jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Steven M. Maley | Doo‐Hyun Kwon | Nick E. Rollins | Johnathan Stanley | Orson L. Sydora | Steven M. Bischof | Daniel H. Ess
Source: ChemRxiv (preprint, 2020-07-27)
Field: Materials Science — Materials Chemistry

Quantum-Mechanical Transition-State Model Combined with Machine Learning Provides Catalyst Design Features for Selective Cr Olefin Oligomerization The use of data science tools to provide the emergence of nontrivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene:1- octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr–N distance, Cr–α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give &gt;95% selectivity for 1-octene&lt;br&gt; Selectivity ; Catalysis ; Olefin fiber ; Transition state ; Transition metal ; Phosphine ; Chemistry ; Random forest ; Molecular descriptor ; Computational chemistry ; Materials science ; Computer science ; Machine learning ; Stereochemistry ; Organic chemistry ; Quantitative structure–activity relationship Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Hydrodesulfurization Studies
Authors: O.A. Egorova | Roohollah Hafizi | David C. Woods | Graeme M. Day
Source: ChemRxiv (preprint, 2020-06-03)
Field: Materials Science — Materials Chemistry

Multi-fidelity Statistical Machine Learning for Molecular Crystal Structure Prediction The prediction of crystal structures from first principles requires highly accurate energies for large numbers of putative crystal structures. The accuracy of solid state density functional theory (DFT) calculations is often required, but hundreds or more structures can be present in the low energy region of interest, so that the associated computational costs are prohibitive. Here, we apply statistical machine learning to predict expensive hybrid functional DFT (PBE0) calculations using a multi-fidelity approach to re-evalute the energies of crystal structures predicted with an inexpensive force field. The method uses an autoregressive Gaussian process, making use of less expensive GGA DFT (PBE) calculations to bridge the gap between the force field and PBE0 energies. The method is benchmarked on the crystal structure landscapes of three small, hydrogen bonding organic molecules and shown to produce accurate predictions of energies and crystal structure ranking using small numbers of the most expensive calculations; the PBE0 energies can be predicted with errors of less than 1 kJ/mol with between 4.2-6.8% of the cost of the full calculations. As the model that we have developed is probabilistic, we discuss how the uncertainties in predicted energies impact on assessment of the energetic ranking of crystal structures. Crystal structure prediction ; Density functional theory ; Crystal (programming language) ; Statistical physics ; Algorithm ; Autoregressive model ; Fidelity ; Computer science ; Field (mathematics) ; Probabilistic logic ; Kriging ; Crystal structure ; Gaussian ; Machine learning ; Molecule ; Artificial intelligence ; Physics ; Chemistry ; Mathematics ; Quantum mechanics ; Crystallography ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Pin-Wen Guan | Venkatasubramanian Viswanathan
Source: arXiv (Cornell University) (preprint, 2020-10-27)
Field: Materials Science — Materials Chemistry

MeltNet: Predicting alloy melting temperature by machine learning Thermodynamics is fundamental for understanding and synthesizing multi-component materials, while efficient and accurate prediction of it still remain urgent and challenging. As a demonstration of the "Divide and conquer" strategy decomposing a phase diagram into different learnable features, quantitative prediction of melting temperature of binary alloys is made by constructing the machine learning (ML) model "MeltNet" in the present work. The influences of model hyperparameters on the prediction accuracy is systematically studied, and the optimal hyperparameters are obtained by Bayesian optimization. A comprehensive error analysis is made on various aspects including training duration, chemistry and input features. It is found that except a few discrepancies mainly caused by less satisfactory treatment of metalloid/semimetal elements and large melting point difference with poor liquid mixing ability between constituent elements, MeltNet achieves overall success in prediction, especially capturing subtle composition-dependent features in the unseen chemical systems for the first time. The reliability, robustness and accuracy of MeltNet is further largely boosted by introducing the ensemble method with uncertainty quantification. Based on the state-of-the-art underlying techniques, MeltNet achieves a prediction mean average error (MAE) as low as about 120 K, at a minimal computational cost. We believe the present work has a general value for significant acceleration of predicting thermodynamics of complicated multi-component systems. Hyperparameter ; Computer science ; Binary number ; Machine learning ; Robustness (evolution) ; Component (thermodynamics) ; Artificial intelligence ; Algorithm ; Thermodynamics ; Mathematics ; Chemistry ; Physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions
Authors: Sen Liu | Branden B. Kappes | Behnam Amin-Ahmadi | Othmane Benafan | Aaron P. Stebner | Xiaoli Zhang
Source: arXiv (Cornell University) (preprint, 2020-03-04)
Field: Materials Science — Materials Chemistry

A physics-informed feature engineering approach to use machine learning with limited amounts of data for alloy design: shape memory alloy demonstration. Machine learning using limited data from physical experiments is shown to work to predict new shape memory alloys in a high dimensional alloy design space that considers chemistry and thermal post-processing. The key to enabling the machine learning algorithms to make predictions of new alloys and their post-processing is shown to be a physics-informed featured engineering approach. Specifically, elemental features previously engineered by the computational materials community to model composition effects in materials are combined with newly engineered heat treatment features. These new features result from pre-processing the heat treatment data using mathematical relationships known to describe the thermodynamics and kinetics of precipitation in alloys. The prior application of the nonlinear physical models to the data in effect linearizes some of the complex alloy development trends a priori using known physics, and results in greatly improved performance of the ML algorithms trained on relatively few data points. Alloy ; A priori and a posteriori ; Feature (linguistics) ; Shape-memory alloy ; Computer science ; Experimental data ; Feature engineering ; Key (lock) ; Artificial intelligence ; Work (physics) ; Machine learning ; Precipitation ; Materials science ; Mechanical engineering ; Metallurgy ; Engineering ; Physics ; Mathematics ; Deep learning Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Min Fu | Thomas Mrziglod | Weiling Luan | Shan‐Tung Tu | Lesław Mleczko
Source: Engineering Reports (article, 2020-02-01)
Field: Materials Science — Materials Chemistry

Neural network modeling and simulation of the synthesis of CuInS<sub>2</sub>/ZnS quantum dots Abstract The development of recipes for synthesis of quantum dots (QDs), a novel semiconductor material for application in optoelectronic devices, is currently purely based on experiments. Since the quality of QDs represented by quantum yield (QY) and emission peak strongly depends on a number of different parameters (route, precursors, conditions, etc), a large number of experiments is necessary. In this article, we show that data‐driven modeling can be used as a supporting tool for optimization and a better understanding of the synthesis process. By using the results collected during the development of CuInS 2 /ZnS (CIS/ZnS) QDs, a neural network model has been established. The model is able to predict the optical properties (QY and emission peak) of CIS/ZnS QDs as a function of the most important synthesis parameters, such as reaction temperature, time of CIS core formation and ZnS shell growth, feed molar ratio of Cu/In and Zn/Cu, various starting precursors, and types of ligands. Finally, a model analysis under various effects influencing the quality of QDs is performed. Quantum dot ; Quantum yield ; Materials science ; Molar ratio ; Yield (engineering) ; Artificial neural network ; Quantum ; Nanotechnology ; Optoelectronics ; Computer science ; Chemistry ; Catalysis ; Fluorescence ; Physics ; Optics Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Semiconductor Quantum Structures and Devices
Authors: Pınar Acar
Source: AIAA Scitech 2020 Forum (article, 2020-01-05)
Field: Materials Science — Materials Chemistry

Machine Learning Reinforced Crystal Plasticity Modeling under Experimental Uncertainty This work addresses a two-step computational approach to build a robust modeling environment for the Titanium-Aluminum alloy, Ti-7Al, a candidate aerospace material owing to superior mechanical performance under high stresses. To be used in aerospace applications, the large deformation behavior of the alloy should be investigated with a high-fidelity crystal plasticity model. However, there is no universal agreement on the crystal plasticity parameters and previous efforts are only based on deterministic techniques. Therefore, our goal is to build a crystal plasticity model for Ti-7Al, which is validated for the global (component-scale) and local (grain-level) features by considering the experimental uncertainty. In the first step, the lower and upper bounds of the crystal plasticity parameters are determined with an inverse problem that is solved to match the computations with the experimental stress-strain data. The second step validates the local features by solving an optimization problem that minimizes the difference between the simulated and experimental microstructural textures. The optimization is performed using an Artificial Neural Network (ANN) based surrogate model that is trained within the lower and upper limits of the parameters obtained in the first step. The outcomes of the two-step approach demonstrate significant improvement over the previous deterministic solutions. Plasticity ; Materials science ; Computation ; Artificial neural network ; Inverse ; Titanium alloy ; Inverse problem ; Crystal (programming language) ; Computer science ; Algorithm ; Machine learning ; Alloy ; Mathematics ; Geometry ; Composite material ; Mathematical analysis Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming
Authors: Rui Shi | Shu-Jia Li | Linxiuzi Yu | Hu‐Jun Qian | Zhong‐Yuan Lu
Source: Soft Materials (article, 2020-04-07)
Field: Materials Science — Materials Chemistry

Physical insights into stress–strain process of polymers under tensile deformation via machine learning ABSTRACT Strain localization is a ubiquitous phenomenon of soft matters subjected to strain. Polymeric materials are a very important class of soft materials and widely used nowadays. Polymers have unique strain localization behavior such as crazing during tensile deformation. How to understand the mechanism at the molecular level of strain localization in polymeric materials has become an important topic in material science. In this work, tensile deformation process of polymers both under a melt state and a glassy state are investigated in MD simulations using a generic coarse-grained model. We use a machine learning technique, i.e., support vector machine (SVM) algorithm, to understand the local molecular structure and the dynamical properties during tensile deformation. By defining “softness” from the SVM model, we investigate the stress–strain behavior of both ductile polymer above glass transition temperature and brittle polymer glass during tensile deformation. We demonstrated that the softness can be used to predict physical properties efficiently; the softness provides deep physical insights into the non-equilibrium stress–strain process. We also find that the Hookean behavior of polymer glasses is mostly contributed by the hard regions of the system, and the elastic limit is quantitatively discussed as well. Crazing ; Materials science ; Deformation (meteorology) ; Brittleness ; Ultimate tensile strength ; Polymer ; Composite material ; Stress (linguistics) ; Stress–strain curve Material Dynamics and Properties ; Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies
Authors: Joanna Krajewska-Śpiewak | Igor Lasota | Barbara Kozub
Source: Archives of Civil and Mechanical Engineering (article, 2020-09-14)
Field: Materials Science — Metals and Alloys

Application of classification neural networks for identification of damage stages of degraded low alloy steel based on acoustic emission data analysis Abstract The paper presents the influence of low alloy steel degradation on the acoustic emission (AE) generated during static tension of notched specimen. The material was cut from a technological pipeline long-term operated in the oil refinery industry. Comparative analysis of AE activity generated by damage process of degraded and new material has been carried out. The different AE parameters were used to detect different stages of fracture process of low alloy steel under quasi-static tensile test. Neural networks with three layers were created with Broyden–Fletcher–Goldfarb–Shanno learning algorithm for a database analysis. The different AE parameters were included in the input layer. Classification neural networks were created in order to determine the stages of material degradation. The results obtained from the carried out studies will be used as the basis for new methodology development of the assessment of the structural condition of in-service equipment. Acoustic emission ; Artificial neural network ; Structural material ; Materials science ; Alloy ; Fracture (geology) ; Alloy steel ; Process (computing) ; Computer science ; Ultimate tensile strength ; Tension (geology) ; Tensile testing ; Structural engineering ; Metallurgy ; Composite material ; Engineering ; Artificial intelligence Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Mineral Processing and Grinding
Authors: Bo Lin | Jincheng Wang | Junjie Li | Zhijun Wang
Source: Journal of Physics Condensed Matter (article, 2020-11-18)
Field: Materials Science — Materials Chemistry

A neural-network based framework of developing cross interaction in alloy embedded-atom method potentials: application to Zr–Nb alloy Interaction potentials are critical to molecular dynamics simulations on fundamental mechanisms at atomic scales. Combination of well-developed single-element empirical potentials via cross interaction (CI) is an important and effective way to develop alloy embedded-atom method (EAM) potentials. In this work, based on neural-network (NN) models, firstly we proposed a framework to construct CI potential functions via utilizing single-element potentials. The framework contained four steps: (1) extracting characteristic points from single-element potential functions, (2) constructing CI functions by cubic spline interpolation, (3) evaluating the accuracy of CI functions by referring to first-principle (FP) data, and (4) searching for reasonable CI functions via NN models. Then with this framework, we developed a Zr-Nb alloy CI potential utilizing the MA-III (pure Zr potential developed by Mendelev and Ackland in 2007) and the Fellinger, Park and Wilkins (FPW) (pure Nb potential developed by FPW in 2010) potentials as single-element parts. The calculated results with this Zr-Nb alloy potential showed that: (1) the newly developed CI potential functions could simultaneously present the potential-function features of Zr and Nb; (2) the normalized energy-volume curves of L1<sub>2</sub> Zr<sub>3</sub>Nb, B2 ZrNb and L1<sub>2</sub> ZrNb<sub>3</sub> calculated by this CI potential reasonably agreed with FP results; (3) the referred MA-III Zr and FPW Nb potentials can satisfactorily reproduce the priority of prismatic slip in Zr and the tension-compression asymmetry of 〈111〉{112} slip in Nb, while other ab initio developed Zr-Nb alloy potentials cannot. Our study indicates that, this NN based framework can take full advantage of single-element potentials, and is very convenient to develop EAM potentials of alloys; moreover, the new-developed Zr-Nb alloy EAM potential can reasonably describe the complicated deformation behaviors in Zr-Nb systems. Alloy ; Atom (system on chip) ; Artificial neural network ; Materials science ; Chemistry ; Computer science ; Metallurgy ; Artificial intelligence ; Embedded system Nuclear Materials and Properties ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Abhik Chakraborty | Raksha Sharma
Source:  (article, 2020-09-01)
Field: Materials Science — Materials Chemistry

See Deeper: Identifying Crystal Structure from X-ray Diffraction Patterns X-ray diffraction is a commonly used experimental science to detect the atomic and molecular structure of crystalline material. The process is called X-ray crystallography (XRC). Traditionally, it is done by human experts with some conjecture about what structure the crystalline material is likely to be. However, the study of crystal structure using X-ray diffraction patterns is applicable in many domains, such as chemistry, physics, biology, etc. It is tedious to have manual crystallography of X-ray diffraction patterns to determine a crystal structure with a massive amount of dataset. With the advent of high computational resources, deep learning techniques have taken classification to its peak. Convolution Neural Network (CNN) maps an input image into a high dimensional space and produce a low-cost function for image classification. In this paper, we deploy a variation of the Convolution Neural Network to predict crystal structure from X-ray diffraction patterns. We compare our approach with a wide range of conventional as well as modern Machine Learning based classification techniques for the structure prediction of a crystal. We report a cross-validation accuracy of 95.6% and Micro F1-score of 0.949 with our model for the proposed task which is significantly better than the other reported baseline methods. X-ray crystallography ; Diffraction ; Crystallography ; Materials science ; Computer science ; Optics ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Naima Melzi | Latifa Khaouane | Salah Hanini | Maamar Laidi | Yamina Ammi | Hamid Zentou
Source: Journal of Applied Mechanics and Technical Physics (article, 2020-03-01)
Field: Chemistry — Spectroscopy

Optimization Methodology of Artificial Neural Network Models for Predicting Molecular Diffusion Coefficients for Polar and Non-Polar Binary Gases In this study, an artificial neural network (ANN) is used to develop predictive models for estimating molecular diffusion coefficients of various gases at multiple pressures over a large field of temperatures. Two feed-forward neural networks NN1 and NN2 are trained using six physicochemical properties: molecular weight, critical volume, critical temperature, dipole moment, temperature, and pressure for NN1 and molecular weight, critical pressure, critical temperature, dipole moment, temperature, and pressure for NN2. The diffusion coefficients are regarded as the output. A set of 1252 gases (941 non-polar gases and 311 polar gases) is used for training and testing the ANN performance, and good correlations are found (R = 0.986 for NN1 and R = 0.988 for NN2). The result of the sensitivity analysis shows the importance of the six input parameters selected for modeling the diffusion coefficient. Moreover, the present ANN model provides more accurate predictions than other models. Polar ; Diffusion ; Chemical polarity ; Materials science ; Moment (physics) ; Binary number ; Artificial neural network ; Thermodynamics ; Dipole ; Volume (thermodynamics) ; Computer science ; Physics ; Mathematics ; Machine learning Adsorption, diffusion, and thermodynamic properties of materials ; Phase Equilibria and Thermodynamics ; Chemical Thermodynamics and Molecular Structure
Authors: Feifeng Wu | Hang Min | Yanwei Wen | Rong Chen | Yunkun Zhao | Michael J. Ford | Bin Shan
Source: Physical review. B./Physical review. B (article, 2020-10-15)
Field: Materials Science — Materials Chemistry

Modeling of metal nanoparticles: Development of neural-network interatomic potential inspired by features of the modified embedded-atom method Interatomic potential plays a key role in ensuring the accuracy and reliability of molecular-dynamics simulation results. While most empirical potentials are benchmarked against a set of carefully chosen bulk material properties, recent advances in machine learning have seen the emergence of neural-network-based mathematical potentials capable of describing highly complex potential energy surfaces for a variety of systems. We report here the development of a neural-network interatomic potential (NNIP) with modified embedded-atom method background density as fingerprint functions, which could accurately model the energetics of metallic nanoparticles and clusters (Cu as a representative example) widely used in catalysis. To appropriately account for the diverse chemical environments encountered in nanoparticles/nanoclusters, an extensive set of atomic configurations (totaling 18 084) were calculated using density-functional-theory (DFT) at the Perdew-Burke-Ernzerhof level. In addition to standard bulk properties such as cohesive energies and elastic constants, the sampled configurations also include a substantial number of differently oriented crystal facets and differently sized nanoparticles and nanoclusters, greatly expanding the value range of NNIP features that was otherwise quite limited. The complex energy potential surface of Cu can be faithfully reproduced, with an average error of 0.011 eV/at for energy states within 3 eV of the ground state. As an illustration, the developed NNIP is used to simulate the molecular dynamics of copper nanoparticles, and good agreement is achieved between DFT and the NNIP. Nanoclusters ; Nanoparticle ; Interatomic potential ; Atom (system on chip) ; Density functional theory ; Molecular dynamics ; Artificial neural network ; Materials science ; Statistical physics ; Nanotechnology ; Chemical physics ; Computer science ; Computational chemistry ; Physics ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies