jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chloe A. Fuller | Lucas S. P. Rudden
Source: IUCrJ (article, 2023-11-14)
Field: Materials Science — Materials Chemistry

Unravelling the components of diffuse scattering using deep learning Many technologically important material properties are underpinned by disorder and short-range structural correlations; therefore, elucidating structure-property relationships in functional materials requires understanding both the average and the local structures. The latter information is contained within diffuse scattering but is challenging to exploit, particularly in single-crystal systems. Separation of the diffuse scattering into its constituent components can greatly simplify analysis and allows for quantitative parameters describing the disorder to be extracted directly. Here, a deep-learning method, DSFU-Net, is presented based on the Pix2Pix generative adversarial network, which takes a plane of diffuse scattering as input and factorizes it into the contributions from the molecular form factor and the chemical short-range order. DSFU-Net was trained on 198 421 samples of simulated diffuse scattering data and performed extremely well on the unseen simulated validation dataset in this work. On a real experimental example, DSFU-Net successfully reproduced the two components with a quality sufficient to distinguish between similar structural models based on the form factor and to refine short-range-order parameters, achieving values comparable to other established methods. This new approach could streamline the analysis of diffuse scattering as it requires minimal prior knowledge of the system, allows access to both components in seconds and is able to compensate for small regions with missing data. DSFU-Net is freely available for use and represents a first step towards an automated workflow for the analysis of single-crystal diffuse scattering. Computer science ; Scattering ; Exploit ; Deep learning ; Artificial intelligence ; Workflow ; Range (aeronautics) ; Characterization (materials science) ; Net (polyhedron) ; Biological system ; Optics ; Physics ; Materials science ; Mathematics ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Tsz Wai Ko | Jonas A. Finkler | Stefan Goedecker | Jörg Behler
Source: arXiv (Cornell University) (preprint, 2023-05-18)
Field: Materials Science — Materials Chemistry

Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in many fields from chemistry to materials science. While most current MLPs are based on environment-dependent atomic energies, the limitations of this locality approximation can be overcome, e.g., in fourth-generation MLPs, which incorporate long-range electrostatic interactions based on an equilibrated global charge distribution. Apart from the considered interactions, the quality of MLPs crucially depends on the information available about the system, i.e., the descriptors. In this work we show that including -- in addition to structural information -- the electrostatic potential arising from the charge distribution in the atomic environments significantly improves the quality and transferability of the potentials. Moreover, the extended descriptor allows to overcome current limitations of two- and three-body based feature vectors regarding artificially degenerate atomic environments. The capabilities of such an electrostatically embedded fourth-generation high-dimensional neural network potential (ee4G-HDNNP), which is further augmented by pairwise interactions, are demonstrated for NaCl as a benchmark system. Employing a data set containing only neutral and negatively charged NaCl clusters, even small energy differences between different cluster geometries can be resolved, and the potential shows an impressive transferability to positively charged clusters as well as the melt. Pairwise comparison ; Computer science ; Electrostatics ; Embedding ; Benchmark (surveying) ; Transferability ; Charge (physics) ; Statistical physics ; Artificial neural network ; Set (abstract data type) ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Yulun Wu | Yumeng Li
Source:  (article, 2023-08-20)
Field: Materials Science — Materials Chemistry

How to Encode Microstructure in Machine Learning: A Comparison Study Abstract Accurately predicting the response of materials under different loading conditions is crucial for designing and developing new materials with desired properties. However, this process can be computationally expensive and challenging, especially for heterogeneous materials with complex microstructures. Recently, machine learning has been widely used to address the challenge for developing predictive models for various material systems with reduced reliance on extensive experimental testings and repetitive expensive physics simulations. The microstructure of a material plays a critical role in determining its properties, making it a key factor that needs to be accounted for in predictive modeling. Heterogeneous materials, specifically, often have complex microstructures with numerous features like pores, inclusions, and grain boundaries, which need to be accurately captured but is hard to be quantified for developing machine learning based predictive models. Therefore, accurate encoding of microstructural features is essential for making reliable predictions. Nevertheless, how to effectively and efficiently capture the complex microstructural features in developing machine learning based predictive models largely remains an open question for researchers in the field of materials science. In this paper, we present a comparison study of different encoding methods for microstructures in machine learning models. Specifically, we investigate pre-defined encoding methods and automatic encoding methods for a synthetic heterogeneous material system. the performance of each machine learning model is evaluated by predicting material responses such as strain energy. Our results show that convolutional neural networks (CNNs) have the ability to auto-encode the microstructure information of material and make promising prediction, especially when good pre-defined descriptors are not available. Overall, this study provides valuable insights into the performance of different encoding methods for microstructures in machine learning models, and can inform the development of more accurate and efficient models for materials science applications. ENCODE ; Machine learning ; Computer science ; Artificial intelligence ; Encoding (memory) ; Convolutional neural network ; Process (computing) ; Field (mathematics) ; Microstructure ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Evan R. Antoniuk | Gowoon Cheon | George Wang | Daniel Bernstein | William Cai | Evan J. Reed
Source: Research Square (preprint, 2023-03-17)
Field: Materials Science — Materials Chemistry

Predicting the Synthesizability of Crystalline Inorganic Materials from the Data of Known Material Compositions Abstract Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery. In this work, we develop a deep learning synthesizability model ( SynthNN ) that leverages the entire corpus of synthesized inorganic chemical compositions. By reformulating material discovery as a synthesizability classification task, SynthNN identifies synthesizable materials with 7x higher precision than with DFT-calculated formation energies. In a head-to-head material discovery comparison against 20 expert material scientists, SynthNN outperforms all experts, achieves 3.6x higher precision and completes the task five orders of magnitude faster than the average human expert. Remarkably, without any prior chemical knowledge, our experiments indicate that SynthNN learns the chemical principles of charge-balancing, chemical family relationships and ionicity, and utilizes these principles to generate synthesizability predictions. The development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials. Materials science ; Mineralogy ; Geology ; Geochemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: M. Zammit | Kristian Zarb Adami
Source: Monthly Notices of the Royal Astronomical Society (article, 2023-11-27)
Field: Chemistry — Spectroscopy

Machine learning applications in Jupiter-host star classification using stellar spectra ABSTRACT The link between stellar host properties, be it chemical, physical, dynamical, or galactic in nature, with the presence of planetary companions, has been one that has been repeatedly tested in the literature. Several corroborated work has argued that the correlation between a stellar atmosphere’s chemistry and the presence of gas giant companions is primordial in nature, implying that the chemical budget in a protoplanetary disc, and by proxy the eventual stellar host, increases the likelihood of gas giant formation. In this work, we aim to use the power of computer vision to build and test a machine learning classifier capable of discriminating between gas giant host stars and a comparison sample, using spectral data of the host stars in the visible regime. High-resolution spectra are used to preserve any inherent information which may contribute to the classification, and are fed into a stacked ensemble design incorporating several convolutional neural networks. The spectral range is binned such that each is assigned to a first-level voter, with the meta-learner aggregating their votes into a final classification. We contextualize and elaborate on the model design and results presented in a prior proceedings publication, and present an amended architecture incorporating semisupervized learning. Both models achieve relatively strong performance metrics and generalize over the holdout sets well, yet still present signs of overfitting. Physics ; Overfitting ; Stellar classification ; Stars ; Astrophysics ; Spectral line ; Classifier (UML) ; Host (biology) ; Stellar atmosphere ; Artificial intelligence ; Machine learning ; Artificial neural network ; Astronomy ; Computer science Spectroscopy and Laser Applications
Authors: Travis Allen | Jake Graser | Ramsey Issa | Taylor D. Sparks
Source: ChemRxiv (preprint, 2023-03-14)
Field: Materials Science — Materials Chemistry

Machine learning predictions of low thermal conductivity: comparing TaVO5 and GdTaO4 Advancements in materials discovery tend to rely disproportionately on happenstance and luck rather than employing a systematic approach. Recently, advances in computational power have allowed researchers to build computer models to predict the material properties of any chemical formula. From energy minimization techniques to machine learning based models, these algorithms have unique strengths and weaknesses. However, a computational model is only as good as its accuracy when compared to real-world measurements. In this work, we take two recommendations from a thermoelectric machine learning model, TaVO5 and GdTaO4, and test their thermoelectric properties of Seebeck coefficient, thermal conductivity, and electrical conductivity. We see that the predictions are mixed; thermal conductivities are correctly predicted, while electrical conductivities and Seebeck coefficients are not. Furthermore, we discover a possible new avenue of research of a low thermal conductivity oxide family. Thermal conductivity ; Thermoelectric effect ; Seebeck coefficient ; Machine learning ; Thermoelectric materials ; Work (physics) ; Artificial intelligence ; Luck ; Computer science ; Materials science ; Minification ; Thermodynamics ; Physics ; Composite material Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Richard Tran | Liqiang Huang | Yuan Zi | Shengguang Wang | Benjamin M. Comer | Xuqing Wu | Stefan J. Raaijman | Nishant K. Sinha | Sajanikumari Sadasivan | Shibin Thundiyil | Kuldeep Mamtani | Ganesh Iyer | Lars C. Grabow | Ligang Lu | Jiefu Chen
Source: arXiv (Cornell University) (preprint, 2023-11-01)
Field: Materials Science — Materials Chemistry

Rational design of nanoscale stabilized oxide catalysts for OER with OC22 The efficiency of $H_2$ production via water electrolysis is typically limited to the sluggish oxygen evolution reaction (OER). As such, significant emphasis has been placed upon improving the rate of OER through the anode catalyst. More recently, the Open Catalyst 2022 (OC22) framework has provided a large dataset of density functional theory (DFT) calculations for OER intermediates on the surfaces of oxides. When coupled with state-of-the-art graph neural network models, total energy predictions can be achieved with a mean absolute error as low as 0.22 eV. In this work, we interpolated a database of the total energy predictions for all slabs and OER surface intermediates for 4,119 oxide materials in the original OC22 dataset using pre-trained models from the OC22 framework. This database includes all terminations of all facets up to a maximum Miller index of 1. To demonstrate the full utility of this database, we constructed a flexible screening framework to identify viable candidate anode catalysts for OER under varying reaction conditions for bulk, surface, and nanoscale Pourbaix stability as well as material cost, overpotential, and metastability. From our assessment, we were able to identify 122 and 68 viable candidates for OER under the bulk and nanoscale regime respectively. Overpotential ; Oxygen evolution ; Catalysis ; Density functional theory ; Oxide ; Materials science ; Anode ; Electrolysis ; Thermodynamics ; Chemistry ; Chemical engineering ; Physical chemistry ; Computational chemistry ; Electrochemistry ; Physics ; Metallurgy ; Organic chemistry ; Electrode Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Nicolas Bertin | Vasily V. Bulatov | Fei Zhou
Source: arXiv (Cornell University) (preprint, 2023-09-25)
Field: Materials Science — Materials Chemistry

Learning dislocation dynamics mobility laws from large-scale MD simulations The computational method of discrete dislocation dynamics (DDD), used as a coarse-grained model of true atomistic dynamics of lattice dislocations, has become of powerful tool to study metal plasticity arising from the collective behavior of dislocations. As a mesoscale approach, motion of dislocations in the DDD model is prescribed via the mobility law; a function which specifies how dislocation lines should respond to the driving force. However, the development of traditional hand-crafted mobility laws can be a cumbersome task and may involve detrimental simplifications. Here we introduce a machine-learning (ML) framework to streamline the development of data-driven mobility laws which are modeled as graph neural networks (GNN) trained on large-scale Molecular Dynamics (MD) simulations of crystal plasticity. We illustrate our approach on BCC tungsten and demonstrate that our GNN mobility implemented in large-scale DDD simulations accurately reproduces the challenging tension/compression asymmetry observed in ground-truth MD simulations while correctly predicting the flow stress at lower straining rate conditions unseen during training, thereby demonstrating the ability of our method to learn relevant dislocation physics. Our DDD+ML approach opens new promising avenues to improve fidelity of the DDD model and to incorporate more complex dislocation motion behaviors in an automated way, providing a faithful proxy for dislocation dynamics several orders of magnitude faster than ground-truth MD simulations. Dislocation ; Molecular dynamics ; Statistical physics ; Computer science ; Asymmetry ; Plasticity ; Artificial intelligence ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Electron Microscopy Techniques and Applications
Authors: Viktor Andonovikj | Mimoza Kovaci Azemi | Beti Andonović | Aleksandar Dimitrov
Source: Proceedings of the ENTRENOVA - Enterprise Research Innovation Conference (article, 2023-09-30)
Field: Materials Science — Materials Chemistry

Towards Optimal Production of Graphene by Electrolysis in Molten Salts Using Machine Learning Graphene is very exciting and one of the best materials in the last decade. The interest in producing non-expensive and high-quality graphene is of great interest to many Departments of materials and researchers worldwide. The main goal of this research considers the design, development, and optimisation of new technology for producing graphene by electrolysis in molten salts using constant cell voltage, reversing cell voltage, and constant overpotential and reversing overpotential methods. Compared to other processes, the electrolysis process is simple, ecological, and economical. Furthermore, it offers possibilities to control various parameters: type of electrolyte, applied voltage, current density, temperature and type of graphite used as a row material for synthesis of graphene. The underlying relationship between the parameters and the quality of the graphene is obtained through an explainable tree-based Machine Learning (ML) model. We trained the model by using the production process parameters as input attributes and the quality of the produced graphene as a target variable. Domain experts provide labels regarding the quality of each experimental instance in our data set. We propose using the extracted rules from the model to serve as a blueprint for optimal graphene production by electrolysis in molten salts. Choosing the correct values for the process parameters results in high-yield graphene production, up to ten times cheaper than the one produced with other existing technologies. Graphene ; Electrolysis ; Overpotential ; Process engineering ; Process (computing) ; Computer science ; Materials science ; Production (economics) ; Nanotechnology ; Electrolytic process ; Graphite ; Electrolyte ; Electrochemistry ; Engineering ; Electrode ; Chemistry ; Metallurgy Graphene research and applications ; Graphite, nuclear technology, radiation studies ; Recycling and Waste Management Techniques
Authors: Kirk Swanson | Jake Williams | Eric Jonas
Source: arXiv (Cornell University) (preprint, 2023-06-13)
Field: Materials Science — Materials Chemistry

Von Mises Mixture Distributions for Molecular Conformation Generation Molecules are frequently represented as graphs, but the underlying 3D molecular geometry (the locations of the atoms) ultimately determines most molecular properties. However, most molecules are not static and at room temperature adopt a wide variety of geometries or $\textit{conformations}$. The resulting distribution on geometries $p(x)$ is known as the Boltzmann distribution, and many molecular properties are expectations computed under this distribution. Generating accurate samples from the Boltzmann distribution is therefore essential for computing these expectations accurately. Traditional sampling-based methods are computationally expensive, and most recent machine learning-based methods have focused on identifying $\textit{modes}$ in this distribution rather than generating true $\textit{samples}$. Generating such samples requires capturing conformational variability, and it has been widely recognized that the majority of conformational variability in molecules arises from rotatable bonds. In this work, we present VonMisesNet, a new graph neural network that captures conformational variability via a variational approximation of rotatable bond torsion angles as a mixture of von Mises distributions. We demonstrate that VonMisesNet can generate conformations for arbitrary molecules in a way that is both physically accurate with respect to the Boltzmann distribution and orders of magnitude faster than existing sampling methods. Boltzmann distribution ; Boltzmann machine ; Boltzmann constant ; Molecule ; Statistical physics ; von Mises yield criterion ; Distribution (mathematics) ; Sampling (signal processing) ; Molecular dynamics ; Physics ; Computer science ; Molecular physics ; Biological system ; Artificial neural network ; Mathematics ; Thermodynamics ; Artificial intelligence ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Shuhui Liu | Ronghe Lin | Wei Liu | Yunjie Ding
Source: ChemPlusChem (article, 2023-05-04)
Field: Materials Science — Surfaces, Coatings and Films

Full Metal Species Quantification of Supported Catalysts: Beyond Metal Dispersion Metal dispersion is a key concept in heterogeneous catalysis. The conventional approaches for its estimation strongly rely on chemisorption with different probe molecules. Albeit they can generally provide an 'averaged' value in a cost-effective manner, the inhomogeneity of the metal species and the complicated metal-support interactions pose formidable challenges for the accurate determination. Full metal species quantification (FMSQ) is introduced as an advanced method to depict the whole distribution of the metal species, ranging from single atoms to clusters and nanoparticles, in a practical solid catalyst. In this approach, automated analysis of massive high-angle annular dark field scanning transmission electron microscopic images is realized through algorithms specialized in combining the electron microscopy-based atom recognition statistics and deep learning-driven nanoparticle segmentation. In this Concept article, different techniques for determining the metal dispersion are discussed with their pros and cons. FMSQ is highlighted for it can circumvent the drawbacks of conventional approaches, allowing more reliable structure-performance relationships beyond the metal size. Dispersion (optics) ; Chemisorption ; Metal ; Nanoparticle ; Catalysis ; Field (mathematics) ; Materials science ; Nanotechnology ; Scanning transmission electron microscopy ; Heterogeneous catalysis ; Transmission electron microscopy ; Computer science ; Chemistry ; Optics ; Physics ; Organic chemistry ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science