jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: AmirMasoud Taheri | Hossein Ebrahimnezhad | Mohammad Hossein Sedaaghi
Source: Materials Today Communications (article, 2022-10-28)
Field: Materials Science — Materials Chemistry

Prediction of the critical temperature of superconducting materials using image regression and ensemble deep learning Superconductivity ; Deep learning ; Machine learning ; Artificial intelligence ; Regression ; Materials science ; Regression analysis ; Ensemble learning ; Computer science ; Data mining ; Condensed matter physics ; Statistics ; Mathematics ; Physics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Christian Henning Gierlich | Stefan Palkovits
Source: Current Opinion in Chemical Engineering (article, 2022-07-18)
Field: Materials Science — Materials Chemistry

Featurizing chemistry for machine learning — methods and a coded example Computer science ; Partition (number theory) ; Field (mathematics) ; Code (set theory) ; Presentation (obstetrics) ; Selection (genetic algorithm) ; Machine learning ; Artificial intelligence ; Theoretical computer science ; Programming language ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Matthew Helmi Leth Larsen | Frederik Dahl | Lars P. Hansen | Bastian Barton | Christian Kisielowski | Stig Helveg | Ole Winther | Thomas W. Hansen | Jakob Schiøtz
Source: Ultramicroscopy (article, 2022-11-04)
Field: Materials Science — Surfaces, Coatings and Films

Reconstructing the exit wave of 2D materials in high-resolution transmission electron microscopy using machine learning Reconstruction of the exit wave function is an important route to interpreting high-resolution transmission electron microscopy (HRTEM) images. Here we demonstrate that convolutional neural networks can be used to reconstruct the exit wave from a short focal series of HRTEM images, with a fidelity comparable to conventional exit wave reconstruction. We use a fully convolutional neural network based on the U-Net architecture, and demonstrate that we can train it on simulated exit waves and simulated HRTEM images of graphene-supported molybdenum disulphide (an industrial desulfurization catalyst). We then apply the trained network to analyse experimentally obtained images from similar samples, and obtain exit waves that clearly show the atomically resolved structure of both the MoS<sub>2</sub> nanoparticles and the graphene support. We also show that it is possible to successfully train the neural networks to reconstruct exit waves for 3400 different two-dimensional materials taken from the Computational 2D Materials Database of known and proposed two-dimensional materials. High-resolution transmission electron microscopy ; Convolutional neural network ; Computer science ; Transmission electron microscopy ; Transmission (telecommunications) ; Materials science ; Artificial neural network ; Optics ; Artificial intelligence ; Nanotechnology ; Physics ; Telecommunications Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Yucheng Fu | William E. Frazier | Kyoo Sil Choi | Lei Li | Zhijie Xu | Vineet V. Joshi | Ayoub Soulami
Source: Scientific Reports (article, 2022-06-28)
Field: Materials Science — Materials Chemistry

Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model Hot rolling and annealing are critical intermediate steps for controlling microstructures and thickness variations when fabricating uranium alloyed with 10% molybdenum (U-10Mo), which is highly relevant to worldwide nuclear non-proliferation efforts. This work proposes a machine-learning surrogate model combined with sensitivity analysis to identify and predict U-10Mo microstructure development during thermomechanical processing. Over 200 simulations were collected using physics-based microstructure models covering a wide range of thermomechanical processing routes and initial alloy grain features. Based on the sensitivity analysis, we determined that an increase in rolling reduction percentage at each processing pass has the strongest effect in reducing the grain size. Multi-pass rolling and annealing can significantly improve recrystallization regardless of the reduction percentage. With a volume fraction below 2%, uranium carbide particles were found to have marginal effects on the average grain size and distribution. The proposed stratified stacking ensemble surrogate predicts the U-10Mo grain size with a mean square error four times smaller than a standard single deep neural network. At the same time, with a significant speedup (1000×) compared to the physics-based model, the machine learning surrogate shows good potential for U-10Mo fabrication process optimization. Materials science ; Microstructure ; Recrystallization (geology) ; Grain size ; Annealing (glass) ; Dynamic recrystallization ; Grain growth ; Metallurgy ; Artificial intelligence ; Computer science ; Hot working Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: R. Graham | Richard J. Wheatley
Source: Chemical Communications (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions Prediction of thermophysical properties from molecular principles requires accurate potential energy surfaces (PES). We present a widely-applicable method to produce first-principles PES from quantum chemistry calculations. Our approach accurately interpolates three-body non-additive interaction data, using the machine learning technique, Gaussian Processes (GP). The GP approach needs no bespoke modification when the number or type of molecules is changed. Our method produces highly accurate interpolation from significantly fewer training points than typical approaches, meaning <i>ab initio</i> calculations can be performed at higher accuracy. As an exemplar we compute the PES for all three-body cross interactions for CO<sub>2</sub>-Ar mixtures. From these we calculate the CO<sub>2</sub>-Ar virial coefficients up to 5th order. The resulting virial equation of state (EoS) is convergent for densities up to the critical density. Where convergent, the EoS makes accurate first-principles predictions for a range of thermophysical properties for CO<sub>2</sub>-Ar mixtures, including the compressibility factor, speed of sound and Joule-Thomson coefficient. Our method has great potential to make wide-ranging first-principles predictions for mixtures of comparably sized molecules. Such predictions can replace the need for expensive, laborious and repetitive experiments and inform the continuum models required for applications. Quantum chemistry ; Intermolecular force ; Quantum ; Computational chemistry ; Quantum chemical ; Chemistry ; Chemical physics ; Nanotechnology ; Materials science ; Physics ; Quantum mechanics ; Organic chemistry ; Molecule ; Supramolecular chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: K. Valli Priyadharshini | A. Vijay | K. Swaminathan | T. Avudaiappan | V. Banupriya
Source: Materials Today Proceedings (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Materials property prediction using feature selection based machine learning technique Feature selection ; Computer science ; Machine learning ; Generalizability theory ; Artificial intelligence ; Field (mathematics) ; Feature (linguistics) ; Ranking (information retrieval) ; Property (philosophy) ; Selection (genetic algorithm) ; Quality (philosophy) ; Noise (video) ; Data mining ; Mathematics Machine Learning in Materials Science ; Mineral Processing and Grinding ; X-ray Diffraction in Crystallography
Authors: Kaoru Hisama | Gerardo Valadez Huerta | Michihisa Koyama
Source: Computational Materials Science (article, 2022-12-08)
Field: Materials Science — Materials Chemistry

Molecular dynamics of electric-field driven ionic systems using a universal neural-network potential Molecular dynamics ; Electric field ; Chemical physics ; Ion ; Ionic conductivity ; Ionic bonding ; Conductivity ; Materials science ; Chemistry ; Computational chemistry ; Physical chemistry ; Physics ; Electrode ; Organic chemistry ; Electrolyte Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrochemical Analysis and Applications
Authors: JingChun Wang | DaDi Zhang | Rui–Xue Xu | ChiYung Yam | GuanHua Chen | Xiao Zheng
Source: The Journal of Physical Chemistry A (article, 2022-02-03)
Field: Materials Science — Materials Chemistry

Improving Density Functional Prediction of Molecular Thermochemical Properties with a Machine-Learning-Corrected Generalized Gradient Approximation The past decade has seen an increasing interest in designing sophisticated density functional approximations (DFAs) by integrating the power of machine learning (ML) techniques. However, application of the ML-based DFAs is often confined to simple model systems. In this work, we construct an ML correction to the widely used Perdew-Burke-Ernzerhof (PBE) functional by establishing a semilocal mapping from the electron density and reduced gradient to the exchange-correlation energy density. The resulting ML-corrected PBE is immediately applicable to any real molecule and yields significantly improved heats of formation while preserving the accuracy for other thermochemical and kinetic properties. This work highlights the prospect of combining the power of data-driven ML methods with physics-inspired derivations for reaching the heaven of chemical accuracy. Density functional theory ; Construct (python library) ; Work (physics) ; Kinetic energy ; Computer science ; Simple (philosophy) ; Power density ; Power (physics) ; Statistical physics ; Algorithm ; Thermodynamics ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Philipp Seitz | C. Scherdel | Gudrun Reichenauer | Jan Schmitt
Source: Computational Materials Science (article, 2022-12-23)
Field: Materials Science — Materials Chemistry

Machine Learning in the development of Si-based anodes using Small-Angle X-ray Scattering for structural property analysis Material development processes are highly iterative and driven by the experience and intuition of the researcher. This can lead to time consuming procedures. Data-driven approaches such as Machine Learning can support decision processes with trained and validated models to predict certain output parameter. In a multifaceted process chain of material synthesis of electrochemical materials and their characterization, Machine Learning has a huge potential to shorten development processes. Based on this, the contribution presents a novel approach to utilize data derived from Small-Angle X-ray Scattering (SAXS) of SiO2 matrix materials for battery anodes with Neural Networks. Here, we use SAXS as an intermediate, high-throughput method to characterize sol–gel based porous materials. A multi-step-method is presented where a Feed Forward Net is connected to a pretrained autoencoder to reliably map parameters of the material synthesis to the SAXS curve of the resulting material. In addition, a direct comparison shows that the prediction error of Neural Networks can be greatly reduced by training each output variable with a separate independent Neural Network. Small-angle X-ray scattering ; Autoencoder ; Artificial neural network ; Scattering ; Computer science ; Materials science ; Artificial intelligence ; Characterization (materials science) ; Process (computing) ; Sol-gel ; Anode ; Machine learning ; Biological system ; Nanotechnology ; Optics ; Electrode ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Yaoyao Song | Yan Zhang | Songhai Xie | Xiaowei Song
Source: Frontiers in Cell and Developmental Biology (article, 2022-12-15)
Field: Chemistry — Spectroscopy

Screening and diagnosis of triple negative breast cancer based on rapid metabolic fingerprinting by conductive polymer spray ionization mass spectrometry and machine learning We present the use of conductive spray polymer ionization mass spectrometry (CPSI-MS) combined with machine learning (ML) to rapidly gain the metabolic fingerprint from 1 μl liquid extraction from the biopsied tissue of triple-negative breast cancer (TNBC) in China. The 76 discriminative metabolite markers are verified at the primary carcinoma site and can also be successfully tracked in the serum. The Lasso classifier featured with 15- and 22-metabolites detected by CPSI-MS achieve a sensitivity of 88.8% for rapid serum screening and a specificity of 91.1% for tissue diagnosis, respectively. Finally, the expression levels of their corresponding upstream enzymes and transporters have been initially confirmed. In general, CPSI-MS/ML serves as a cost-effective tool for the rapid screening, diagnosis, and precise characterization for the TNBC metabolism reprogramming in the clinical practice. Triple-negative breast cancer ; Mass spectrometry ; Breast cancer ; Metabolite ; Metabolomics ; Chromatography ; Chemistry ; Cancer research ; Cancer ; Medicine ; Biochemistry ; Internal medicine Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Karthik Ganeshan | Rabi Khanal | Murali Gopal Muraleedharan | Matti Hellström | Paul R. C. Kent | Stephan Irle | Adri C. T. van Duin
Source: Journal of Chemical Theory and Computation (article, 2022-10-21)
Field: Materials Science — Materials Chemistry

Importance of Nuclear Quantum Effects on Aqueous Electrolyte Transport under Confinement in Ti<sub>3</sub>C<sub>2</sub> MXenes Protons display a high chemical activity and strongly affect the charge storage capability in confined interlayer spaces of two-dimensional (2D) materials. As such, an accurate representation of proton dynamics under confinement is important for understanding and predicting charge storage dynamics in these materials. While often ignored in atomistic-scale simulations, nuclear quantum effects (NQEs), e.g., tunneling, can be significant under confinement even at room temperature. Using the thermostatted ring polymer molecular dynamics implementation of path integral molecular dynamics (PIMD) in conjunction with the ReaxFF force field, density functional tight binding (DFTB), and NequIP neural network potential simulations, we investigate the role of NQEs on proton and water transport in bulk water and aqueous electrolytes under confinement in Ti<sub>3</sub>C<sub>2</sub> MXenes. Although overall NQEs are relatively small, especially in bulk, we find that they can alter both quantitative values and qualitative trends on both proton transport and water self-diffusion under confinement relative to classical MD predictions. Therefore, our results suggest the need for NQEs to be considered to simulate aqueous systems under confinement for both qualitative and quantitative accuracy. MXenes ; Chemical physics ; Molecular dynamics ; Proton ; Aqueous solution ; Quantum tunnelling ; Electrolyte ; Proton transport ; ReaxFF ; Materials science ; Chemistry ; Nanotechnology ; Physics ; Computational chemistry ; Physical chemistry ; Condensed matter physics ; Nuclear physics MXene and MAX Phase Materials ; Graphene research and applications ; 2D Materials and Applications
Authors: Shumpei Akita | Jing‐Yao Guo | Falk William Seidel | Matthew S. Sigman | Kyoko Nozaki
Source: Organometallics (article, 2022-03-29)
Field: Chemistry — Organic Chemistry

Statistical Analysis of Catalytic Performance in Ethylene/Methyl Acrylate Copolymerization Using Palladium/Phosphine-Sulfonate Catalysts For various types of palladium complexes bearing phosphine-sulfonate (PS) ligands used in the coordination–insertion copolymerization of olefins with polar monomers, characteristic features of the ligands, such as electronic and steric properties, have been discussed to describe their catalytic performance. Aiming at further analysis of the literature data, here we report the development of a statistical method for how the ligand impacts the performance of a Pd-catalyzed copolymerization of ethylene and methyl acrylate (MA). During our investigation, ligand features important for the resultant molecular weight of the obtained polymers were identified. Consistent with previously suggested important parameters, the electron density on the palladium center and maximum width of the substituents on the phosphorus atom (B5) were found to be significant for catalyst performance. We also found that additional features impact reaction outputs. As an example, the lower occupancy of the palladium dz2 orbital results in an increase of molecular weight and catalyst activity in both ethylene homopolymerization and ethylene/methyl acrylate copolymerization. Furthermore, it was predicted that a larger bite angle of the ligand increased the activity of ethylene/methyl acrylate copolymerization without impacting the molecular weight. On the basis of these machine learning predictions, three thiophene derived PS-type catalysts were synthesized and tested for MA/ethylene copolymerization. Unexpectedly, rather than the one predicted to enhance catalytic performance, a synthetic intermediate to this ligand exhibited higher activity albeit with the expense of molecular weight and MA incorporation. The inconsistency between the prediction and the experimental result is likely a result of insufficient training data for the catalyst with a different linker moiety. However, the unexpected finding that chlorination of the ligand backbone increases the overall catalyst performance will inspire an avenue for PS catalyst development. Copolymer ; Palladium ; Ethylene ; Methyl acrylate ; Chemistry ; Phosphine ; Steric effects ; Polymer chemistry ; Catalysis ; Ligand (biochemistry) ; Monomer ; Acrylate ; Organic chemistry ; Polymer Organometallic Complex Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis ; Carbon dioxide utilization in catalysis
Authors: Wenhao Zhang | David Berthebaud | Jean‐François Halet | Takao Mori
Source: The Journal of Physical Chemistry A (article, 2022-09-30)
Field: Materials Science — Materials Chemistry

Electronic Configurations of 3d Transition-Metal Compounds Using Local Structure and Neural Networks Machine learning (ML) methods extract statistical relationships between inputs and results. When the inputs are solid-state crystal structures, structure-property relationships can be obtained. In this work, we investigate whether a simple neural network is able to learn the 3d orbital occupations for the transition-metal (TM) centers in crystalline inorganic solid-state compounds using only the local structure around the transition-metal centers described by rotationally invariant fingerprints based on spherical harmonics and one-hot elemental encoding. A multilayer neural network trained on density functional theory (DFT) results of about 1800 samples was developed and showed good performance in predicting the TM orbital occupations (for both spin channels). We study in detail how the local structure affects the predictions of the local properties and how they provide physical insights for the design of a future machine learning model for materials chemistry. The proposed ML method is illustrated in practical application by predicting local magnetic moments of the transition-metal atoms in a full set of inorganic structures with large unit cells. Although less accurate compared to the experimental data, the ML results compared well with the DFT results, suggesting the feasibility of electronic property prediction based only on structure input. Artificial neural network ; Transition metal ; Electronic structure ; Density functional theory ; Crystal structure prediction ; Crystal structure ; Materials science ; Spherical harmonics ; Statistical physics ; Property (philosophy) ; Harmonics ; Computer science ; Biological system ; Chemical physics ; Topology (electrical circuits) ; Chemistry ; Artificial intelligence ; Computational chemistry ; Crystallography ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Xiao Wang | Wenyi Bian | Tianyang Zhang | Yu Zhao | Mingwang Shao | Haiping Lin | Yang Liu | Hui Huang | Zhenhui Kang
Source: Nano Research (article, 2022-06-10)
Field: Materials Science — Materials Chemistry

Highly crystalline core dominated the catalytic performance of carbon dot for cyclohexane to adipic acid reaction Carbon dots (CDs) have uniquely structural, physicochemical and photochemical properties, suggesting a promising platform for catalysis applications. The in-depth understanding of the structure-activity relationship in the CDs-based catalyst system needs to know the effect of the crystalline core on their catalytic performance. The efficient catalytic oxidation of cyclohexane is an urgent challenge in current chemical industry, in which, adipic acid (AA) plays an important role in industry for synthesis of nylon-6 and nylon-66. Here, we fabricated the pristine CDs by electrochemical etching graphite rod method and derived CDs with high crystalline core (CD-600, CD-800, and CD-1100) through a thermal treatment method in tube furnace. Furthermore, these CDs performed an outstanding catalytic performance for one-step synthesis of AA from cyclohexane. With the help of machine learning (ML), the deep correlations between features (structures of CDs, catalytic conditions) and catalytic performances were investigated by XGBoost (XGB) model. Then under the optimization and prediction of XGB, it was found that high crystalline core preceded the other features and CD-1100 could get the best conversion of 30.696% and selectivity to AA of 92.52% at reaction conditions of 130 °C, 1.5 MPa, and 10 h. This work pioneered the application of ML in industrial issues and demonstrated a comprehensive understanding on CDs as catalyst to realize one-step synthesis of AA. Cyclohexane ; Catalysis ; Adipic acid ; Materials science ; Graphite ; Chemical engineering ; Selectivity ; Carbon fibers ; Nanotechnology ; Chemistry ; Organic chemistry ; Polymer chemistry ; Composite material Carbon and Quantum Dots Applications ; Supercapacitor Materials and Fabrication ; Electrochemical sensors and biosensors