jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Elham Kiyani | Hamidreza Yazdani Sarvestani | Hossein Ravanbakhsh | Razyeh Behbahani | Behnam Ashrafi | Meysam Rahmat | Mikko Karttunen
Source: arXiv (Cornell University) (preprint, 2023-05-19)
Field: Materials Science — Materials Chemistry

Designing architectured ceramics for transient thermal applications using finite element and deep learning Topologically interlocking architectures can generate tough ceramics with attractive thermo-mechanical properties. This concept can make the material design pathway a challenging task, since modeling the whole design space is neither effective nor feasible. We propose an approach to design high-performance architectured ceramics using machine learning (ML) with data from finite element analysis (FEA). Convolutional neural networks (CNNs) and Multilayer Perceptrons (MLPs) are used as the deep learning approaches. A limited set of FEA simulation data containing a variety of architectural design parameters is used to train our neural networks, including learning how independent and dependent design parameters are related. A trained network is then used to predict the optimum structure from the configurations. A FEA simulation is run on the best predictions of both MLP and CNN algorithms to evaluate the performance of our networks. Although a limited amount of simulation data are available, our networks are effective in predicting the transient thermo-mechanical responses of possible panel designs. For example, the optimal design after using CNN prediction resulted in $\approx \! 30\%$ improvement in terms of edge temperature. Finite element method ; Transient (computer programming) ; Convolutional neural network ; Computer science ; Perceptron ; Artificial neural network ; Artificial intelligence ; Deep learning ; Machine learning ; Computer engineering ; Engineering ; Structural engineering Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Injection Molding Process and Properties
Authors: Federico Ottomano | Giovanni de Felice | Vladimir V. Gusev | Taylor D. Sparks
Source: ChemRxiv (preprint, 2023-08-08)
Field: Materials Science — Materials Chemistry

Not as simple as we thought: a rigorous examination of data aggregation in materials informatics Recent Machine Learning (ML) developments have opened new perspectives on accelerating the discovery of new materials. However, in the field of materials informatics, the performance of ML estimators is heavily limited by the nature of the available training datasets, which are often severely restricted and unbalanced. Among practitioners, it is usually taken for granted that more data corresponds to better performance. Here, we investigate whether different ML models for property predictions benefit from the aggregation of large databases into smaller repositories. To do this, we probe three different aggregation strategies prioritizing training size, element diversity, and composition diversity. For classic ML models, our results consistently show a reduction in performance under all the considered strategies. Deep Learning models show more robustness, but most changes are not significant. Furthermore, to assess whether this is a consequence of a distribution mismatch between datasets, we simulate the data acquisition process of a single dataset and compare a random selection with prioritizing chemical diversity. We observe that prioritizing composition diversity generally leads to a slower convergence toward better accuracy. Overall, our results suggest caution when merging different data sources and discourage a biased acquisition of novel chemistries when building a training dataset. Computer science ; Robustness (evolution) ; Estimator ; Machine learning ; Diversity (politics) ; Data science ; Artificial intelligence ; Data mining ; Training set ; Field (mathematics) ; Data aggregator ; Statistics ; Mathematics Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; Computational Drug Discovery Methods
Authors: Hongyang Dong | Simon D. M. Jacques | Keith T. Butler | Olof Gutowski | Ann‐Christin Dippel | Martin von Zimmerman | Andrew M. Beale | Antonis Vamvakeros
Source: ChemRxiv (preprint, 2023-09-27)
Field: Materials Science — Materials Chemistry

A neural network reconstruction approach for obtaining parallax-free X-ray powder diffraction computed tomography data from large samples In this study, we introduce an innovative method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples. Our approach integrates a unique 3D neural network architecture with a forward projector that accounts for the experimental geometry. This self-supervised technique for tomographic volume reconstruction is designed to be chemistry-agnostic, eliminating the need for prior knowledge of the sample's chemical composition. We showcase the efficacy of this method through its application on both simulated and experimental X-ray diffraction tomography data, acquired from a phantom sample and a commercially available and industrially relevant NMC532 cylindrical Lithium-ion battery. Parallax ; Projector ; Sample (material) ; Diffraction ; Tomography ; Computer science ; Imaging phantom ; Iterative reconstruction ; Powder diffraction ; Computed tomography ; Artificial neural network ; Optics ; Artificial intelligence ; Computer graphics (images) ; Computer vision ; Physics ; Radiology ; Nuclear magnetic resonance X-ray Diffraction in Crystallography ; Medical Imaging Techniques and Applications ; Machine Learning in Materials Science
Authors: Xiaojing Li | Bahram Vaghefinazari | Tim Würger | Sviatlana V. Lamaka | Mikhail L. Zheludkevich | Christian Feiler
Source: Research Square (preprint, 2023-01-13)
Field: Materials Science — Materials Chemistry

Predicting corrosion inhibition efficiencies of small organic molecules using data-driven techniques Corrosion ; Organic molecules ; Small molecule ; Materials science ; Molecule ; Environmental science ; Computer science ; Chemistry ; Metallurgy ; Organic chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Yan Xiang | Yuhang Tang | Guang Lin | Daniel Reker
Source: ChemRxiv (preprint, 2023-02-20)
Field: Materials Science — Materials Chemistry

Interpretable Molecular Property Predictions Using Marginalized Graph Kernels Marginalized graph kernels have shown competitive performance in molecular machine learning tasks but currently lack measures of interpretability, which are important to improve trust in the models, detect biases, and inform molecular optimization campaigns. We here conceive and implement two interpretability measures for Gaussian process regression using a marginalized graph kernel (GPR-MGK) to quantify (1) the contribution of specific training data to the prediction and (2) the contribution of specific nodes of the graph to the prediction. We demonstrate the applicability of these interpretability measures for molecular property prediction. We compare GPR-MGK to graph neural networks on four logic datasets and find that the atomic attribution of GPR-MGK generally outperforms the atomic attribution of graph neural networks. We also perform a detailed molecular attribution analysis using the FreeSolv dataset, showing how molecules in the training set influence machine learning predictions and why Morgan fingerprints perform poorly on this dataset. This is the first systematic examination of the interpretability of GPR-MGK and thereby an important step in the further maturation of marginalized graph kernel methods for interpretable molecular predictions. Interpretability ; Kriging ; Artificial intelligence ; Graph ; Machine learning ; Computer science ; Attribution ; Molecular graph ; Property (philosophy) ; Kernel (algebra) ; Artificial neural network ; Theoretical computer science ; Mathematics ; Psychology ; Discrete mathematics ; Social psychology ; Epistemology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Yuanqing Lu | Timur R. Fazletdinov | Zhiwen Pan | Katrin Wondraczek | Lothar Wondraczek
Source: ChemRxiv (preprint, 2023-11-09)
Field: Materials Science — Materials Chemistry

Prediction of phase composition and process resilience in plasma-assisted hetero-aggregate synthesis using a machine-learning model with multivariate output The synthesis of nanoscale particles and particle aggregates from liquid or gaseous precursors is affected by a variety of trade-off relations, for example, in terms of product composition, yield, or energy efficiency. Machine-supported process evaluation and learning (ML) of these relations enables optimization strategies for advanced material processing. We demonstrate such a workflow on the example of plasma-assisted aerosol deposition (PAAD) of alumina powders. Depending on processing conditions, these powders comprise of hetero-aggregate mixtures of crystalline and amorphous polymorphs. Process optimization towards a specific target composition calls for ML approaches. For this, a sufficiently large and consistent dataset of PAAD input (processing) and output (product) parameters was initially generated by real-world processing, and subsequently extrapolated into a cloud of ~ 106 input-output parameter matrices using Gaussian process regression with multivariate output and input-output feature analysis. We subsequently demonstrate how not only the phase composition of obtained alumina powders, but also product resilience to variations in specific processing parameters, or - as a perspective - the energy efficiency of material processing can be predicted. Multivariate statistics ; Process engineering ; Workflow ; Aggregate (composite) ; Amorphous solid ; Process (computing) ; Materials science ; Computer science ; Yield (engineering) ; Biological system ; Machine learning ; Computational science ; Nanotechnology ; Engineering ; Chemistry ; Composite material Machine Learning in Materials Science ; Forecasting Techniques and Applications
Authors: Soumya Sanyal | Arun K. Sagotra | Narendra Kumar | Sharad Rathi | M. Bala Krishna | Nagesh Somayajula | Duraivelan Palanisamy | Ram R. Ratnakar | Suchismita Sanyal | Partha Talukdar | Umesh V. Waghmare | Janakiraman Balachandran
Source: arXiv (Cornell University) (preprint, 2023-01-28)
Field: Materials Science — Materials Chemistry

Potential energy surface prediction of Alumina polymorphs using graph neural network The process of design and discovery of new materials can be significantly expedited and simplified if we can learn effectively from available data. Deep learning (DL) approaches have recently received a lot of interest for their ability to speed up the design of novel materials by predicting material properties with precision close to experiments and ab-initio calculations. The application of deep learning to predict materials properties measured by experiments are valuable yet challenging due to the limited amount of experimental data. Most of the existing approaches to predict properties from computational data have also been directed towards specific material properties. In this work, we extend this approach, by proposing Landscape Crystal Graph Convolution Network(LCGCN), an accurate and transferable deep learning framework based on graph convolutional networks. LCGCN directly learns the potential energy surface (PES) from atomic configurations. This approach can enable transferable models that can predict different material properties. We apply this framework to bulk crystals (i.e. Al2O3), and test it by calculating potential energy surfaces at different temperatures and across different phases of crystal. Computer science ; Convolutional neural network ; Graph ; Deep learning ; Convolution (computer science) ; Artificial neural network ; Surface (topology) ; Ab initio ; Artificial intelligence ; Algorithm ; Machine learning ; Theoretical computer science ; Mathematics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Gang Seob Jung | Sangkeun Lee | Jong Youl Choi
Source: arXiv (Cornell University) (preprint, 2023-11-09)
Field: Materials Science — Materials Chemistry

Data Distillation for Neural Network Potentials toward Foundational Dataset Machine learning (ML) techniques and atomistic modeling have rapidly transformed materials design and discovery. Specifically, generative models can swiftly propose promising materials for targeted applications. However, the predicted properties of materials through the generative models often do not match with calculated properties through ab initio calculations. This discrepancy can arise because the generated coordinates are not fully relaxed, whereas the many properties are derived from relaxed structures. Neural network-based potentials (NNPs) can expedite the process by providing relaxed structures from the initially generated ones. Nevertheless, acquiring data to train NNPs for this purpose can be extremely challenging as it needs to encompass previously unknown structures. This study utilized extended ensemble molecular dynamics (MD) to secure a broad range of liquid- and solid-phase configurations in one of the metallic systems, nickel. Then, we could significantly reduce them through active learning without losing much accuracy. We found that the NNP trained from the distilled data could predict different energy-minimized closed-pack crystal structures even though those structures were not explicitly part of the initial data. Furthermore, the data can be translated to other metallic systems (aluminum and niobium), without repeating the sampling and distillation processes. Our approach to data acquisition and distillation has demonstrated the potential to expedite NNP development and enhance materials design and discovery by integrating generative models. Computer science ; Artificial neural network ; Generative grammar ; Distillation ; Process (computing) ; Artificial intelligence ; Generative model ; Range (aeronautics) ; Machine learning ; Materials science ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Dong Jae Shin | Geonyeong Choi | Charmgil Hong | Jeong Woo Han
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Surface Segregation Machine-Learned with Inexpensive Numerical Fingerprint for the Design of Alloy Catalysts Fingerprint (computing) ; Alloy ; Catalysis ; Surface (topology) ; Materials science ; Mechanical engineering ; Computer science ; Engineering drawing ; Composite material ; Engineering ; Artificial intelligence ; Chemistry ; Mathematics ; Geometry ; Organic chemistry Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Catalysis and Hydrodesulfurization Studies
Authors: Max Hodapp | Alexander V. Shapeev
Source: arXiv (Cornell University) (preprint, 2023-04-17)
Field: Materials Science — Materials Chemistry

Equivariant Tensor Network Potentials Machine-learning interatomic potentials (MLIPs) have made a significant contribution to the recent progress in the fields of computational materials and chemistry due to the MLIPs' ability of accurately approximating energy landscapes of quantum-mechanical models while being orders of magnitude more computationally efficient. However, the computational cost and number of parameters of many state-of-the-art MLIPs increases exponentially with the number of atomic features. Tensor (non-neural) networks, based on low-rank representations of high-dimensional tensors, have been a way to reduce the number of parameters in approximating multidimensional functions, however, it is often not easy to encode the model symmetries into them. In this work we develop a formalism for rank-efficient equivariant tensor networks (ETNs), i.e. tensor networks that remain invariant under actions of SO(3) upon contraction. All the key algorithms of tensor networks like orthogonalization of cores and DMRG-based algorithms carry over to our equivariant case. Moreover, we show that many elements of modern neural network architectures like message passing, pulling, or attention mechanisms, can in some form be implemented into the ETNs. Based on ETNs, we develop a new class of polynomial-based MLIPs that demonstrate superior performance over existing MLIPs for multicomponent systems. Equivariant map ; Homogeneous space ; Computer science ; Tensor (intrinsic definition) ; Artificial neural network ; Formalism (music) ; Theoretical computer science ; Quantum entanglement ; Invariant (physics) ; Scalability ; Spin network ; Quantum ; Statistical physics ; Mathematics ; Artificial intelligence ; Pure mathematics ; Physics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Physics and Python Applications ; Parallel Computing and Optimization Techniques
Authors: Jehad Abed | Javier Heras‐Domingo | Mingchuan Luo | Rohan Yuri Sanspeur | Wajdi Alnoush | Débora Motta Meira | Hsiao‐Tsu Wang | Jian Wang | Jigang Zhou | Daojin Zhou | Khalid Fatih | Drew Higgins | Zachary W. Ulissi | Edward H. Sargent
Source: Research Square (preprint, 2023-01-04)
Field: Materials Science — Materials Chemistry

Multi-Descriptor Design of Ruthenium Catalysts for Durable Acidic Water Oxidation Abstract Further improvements in the performance and cost-effectiveness of water electrolyzers are urgently needed to accelerate decarbonization of hydrogen production. Iridium-free oxygen evolution reaction (OER) electrocatalysts are needed that are active and durable under acidic conditions. Here we report Ru 0.6 Cr 0.2 Ti 0.2 O 2 , identified from a machine-learning aided density functional theory (DFT) model using Pourbaix decomposition energy and metal-oxygen covalency as descriptors for electrochemical stability. To screen the entire space of bimetallic oxides for stability under harsh acidic conditions, we employ a graph convolution neural network to predict the Pourbaix decomposition energy accurately from unrelaxed structures. This was accomplished with an accuracy of 32 meV/atom. Notably, utilizing an optimized hyperbolic tangent activation function and dropout algorithm reduced the prediction error by 90%. Experimentally, the catalyst has an overpotential of 267 mV at 100 mA/cm 2 , accompanied by 200 hours of operation with an overpotential increase of less than 5 mV. DFT calculations show that adding Ti into the structure increases the metal-oxygen covalency of the system, improving the stability of the mixed-metal-oxide. At the same time, adding Cr lowers the energy barrier of the HOO* formation rate-determining step, thus improving activity compared to RuO 2 . We investigate structural and chemical changes during the reaction using in situ X-ray absorption spectroscopy and ptychography-scanning transmission X-ray microscopy. These evidence the evolution of a metastable structure compromised of a strong Ti-oxo network and a hydrous Cr-O passivation layer during the reaction – a structure that slows the dissolution of Ru by 20x while simultaneously suppressing lattice oxygen participation by &gt; 60% compared to the case of RuO 2 . Ruthenium ; Catalysis ; Chemistry ; Computer science ; Chemical engineering ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Dibakar Datta
Source: arXiv (Cornell University) (preprint, 2023-11-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring the Mechanical Behaviors of 2D Materials in Electrochemical Energy Storage Systems: Present Insights and Future Prospects 2D materials (2DM) and their heterostructures (2D + nD, n = 0,1,2,3) hold significant promise for applications in Electrochemical Energy Storage Systems (EESS), such as batteries. 2DM can serve as van der Waals (vdW) slick interface between conventional active materials (e.g., Silicon) and current collectors, modifying interfacial adhesion and preventing stress-induced fractures. Additionally, 2DM can replace traditional polymer binders (e.g., MXenes). This arrangement also underscores the critical role of interfacial mechanics between 2DM and active materials. Furthermore, 2DM can be designed to function as an electrode itself. For instance, a porous graphene network has been reported to possesses approximately five times the capacity of a traditional graphite anode. Consequently, gaining a comprehensive understanding of the mechanical properties of 2DM in EESS is paramount. However, modeling 2DM in EESS poses significant challenges due to the intricate coupling of mechanics and electrochemistry. For instance, defective graphene tends to favor adatom adsorption (e.g., Li+) during charging. In cases of strong adsorption, adatoms may not readily detach from electrodes during discharging. As a result, in such scenarios, adsorption-desorption (charge-discharge) processes govern the mechanical properties of 2DM when used as binders and current collectors. Regrettably, most existing studies on the mechanical properties of 2DM in EESS have failed to adequately address these critical issues. This perspective paper aims to provide a comprehensive overview of recent progress in the chemo-mechanics of 2DM's mechanical properties. A wide spectrum of multiscale modeling approaches, including atomistic/molecular simulations, continuum modeling, and machine learning, are discussed. Adsorption ; Anode ; Graphene ; Nanotechnology ; Materials science ; Electrochemistry ; Computer science ; Mechanical engineering ; Physics ; Chemistry ; Electrode ; Engineering ; Quantum mechanics ; Physical chemistry Supercapacitor Materials and Fabrication ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Shaoqin Wang | Xiangdong Wang | Zhili Li | Pengfei Luo | Jiye Zhang | Jiong Yang | Jun Luo
Source: Advanced Materials Technologies (article, 2023-12-03)
Field: Materials Science — Materials Chemistry

Discovery of the Layered Thermoelectric Compound GeBi<sub>2</sub>Se<sub>4</sub> and Accelerating Its Performance Optimization by Machine Learning Searching for new materials with intrinsically low lattice thermal conductivity is crucial for the exploration of high‐performance thermoelectric materials. Herein, the layered compound GeBi 2 Se 4 with intrinsically low lattice thermal conductivity is discovered, and its thermoelectric performance optimization is accelerated by machine learning. The ultralow lattice thermal conductivity of 0.53 W m −1 K −1 at room temperature for the GeBi 2 Se 4 sample can be ascribed to the large anharmonicity and miscellaneous crystal defects. By alloying tellurium (Te) at the selenium (Se) site, the lattice thermal conductivity is further reduced due to the alloy scattering effect and chemical bond softening while the density‐of‐states effective mass of electrons is significantly increased. Finally, the best n‐type thermoelectric GeBi 2 Se 1.9 Te 2.1 sample with a dimensionless figure of merit zT of 0.56 at 460 K is screened out by machine learning and verified by experiments, which increases by 140% in comparison with the pristine GeBi 2 Se 4 . Thermoelectric effect ; Materials science ; Thermoelectric materials ; Thermal conductivity ; Tellurium ; Anharmonicity ; Condensed matter physics ; Lattice (music) ; Thermodynamics ; Composite material ; Metallurgy ; Physics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; 2D Materials and Applications