jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nikolay V. Tkachenko | Anastasiia Tkachenko | Benjamin Nebgen | Sergei Tretiak | Alexander I. Boldyrev
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Neural network atomistic potentials for global energy minima search in carbon clusters The global energy optimization problem is an acute and important problem in chemistry. It is crucial to know the geometry of the lowest energy isomer (global minimum, GM) of a given compound for the evaluation of its chemical and physical properties. This problem is especially relevant for atomic clusters. Due to the exponential growth of the number of local minima geometries with the increase of the number of atoms in the cluster, it is important to find a computationally efficient and reliable method to navigate the energy landscape and locate a true global minima structure. Newly developed neural network (NN) atomistic potentials offer a numerically efficient and relatively accurate approach for molecular structure optimization. An important question that needs to be answered is "Can NN potentials, trained on a given set, represent the potential energy surface (PES) of a neighboring domain?". In this work, we tested the applicability of ANI-1ccx and ANI-nr NN atomistic potentials for the global minima optimization of carbon clusters C<sub><i>n</i></sub> (<i>n</i> = 3-10). We showed that with the introduction of the cluster connectivity restriction and consequent DFT or <i>ab initio</i> calculations, ANI-1ccx and ANI-nr can be considered as robust PES pre-samplers that can capture the GM structure even for large clusters such as C<sub>20</sub>. Maxima and minima ; Cluster (spacecraft) ; Potential energy surface ; Global optimization ; Work (physics) ; Energy minimization ; Artificial neural network ; Potential energy ; Molecular dynamics ; Set (abstract data type) ; Ab initio ; Energy landscape ; Statistical physics ; Energy (signal processing) ; Computer science ; Physics ; Computational chemistry ; Chemistry ; Mathematics ; Algorithm ; Atomic physics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Alex Morehead | Jianlin Cheng
Source: arXiv (Cornell University) (preprint, 2023-02-08)
Field: Chemistry — Spectroscopy

Geometry-Complete Diffusion for 3D Molecule Generation and Optimization Denoising diffusion probabilistic models (DDPMs) have pioneered new state-of-the-art results in disciplines such as computer vision and computational biology for diverse tasks ranging from text-guided image generation to structure-guided protein design. Along this latter line of research, methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a DDPM framework. However, such methods are unable to learn important geometric properties of 3D molecules, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which notably hinders their ability to generate valid large 3D molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation, which outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings for the QM9 dataset and the larger GEOM-Drugs dataset, respectively, and generates more novel and unique unconditional 3D molecules for the QM9 dataset compared to previous methods. Importantly, we demonstrate that the geometry-complete denoising process of GCDM learned for 3D molecule generation enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn. Additionally, we show that extensions of GCDM can not only effectively design 3D molecules for specific protein pockets but also that GCDM's geometric features can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity, demonstrating new versatility of molecular diffusion models. Our source code and data are freely available at https://github.com/BioinfoMachineLearning/Bio-Diffusion. Diffusion ; Molecule ; Geometry ; Computer science ; Materials science ; Physics ; Mathematics ; Thermodynamics ; Quantum mechanics Analytical Chemistry and Chromatography
Authors: Sen Liu | Behnam Amin-Ahmadi | Rui Liu | Qiaojie Zheng | Xiaoli Zhang
Source: Materials Characterization (article, 2023-03-06)
Field: Materials Science — Surfaces, Coatings and Films

Automated phase segmentation and quantification of high-resolution TEM image for alloy design High-resolution transmission electron microscopy ; Materials science ; Phase (matter) ; Computer science ; Artificial intelligence ; Nanotechnology ; Image processing ; Biological system ; Transmission electron microscopy ; Image (mathematics) Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Knut Andreas Meyer | Fredrik Ekre
Source:  (preprint, 2023-04-17)
Field: Materials Science — Materials Chemistry

Thermodynamically consistent neural network plasticity modeling and discovery of evolution laws Over the past decade, advancements in computational frameworks and processing power have made deep neural networks increasingly viable for material modeling.However, purely data-driven models can yield non-physical predictions due to the lack of physical constraints.While recent works that incorporate physics into the training process partially address this issue, they offer no guarantees beyond the scope of the training data.To tackle this challenge, we propose a novel approach that embeds the neural network within the material model, inherently fulfilling thermodynamic laws.The network represents only the unknown physics allowing us to integrate knowledge accumulated from decades of constitutive modeling research into the data-driven methodology.By analyzing the trained, embedded networks, we recover existing evolution laws from artificial training data and discover new evolution laws from experimental data.The discovered evolution laws for isotropic and kinematic hardening can qualitatively predict an experimentally observed yield strength evolution, which conventional evolution laws cannot describe. Artificial neural network ; Plasticity ; Computer science ; Statistical physics ; Artificial intelligence ; Physics ; Thermodynamics Material Properties and Failure Mechanisms ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Jing Cai | Yulun Wu | Haohao Bai | Yingsheng He | Yazhou Qin
Source: Heliyon (article, 2023-11-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

SERS and machine learning based effective feature extraction for detection and identification of amphetamine analogs Surface-enhanced Raman spectroscopy (SERS) is extensively researched in diverse disciplines due to its sensitivity and non-destructive nature. It is particularly considered a potential and promising technology for rapid on-site screening in drug detection. In this investigation, a technique was developed for fabricating nanocrystals of Ag@Au SNCs. Ag@Au SNCs, as the basic material of SERS, can detect amphetamine at concentrations as low as 1 μg/mL. The Ag@Au SNCs exhibits a strong surface plasmon resonance effect, which amplifies molecular signals. The SERS spectra of ten substances, including amphetamine and its analogs, showed a strong peak signal. To establish a qualitative distinction, we examined the Raman spectra and conducted density functional theory (DFT) calculations on the ten aforementioned species. The DFT calculation enabled us to determine the vibrational frequency and assign normal modes, thereby facilitating the qualitative differentiation of amphetamines and its analogs. Furthermore, the SERS spectrum of the ten mentioned substances was analysed using the support vector machine learning algorithm, which yielded a discrimination accuracy of 98.0 %. Raman spectroscopy ; Drug detection ; Density functional theory ; Raman scattering ; Plasmon ; Chemistry ; Analytical Chemistry (journal) ; Materials science ; Computer science ; Nanotechnology ; Computational chemistry ; Optoelectronics ; Chromatography ; Physics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Yuki K. Wakabayashi | Takuma Otsuka | Yoshiharu Krockenberger | Hiroshi Sawada | Yoshitaka Taniyasu | Hideki Yamamoto
Source: APL Machine Learning (article, 2023-04-06)
Field: Materials Science — Materials Chemistry

Stoichiometric growth of SrTiO3 films via Bayesian optimization with adaptive prior mean Perovskite insulator SrTiO3 (STO) is expected to be applied to the next generation of electronic and photonic devices as high-k capacitors and photocatalysts. However, reproducible growth of highly insulating stoichiometric (STO) films remains challenging due to the difficulty of precise stoichiometry control in perovskite oxide films. Here, to grow stoichiometric (STO) thin films by fine-tuning multiple growth conditions, we developed a new Bayesian optimization (BO)-based machine learning method that encourages exploration of the search space by varying the prior mean to get out of suboptimal growth condition parameters. Using simulated data, we demonstrate the efficacy of the new BO method, which reproducibly reaches the global best conditions. With the BO method implemented in machine-learning-assisted molecular beam epitaxy (ML-MBE), a highly insulating stoichiometric (STO) film with no absorption in the bandgap was developed in only 44 MBE growth runs. The proposed algorithm provides an efficient experimental design platform that is not as dependent on the experience of individual researchers and will accelerate not only oxide electronics but also various material syntheses. Stoichiometry ; Bayesian optimization ; Materials science ; Molecular beam epitaxy ; Perovskite (structure) ; Thin film ; Band gap ; Electronics ; Oxide ; Capacitor ; Optoelectronics ; Nanotechnology ; Computer science ; Epitaxy ; Machine learning ; Chemical engineering ; Chemistry ; Electrical engineering Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Daniel F. Thomas du Toit | Volker L. Deringer
Source: The Journal of Chemical Physics (article, 2023-07-11)
Field: Materials Science — Materials Chemistry

Cross-platform hyperparameter optimization for machine learning interatomic potentials Machine-learning (ML)-based interatomic potentials are increasingly popular in material modeling, enabling highly accurate simulations with thousands and millions of atoms. However, the performance of machine-learned potentials depends strongly on the choice of hyperparameters-that is, of those parameters that are set before the model encounters data. This problem is particularly acute where hyperparameters have no intuitive physical interpretation and where the corresponding optimization space is large. Here, we describe an openly available Python package that facilitates hyperparameter optimization across different ML potential fitting frameworks. We discuss methodological aspects relating to the optimization itself and to the selection of validation data, and we show example applications. We expect this package to become part of a wider computational framework to speed up the mainstream adaptation of ML potentials in the physical sciences. Hyperparameter ; Python (programming language) ; Computer science ; Machine learning ; Artificial intelligence ; Set (abstract data type) ; Parameter space ; Mathematics ; Programming language ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: R.S. Monzamodeth | B. Campillo | Julio C. Villalobos | Fermín Castillo | Osvaldo Flores
Source: Materials (article, 2023-10-10)
Field: Materials Science — Metals and Alloys

Hydrogen Diffusion in Nickel Superalloys: Electrochemical Permeation Study and Computational AI Predictive Modeling Ni-based superalloys are materials utilized in high-performance services that demand excellent corrosion resistance and mechanical properties. Its usages can include fuel storage, gas turbines, petrochemistry, and nuclear reactor components, among others. On the other hand, hydrogen (H), in contact with metallic materials, can cause a phenomenon known as hydrogen embrittlement (HE), and its study related to the superalloys is fundamental. This is related to the analysis of the solubility, diffusivity, and permeability of H and its interaction with the bulk, second-phase particles, grain boundaries, precipitates, and dislocation networks. The aim of this work was mainly to study the effect of chromium (Cr) content on H diffusivity in Ni-based superalloys; additionally, the development of predictive models using artificial intelligence. For this purpose, the permeability test was employed based on the double cell experiment proposed by Devanathan-Stachurski, obtaining the effective diffusion coefficient (D<sub>eff</sub>), steady-state flux (J<sub>ss</sub>), and the trap density (N<sub>T</sub>) for the commercial and experimentally designed and manufactured Ni-based superalloys. The material was characterized with energy-dispersed X-ray spectroscopy (EDS), atomic absorption, CHNS/O chemical analysis, X-ray diffraction (XRD), brightfield optical microscopy (OM), and scanning electron microscopy (SEM). On the other hand, predictive models were developed employing artificial neural networks (ANNs) using experimental results as a database. Furthermore, the relative importance of the main parameters related to the H diffusion was calculated. The D<sub>eff</sub>, J<sub>ss</sub>, and N<sub>T</sub> achieved showed relatively higher values considering those reported for Ni alloys and were found in the following orders of magnitude: [1 × 10<sup>-8</sup>, 1 × 10<sup>-11</sup> m<sup>2</sup>/s], [1 × 10<sup>-5</sup>, 9 × 10<sup>-7</sup> mol/cm<sup>2</sup>s], and [7 × 10<sup>25</sup> traps/m<sup>3</sup>], respectively. Regarding the predictive models, linear correlation coefficients of 0.96 and 0.80 were reached, corresponding to the D<sub>eff</sub> and J<sub>ss</sub>. Due to the results obtained, it was suitable to dismiss the effect of Cr in solid solution on the H diffusion. Finally, the predictive models developed can be considered for the estimation of D<sub>eff</sub> and J<sub>ss</sub> as functions of the characterized features. Superalloy ; Materials science ; Thermal diffusivity ; Scanning electron microscope ; Corrosion ; Metallurgy ; Chemical engineering ; Analytical Chemistry (journal) ; Microstructure ; Composite material ; Thermodynamics ; Chemistry ; Chromatography Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; High Temperature Alloys and Creep
Authors: Jintu Zhang | Haotian Zhang | Zhixin Qin | Yu Kang | Xin Hong | Tingjun Hou
Source: Journal of Chemical Information and Modeling (article, 2023-02-15)
Field: Materials Science — Materials Chemistry

Quasiclassical Trajectory Simulation as a Protocol to Build Locally Accurate Machine Learning Potentials Direct trajectory calculations have become increasingly popular in recent computational chemistry investigations. However, the exorbitant computational cost of <i>ab initio</i> trajectory calculations usually limits its application in mechanistic explorations. Recently, machine learning-based potential energy surface (ML-PES) provides a powerful strategy to circumvent the heavy computational cost and meanwhile maintain the required accuracy. Despite the appealing potential, constructing a robust ML-PES is still challenging since the training set of the PES should cover a broad enough configuration space. In this work, we demonstrate that when the concerned properties could be collected by the localized sampling of the configuration space, quasiclassical trajectory (QCT) calculations can be invoked to efficiently obtain locally accurate ML-PESs. We prove our concept with two model reactions: methyl migration of <i>i</i>-pentane cation and dimerization of cyclopentadiene. We found that the locally accurate ML-PESs are sufficiently robust for reproducing the static and dynamic features of the reactions, including the time-resolved free energy and entropy changes, and time gaps. Computer science ; Trajectory ; Potential energy surface ; Ab initio ; Potential energy ; Molecular dynamics ; Set (abstract data type) ; Mathematical optimization ; Statistical physics ; Computational chemistry ; Chemistry ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Lisa V. Winkler | Christian Nölleke
Source: Optics Express (article, 2023-08-14)
Field: Chemistry — Spectroscopy

Artificial neural networks for laser frequency stabilization In order to stabilize a laser's emission frequency, absolute references such as molecular absorption lines are widely used. To automate the stabilization process, the desired absorption line needs to be identified reliably from a spectrum by a computer. We present an artificial neural network solving this task using the iodine spectrum as an example. The neural network is trained using only simulated data and subsequently tested using measured data. We show that this approach is robust against large variations of operating and environmental conditions. Artificial neural network ; Laser ; Optics ; Computer science ; Absorption spectroscopy ; Absorption (acoustics) ; Line (geometry) ; Artificial intelligence ; Physics ; Mathematics Spectroscopy and Laser Applications ; Laser Design and Applications ; Advanced Fiber Laser Technologies
Authors: Pema Lhamo | Biswanath Mahanty
Source: Journal of Polymers and the Environment (article, 2023-05-27)
Field: Materials Science — Biomaterials

Tuning Material Properties of Microbially Synthesized Poly(3-hydroxybutyrate-co-hydroxyvalerate) and Their Composites for Various Applications Polyhydroxyalkanoates ; Materials science ; Bioplastic ; Ultimate tensile strength ; Biodegradation ; Substrate (aquarium) ; Polymer ; Composite material ; Waste management ; Organic chemistry ; Bacteria biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Carbon dioxide utilization in catalysis
Authors: Cameron J. Foss | Zlatan Akšamija
Source: Applied Physics Letters (article, 2023-02-06)
Field: Materials Science — Materials Chemistry

Machine learning enables robust prediction of thermal boundary conductance of 2D substrate interfaces Two-dimensional van der Waals (vdW) materials exhibit a broad palette of unique and superlative properties, including high electrical and thermal conductivities, paired with the ability to exfoliate or grow and transfer single layers onto a variety of substrates thanks to the relatively weak vdW interlayer bonding. However, the same vdW bonds also lead to relatively low thermal boundary conductance (TBC) between the 2D layer and its 3D substrate, which is the main pathway for heat removal and thermal management in devices, leading to a potential thermal bottleneck and dissipation-driven performance degradation. Here, we use first-principles phonon dispersion with our 2D–3D Boltzmann phonon transport model to compute the TBC of 156 unique 2D/3D interface pairs, many of which are not available in the literature. We then employ machine learning to develop streamlined predictive models, of which a neural network and a Gaussian process display the highest predictive accuracy (RMSE &amp;lt; 5 MW m−2 K−1 and R2&amp;gt; 0.99) on the complete descriptor set. Then we perform sensitivity analysis to identify the most impactful descriptors, consisting of the vdW spring coupling constant, 2D thermal conductivity, ZA phonon bandwidth, the ZA phonon resonance gap, and the frequency of the first van Hove singularity or Boson peak. On that reduced set, we find that a decision-tree algorithm can make accurate predictions (RMSE &amp;lt; 20 MW m−2 K−1 and R2&amp;gt; 0.9) on materials it has not been trained on by performing a transferability analysis. Our model allows optimal selection of 2D-substrate pairings to maximize heat transfer and will improve thermal management in future 2D nanoelectronics. Thermal conductivity ; Phonon ; Machine learning ; Materials science ; Condensed matter physics ; Artificial intelligence ; Computer science ; Thermodynamics ; Physics Thermal properties of materials ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Lijun Jing | Wei-Qi Yan | Han-Jie Xiao | Ming Lei | Yueqiang Cao | Zhi‐Jun Sui | Jinghong Zhou | Xinggui Zhou | De Chen | Yi‐An Zhu
Source: Chemical Engineering Science (article, 2023-08-16)
Field: Materials Science — Materials Chemistry

Interface-enhanced catalytic performance of TiO2-supported Cu and Au for dimethyl oxalate hydrogenation: A comparative microkinetic analysis Catalysis ; Chemistry ; Oxalate ; Adsorption ; Selectivity ; Oxygen ; Density functional theory ; Inorganic chemistry ; Ion ; Metal ; Physical chemistry ; Computational chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Electrocatalysts for Energy Conversion
Authors: Shujun Zhao | Wei Liu | Dapeng Song
Source: Journal of Food Measurement & Characterization (article, 2023-08-12)
Field: Chemistry — Electrochemistry

Rapid detection and prediction model establishment of propachlor residues in food assisted by machine learning Detection limit ; Colloidal gold ; Reproducibility ; Relative standard deviation ; Food safety ; Chemistry ; Linear range ; Molecularly imprinted polymer ; Chromatography ; Environmental chemistry ; Nanotechnology ; Environmental science ; Materials science ; Selectivity ; Nanoparticle ; Food science Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical chemistry methods development
Authors: Qinghua Guo | Peiyan Ye | Zhiming Zhang | Qiao Xu
Source: Nanomaterials (article, 2023-11-30)
Field: Materials Science — Biomaterials

Optimization Mechanism of Nozzle Parameters and Characterization of Nanofibers in Centrifugal Spinning Nanofibers are an emerging kind of nano-material, widely used in several application domains such as biomedicine, high-efficiency filtration media, precision electronics, and optical devices. Centrifugal spinning, which is a novel nanofiber production technology, has been widely studied. This paper proposes a structural parameter optimization design method of a bent-tube nozzle. The mathematical model of the spinning solution motion in the nozzle is first developed. The optimization function of the structure parameters of the bent-tube nozzle is then obtained by calculation. Afterwards, these parameters are optimized using a neural network algorithm. The obtained results show that, when the bending angle is 15°, the curvature radius is 10 mm, the outlet radius is 0.205 mm, and the head loss of the solution can be minimized. Finally, centrifugal spinning experiments are conducted and the influence of the centrifugal spinning parameters on the nanofibers is analyzed. In addition, the optimized bent-tube nozzle improves the surface morphology of the nanofibers as their diameter distribution becomes more uniform. Nozzle ; Spinning ; Materials science ; Nanofiber ; RADIUS ; Bent molecular geometry ; Radius of curvature ; Tube (container) ; Centrifugal force ; Bend radius ; Composite material ; Bending ; Mechanical engineering ; Curvature ; Computer science ; Geometry ; Engineering Electrospun Nanofibers in Biomedical Applications ; Additive Manufacturing and 3D Printing Technologies ; Advanced Sensor and Energy Harvesting Materials