jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yifei Wang | Shiyang Chen | Guobin Chen | Ethan Shurberg | Hang Liu | Pengyu Hong
Source: arXiv (Cornell University) (preprint, 2022-08-09)
Field: Materials Science — Materials Chemistry

Motif-based Graph Representation Learning with Application to Chemical Molecules This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real applications. Existing graph neural networks offer limited ability to capture complex interactions within local structural contexts, which hinders them from taking advantage of the expression power of ARGs. We propose Motif Convolution Module (MCM), a new motif-based graph representation learning technique to better utilize local structural information. The ability to handle continuous edge and node features is one of MCM's advantages over existing motif-based models. MCM builds a motif vocabulary in an unsupervised way and deploys a novel motif convolution operation to extract the local structural context of individual nodes, which is then used to learn higher-level node representations via multilayer perceptron and/or message passing in graph neural networks. When compared with other graph learning approaches to classifying synthetic graphs, our approach is substantially better in capturing structural context. We also demonstrate the performance and explainability advantages of our approach by applying it to several molecular benchmarks. Computer science ; ENCODE ; Motif (music) ; Graph ; Theoretical computer science ; Feature learning ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Connor Allen | Albert P. Bartók
Source: arXiv (Cornell University) (preprint, 2022-07-24)
Field: Materials Science — Materials Chemistry

Optimal data generation for machine learned interatomic potentials Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generating databases of atomic configurations used in fitting these models is a laborious process, requiring significant computational and human effort. A computationally efficient method is presented to generate databases of atomic configurations that contain optimal information on the small-displacement regime of the potential energy surface of bulk crystalline matter. Utilising non-diagonal supercell (NDSC), an automatic process is suggested for ab initio data generation. MLIPs were fitted for Al, W, Mg and Si, which very closely reproduce the ab initio phonon and elastic properties. The protocol can be easily adapted to other materials and can be inserted in the workflow of any flavour of MLIP generation. Interatomic potential ; Ab initio ; Workflow ; Computer science ; Ab initio quantum chemistry methods ; Phonon ; Process (computing) ; Supercell ; Diagonal ; Computational science ; Algorithm ; Physics ; Molecular dynamics ; Chemistry ; Computational chemistry ; Database ; Condensed matter physics ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Quintin N. Meier | Natalio Mingo | Ambroise van Roekeghem
Source: arXiv (Cornell University) (preprint, 2022-06-16)
Field: Materials Science — Materials Chemistry

Finite temperature dielectric properties of KTaO$_3$ from first principles and machine learning: Phonon spectra, Barrett law, strain engineering and electrostriction Despite important breakthroughs in the last decade, the calculation of temperature dependent properties of solids still remains a challenging task, especially in the vicinity of structural phase transitions. We show that the combination of machine-learning interatomic potentials with quantum self-consistent ab initio lattice dynamics allows to calculate efficiently the temperature dependence of dielectric properties of the quantum paraelectric perovskite KTaO$_3$, with a precision beyond what could be reasonably achieved using plain density functional theory. We first follow the strong anharmonic softening of the polar mode in this incipient ferroelectric material, and the resulting divergence of the dielectric constant that eventually saturates due to the interplay between temperature and quantum fluctuations. Further, we predict the stability range of the quantum paraelectric state under the application of epitaxial strain at 0 K and 300 K. Finally, we calculate the temperature dependence of electrostrictive tensors for this material and show that giant electrostriction in KTaO$_3$ is to be expected also at room temperature under the condition of strain engineering. Electrostriction ; Condensed matter physics ; Phonon ; Dielectric ; Ferroelectricity ; Materials science ; Anharmonicity ; Ab initio ; Quantum ; Physics ; Piezoelectricity ; Quantum mechanics ; Composite material ; Optoelectronics Machine Learning in Materials Science
Authors: Junxiong Hu | Yerin Han | X. Chi | Ganesh Ji Omar | Mohammed M. Al Ezzi | J. Y. Tan | J. Gou | X. J. Yu | K. Watanabe | T. Taniguchi | A. T. S. Wee | Z. H. Qiao | A. Ariando
Source: arXiv (Cornell University) (preprint, 2022-05-23)
Field: Materials Science — Materials Chemistry

Direct Determination of Spin-Splitting Energy in Magnetic Graphene by Landau Fan Shifts Spin-polarized two-dimensional materials with large and tunable spin-splitting energy promise the field of 2D spintronics. While graphene has been a canonical 2D material, its spin properties and tunability are limited. Here, we demonstrate the emergence of robust spin-polarization in graphene with large and tunable spin-splitting energy of up to 132 meV at zero applied magnetic fields. The spin polarization is induced through a magnetic exchange interaction between graphene and the underlying ferrimagnetic oxide insulating layer, Tm3Fe5O12, as confirmed by its X-ray magnetic circular dichroism. The spin-splitting energies are directly measured and visualized by the shift in their landau fan diagram mapped by analyzing the measured subnikov-de-Haas oscillations as a function of applied electric fields, showing consistent fit with our first-principles and machine learning calculations. Further, the observed spin-splitting energies can be tuned over a broad range between 98 and 166 meV by cooling fields. Our methods and results are applicable to other two-dimensional (magnetic) materials and heterostructures, and offer great potential for developing next-generation spin logic and memory devices. Spintronics ; Condensed matter physics ; Graphene ; Ferrimagnetism ; Spin polarization ; Landau quantization ; Magnetic field ; Spin (aerodynamics) ; Zero field splitting ; Physics ; Spin pumping ; Materials science ; Spin Hall effect ; Ferromagnetism ; Magnetization ; Quantum mechanics Graphene research and applications ; Quantum and electron transport phenomena ; Magnetic properties of thin films
Authors: Marwa A. Fouad | Nouran Al-Khalil Bek | Asmaa A. El‐Zaher | Mohammad Sammany | Mohamed K. El‐Ashrey
Source: Egyptian Journal of Chemistry (article, 2022-02-07)
Field: Chemistry — Analytical Chemistry

Quantitative Structure Retention/Activity Relationship Study of some Dialkoxybenzamide Phosphodiesterase-4B Inhibitors Recently synthesized dialkoxybenzamide derivatives, structurally analogous to Roflumilast with more selectivity against phosphodiesterase 4B, were highlighted in the present work. To find optimum chromatographic conditions for the elution of these compounds, a central composite experimental design was carried out by varying the stationary phase type as a categorical factor and mobile phase composition including the percentage of acetonitrile and the pH of the buffered water as continuous factors; the obtained retention times were utilized in the quantitative structure retention relationship (QSRR) studies. Furthermore, quantitative structure activity relationship (QSAR) studies for these promising compounds were performed. QSAR and QSRR models were built by different techniques namely multiple linear regression (MLR), principal component regression (PCR) for linear modelling and Principal Component-Artificial Neural Networks (PC-ANN) for nonlinear modelling. Internal validation (leave many out method), and external validation were used to evaluate the performance of the generated models. Depending on the calculated statistical parameters, PC-ANN QSAR model showed the best predictive power for the biological activities of the test set compounds. Whereas MLR technique was more suitable to build QSRR model, this model can help in understanding how the chemical structure and the lipophilicity of the compounds can affect the retention time and chromatographic behavior. Phosphodiesterase ; Chemistry ; Structure–activity relationship ; Pharmacology ; Biochemistry ; Enzyme ; Medicine ; In vitro Analytical Methods in Pharmaceuticals ; Synthesis and Biological Activity ; Phosphodiesterase function and regulation
Authors: Takumi ADACHI | Ken Itomi | Ryouta YAMAMOTO | Shou KUBOUCHI | Jun Morita | Shin Horiuchi | Hiroshi MORITA
Source: NIPPON GOMU KYOKAISHI (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Informatics Research in Functional Synthetic Rubber Materials The relationship between structures and physical properties for rubber blend was investigated by materials informatics approach. SBR/IR/Silica blend rubber, which is commonly used as tire rubber, was targeted in this paper. Model structures were created on a computer, and physical properties were calculated by finite element method (FEM) simulation. Datasets with structural features as explanatory variables and calculated values as objective variables were obtained by three-dimensional structure analysis for model structures. To clarify the contribution of each structural features to physical properties, machine learning was performed using the dataset with the modulus at 50% elongation (M50) as the objective variable. The prediction accuracy of the model was as high as 0.87 in R2 value. Furthermore, SHapley Additive exPlanation (SHAP) analysis revealed that the continuity of SBR phase and the shape of the phase separation interface are particularly important for physical characteristics. Finally, we created a structure-property correlation diagram that allow us to narrow down the compositions and structures that satisfied target physical properties without actual experiments. It follows that material development period will be shortened. Informatics ; Synthetic rubber ; Natural rubber ; Computer science ; Data science ; Engineering ; Materials science ; Composite material Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Innovations in Concrete and Construction Materials
Authors: Alberto Hernandez | Tim Mueller
Source: arXiv (Cornell University) (preprint, 2022-10-27)
Field: Materials Science — Materials Chemistry

Generalizability of Functional Forms for Interatomic Potential Models Discovered by Symbolic Regression In recent years there has been great progress in the use of machine learning algorithms to develop interatomic potential models. Machine-learned potential models are typically orders of magnitude faster than density functional theory but also orders of magnitude slower than physics-derived models such as the embedded atom method. In our previous work, we used symbolic regression to develop fast, accurate and transferrable interatomic potential models for copper with novel functional forms that resemble those of the embedded atom method. To determine the extent to which the success of these forms was specific to copper, here we explore the generalizability of these models to other face-centered cubic transition metals and analyze their out-of-sample performance on several material properties. We found that these forms work particularly well on elements that are chemically similar to copper. When compared to optimized Sutton-Chen models, which have similar complexity, the functional forms discovered using symbolic regression perform better across all elements considered except gold where they have a similar performance. They perform similarly to a moderately more complex embedded atom form on properties on which they were trained, and they are more accurate on average on other properties. We attribute this improved generalized accuracy to the relative simplicity of the models discovered using symbolic regression. The genetic programming models are found to outperform other models from the literature about 50% of the time in a variety of property predictions, with about 1/10th the model complexity on average. We discuss the implications of these results to the broader application of symbolic regression to the development of new potentials and highlight how models discovered for one element can be used to seed new searches for different elements. Generalizability theory ; Symbolic regression ; Regression ; Computer science ; Genetic programming ; Simplicity ; Regression analysis ; Atom (system on chip) ; Artificial intelligence ; Machine learning ; Mathematics ; Statistics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Stefan Hiemer | Paolo Moretti | Stefano Zapperi | Michael Zaiser
Source: arXiv (Cornell University) (preprint, 2022-08-14)
Field: Materials Science — Materials Chemistry

Predicting Creep Failure by Machine Learning -- Which Features Matter? Spatial and temporal features are studied with respect to their predictive value for failure time prediction in subcritical failure with machine learning (ML). Data are generated from simulations of a novel, brittle random fuse model (RFM), as well as elasto-plastic finite element simulations (FEM) of a stochastic plasticity model with damage, both models considering stochastic thermally activated damage/failure processes in disordered materials. Fuse networks are generated with hierarchical and nonhierarchical architectures. Random forests - a specific ML algorithm - allow us to measure the feature importance through a feature's average error reduction. RFM simulation data are found to become more predictable with increasing system size and temperature. Increasing the load or the scatter in local materials properties has the opposite effect. Damage accumulation in these models proceeds in stochastic avalanches, and statistical signatures such as avalanche rate or magnitude have been discussed in the literature as predictors of incipient failure. However, in the present study such features proved of no measurable use to the ML models, which mostly rely on global or local strain for prediction. This suggests the strain as viable quantity to monitor in future experimental studies as it is accessible via digital image correlation. Fuse (electrical) ; Feature (linguistics) ; Computer science ; Brittleness ; Finite element method ; Creep ; Plasticity ; Artificial intelligence ; Machine learning ; Structural engineering ; Materials science ; Engineering Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Microstructure and mechanical properties
Authors: Michael Deffner | Marc Philipp Weise | Haitao Zhang | Maike Mücke | Jonny Proppe | Ignacio Franco | Carmen Herrmann
Source: ChemRxiv (preprint, 2022-06-22)
Field: Materials Science — Materials Chemistry

Learning Conductance: Gaussian Process Regression for Molecular Electronics Experimental studies of charge transport through single molecules often rely on break junction setups, where molecular junctions are repeatedly formed and broken while measuring the conductance, leading to a statistical distribution of conductance values. Modeling this experimental situation and the resulting conductance histograms is challenging for theoretical methods, as computations need to capture structural changes in experiments, including the statistics of junction formation and rupture. This type of extensive structural sampling implies that even when evaluating conductance from computationally efficient electronic structure methods, which typically are of reduced accuracy, the evaluation of conductance histograms is too expensive to be a routine task. Highly accurate quantum transport computations are only computationally feasible for a few selected conformations and thus necessarily ignore the rich conformational space probed in experiments. To overcome these limitations, we investigate the potential of machine learning for modeling conductance histograms, in particular by Gaussian process regression. We show that by selecting specific structural parameters as features, Gaussian process regression can be used to efficiently predict the zero-bias conductance from molecular structures, reducing the computational cost of simulating conductance histograms by an order of magnitude. This enables the efficient calculation of conductance histograms even on the basis of computationally expensive first-principles approaches by effectively reducing the number of necessary charge transport calculations, paving the way towards their routine evaluation. Conductance ; Histogram ; Computation ; Gaussian ; Gaussian process ; Computer science ; Kriging ; Algorithm ; Statistical physics ; Artificial intelligence ; Biological system ; Machine learning ; Chemistry ; Physics ; Computational chemistry ; Condensed matter physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Jim Magiera | Christian Rohde
Source: Journal of Computational Physics (article, 2022-08-22)
Field: Materials Science — Materials Chemistry

A molecular–continuum multiscale model for inviscid liquid–vapor flow with sharp interfaces Inviscid flow ; Microscale chemistry ; Mechanics ; Discretization ; Statistical physics ; Finite volume method ; Flow (mathematics) ; Boundary value problem ; Two-phase flow ; Multiscale modeling ; Scale (ratio) ; Classical mechanics ; Physics ; Mathematics ; Chemistry ; Mathematical analysis Block Copolymer Self-Assembly ; Advanced Mathematical Modeling in Engineering ; Model Reduction and Neural Networks
Authors: Hiba Kobeissi | Saeed Mohammadzadeh | Emma Lejeune
Source: Journal of Biomechanical Engineering (article, 2022-06-29)
Field: Materials Science — Materials Chemistry

Enhancing Mechanical Metamodels With a Generative Model-Based Augmented Training Dataset Modeling biological soft tissue is complex in part due to material heterogeneity. Microstructural patterns, which play a major role in defining the mechanical behavior of these tissues, are both challenging to characterize and difficult to simulate. Recently, machine learning (ML)-based methods to predict the mechanical behavior of heterogeneous materials have made it possible to more thoroughly explore the massive input parameter space associated with heterogeneous blocks of material. Specifically, we can train ML models to closely approximate computationally expensive heterogeneous material simulations where the ML model is trained on datasets of simulations with relevant spatial heterogeneity. However, when it comes to applying these techniques to tissue, there is a major limitation: the number of useful examples available to characterize the input domain under study is often limited. In this work, we investigate the efficacy of both ML-based generative models and procedural methods as tools for augmenting limited input pattern datasets. We find that a style-based generative adversarial network with an adaptive discriminator augmentation mechanism is able to successfully leverage just 1000 example patterns to create authentic generated patterns. In addition, we find that diverse generated patterns with adequate resemblance to real patterns can be used as inputs to finite element simulations to meaningfully augment the training dataset. To enable this methodological contribution, we have created an open access finite element analysis simulation dataset based on Cahn-Hilliard patterns. We anticipate that future researchers will be able to leverage this dataset and build on the work presented here. Computer science ; Leverage (statistics) ; Discriminator ; Generative grammar ; Artificial intelligence ; Machine learning ; Domain (mathematical analysis) ; Mathematics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Elasticity and Material Modeling
Authors: J. Zhao | Jesper Byggmästar | Huan He | K. Nordlund | Flyura Djurabekova | Mengyuan Hua
Source: arXiv (Cornell University) (preprint, 2022-12-06)
Field: Materials Science — Materials Chemistry

Complex $\mathrm{Ga}_{2}\mathrm{O}_{3}$ Polymorphs Explored by Accurate and General-Purpose Machine-Learning Interatomic Potentials $\mathrm{Ga}_{2}\mathrm{O}_{3}$ is a wide-bandgap semiconductor of emergent importance for applications in electronics and optoelectronics. However, vital information of the properties of complex coexisting $\mathrm{Ga}_{2}\mathrm{O}_{3}$ polymorphs and low-symmetry disordered structures is missing. In this work, we develop two types of kernel-based machine-learning Gaussian approximation potentials (ML-GAPs) for $\mathrm{Ga}_{2}\mathrm{O}_{3}$ with high accuracy for $β$/$κ$/$α$/$δ$/$γ$ polymorphs and generality for disordered stoichiometric structures. We release two versions of interatomic potentials in parallel, namely soapGAP and tabGAP, for excellent accuracy and exceeding speedup, respectively. We systematically show that both the soapGAP and tabGAP can reproduce the structural properties of all the five polymorphs in an exceptional agreement with ab initio results, meanwhile boost the computational efficiency with $5\times10^{2}$ and $2\times10^{5}$ computing speed increases compared to density functional theory, respectively. The results show that the liquid-solid phase transition proceeds in three different stages, a "slow transition", "fast transition" and "only Ga migration". We show that this complex dynamics can be understood in terms of different behavior of O and Ga sublattices in the interfacial layer. Ab initio ; Physics ; Molecular dynamics ; Stoichiometry ; Crystallography ; Band gap ; Materials science ; Condensed matter physics ; Chemistry ; Physical chemistry ; Quantum mechanics Machine Learning in Materials Science ; Ga2O3 and related materials