jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jan N. Fuhg | Lloyd van Wees | Mark Obstalecki | Paul A. Shade | Nikolaos Bouklas | Matthew Kasemer
Source: arXiv (Cornell University) (preprint, 2022-01-28)
Field: Materials Science — Materials Chemistry

Machine-learning convex and texture-dependent macroscopic yield from crystal plasticity simulations The influence of the microstructure of a polycrystalline material on its macroscopic deformation response is still one of the major problems in materials engineering. For materials characterized by elastic-plastic deformation responses, predictive computational models to characterize crystal-plasticity (CP) have been developed. However, due to their large demand of computational resources, CP simulations cannot be straightforwardly implemented in hierarchical computational models such as FE$^{2}$. This bottleneck intensifies the need for the development of macroscopic simulation tools that can be directly informed by microstructural quantities. Using a 3D Finite-Element solver for CP, we generate a macroscopic yield function database based on general loading conditions and crystallographic texture. We furthermore assume an independence of the yield function to hydrostatic pressure of the yield function. Leveraging the advancement in statistical modeling we describe and apply a machine learning framework for predicting macroscopic yield as a function of crystallographic texture. The convexity of the data-driven yield function is guaranteed by using partially input convex neural networks as the predictive tool. Furthermore, in order to allow for the predicted yield function to be directly incorporated in time-integration schemes, as needed for the Finite Element method, the yield surfaces are interpreted as the boundaries of signed distance function level sets. Finite element method ; Texture (cosmology) ; Yield (engineering) ; Materials science ; Convexity ; Function (biology) ; Solver ; Bottleneck ; Statistical physics ; Machine learning ; Computer science ; Geometry ; Artificial intelligence ; Mathematical optimization ; Mathematics ; Physics ; Composite material ; Image (mathematics) ; Thermodynamics Machine Learning in Materials Science ; Orthopaedic implants and arthroplasty ; Mineral Processing and Grinding
Authors: Daniel Probst
Source: ChemRxiv (preprint, 2022-11-22)
Field: Materials Science — Materials Chemistry

Growing pains: Reacting to negative impacts of deep learning on machine learning for chemistry While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that deep learning has potential negative impacts on the scientific community and society as a whole. An ever-growing need for more computational resources may exacerbate the concentration of funding and the exclusiveness of research between countries, sectors, and institutions. Here, I introduce recent concerns and considerations of the machine learning research community and present potential solutions. Deep learning ; Artificial intelligence ; Academic community ; Computer science ; Engineering ethics ; Data science ; Political science ; Chemistry ; Engineering ; Library science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Massimiliano Lupo Pasini | GS Jung | Stephan Irle
Source: ChemRxiv (preprint, 2022-10-25)
Field: Materials Science — Materials Chemistry

Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases We developed a graph convolutional neural network (GCNN) to predict the formation energy and the bulk modulus for models of solid solution alloys for various atomic crystal structures and relaxed volumes. We trained the GCNN model on a dataset for nickel-niobium (NiNb) that was generated for simplicity with the embedded atom model (EAM) empirical interatomic potential. The dataset has been generated by calculating the formation energy and the bulk modulus as a prototypical elastic property for optimized geometries starting from initial body-centered cubic (BCC), face-centered cubic (FCC), and hexagonal compact packed (HCP) crystal structures, with configurations spanning the possible compositional range for each of the three types of initial crystal structure. Numerical results show that the GCNN model effectively predicts both the formation energy and the bulk modulus as functions of the optimized crystal structure, relaxed volume, and configurational entropy of the model structures for solid solution alloys. Cubic crystal system ; Materials science ; Alloy ; Crystal structure ; Solid solution ; Crystal (programming language) ; Niobium ; Modulus ; Atom (system on chip) ; Bulk modulus ; Crystallography ; Computer science ; Chemistry ; Composite material ; Metallurgy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Abeer Abdulaziz AlArfaj | Hanan A. Hosni Mahmoud
Source: Crystals (article, 2022-09-19)
Field: Materials Science — Materials Chemistry

Feature Fusion Deep Learning Model for Defects Prediction in Crystal Structures Detection of defective crystal structures can help in refute such defective structures to decrease industrial defects. In our research, we are concerned with Silicon nitride crystals. There are four types of crystal structure classes, namely no-defect structures, pristine crystal structures, defective random displacement crystal structures, and defective 25% vacancies crystal structures. This paper proposes a deep learning model to detect the four types of crystal structures with high accuracy and precision. The proposed model consists of both classification and regression models with a new loss function definition. After training both models, the features extracted are fused and utilized as an input to a perceptron classifier to identify the four types of crystal structures. A novel dense neural network (DNN) is proposed with a multitasking tactic. The developed multitask tactic is validated using a dataset of 16,000 crystal structures, with 30% highly defective crystals. Crystal structure images are captured under cobalt blue light. The multitask DNN model achieves an accuracy and precision of 97% and 96% respectively. Also, the average area under the curve (AUC) is 0.96 on average, which outperforms existing detection methods for crystal structures. The experiments depict the computational time comparison of a single training epoch of our model versus state-of-the-art models. the training computational time is performed using crystal structures diffraction image database of twelve image batches. It can be realized that the prediction computational time of our multitasking model is the least time of 21 s. Artificial intelligence ; Computer science ; Artificial neural network ; Human multitasking ; Crystal (programming language) ; Machine learning ; Feature (linguistics) ; Multilayer perceptron ; Algorithm ; Crystal structure ; Pattern recognition (psychology) ; Materials science ; Crystallography ; Chemistry Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Advanced X-ray and CT Imaging
Authors: Yalei Fu | Rui Zhang | Godwin Enemali | Abhishek Upadhyay | Michael Lengden | Chang Liu
Source: 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (article, 2022-05-16)
Field: Chemistry — Spectroscopy

Convolutional Neural Network Aided Chemical Species Tomography for Dynamic Temperature Imaging Chemical Species Tomography (CST) using Tunable Diode Laser Absorption Spectroscopy (TDLAS) is an in-situ technique to reconstruct the two-dimensional temperature distributions in combustion diagnosis. However, limited by the lack of projection data, traditionally computational tomographic algorithms are inherently rank-deficient, causing artefacts and severe uncertainty in the retrieved images. Recently, data-driven approaches, such as deep learning algorithms, have been validated to be more accurate and stable for CST. However, most attempts modelled the phantoms using two-dimensional Gaussian profiles to construct the training set, enabling reconstruction of only simple and static temperature fields and can seldom retrieve the dynamic and instantaneous temperature imaging. To address this problem, we use Fire Dynamics Simulator (FDS) to simulate the dynamic and fire-driven reacting flows for training set construction. Based on this training set, a Convolutional Neural Network (CNN) is designed. This newly introduced method is validated by numerical simulation, indicating good accuracy and sensitivity in monitoring dynamic flames. Convolutional neural network ; Computer science ; Tomographic reconstruction ; Tomography ; Gaussian ; Dynamic imaging ; Artificial intelligence ; Set (abstract data type) ; Projection (relational algebra) ; Algorithm ; Temperature measurement ; Tunable diode laser absorption spectroscopy ; Iterative reconstruction ; Laser ; Optics ; Image processing ; Physics ; Tunable laser Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Combustion and flame dynamics
Authors: Nanyu Wang | Jiayidaer Jieensi | Zhen Zheng | Yanguang Zhou | Shenghong Ju
Source: 2022 23rd International Conference on Electronic Packaging Technology (ICEPT) (article, 2022-08-10)
Field: Materials Science — Materials Chemistry

Predicting the interfacial thermal resistance of electronic packaging materials via machine learning With the wide application of nanomaterials in electronic devices, the interfacial thermal property has become an indispensable parameter in the thermal management of electronic packaging. Materials informatics, the combination of simulation/experimentation and machine learning, which is now generating a great deal of attention as a tool to accelerating the process of searching for new interfacial thermal materials. In this work, we have proposed a high-throughput screening framework to design interfacial materials with high/low interfacial thermal conductance. The prediction model was trained and established based on the collection of interfacial thermal resistance via the diffuse mismatch model. The input properties for diffuse mismatch model were obtained by the first-principles harmonic lattice dynamics calculation with 207 crystals. The crystal structure parameters and elemental properties are adopted as descriptors as they are quickly accessible from the materials database. The prediction models for interface thermal resistance were successfully built up by using the random forest and support vector machine algorithms. The descriptors of energy, lattice constant, volume and density of the crystals were found to have strong impact on determining the interface thermal resistance of heterogeneous interfaces. Materials science ; Thermal ; Thermal resistance ; Interfacial thermal resistance ; Lattice (music) ; Computer science ; Machine learning ; Thermodynamics Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Xinyuan Wu | Qilei Liu | Yujing Zhao | Lei Zhang | Jian Du
Source: Industrial & Engineering Chemistry Research (article, 2022-10-05)
Field: Materials Science — Materials Chemistry

Reaction Kinetic Model Considering the Solvation Effect Based on the FMO Theory and Deep Learning Reaction solvents are capable of significantly enhancing the kinetic rate and selectivity of chemical reactions through the solvation effects. However, the commonly used reaction kinetic models considering the solvation effects are based on the transition state theory, which is generally limited to the demanding exploration of transition states. In this paper, a reaction solvent screen framework is established for screening potential reaction solvents, where a novel reaction kinetic model is proposed based on the frontier molecular orbital theory correlating the reaction rate constants with the numbers of hydrogen bond donors/acceptors of solvents and the solvation HOMO/LUMO energies of reactants, which quantify the solvation effects on reaction systems independent of transition states and are calculated through the implicit solvation model using the solvent dielectric constants. To facilitate high-throughput screening of reaction solvents in a wide search space, a tailor-made text-based deep learning model is then developed for fast and accurate predictions of dielectric constants. Finally, two case studies, namely, SNAr and Menschutkin reactions, are presented to demonstrate the feasibility and effectiveness of the proposed reaction solvent screen framework. Solvation ; Chemistry ; Reaction rate constant ; Solvent effects ; Reaction rate ; Computational chemistry ; Implicit solvation ; Solvent ; Molecular orbital ; Physical chemistry ; Transition state ; HOMO/LUMO ; Thermodynamics ; Molecule ; Organic chemistry ; Kinetics ; Catalysis ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Hasan M. Jamil | Lan Li | Amin Mirkouei
Source: 2022 IEEE International Conference on Big Data (Big Data) (article, 2022-12-17)
Field: Materials Science — Materials Chemistry

MatFlow: A System for Knowledge-based Novel Materials Design using Machine Learning Designing novel materials and analyzing their properties is a computation intensive process. Increasingly modern machine learning techniques are being exploited in contemporary research to expedite and advance materials studies. One powerful tool available to researchers is the body of scientific knowledge that aids in selecting design models, algorithms, meta-data, and visualization tools to process and analyze experimental and empirical data for an iterative design process, potentially involving a human in the loop, In this preliminary research paper, our goal is to introduce a new machine learning platform, called MatFlow, for automated and knowledge driven design of novel materials and their usage. We outline its architecture and illustrate its functionality with an application in Transition Metal Dichalcogenide (TMD) Heterostructures design of electronic and energy devices. Computer science ; Process (computing) ; Visualization ; Design process ; Architecture ; Iterative and incremental development ; Iterative design ; Artificial intelligence ; Machine learning ; Software engineering ; Data science ; Systems engineering ; Work in process ; Engineering Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; 2D Materials and Applications
Authors: Yang Ling | Zhengxin Chen | Site Li | Yunxiao Guan | Minmin Shi | Jun Zhu | Zhihai Cheng | Jiang Wu | Chaojie Yin | Mengjie Bai
Source: Fuel (article, 2022-09-15)
Field: Materials Science — Materials Chemistry

Mapping between density of states and energy band gap for bismuth-based semiconductors via machine learning prediction Gradient boosting ; Random forest ; Ensemble learning ; Hyperparameter optimization ; Boosting (machine learning) ; Computer science ; Support vector machine ; Mean squared error ; Artificial intelligence ; Hyperparameter ; Machine learning ; Gaussian process ; Band gap ; Semiconductor ; Gaussian ; Mathematics ; Materials science ; Statistics ; Physics ; Condensed matter physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Lixue Cheng | Jiace Sun | J. Emiliano Deustua | Vignesh C. Bhethanabotla | Thomas F. Miller
Source: arXiv (Cornell University) (preprint, 2022-07-17)
Field: Materials Science — Materials Chemistry

Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression We introduce a novel machine learning strategy, kernel addition Gaussian process regression (KA-GPR), in molecular-orbital-based machine learning (MOB-ML) to learn the total correlation energies of general electronic structure theories for closed- and open-shell systems by introducing a machine learning strategy. The learning efficiency of MOB-ML (KA-GPR) is the same as the original MOB-ML method for the smallest criegee molecule, which is a closed-shell molecule with multi-reference characters. In addition, the prediction accuracies of different small free radicals could reach the chemical accuracy of 1 kcal/mol by training on one example structure. Accurate potential energy surfaces for the H10 chain (closed-shell) and water OH bond dissociation (open-shell) could also be generated by MOB-ML (KA-GPR). To explore the breadth of chemical systems that KA-GPR can describe, we further apply MOB-ML to accurately predict the large benchmark datasets for closed- (QM9, QM7b-T, GDB-13-T) and open-shell (QMSpin) molecules. Open shell ; Kriging ; Ground-penetrating radar ; Dissociation (chemistry) ; Support vector machine ; Computer science ; Benchmark (surveying) ; Kernel (algebra) ; Shell (structure) ; Gaussian ; Artificial intelligence ; Molecule ; Regression ; Chemistry ; Machine learning ; Computational chemistry ; Physics ; Materials science ; Atomic physics ; Mathematics ; Physical chemistry ; Quantum mechanics ; Discrete mathematics ; Statistics ; Radar Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Xue Jiang | Yu Yan | Yanjing Su
Source: Anti-Corrosion Methods and Materials (article, 2022-03-11)
Field: Materials Science — Materials Chemistry

Predicting the corrosion properties of cast and hot isostatic pressed CoCrMo/W alloys in seawater by machine learning Purpose Cobalt-based alloys exhibit a unique combination of wear resistance, strength and corrosion resistance. Localized corrosion of such alloys in seawater system can be several orders of magnitude faster than general corrosion, and direct experimental evidence of the local activation process is still lacking, which makes the accurate prediction for properties difficult, especially for long-term corrosion. The purpose of this study is revealing the relationship between multiple environments and corrosion properties to predict the corrosion of cobalt-based alloys. Design/methodology/approach A data-driven method for the prediction of the corrosion behavior of cast and hot isostatic-pressed CoCrMo/W alloys in seawater is proposed. The gradient boosting regression models calculate mean relative errors (MREs) of 0.160 and 0.435 by evaluating a hold-out set for breakdown potential (E b ) and maximum current density (i max ), respectively, considering various compositions, synthesis methods and corrosion environments. Findings The models can be used to estimate the “unseen” cobalt-based alloy after immersion in 3.5 Wt.% NaCl solution for one, two, four and eight months to obtain high precision with MREs of 7.8% and 9.8% for E b and i max , respectively. Originality/value Machine learning method provides novel and promising insights for the prediction of localized corrosion properties. Corrosion ; Materials science ; Seawater ; Alloy ; Metallurgy ; Immersion (mathematics) ; Mathematics ; Geology ; Geometry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; High-Temperature Coating Behaviors
Authors: Jiace Sun | Lixue Cheng | Thomas F. Miller
Source: The Journal of Chemical Physics (article, 2022-08-16)
Field: Materials Science — Materials Chemistry

Molecular dipole moment learning via rotationally equivariant derivative kernels in molecular-orbital-based machine learning This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree-Fock computations. A MOB pairwise decomposition of the correlation part of the dipole moment is applied, and these pair dipole moments could be further regressed as a universal function of MOs. The dipole MOB features consist of the energy MOB features and their responses to electric fields. An interpretable and rotationally equivariant derivative kernel for Gaussian process regression (GPR) is introduced to learn the dipole moment more efficiently. The proposed problem setup, feature design, and ML algorithm are shown to provide highly accurate models for both dipole moments and energies on water and 14 small molecules. To demonstrate the ability of MOB-ML to function as generalized density-matrix functionals for molecular dipole moments and energies of organic molecules, we further apply the proposed MOB-ML approach to train and test the molecules from the QM9 dataset. The application of local scalable GPR with Gaussian mixture model unsupervised clustering GPR scales up MOB-ML to a large-data regime while retaining the prediction accuracy. In addition, compared with the literature results, MOB-ML provides the best test mean absolute errors of 4.21 mD and 0.045 kcal/mol for dipole moment and energy models, respectively, when training on 110 000 QM9 molecules. The excellent transferability of the resulting QM9 models is also illustrated by the accurate predictions for four different series of peptides. Dipole ; Moment (physics) ; Kriging ; Bond dipole moment ; Artificial intelligence ; Electric dipole moment ; Computational chemistry ; Gaussian ; Chemistry ; Physics ; Statistical physics ; Algorithm ; Computer science ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Xabier Telleria‐Allika | José M. Mercero | Xabier López | Jon M. Matxain
Source: AIP Advances (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

Building machine learning assisted phase diagrams: Three chemically relevant examples In this work, we present a systematic procedure to build phase diagrams for chemically relevant properties by the use of a semi-supervised machine learning technique called uncertainty sampling. Concretely, we focus on ground state spin multiplicity and chemical bonding properties. As a first step, we have obtained single-eutectic-point-containing solid–liquid systems that have been suitable for contrasting the validity of this approach. Once this was settled, on the one hand, we built magnetic phase diagrams for several Hooke atoms containing a few electrons (4 and 6) trapped in spheroidal harmonic potentials. Changing the parameters of the confinement potential, such as curvature and anisotropy, and interelectronic interaction strength, we have been able to obtain and rationalize magnetic phase transitions flipping the ground state spin multiplicity from singlet (nonmagnetic) to triplet (magnetic) states. On the other hand, Bader’s analysis is performed upon helium dimers confined by spherical harmonic potentials. Covalency is studied using descriptors as the sign for Δρ(rC) and H(rC), and the dependency on the degrees of freedom of the system is studied, i.e., potential curvature ω2 and interatomic distance R. As a result, we have observed that there may exist a covalent bond between He atoms for short enough distances and strong enough confinement. This machine learning procedure could, in principle, be applied to the study of other chemically relevant properties involving phase diagrams, saving a lot of computational resources. Phase diagram ; Ground state ; Singlet state ; Curvature ; Materials science ; Physics ; Condensed matter physics ; Excited state ; Phase (matter) ; Atomic physics ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies