. Taking the superalloy data extraction in our previous work as an example, the proposed framework using Snorkel automatically labels the corpus containing property values. Then Ordered Neurons-Long Short-Term Memory (ON-LSTM) network is adopted to train an information extraction model on the generated corpus. The experimental results show that the F1-score of γ' solvus temperature, density and solidus temperature of superalloys are 83.90%, 94.02%, 89.27%, respectively. Furthermore, we conduct similar experiments on other materials, the experimental results show that the proposed framework is universal in the field of materials. Computer science ; Natural language processing ; Artificial intelligence ; Information extraction ; Task (project management) ; Natural language ; Information retrieval ; Field (mathematics) ; Domain (mathematical analysis) Machine Learning in Materials Science ; Text and Document Classification Technologies ; Rough Sets and Fuzzy Logic
Authors: Kritesh Kumar Gupta | Aditya Roy | T. Mukhopadhyay | Lintu Roy | S. Dey
Source: Materials Today Communications (article, 2022-07-03)
Field: Materials Science — Materials Chemistry

Probing the stochastic fracture behavior of twisted bilayer graphene: Efficient ANN based molecular dynamics simulations for complete probabilistic characterization Materials science ; Monte Carlo method ; Molecular dynamics ; Fracture (geology) ; Uniaxial tension ; Probabilistic logic ; Ultimate tensile strength ; Condensed matter physics ; Statistical physics ; Computer science ; Artificial intelligence ; Composite material ; Mathematics ; Physics ; Statistics Graphene research and applications ; Carbon Nanotubes in Composites ; Nanopore and Nanochannel Transport Studies
Authors: Ganesh Sivaraman | Nicholas E. Jackson
Source: Journal of Chemical Theory and Computation (article, 2022-01-12)
Field: Materials Science — Materials Chemistry

Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) resolutions using deep neural networks (DNNs). While DNNs can learn complex representations that prove challenging for kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within a GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions using range-separated hybrid density functional theory (DFT), obtaining a 10<sup>7</sup> speedup relative to naive all-atom QC. By treating the predicted electronic properties as random Gaussian Processes, DKL incorporates CG mapping degeneracy by learning the distribution of electronic energies as a function of CG configuration. DKL-ECG accurately reproduces molecular orbital energies from range-separated DFT while facilitating efficient training via active learning using the uncertainties provided by DKL. We show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, all explored query methods exhibit comparable performance for the examined system. We attribute this result to the significant overlap of the feature space and output property distributions across multiple temperatures. Computer science ; Overfitting ; Scalability ; Range (aeronautics) ; Kernel (algebra) ; Density functional theory ; Artificial intelligence ; Statistical physics ; Deep learning ; Algorithm ; Machine learning ; Artificial neural network ; Physics ; Mathematics ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Fangyan Zhu | Jiantao Leng | Jin-Wu Jiang | Tienchong Chang | Tong‐Yi Zhang | Huajian Gao
Source: Journal of the Mechanics and Physics of Solids (article, 2022-03-17)
Field: Materials Science — Materials Chemistry

Thermal-fluctuation gradient induced tangential entropic forces in layered two-dimensional materials Nanomechanics ; Boron nitride ; Hexagonal boron nitride ; Nanomaterials ; Graphane ; Carbon nanotube ; Thermal ; Graphene ; Graphyne ; Enhanced Data Rates for GSM Evolution ; Nanotechnology ; Materials science ; Substrate (aquarium) ; Molecular dynamics ; Temperature gradient ; Chemical physics ; Mechanics ; Chemistry ; Thermodynamics ; Physics ; Computational chemistry ; Atomic force microscopy ; Computer science Graphene research and applications ; Thermal properties of materials ; Carbon Nanotubes in Composites
Authors: Loredana Himiniuc | Bogdan Florin Toma | Răzvan Popovici | Ana Maria Grigore | Alexandru Hamod | Constantin Volovăț | Simona Ruxandra Volovăț | Irina Nica | Decebal Vasincu | Maricel Agop | Mihaela Camelia Tîrnovanu | Lăcrămioara Ochiuz | Anca Negură | Mihaela Grigore
Source: Journal of Immunology Research (review, 2022-05-04)
Field: Materials Science — Biomaterials

Update on the Use of Nanocarriers and Drug Delivery Systems and Future Directions in Cervical Cancer Cervical cancer represents a major health problem among females due to its increased mortality rate. The conventional therapies are very aggressive and unsatisfactory when it comes to survival rate, especially in terminal stages, which requires the development of new treatment alternatives. With the use of nanotechnology, various chemotherapeutic drugs can be transported via nanocarriers directly to cervical cancerous cells, thus skipping the hepatic first-pass effect and decreasing the rate of chemotherapy side effects. This review comprises various drug delivery systems that were applied in cervical cancer, such as lipid-based nanocarriers, polymeric and dendrimeric nanoparticles, carbon-based nanoparticles, metallic nanoparticles, inorganic nanoparticles, micellar nanocarriers, and protein and polysaccharide nanoparticles. Nanoparticles have a great therapeutic potential by increasing the pharmacological activity, drug solubility, and bioavailability. Through their mechanisms, they highly increase the toxicity in the targeted cervical tumor cells or tissues by linking to specific ligands. In addition, a nondifferentiable model is proposed through holographic implementation in the dynamics of drug delivery dynamics. As any hologram functions as a deep learning process, the artificial intelligence can be proposed as a new analyzing method in cervical cancer. Nanocarriers ; Drug delivery ; Drug ; Nanotechnology ; Bioavailability ; Nanoparticle ; Cancer ; Cervical cancer ; Targeted drug delivery ; Medicine ; Pharmacology ; Chemistry ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Hongjian Tang | Jianwen Jiang
Source: AIChE Journal (article, 2022-08-07)
Field: Chemistry — Inorganic Chemistry

Active learning boosted computational discovery of covalent–organic frameworks for ultrahigh <scp>CH<sub>4</sub></scp> storage Abstract As an environmental‐benign fuel, methane (CH 4 ) has received considerable interest for developing high‐capacity energy storage systems. Herein, we aim to rapidly discover covalent–organic frameworks (COFs) for ultrahigh CH 4 storage among 530,000+ COFs, including one experimental (Curated) and two hypothetical (Berkeley and Genomic) databases. First, the feature space of all the three COF databases is projected by t‐Distributed Stochastic Neighbor Embedding (t‐SNE) technique, which reveals a potential but unexplored regime in Genomic COFs. Subsequently, an active learning (AL) approach is developed by integrating parallel acquisition with molecular simulation to efficiently explore Genomic COFs. The parallel AL model demonstrates remarkable screening efficiency and shortlists top COFs by evaluating only 50 out of 445,845 Genomic COFs. A record‐breaking Genomic COF is identified with CH 4 deliverable capacity of 222.2 v/v, surpassing the current world record (208.0 v/v from experiment and 217.9 v/v from simulation). Our AL approach is significantly faster than brute‐force simulation and conventional machine learning, it would accelerate the discovery of advanced porous materials for broad applications. Embedding ; Computer science ; Covalent bond ; Nanotechnology ; Chemistry ; Artificial intelligence ; Materials science Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Sergei Manzhos | Manabu Ihara
Source: Journal of Mathematical Chemistry (article, 2022-10-03)
Field: Materials Science — Materials Chemistry

Optimization of hyperparameters of Gaussian process regression with the help of а low-order high-dimensional model representation: application to a potential energy surface Hyperparameter ; Overfitting ; Gaussian process ; Mathematics ; Kriging ; Hyperparameter optimization ; Representation (politics) ; Basis (linear algebra) ; Kernel (algebra) ; Mathematical optimization ; Algorithm ; Artificial intelligence ; Computer science ; Gaussian ; Statistics ; Artificial neural network ; Support vector machine Machine Learning in Materials Science ; Nuclear reactor physics and engineering ; Gaussian Processes and Bayesian Inference
Authors: Bora Karasulu | Jean‐Marc Leyssale | P.N. Rowe | Cédric Weber | Carla de Tomás
Source: Carbon (article, 2022-01-20)
Field: Materials Science — Materials Chemistry

Accelerating the prediction of large carbon clusters via structure search: Evaluation of machine-learning and classical potentials Cluster (spacecraft) ; Nanoclusters ; Fullerene ; Transferability ; Cluster analysis ; Carbon fibers ; Computer science ; Range (aeronautics) ; Statistical physics ; Materials science ; Machine learning ; Nanotechnology ; Algorithm ; Physics Machine Learning in Materials Science ; Fullerene Chemistry and Applications ; Graphene research and applications
Authors: Shobhit K. Patel | Juveriya Parmar | Vijay Katkar | Fahad Ahmed Al-Zahrani | Kawsar Ahmed
Source: Alexandria Engineering Journal (article, 2022-04-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultra-broadband and polarization-insensitive metasurface absorber with behavior prediction using machine learning The solar spectrum energy absorption is very important for designing any solar absorber. The need for absorbing visible, infrared, and ultraviolet regions is increasing as most of the absorbers absorb visible regions. We propose a metasurface solar absorber based on Ge2Sb2Te5 (GST) substrate which increases the absorption in visible, infrared and ultraviolet regions. GST is a phase-changing material having two different phases amorphous (aGST) and crystalline (cGST). The absorber is also analyzed using machine learning algorithm to predict the absorption values for different wavelengths. The solar absorber is showing an ultra-broadband response covering a 0.2–1.5 µm wavelength. The absorption analysis for ultra-violet, visible, and near-infrared regions for aGST and cGST is presented. The absorption of aGST design is better compared to cGST design. Furthermore, the design is showing polarization insensitiveness. Experiments are performed to check the K-Nearest Neighbors (KNN)-Regressor model’s prediction efficiency for predicting missing/intermediate wavelengths values of absorption. Different values of K and test scenarios; C-30, C-50 are used to evaluate regressor models using adjusted R2 Score as an evaluation metric. It is detected from the experimental results that, high prediction proficiency (more than 0.9 adjusted R2score) can be accomplished using a lower value of K in KNN-Regressor model. The design results are optimized for geometrical parameters like substrate thickness, metasurface thickness, and ground plane thickness. The proposed metasurface solar absorber is absorbing ultraviolet, visible, and near-infrared regions which will be used in solar thermal energy applications. Materials science ; Ultraviolet ; Wavelength ; Absorption (acoustics) ; Optics ; Polarization (electrochemistry) ; Infrared ; Broadband ; Near-infrared spectroscopy ; Substrate (aquarium) ; Visible spectrum ; Optoelectronics ; Physics ; Chemistry Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Optical Wireless Communication Technologies
Authors: Olusola Solomon Amodu | Moradeyo O. Odunlami | Joseph T. Akintola | Tunde V. Ojumu | Olushola S. Ayanda
Source: Heliyon (article, 2022-11-29)
Field: Materials Science — Materials Chemistry

Artificial neural network and response surface methodology for optimization of corrosion inhibition of mild steel in 1 M HCl by Musa paradisiaca peel extract Banana (<i>Musa paradisiaca</i>) peel extract (BPE) was used as an environmentally benign corrosion inhibitor for mild steel in 1.0 M HCl. The efficiency of BPE was assessed by monitoring the pH of HCl solution and the quantity of hydrogen gas evolved during the reaction, using gasometric and thermometric methods. Moreover, the effect of concentration and temperature on the inhibition efficiency was modelled and optimized by response surface methodology (RSM) and artificial neural network (ANN). It was observed that the evolution of hydrogen gas decreases with increasing concentration of BPE, while it increases with time for the various concentrations up till 8 min before attaining constant values. By numerical optimization of RSM, the optimum corrosion inhibition efficiency of 60.08% was obtained at 308.08 K and concentration of 7.44 g/L for gasometric method, while an optimum of 61.25% was obtained at 308 K and 7.50 g/L for thermometric method. Optimization of inhibition parameters with ANN revealed the optimum number of neurons for both gasometric and thermometric methods to be 7; while the MSE are 2.2788 and 2.7306, and R<sup>2</sup> are 96.21 and 98.86 respectively. Comparing the performance models of RSM and ANN: for gasometric method, R<sup>2</sup> was 98.93 for RSM and 96.21 for ANN, while for thermometric method, R<sup>2</sup> was obtained as 95.78 and 98.86 for RSM and ANN, respectively. Both RSM and ANN have demonstrated to be robust optimization techniques; particularly, ANN was found to give a good prediction of the available small dataset. Response surface methodology ; Central composite design ; Musa × paradisiaca ; Chemistry ; Corrosion ; Hydrogen ; Nuclear chemistry ; Analytical Chemistry (journal) ; Materials science ; Chromatography ; Food science Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Pier Paolo Poier | Théo Jaffrelot Inizan | Olivier Adjoua | Louis Lagardère | Jean‐Philip Piquemal
Source: The Journal of Physical Chemistry Letters (article, 2022-05-11)
Field: Materials Science — Materials Chemistry

Accurate Deep Learning-Aided Density-Free Strategy for Many-Body Dispersion-Corrected Density Functional Theory Using a deep neuronal network (DNN) model trained on the large ANI-1 data set of small organic molecules, we propose a transferable density-free many-body dispersion (DNN-MBD) model. The DNN strategy bypasses the explicit Hirshfeld partitioning of the Kohn-Sham electron density required by MBD models to obtain the atom-in-molecules volumes used by the Tkatchenko-Scheffler polarizability rescaling. The resulting DNN-MBD model is trained with minimal basis iterative Stockholder atomic volumes and, coupled to density functional theory (DFT), exhibits comparable (if not greater) accuracy to other approaches based on different partitioning schemes. Implemented in the Tinker-HP package, the DNN-MBD model decreases the overall computational cost compared to MBD models where the explicit density partitioning is performed. Its coupling with the recently introduced Stochastic formulation of the MBD equations (<i>J. Chem. Theory Comput.</i> <b>2022</b>, <i>18</i> (3), 1633-1645) enables large routine dispersion-corrected DFT calculations at preserved accuracy. Furthermore, the DNN electron density-free features extend the MBD model's applicability beyond electronic structure theory within methodologies such as force fields and neural networks. Density functional theory ; Polarizability ; Computer science ; Statistical physics ; Artificial neural network ; Orbital-free density functional theory ; Atom (system on chip) ; Algorithm ; Biological system ; Artificial intelligence ; Physics ; Quantum mechanics ; Molecule ; Hybrid functional Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Suman Sinha | Muhammed Sahad E | Rakesh Mondal | Siuli Das | Litty Thomas Manamel | Paula Brandão | Bas de Bruin | Bikas C. Das | Nanda D. Paul
Source: Journal of the American Chemical Society (article, 2022-10-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Singlet-Diradical Co(III)-Dimer as a Nonvolatile Resistive Switching Device: Synthesis, Redox-Induced Interconversion, and Current–Voltage Characteristics Herein we report a ligand-centered redox-controlled strategy for the synthesis of an unusual binuclear diradical cobalt(III) complex, [Co<sub>2</sub><sup>III</sup>(L<sup>•3-</sup>)<sub>2</sub>] (<b>1</b>), featuring two three-electron reduced trianionic monoradical 2,9-bis(phenyldiazo)-1,10-phenanthroline ligands (<b>L</b><sup><b>•</b>3-</sup>) and two intermediate-spin cobalt(III) centers having a Co-Co bond. Controlled ligand-centered oxidation of <b>1</b> afforded two mononuclear complexes, [Co<sup>II</sup>(L<sup><b>•</b>-</sup>)(L<sup>0</sup>)]<sup>+</sup> ([<b>3</b>])<sup>+</sup> and [Co<sup>II</sup>(L<sup>0</sup>)<sub>2</sub>]<sup>2+</sup> ([<b>2</b>]<sup>2+</sup>), which upon further ligand-centered reduction yielded a di-azo-anion diradical complex, [Co<sup>II</sup>(L<sup><b>•</b>-</sup>)<sub>2</sub>] (<b>4</b>). In complex <b>1</b>, two three-electron reduced di-azo-anion monoradical ligands (<b>L</b><sup><b>•</b>3-</sup>) bridge two intermediate Co(III) centers at a distance of 2.387(2) Å, while upon oxidation, one of the coordinating azo-arms of <b>L</b> becomes pendent, and in complexes [<b>2</b>]<sup>2+</sup>, [<b>3</b>]<sup>+</sup>, and <b>4</b>, two tetradentate ligands coordinate a single Co(II) center in a tridentate meridional fashion with one uncoordinated azo-arm from each of the ligands. In the presence of reducing agents, the monomers [<b>2</b>]<sup>2+</sup>, [<b>3</b>]<sup>+</sup>, and <b>4</b> undergo ligand-centered reduction to form azo-anion radicals, and the otherwise pendent azo-arms in the presence of cobalt(II)-salts like Co(ClO<sub>4</sub>)<sub>2</sub> or CoCl<sub>2</sub> bind the second Co(II)-ion; further internal electron transfer from the cobalt center to the arylazo backbone produces the binuclear complex <b>1</b>. Spectroscopic analysis, DFT studies, and control experiments were performed to understand the electronic structures and the ligand-centered redox-controlled interconversion. The application of complex <b>1</b> as a molecular memory device (memristor) was also explored. Complex <b>1</b> showed encouraging results as a memristor with a current ON/OFF ratio > 10<sup>4</sup> and is highly promising for resistive RAM/ROM applications. Diradical ; Chemistry ; Cobalt ; Ligand (biochemistry) ; Redox ; Photochemistry ; Singlet state ; Electron transfer ; Viologen ; Non-innocent ligand ; Dimer ; Crystallography ; Stereochemistry ; Inorganic chemistry ; Organic chemistry Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Electron Spin Resonance Studies