jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Colin Burdine | Edward P. Blair
Source:  (article, 2023-09-17)
Field: Materials Science — Materials Chemistry

Discovery of Novel Superconducting Materials with Deep Learning Superconducting devices are crucial to emerging quantum technologies, contributing to innovations in the areas of computing, metrology, and communication systems. However, many of these devices are based on conventional BCS (Bardeen-Cooper-Schrieffer) superconductors, which have operating critical temperatures <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(T_{c})$</tex> that require expensive helium-based cryogenic cooling. This has motivated the exploration of unconventional non-BCS superconductors as an alternative platform for these technologies, since some of these superconductors exhibit desirable properties such as high <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T_{c}$</tex> and topologically robust states. Although unconventional superconductors have been known to exist for several decades, a comprehensive theory of superconductivity in these materials has not yet been developed, making the discovery of new superconductors a challenging endeavor. In this paper, we present ongoing work in the autonomous discovery of conventional, unconventional, and exotic superconductors with deep learning methods. Using experimental data from over 36,000 superconducting materials, we demonstrate that graph neural network models trained on raw crystalline structure can be used to discover superconductors with desirable properties such as high <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T_{c}$</tex>. Superconductivity ; Computer science ; Deep learning ; Artificial intelligence ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Electronic and Structural Properties of Oxides
Authors: Q. Ge | Weiping Zhu | Jin-Wu Jiang
Source: Nanotechnology (article, 2023-11-03)
Field: Materials Science — Materials Chemistry

Machine learning accelerated search for the impact limit of the graphene/aluminum alloy whipple structure This paper proposes a Whipple structure to enhance the impact resistance of graphene/aluminum alloy composites by varying the interlayer spacing between graphene and aluminum alloy. The increased interlayer spacing provides more deformation space for the graphene to absorb more deformation energy, and enables the formation of a debris cloud from the bullet fragments and graphene fragments, significantly reducing the impact energy per unit area of the next material. The impact limit serves as a critical metric for assessing the impact resistance of the Whipple structure. Based on molecular dynamics simulations, we developed a machine learning model to predict the protection of aluminum alloy, and quickly determined the impact limits of velocity, bullet radius, and interlayer spacing by using the machine learning model. An empirical equation for the impact limit of interlayer spacing was established. The results showed that non-zero interlayer spacing can significantly improve the impact resistance of the hybrid structure; to fully exploit the superior impact resistance of this Whipple structure, the number of graphene layers should be at least 3. Furthermore, at high impact velocities and large bullet radii, the impact limit of the interlayer spacing exhibits a substantial correlation with the number of graphene layers. These results provide valuable information for the design of the impact resistance of the graphene/aluminum alloy composites. Graphene ; Materials science ; Alloy ; Deformation (meteorology) ; RADIUS ; Aluminium ; Limit (mathematics) ; Composite material ; Molecular dynamics ; Nanotechnology ; Computer science ; Physics Graphite, nuclear technology, radiation studies ; Graphene research and applications ; Fiber-reinforced polymer composites
Authors: Felix Utama Kosasih | Fanzhi Su | Tian Du | Sinclair R. Ratnasingham | Joe Briscoe | Caterina Ducati
Source: Microscopy and Microanalysis (article, 2023-03-30)
Field: Materials Science — Materials Chemistry

Deep Learning-Assisted Multivariate Analysis for Nanoscale Characterization of Heterogeneous Beam-Sensitive Materials Nanoscale materials characterization often uses highly energetic probes which can rapidly damage beam-sensitive materials, such as hybrid organic-inorganic compounds. Reducing the probe dose minimizes the damage, but often at the cost of lower signal-to-noise ratio (SNR) in the acquired data. This work reports the optimization and validation of principal component analysis (PCA) and nonnegative matrix factorization for the postprocessing of low-dose nanoscale characterization data. PCA is found to be the best approach for data denoising. However, the popular scree plot-based method for separation of principal and noise components results in inaccurate or excessively noisy models of the heterogeneous original data, even after Poissonian noise weighting. Manual separation of principal and noise components produces a denoised model which more accurately reproduces physical features present in the raw data while improving SNR by an order of magnitude. However, manual selection is time-consuming and potentially subjective. To suppress these disadvantages, a deep learning-based component classification method is proposed. The neural network model can examine PCA components and automatically classify them with an accuracy of >99% and a rate of ∼2 component/s. Together, multivariate analysis and deep learning enable a deeper analysis of nanoscale materials' characterization, allowing as much information as possible to be extracted. Characterization (materials science) ; Nanoscopic scale ; Materials science ; Nanotechnology ; Multivariate statistics ; Computer science ; Machine learning Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Thermography and Photoacoustic Techniques
Authors: Ivan Flaminio Cozza | Maria Pia Centini | Francesco Sapio | Venkatesh Gopalakrishnan | Giacomo Buitoni | Marco Tabarrini
Source: SAE technical papers on CD-ROM/SAE technical paper series (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

Application of a Machine Learning Approach for Selective Catalyst Reduction Catalyst 3D-CFD Modeling: Numerical Method Development and Experimental Validation &lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Internal combustion engines (ICEs) exhaust emissions, particularly nitrogen oxides (NOx), have become a growing environmental and health concern. The biggest challenge for contemporary ICE industry is the development of clean ICEs, and the use of advanced design tools like Computational Fluid Dynamics (CFD) simulation is paramount to achieve this goal. In particular, the development of aftertreatment systems like Selective Catalyst Reduction (SCR) is a key step to reduce NOx emissions, and accurate and efficient CFD models are essential for its design and optimization. In this work, we propose a novel 3D-CFD methodology, which uses a Machine Learning (ML) approach as a surrogate model for the SCR catalyst chemistry, which aims to enhance accuracy of the simulations with a moderate computational cost. The ML approach is trained on a dataset generated from a set of 1D-CFD simulations of a single channel of an SCR catalyst. The trained model is then applied to the 3D-CFD simulation as a surrogate model for the SCR chemistry, allowing the calculation of the spatial distribution of chemical species at the catalyst’s outlet. The model is finally applied to the calculation of the SCR conversion efficiency and to the simulation of NOx sensors reading on the tailpipe. Experimental validation of the developed 3D-CFD models was performed on a dedicated test bench. An ad-hoc local sampling probe has been used to measure the spatial distribution of the chemical species downstream the SCR system, together with the reading of three commercial NOx sensors. Our findings suggest that the ML-based surrogate model for the SCR is a reliable and efficient methodology for including the SCR chemistry in a 3D-CFD simulation at a moderate computational cost. The developed numerical method could provide valuable insights into the SCR catalyst&lt;b&gt;’&lt;/b&gt;s design and optimization and aid in the development of ultra-low NOx emission ICEs.&lt;/div&gt;&lt;/div&gt; Computational fluid dynamics ; NOx ; Selective catalytic reduction ; Computer science ; Combustion ; Simulation ; Process engineering ; Engineering ; Catalysis ; Chemistry ; Aerospace engineering Catalytic Processes in Materials Science ; Air Quality Monitoring and Forecasting ; Vehicle emissions and performance
Authors: Tiehan Fan | Jianxin Hou | Jian Hu
Source: Metals (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

An Effective Framework for Predicting Performance of Solid-Solution Copper Alloys Using a Feature Engineering Technique in Machine Learning Utilized extensively in a myriad of industries, solid-solution copper alloys are prized for their superior electrical conductivity and mechanical properties. However, optimizing these often mutually exclusive properties poses a challenge, especially considering the complex interplay of alloy composition and processing techniques. To address this, we introduce a novel computational framework that employs advanced feature engineering within machine learning algorithms to accurately predict the alloy’s microhardness and electrical conductivity. Our methodology demonstrates a substantial enhancement over traditional data-driven models, achieving remarkable increases in R2 scores—from 0.939 to 0.971 for microhardness predictions and from −1.05 to 0.934 for electrical conductivity. Through machine learning, we also spotlight key determinants that significantly influence overall performance of solid-solution copper alloys, providing actionable insights for future alloy design and material optimization. Alloy ; Indentation hardness ; Copper ; Electrical resistivity and conductivity ; Feature (linguistics) ; Materials science ; Conductivity ; Key (lock) ; Feature engineering ; Computer science ; Machine learning ; Artificial intelligence ; Solid solution ; Process engineering ; Mechanical engineering ; Metallurgy ; Deep learning ; Microstructure ; Engineering ; Electrical engineering ; Physics Corrosion Behavior and Inhibition ; Microstructure and mechanical properties ; Nanoporous metals and alloys
Authors: Quin Hu | Andrew Johannesen | Daniel S. Graham | Jason D. Goodpaster
Source: ChemRxiv (preprint, 2023-05-04)
Field: Materials Science — Materials Chemistry

Neural Network Potentials for Reactive Chemistry: CASPT2 Quality Potential Energy Surfaces for Bond Breaking Neural Network potentials are developed which accurately make and break bonds for use in molecular simulations. We report a neural network potential that can describe the potential energy surface for carbon-carbon bond dissociation with less than 1 kcal/mol error compared to complete active space second-order perturbation theory (CASPT2), and maintains this accuracy for both the minimum energy path and molecular dynamic calculations up to 2000K. We utilize a transfer learning algorithm to develop neural network potentials to generate potential energy surfaces; this method aims to use the minimum amount of CASPT2 data on small systems to train neural network potentials while maintaining excellent transferability to larger systems. First, we generate homolytic carbon-carbon bond dissociation data of small size alkanes with density functional theory (DFT) energies to train the potentials to accurately predict bond dissociation at the DFT level. Then, using transfer learning, we retrained the neural network potential to CASPT2 level of accuracy. We demonstrate that the neural network potential only requires bond dissociation data of a few small alkanes to accurately predict bond dissociation energy in larger alkanes. We then perform additional training on molecular dynamic simulations to refine our neural network potentials to obtain high accuracy for general use in molecular simulation. This training algorithm is generally applicable to any type of bond or any level of theory and will be useful for the generation of new reactive neural network potentials. Artificial neural network ; Dissociation (chemistry) ; Potential energy surface ; Chemistry ; Transferability ; Potential energy ; Molecular dynamics ; Bond-dissociation energy ; Bond order ; Computer science ; Biological system ; Computational chemistry ; Bond length ; Chemical physics ; Statistical physics ; Artificial intelligence ; Molecule ; Atomic physics ; Physics ; Machine learning ; Physical chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Akhil Thomas | Ali Riza Durmaz | Mehwish Alam | Peter Gumbsch | Harald Sack | Christoph Eberl
Source: Research Square (preprint, 2023-02-15)
Field: Materials Science — Materials Chemistry

Materials fatigue prediction using graph neural networks on microstructure representations Microstructure ; Graph ; Artificial neural network ; Computer science ; Artificial intelligence ; Materials science ; Geology ; Theoretical computer science ; Composite material Machine Learning in Materials Science ; Mineral Processing and Grinding ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Qi‐Jun Hong
Source: arXiv (Cornell University) (preprint, 2023-07-10)
Field: Materials Science — Materials Chemistry

Deep learning for CALPHAD modeling: Universal parameter learning solely based on chemical formula Empowering the creation of thermodynamic and property databases, the CALPHAD (CALculation of PHAse Diagrams) methodology plays a vital role in enhancing materials and manufacturing process design. In this study, we propose a deep learning approach to train parameters in CALPHAD models solely based on chemical formula. We demonstrate its application through an example of calculating the mixing parameter of liquids. This work showcases the integration of CALPHAD and deep learning, highlighting its potential for achieving automated comprehensive CALPHAD modeling. CALPHAD ; Process (computing) ; Computer science ; Work (physics) ; Phase (matter) ; Phase diagram ; Thermodynamics ; Chemistry ; Physics ; Programming language ; Organic chemistry Machine Learning in Materials Science ; Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography
Authors: Pavlo O. Dral | Fuchun Ge | Yi-Fan Hou | Peikun Zheng | Yuxinxin Chen | Mario Barbatti | Olexandr Isayev | Cheng Wang | Bao-Xin Xue | Max Pinheiro | Yuming Su | Yiheng Dai | Yangtao Chen | Lina Zhang | Shuang Zhang | Arif Ullah | Quanhao Zhang | Yanchi Ou
Source: arXiv (Cornell University) (preprint, 2023-10-31)
Field: Materials Science — Materials Chemistry

MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pre-trained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries. Workflow ; Python (programming language) ; Computer science ; Computational science ; Software ; Leverage (statistics) ; Scripting language ; Modelica ; Simulation ; Artificial intelligence ; Programming language ; Database Machine Learning in Materials Science ; Various Chemistry Research Topics ; Catalysis and Oxidation Reactions
Authors: Takashiro Akitsu | Yuji Takiguchi | Shintaro Suda | Daisuke Nakane
Source: FirePhysChem (article, 2023-12-30)
Field: Chemistry — Physical and Theoretical Chemistry

Deep-learning prediction of safety moiety of salen-type complex crystals towards explosive perchlorate salts Perchlorate compounds are well-known for their explosive and hazardous nature. Considering previously reported perchlorate crystals of salen-type manganese(III) complexes, our study aimed to identify the specific molecular/crystal structure responsible for their explosive properties. Employing deep learning, we conducted an analysis of the Hirschfeld surface for salen-type metal complexes within a crystal structure database. The results indicate that the salen-type complex site lacks distinctive structural features, attributing its explosive potential to the chemical bonding of the perchlorate ion and the surrounding intermolecular interactions. Perchlorate ; Explosive material ; Crystal structure ; Materials science ; Manganese ; Intermolecular force ; Metal ; Crystallography ; Chemistry ; Molecule ; Ion ; Organic chemistry ; Metallurgy Crystallography and molecular interactions ; Energetic Materials and Combustion ; Chemical Analysis and Environmental Impact
Authors: Jian Chang | Yiming Kuai | Xian Wei | Hui Yu | Hai Lan
Source: Chinese Journal of Chemical Physics (article, 2023-02-21)
Field: Materials Science — Materials Chemistry

Molecular potential energy computation via graph edge aggregate attention neural network Accurate potential energy surface (PES) calculation is the basis of molecular dynamics research. Using deep learning (DL) methods can improve the speed of PES calculation while achieving competitive accuracy to ab initio methods. However, the performance of DL model is extremely sensitive to the distribution of training data. Without sufficient training data, the DL model suffers from overfitting issues that lead to catastrophic performance degradation on unseen samples. To solve this problem, based on the message passing paradigm of graph neural networks, we innovatively propose an edge-aggregate-attention mechanism, which specifies the weight based on node and edge information. Experiments on MDI7 and QM9 datasets show that our model not only achieves higher PES calculation accuracy but also has better generalization ability compared with Schnet, which demonstrates that edge-aggregate-attention can better capture the inherent features of equilibrium and non-equilibrium molecular conformations. Overfitting ; Computer science ; Aggregate (composite) ; Enhanced Data Rates for GSM Evolution ; Generalization ; Computation ; Graph ; Artificial neural network ; Artificial intelligence ; Potential energy surface ; Machine learning ; Ab initio ; Algorithm ; Theoretical computer science ; Chemistry ; Mathematics ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Shuang Zhou | Qian Lei | Jie Yin | Yuxin Liang | Qianye Ren | Can Dai | Zhou Li
Source: JOM (article, 2023-03-14)
Field: Chemistry — Inorganic Chemistry

Design Strategy for Art Copper Alloys’ Colors Through Machine Learning and Oxidation Treatment Copper ; Materials science ; Metallurgy ; Computer science Pigment Synthesis and Properties ; X-ray Diffraction in Crystallography ; Welding Techniques and Residual Stresses
Authors: Ali Hamza | Umar Hayat | Wahid Hussain | Anam Mumtaz
Source:  (article, 2023-02-22)
Field: Materials Science — Materials Chemistry

Machine Learning Based Classification of crystal system using rendered images from X-ray diffraction (XRD) dataset X-ray diffraction(XRD) is an essential characterization technique to study the properties of the materials. Finding a material's crystal system is an important step in its analysis. So, the process should be fast as well as accurate. In total there are seven crystal systems: triclinic, monoclinic, orthorhombic, tetragonal, trigonal, hexagonal, and cubic. Previous studies have worked on finding the material crystal structure by introducing machine learning approaches. In, recent studies the X-ray diffraction(XRD) dataset in the tabular form was used to train a machine learning model to classify the material's crystal system. The machine learning models trained on the tabular X-ray diffraction(XRD) dataset didn't maximize their performance. In the scope of this study rendered X-ray diffraction(XRD) images had been used to maximize the performance of the machine learning models. By using the rendered images as input from the X-ray diffraction(XRD) tabular dataset the machine learning model was able to achieve an accuracy of 98%-99%. The final findings had shown that rendered images datasets had improved the machine learning model's ability to correctly classify the crystal systems of materials. Triclinic crystal system ; Monoclinic crystal system ; Orthorhombic crystal system ; Artificial intelligence ; Diffraction ; Machine learning ; Crystal (programming language) ; Tetragonal crystal system ; Materials science ; X-ray crystallography ; Crystallography ; Computer science ; Crystal structure ; Algorithm ; Optics ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Mineral Processing and Grinding
Authors: Ying Fang | Paul R. Ohodnicki | Guofeng Wang
Source: The Journal of Chemical Physics (article, 2023-05-15)
Field: Materials Science — Materials Chemistry

A machine learning based computational approach for prediction of cation distribution in spinel crystal In this study, a machine learning based computational approach has been developed to investigate the cation distribution in spinel crystals. The computational approach integrates the construction of datasets consisting of the energies calculated from density functional theory, the training of machine learning models to derive the relationship between system energy and structural features, and atomistic Monte Carlo simulations to sample the thermodynamic equilibrium structures of spinel crystals. It is found that the support vector machine model yields excellent performance in energy predictions based on spinel crystal structures. Furthermore, the developed computational approach has been applied to predict the cation distribution in single spinel MgAl2O4 and MgFe2O4 and double spinel MgAl2-aFeaO4. Agreeing with the available experimental data, the computational approach correctly predicts that the equilibrium degree of inversion of MgAl2O4 increases with temperature, whereas the degree of inversion of MgFe2O4 decreases with temperature. Additionally, it is predicted that the equilibrium occupancy of Mg cations at the tetrahedral and octahedral sites in MgAl2-aFeaO4 could be tuned as a function of chemical composition. Therefore, this study presents a reliable computational approach that can be extended to study the variation of cation distribution with processing temperature and chemical composition in a wide range of complex multi-cation spinel oxides with numerous applications. Spinel ; Lattice (music) ; Monte Carlo method ; Density functional theory ; Statistical physics ; Materials science ; Algorithm ; Computer science ; Thermodynamics ; Chemistry ; Computational chemistry ; Physics ; Mathematics Magnetic Properties and Synthesis of Ferrites ; Advanced Condensed Matter Physics ; X-ray Diffraction in Crystallography