jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: J. Zhao | Jesper Byggmästar | Zhaofu Zhang | Flyura Djurabekova | K. Nordlund | Mengyuan Hua
Source: Physical review. B./Physical review. B (article, 2021-08-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase transition of two-dimensional ferroelectric and paraelectric <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Ga</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> monolayers: A density functional theory and machine learning study ${\mathrm{Ga}}_{2}{\mathrm{O}}_{3}$ is a wide-band-gap semiconductor of great interest for applications in electronics and optoelectronics. Two-dimensional (2D) ${\mathrm{Ga}}_{2}{\mathrm{O}}_{3}$ synthesized from top-down or bottom-up processes can reveal new heterogeneous structures and promising applications. In this paper, we study phase transitions among three low-energy stable ${\mathrm{Ga}}_{2}{\mathrm{O}}_{3}$ monolayer configurations using density functional theory and a machine learning Gaussian approximation potential, together with solid-state nudged elastic band calculations. Kinetic minimum energy paths involving direct atomic jump as well as concerted layer motion are investigated. The low phase transition barriers indicate feasible tunability of the phase transition and orientation via strain engineering and external electric fields. Large-scale calculations using the trained machine learning potential on the thermally activated single-atom jumps reveal the clear nucleation and growth processes of different domains. The results provide useful insights into future experimental synthesis and characterization of 2D ${\mathrm{Ga}}_{2}{\mathrm{O}}_{3}$ monolayers. Phase transition ; Materials science ; Nucleation ; Ferroelectricity ; Band gap ; Algorithm ; Phase (matter) ; Semiconductor ; Condensed matter physics ; Dielectric ; Machine learning ; Physics ; Computer science ; Optoelectronics ; Thermodynamics ; Quantum mechanics Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; Electronic and Structural Properties of Oxides
Authors: Cody Kunka | Apaar Shanker | Elton Chen | Surya R. Kalidindi | Rémi Dingreville
Source: npj Computational Materials (article, 2021-05-17)
Field: Materials Science — Materials Chemistry

Decoding defect statistics from diffractograms via machine learning Abstract Diffraction techniques can powerfully and nondestructively probe materials while maintaining high resolution in both space and time. Unfortunately, these characterizations have been limited and sometimes even erroneous due to the difficulty of decoding the desired material information from features of the diffractograms. Currently, these features are identified non-comprehensively via human intuition, so the resulting models can only predict a subset of the available structural information. In the present work we show (i) how to compute machine-identified features that fully summarize a diffractogram and (ii) how to employ machine learning to reliably connect these features to an expanded set of structural statistics. To exemplify this framework, we assessed virtual electron diffractograms generated from atomistic simulations of irradiated copper. When based on machine-identified features rather than human-identified features, our machine-learning model not only predicted one-point statistics (i.e. density) but also a two-point statistic (i.e. spatial distribution) of the defect population. Hence, this work demonstrates that machine-learning models that input machine-identified features significantly advance the state of the art for accurately and robustly decoding diffractograms. Decoding methods ; Statistic ; Computer science ; Artificial intelligence ; Machine learning ; Algorithm ; Population ; Intuition ; Support vector machine ; Pattern recognition (psychology) ; Statistics ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Hamza Y. Ismail | Sary Fayyad | Mohammad N. Ahmad | James J. Leahy | Mu. Naushad | Gavin Walker | Ahmad B. Albadarin | Witold Kwapiński
Source: Journal of Cleaner Production (article, 2021-09-27)
Field: Chemistry — Analytical Chemistry

Modelling of yields in torrefaction of olive stones using artificial intelligence coupled with kriging interpolation Artificial neural network ; Kriging ; Torrefaction ; Interpolation (computer graphics) ; Biological system ; Particle size ; Mathematics ; Chemistry ; Materials science ; Process engineering ; Statistics ; Computer science ; Engineering ; Machine learning ; Chemical engineering ; Artificial intelligence ; Pyrolysis ; Organic chemistry ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Iron and Steelmaking Processes ; Mineral Processing and Grinding
Authors: Akshay Bhutada | Sunni Kumar | Dayalan Gunasegaram | Alankar Alankar
Source: Metals (article, 2021-07-22)
Field: Materials Science — Materials Chemistry

Machine Learning Based Methods for Obtaining Correlations between Microstructures and Thermal Stresses The microstructure–property relationship is critical for parts made using the emerging additive manufacturing process where highly localized cooling rates bestow spatially varying microstructures in the material. Typically, large temperature gradients during the build stage are known to result in significant thermally induced residual stresses in parts made using the process. Such stresses are influenced by the underlying local microstructures. Given the extensive range of variations in microstructures, it is useful to have an efficient method that can detect and quantify cause and effect. In this work, an efficient workflow within the machine learning (ML) framework for establishing microstructure–thermal stress correlations is presented. While synthetic microstructures and simulated properties were used for demonstration, the methodology may equally be applied to actual microstructures and associated measured properties. The dataset for ML consisted of images of synthetic microstructures along with thermal stress tensor fields simulated using a finite element (FE) model. The FE model considered various grain morphologies, crystallographic orientations, anisotropic elasticity and anisotropic thermal expansion. The overall workflow was divided into two parts. In the first part, image classification and clustering were performed for a sanity test of data. Accuracies of 97.33% and 99.83% were achieved using the ML based method of classification and clustering, respectively. In the second part of the work, convolution neural network model (CNN) was used to correlate the microstructures against various components and measures of stress. The target vectors of stresses consisted of individual components of stress tensor, principal stresses and hydrostatic stress. The model was able to show a consistent correlation between various morphologies and components of thermal stress. The overall predictions by the model for all the microstructures resulted into R2≈0.96 for all the stresses. Such a correlation may be used for finding a range of microstructures associated with lower amounts of thermally induced stresses. This would allow the choice of suitable process parameters that can ensure that the desired microstructures are obtained, provided the relationship between those parameters and microstructures are also known. Microstructure ; Materials science ; Residual stress ; Anisotropy ; Stress (linguistics) ; Cluster analysis ; Cauchy stress tensor ; Tensor (intrinsic definition) ; Artificial intelligence ; Computer science ; Composite material ; Geometry ; Mathematics ; Optics ; Mathematical analysis ; Physics Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Injection Molding Process and Properties
Authors: Pan Huang | Zhi-Jian Yin | Yun Tian | Jie Yang | Wei Zhong | Chunzhong Li | Cheng Lian | Li Yang | Honglai Liu
Source: Chemical Engineering Science (article, 2021-08-03)
Field: Chemistry — Inorganic Chemistry

Anomalous diffusion in zeolites Diffusion ; Anomalous diffusion ; Thermodynamics ; Chemistry ; Materials science ; Chemical engineering ; Physics ; Computer science ; Innovation diffusion ; Engineering Zeolite Catalysis and Synthesis ; Phase Equilibria and Thermodynamics ; Catalysis and Oxidation Reactions
Authors: Viktor Zaverkin | Johannes Kästner
Source: Machine Learning Science and Technology (article, 2021-02-03)
Field: Materials Science — Materials Chemistry

Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design Abstract Machine learning has been proven to have the potential to bridge the gap between the accuracy of ab initio methods and the efficiency of empirical force fields. Neural networks are one of the most frequently used approaches to construct high-dimensional potential energy surfaces. Unfortunately, they lack an inherent uncertainty estimation which is necessary for efficient and automated sampling through the chemical and conformational space to find extrapolative configurations. The identification of the latter is needed for the construction of transferable and uniformly accurate potential energy surfaces. In this paper, we propose an active learning approach that uses the estimated model’s output variance derived in the framework of the optimal experimental design. This method has several advantages compared to the established active learning approaches, e.g. Query-by-Committee, Monte Carlo dropout, feature and latent distances, in terms of the predictive power and computational efficiency. We have shown that the application of the proposed active learning scheme leads to transferable and uniformly accurate potential energy surfaces constructed using only a small fraction of data points. Additionally, it is possible to define a natural threshold value for the proposed uncertainty metric which offers the possibility to generate highly informative training data on-the-fly. Computer science ; Artificial neural network ; Monte Carlo method ; Metric (unit) ; Dropout (neural networks) ; Variance (accounting) ; Importance sampling ; Active learning (machine learning) ; Artificial intelligence ; Machine learning ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Force Microscopy Techniques and Applications
Authors: Seongyong Park | Jaeseok Lee | Shujaat Khan | Abdul Wahab | Minseok Kim
Source: Biosensors (article, 2021-11-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are lacking. Towards this end, we provide the SERS spectral benchmark dataset of Rhodamine 6G (<i>R</i>6<i>G</i>) for a molecule detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. Our best model, coined as the SERSNet, robustly identifies <i>R</i>6<i>G</i> molecule with excellent independent test performance. In particular, SERSNet shows 95.9% balanced accuracy for the cross-batch testing task. Benchmark (surveying) ; Rhodamine 6G ; Preprocessor ; Biomolecule ; Computer science ; Artificial intelligence ; Task (project management) ; Surface-enhanced Raman spectroscopy ; Deep learning ; Artificial neural network ; Machine learning ; Raman spectroscopy ; Pattern recognition (psychology) ; Biological system ; Raman scattering ; Materials science ; Nanotechnology ; Chemistry ; Molecule ; Engineering ; Optics ; Physics ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Omer Keles | Alper Taner
Source: Spanish Journal of Agricultural Research (article, 2021-11-23)
Field: Chemistry — Analytical Chemistry

Classification of hazelnut varieties by using artificial neural network and discriminant analysis Aim of study: This study was conducted to classify hazelnut (Corylus avellana L.) varieties by using artificial neural network and discriminant analysis. Area of study: Samsun Province, Turkey. Material and methods: The physical, mechanical and optical properties of 11 hazelnut varieties were determined for three major axes. The parameters of physical, mechanical and optical properties were included as independent variables, while hazelnut varieties were included as dependent variables. Models were created for each of the three axes to classify hazelnut varieties. Main results: Classification success rates with Artificial Neural Networks (ANN) and Discriminant Analysis (DA) were found as 89.1% and 92.7% for X axis, as 92.7% and 92.7% for Y axis and as 86.8% and 88.7% for Z axis, respectively. The classification results of ANN and DA models were found to be very close to each other. Both models can be used in the classification of hazelnut varieties. Research highlights: The results obtained for the identification and classification of hazelnut varieties show the feasibility and effectiveness of the proposed models. Linear discriminant analysis ; Artificial neural network ; Artificial intelligence ; Mathematics ; Pattern recognition (psychology) ; Computer science Spectroscopy and Chemometric Analyses ; Nuts composition and effects ; Agricultural Engineering and Mechanization
Authors: Taavi Repän | Ramakrishna Venkitakrishnan | Carsten Rockstuhl
Source: Optics Express (article, 2021-09-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial neural networks used to retrieve effective properties of metamaterials We propose using deep neural networks for the fast retrieval of effective properties of metamaterials based on their angular-dependent reflection and transmission spectra from thin slabs. While we noticed that non-uniqueness is an issue for a successful application, we propose as a solution an automatic algorithm to subdivide the entire parameter space. Then, in each sub-space, the mapping between the optical response (complex reflection and transmission coefficients) and the corresponding material parameters (dielectric permittivity and permeability) is unique. We show that we can easily train one neural network per sub-space. For the final parameter retrieval, predictions from the different sub-networks are compared, and the one with the smallest error expresses the desired effective properties. Our approach allows a significant reduction in run-time, compared to more traditional least-squares fitting. Using deep neural networks to retrieve effective properties of metamaterials is a significant showcase for the application of AI technology to nanophotonic problems. Once trained, the nets can be applied to retrieve properties of a larger number of different metamaterials. Metamaterial ; Artificial neural network ; Computer science ; Permittivity ; Optics ; Algorithm ; Reflection (computer programming) ; Transmission (telecommunications) ; Dielectric ; Uniqueness ; Reflection coefficient ; Artificial intelligence ; Physics ; Mathematics ; Telecommunications ; Optoelectronics ; Mathematical analysis Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research
Authors: Saransh Gupta | Prakash Periasamy | Badri Narayanan
Source: Nanoscale (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Defect dynamics in two-dimensional black phosphorus under argon ion irradiation Fundamental understanding of the atomic-scale mechanisms underlying production, accumulation, and temporal evolution of defects in phosphorene during noble-gas ion irradiation is crucial to design efficient defect engineering routes to fabricate next-generation materials for energy technologies. Here, we employed classical molecular dynamics (CMD) simulations using a reactive force field to unravel the effect of defect dynamics on the structural changes in a monolayer of phosphorene induced by argon-ion irradiation, and its subsequent relaxation during post-radiation annealing treatment. Analysis of our CMD trajectories using unsupervised machine learning methods showed that radiation fluence strongly influences the types of defect that form, their dynamics, and their relaxation mechanisms during subsequent annealing. Low ion fluences yielded a largely crystalline sheet featuring isolated small voids (up to 2 nm), Stone-Wales defects, and mono-/di-vacancies; while large nanopores (∼10 nm) can form beyond a critical fluence of ∼10<sup>14</sup> ions per cm<sup>2</sup>. During post-radiation annealing, we found two distinct relaxation mechanisms, depending on the fluence level. The isolated small voids (1-2 nm) formed at low ion-fluences heal via local re-arrangement of rings, which is facilitated by a cooperative mechanism involving a series of atomic motions that include thermal rippling, bond formation, bond rotation, angle bending and dihedral twisting. On the other hand, damaged structures obtained at high fluences exhibit pronounced coalescence of nanopores mediated by 3D networks of P-centered tetrahedra. These findings provide new perspectives to use ion beams to precisely control the concentration and distribution of specific defect types in phosphorene for emerging applications in electronics, batteries, sensing, and neuromorphic computing. Phosphorene ; Black phosphorus ; Monolayer ; Irradiation ; Argon ; Ion ; Molecular dynamics ; Chemical physics ; Fluence ; Materials science ; Noble gas ; Phosphorus ; Dynamics (music) ; Molecular physics ; Atomic physics ; Nanotechnology ; Chemistry ; Optoelectronics ; Computational chemistry ; Physics ; Nuclear physics ; Metallurgy 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Judy Kuan‐Yu Liu | Edouard Niyonsaba | Kawthar Z. Alzarieni | Victoria M. Boulos | Ravikiran Yerabolu | Hilkka I. Kenttämaa
Source: Mass Spectrometry Reviews (review, 2021-08-26)
Field: Chemistry — Spectroscopy

Determination of the compound class and functional groups in protonated analytes via diagnostic gas‐phase ion‐molecule reactions Diagnostic gas-phase ion-molecule reactions serve as a powerful alternative to collision-activated dissociation for the structural elucidation of analytes when using tandem mass spectrometry. The use of such ion-molecule reactions has been demonstrated to provide a robust tool for the identification of specific functional groups in unknown ionized analytes, differentiation of isomeric ions, and classification of unknown ions into different compound classes. During the past several years, considerable efforts have been dedicated to exploring various reagents and reagent inlet systems for functional-group selective ion-molecule reactions with protonated analytes. This review provides a comprehensive coverage of literature since 2006 on general and predictable functional-group selective ion-molecule reactions of protonated analytes, including simple monofunctional and complex polyfunctional analytes, whose mechanisms have been explored computationally. Detection limits for experiments involving high-performance liquid chromatography coupled with tandem mass spectrometry based on ion-molecule reactions and the application of machine learning to predict diagnostic ion-molecule reactions are also discussed. Chemistry ; Protonation ; Gas phase ; Molecule ; Analyte ; Ion ; Functional group ; Computational chemistry ; Combinatorial chemistry ; Chromatography ; Organic chemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Analytical chemistry methods development
Authors: Bryant Kim | Yihan Shao | Jingzhi Pu
Source: Journal of Chemical Theory and Computation (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

Doubly Polarized QM/MM with Machine Learning Chaperone Polarizability A major shortcoming of semiempirical (SE) molecular orbital methods is their severe underestimation of molecular polarizability compared with experimental and <i>ab initio</i> (AI) benchmark data. In a combined quantum mechanical and molecular mechanical (QM/MM) treatment of solution-phase reactions, solute described by SE methods therefore tends to generate inadequate electronic polarization response to solvent electric fields, which often leads to large errors in free energy profiles. To address this problem, here we present a hybrid framework that improves the response property of SE/MM methods through high-level molecular-polarizability fitting. Specifically, we place on QM atoms a set of corrective polarizabilities (referred to as chaperone polarizabilities), whose magnitudes are determined from machine learning (ML) to reproduce the condensed-phase AI molecular polarizability along the minimum free energy path. These chaperone polarizabilities are then used in a machinery similar to a polarizable force field calculation to compensate for the missing polarization energy in the conventional SE/MM simulations. Because QM atoms in this treatment host SE wave functions as well as classical polarizabilities, both polarized by MM electric fields, we name this method doubly polarized QM/MM (dp-QM/MM). We demonstrate the new method on the free energy simulations of the Menshutkin reaction in water. Using AM1/MM as a base method, we show that ML chaperones greatly reduce the error in the solute molecular polarizability from 6.78 to 0.03 Å<sup>3</sup> with respect to the density functional theory benchmark. The chaperone correction leads to ∼10 kcal/mol of additional polarization energy in the product region, bringing the simulated free energy profiles to closer agreement with the experimental results. Furthermore, the solute-solvent radial distribution functions show that the chaperone polarizabilities modify the free energy profiles through enhanced solvation corrections when the system evolves from the charge-neutral reactant state to the charge-separated transition and product states. These results suggest that the dp-QM/MM method, enabled by ML chaperone polarizabilities, provides a very physical remedy for the underpolarization problem in SE/MM-based free energy simulations. Polarizability ; QM/MM ; Polarization (electrochemistry) ; Molecular dynamics ; Molecular physics ; Chemical physics ; Quantum ; Chemistry ; Computational chemistry ; Physics ; Molecule ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies