jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dominique Beaini | Shenyang Huang | Joao Alex Cunha | Zhiyi Li | Gabriela Moisescu-Pareja | Oleksandr Dymov | Samuel Maddrell-Mander | Callum McLean | Ali Parviz | Luis T. Díaz Müller | Jama Hussein Mohamud | Frederik Wenkel | Michael Craig | Michał Koziarski | Jiarui Lu | Zhaocheng Zhu | Cristian Gabellini | Guillaume Rabusseau | Reihaneh Rabbany | Jian Tang | Christopher G. Morris | Mirco Ravanelli | Guy Wolf | Prudencio Tossou | Hadrien Mary | Błażej Banaszewski | Chad Martin | Dominic Masters
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-06-02)
Field: Materials Science — Materials Chemistry

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets Recently, pre-trained foundation models have shown significant advancements in multiple fields. However, the lack of datasets with labeled features and codebases has hindered the development of a supervised foundation model for molecular tasks. Here, we have carefully curated seven datasets specifically tailored for node- and graph-level prediction tasks to facilitate supervised learning on molecules. Moreover, to support the development of multi-task learning on our proposed datasets, we created the Graphium graph machine learning library. Our dataset collection encompasses two distinct categories. Firstly, the TOYMIX category modifies three small existing datasets with additional data for multi-task learning. Secondly, the LARGEMIX category includes four large-scale datasets with 344M graph-level data points and 409M node-level data points from ∼5M unique molecules. Finally, the ultra-large dataset contains 2,210M graph-level data points and 2,031M node-level data points coming from 86M molecules. Hence our datasets represent an order of magnitude increase in data volume compared to other 2D-GNN datasets. In addition, recognizing that molecule-related tasks often span multiple levels, we have designed our library to explicitly support multi-tasking, offering a diverse range of multi-level representations, i.e., representations at the graph, node, edge, and node-pair level. We equipped the library with an extensive collection of models and features to cover different levels of molecule analysis. By combining our curated datasets with this versatile library, we aim to accelerate the development of molecule foundation models. Datasets and code are available at https://github.com/datamol-io/graphium. Task (project management) ; Scale (ratio) ; Computer science ; Artificial intelligence ; Data science ; Natural language processing ; Geography ; Engineering ; Systems engineering ; Cartography Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Machine Learning in Bioinformatics
Authors: Dominique Beaini | Shenyang Huang | Joao Alex Cunha | Zhiyi Li | Gabriela Moisescu-Pareja | Oleksandr Dymov | Samuel Maddrell-Mander | Callum McLean | Ali Parviz | Luis T. Díaz Müller | Jama Hussein Mohamud | Frederik Wenkel | Michael Craig | Michał Koziarski | Jiarui Lu | Zhaocheng Zhu | Cristian Gabellini | Guillaume Rabusseau | Reihaneh Rabbany | Jian Tang | Christopher G. Morris | Mirco Ravanelli | Guy Wolf | Prudencio Tossou | Hadrien Mary | Błażej Banaszewski | Chad Martin | Dominic Masters
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-06-02)
Field: Materials Science — Materials Chemistry

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets Recently, pre-trained foundation models have shown significant advancements in multiple fields. However, the lack of datasets with labeled features and codebases has hindered the development of a supervised foundation model for molecular tasks. Here, we have carefully curated seven datasets specifically tailored for node- and graph-level prediction tasks to facilitate supervised learning on molecules. Moreover, to support the development of multi-task learning on our proposed datasets, we created the Graphium graph machine learning library. Our dataset collection encompasses two distinct categories. Firstly, the TOYMIX category modifies three small existing datasets with additional data for multi-task learning. Secondly, the LARGEMIX category includes four large-scale datasets with 344M graph-level data points and 409M node-level data points from ∼5M unique molecules. Finally, the ultra-large dataset contains 2,210M graph-level data points and 2,031M node-level data points coming from 86M molecules. Hence our datasets represent an order of magnitude increase in data volume compared to other 2D-GNN datasets. In addition, recognizing that molecule-related tasks often span multiple levels, we have designed our library to explicitly support multi-tasking, offering a diverse range of multi-level representations, i.e., representations at the graph, node, edge, and node-pair level. We equipped the library with an extensive collection of models and features to cover different levels of molecule analysis. By combining our curated datasets with this versatile library, we aim to accelerate the development of molecule foundation models. Datasets and code are available at https://github.com/datamol-io/graphium. Task (project management) ; Scale (ratio) ; Computer science ; Artificial intelligence ; Data science ; Machine learning ; Engineering ; Geography ; Systems engineering ; Cartography Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Machine Learning in Bioinformatics
Authors: Dominique Beaini | Shenyang Huang | Joao Alex Cunha | Zhiyi Li | Gabriela Moisescu-Pareja | Oleksandr Dymov | Samuel Maddrell-Mander | Callum McLean | Ali Parviz | Luis T. Díaz Müller | Jama Hussein Mohamud | Frederik Wenkel | Michael Craig | Michał Koziarski | Jiarui Lu | Zhaocheng Zhu | Cristian Gabellini | Guillaume Rabusseau | Reihaneh Rabbany | Jian Tang | Christopher G. Morris | Mirco Ravanelli | Guy Wolf | Prudencio Tossou | Hadrien Mary | Błażej Banaszewski | Chad Martin | Dominic Masters
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-06-02)
Field: Materials Science — Materials Chemistry

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets Recently, pre-trained foundation models have shown significant advancements in multiple fields. However, the lack of datasets with labeled features and codebases has hindered the development of a supervised foundation model for molecular tasks. Here, we have carefully curated seven datasets specifically tailored for node- and graph-level prediction tasks to facilitate supervised learning on molecules. Moreover, to support the development of multi-task learning on our proposed datasets, we created the Graphium graph machine learning library. Our dataset collection encompasses two distinct categories. Firstly, the TOYMIX category modifies three small existing datasets with additional data for multi-task learning. Secondly, the LARGEMIX category includes four large-scale datasets with 344M graph-level data points and 409M node-level data points from ∼5M unique molecules. Finally, the ultra-large dataset contains 2,210M graph-level data points and 2,031M node-level data points coming from 86M molecules. Hence our datasets represent an order of magnitude increase in data volume compared to other 2D-GNN datasets. In addition, recognizing that molecule-related tasks often span multiple levels, we have designed our library to explicitly support multi-tasking, offering a diverse range of multi-level representations, i.e., representations at the graph, node, edge, and node-pair level. We equipped the library with an extensive collection of models and features to cover different levels of molecule analysis. By combining our curated datasets with this versatile library, we aim to accelerate the development of molecule foundation models. Datasets and code are available at https://github.com/datamol-io/graphium. Task (project management) ; Scale (ratio) ; Computer science ; Data science ; Geography ; Systems engineering ; Engineering ; Cartography Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Machine Learning in Bioinformatics
Authors: Dominique Beaini | Shenyang Huang | Joao Alex Cunha | Zhiyi Li | Gabriela Moisescu-Pareja | Oleksandr Dymov | Samuel Maddrell-Mander | Callum McLean | Ali Parviz | Luis T. Díaz Müller | Jama Hussein Mohamud | Frederik Wenkel | Michael Craig | Michał Koziarski | Jiarui Lu | Zhaocheng Zhu | Cristian Gabellini | Guillaume Rabusseau | Reihaneh Rabbany | Jian Tang | Christopher G. Morris | Mirco Ravanelli | Guy Wolf | Prudencio Tossou | Hadrien Mary | Błażej Banaszewski | Chad Martin | Dominic Masters
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-06-02)
Field: Materials Science — Materials Chemistry

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets Recently, pre-trained foundation models have shown significant advancements in multiple fields. However, the lack of datasets with labeled features and codebases has hindered the development of a supervised foundation model for molecular tasks. Here, we have carefully curated seven datasets specifically tailored for node- and graph-level prediction tasks to facilitate supervised learning on molecules. Moreover, to support the development of multi-task learning on our proposed datasets, we created the Graphium graph machine learning library. Our dataset collection encompasses two distinct categories. Firstly, the TOYMIX category modifies three small existing datasets with additional data for multi-task learning. Secondly, the LARGEMIX category includes four large-scale datasets with 344M graph-level data points and 409M node-level data points from ∼5M unique molecules. Finally, the ultra-large dataset contains 2,210M graph-level data points and 2,031M node-level data points coming from 86M molecules. Hence our datasets represent an order of magnitude increase in data volume compared to other 2D-GNN datasets. In addition, recognizing that molecule-related tasks often span multiple levels, we have designed our library to explicitly support multi-tasking, offering a diverse range of multi-level representations, i.e., representations at the graph, node, edge, and node-pair level. We equipped the library with an extensive collection of models and features to cover different levels of molecule analysis. By combining our curated datasets with this versatile library, we aim to accelerate the development of molecule foundation models. Datasets and code are available at https://github.com/datamol-io/graphium. Task (project management) ; Scale (ratio) ; Computer science ; Machine learning ; Artificial intelligence ; Data science ; Engineering ; Geography ; Systems engineering ; Cartography Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Machine Learning in Bioinformatics
Authors: Dominique Beaini | Shenyang Huang | Joao Alex Cunha | Zhiyi Li | Gabriela Moisescu-Pareja | Oleksandr Dymov | Samuel Maddrell-Mander | Callum McLean | Ali Parviz | Luis T. Díaz Müller | Jama Hussein Mohamud | Frederik Wenkel | Michael Craig | Michał Koziarski | Jiarui Lu | Zhaocheng Zhu | Cristian Gabellini | Guillaume Rabusseau | Reihaneh Rabbany | Jian Tang | Christopher G. Morris | Mirco Ravanelli | Guy Wolf | Prudencio Tossou | Hadrien Mary | Błażej Banaszewski | C.E. Martín | Dominic Masters
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-06-02)
Field: Materials Science — Materials Chemistry

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets Recently, pre-trained foundation models have shown significant advancements in multiple fields. However, the lack of datasets with labeled features and codebases has hindered the development of a supervised foundation model for molecular tasks. Here, we have carefully curated seven datasets specifically tailored for node- and graph-level prediction tasks to facilitate supervised learning on molecules. Moreover, to support the development of multi-task learning on our proposed datasets, we created the Graphium graph machine learning library. Our dataset collection encompasses two distinct categories. Firstly, the TOYMIX category modifies three small existing datasets with additional data for multi-task learning. Secondly, the LARGEMIX category includes four large-scale datasets with 344M graph-level data points and 409M node-level data points from ∼5M unique molecules. Finally, the ultra-large dataset contains 2,210M graph-level data points and 2,031M node-level data points coming from 86M molecules. Hence our datasets represent an order of magnitude increase in data volume compared to other 2D-GNN datasets. In addition, recognizing that molecule-related tasks often span multiple levels, we have designed our library to explicitly support multi-tasking, offering a diverse range of multi-level representations, i.e., representations at the graph, node, edge, and node-pair level. We equipped the library with an extensive collection of models and features to cover different levels of molecule analysis. By combining our curated datasets with this versatile library, we aim to accelerate the development of molecule foundation models. Datasets and code are available at https://github.com/datamol-io/graphium. Task (project management) ; Scale (ratio) ; Computer science ; Artificial intelligence ; Natural language processing ; Engineering ; Systems engineering ; Geography ; Cartography Machine Learning in Materials Science ; Genetics, Bioinformatics, and Biomedical Research ; Machine Learning in Bioinformatics
Authors: Dominique Beaini | Shenyang Huang | Joao Alex Cunha | Zhiyi Li | Gabriela Moisescu-Pareja | Oleksandr Dymov | Samuel Maddrell-Mander | Callum McLean | Ali Parviz | Luis T. Díaz Müller | Jama Hussein Mohamud | Frederik Wenkel | Michael Craig | Michał Koziarski | Jiarui Lu | Zhaocheng Zhu | Cristian Gabellini | Guillaume Rabusseau | Reihaneh Rabbany | Jian Tang | Christopher G. Morris | Mirco Ravanelli | Guy Wolf | Prudencio Tossou | Hadrien Mary | Błażej Banaszewski | Chad Martin | Dominic Masters
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-09-22)
Field: Materials Science — Materials Chemistry

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets (Ultra Large Dataset) Recently, pre-trained foundation models have shown significant advancements in multiple fields. However, the lack of datasets with labeled features and codebases has hindered the development of a supervised foundation model for molecular tasks. Here, we have carefully curated seven datasets specifically tailored for node- and graph-level prediction tasks to facilitate supervised learning on molecules. Moreover, to support the development of multi-task learning on our proposed datasets, we created the Graphium graph machine learning library. Our dataset collection encompasses two distinct categories. Firstly, the TOYMIX category modifies three small existing datasets with additional data for multi-task learning. Secondly, the LARGEMIX category includes four large-scale datasets with 344M graph-level data points and 409M node-level data points from ∼5M unique molecules. Finally, the ultra-large dataset contains 2,210M graph-level data points and 2,031M node-level data points coming from 86M molecules. Hence our datasets represent an order of magnitude increase in data volume compared to other 2D-GNN datasets. In addition, recognizing that molecule-related tasks often span multiple levels, we have designed our library to explicitly support multi-tasking, offering a diverse range of multi-level representations, i.e., representations at the graph, node, edge, and node-pair level. We equipped the library with an extensive collection of models and features to cover different levels of molecule analysis. By combining our curated datasets with this versatile library, we aim to accelerate the development of molecule foundation models. Datasets and code are available at https://github.com/datamol-io/graphium. Task (project management) ; Scale (ratio) ; Computer science ; Artificial intelligence ; Machine learning ; Data science ; Geography ; Engineering ; Cartography ; Systems engineering Machine Learning in Materials Science
Authors: Zeneng Cheng | Kan Wang | A. N. M. Tanvir | Wei Shang | Yingying Zhang | Tengfei Luo | Alexander W. Dowling | David B. Go
Source:  (article, 2023-05-21)
Field: Materials Science — Materials Chemistry

Non-thermal Plasma Jet Sintering of Indium Tin Oxide Thin Films based on Bayesian Optimization Indium tin oxide (ITO) is a widely used thin-film material due to its electrical conductivity and optical transparency. It is an ideal candidate for flexible electronics, solar cell panels, and touch screens. The significance of effectively processing ITO thin films is becoming increasingly important. Non-thermal plasma jet sintering is a technique for sintering printed nanoparticle inks, which has previously been proven to enhance the electrical conductivity of printed silver nanoparticle thin films. It can be easily set up based on a dielectric barrier discharge (DBD) plasma jet configuration and operate under atmospheric pressure and temperature without damaging the thin films or thermally-sensitive substrates. In this research, we used blade coating to deposit ITO thin films on glass and quartz surfaces and sintered the deposited inks using an argon plasma jet. To identify the optimal setting for several key input parameters, including sintering time, argon gas flow rate, applied voltage, the gap distance between the ITO thin film surface and plasma jet, and input frequency, that influences the electrical conductivity of the sintered film, we employ a Bayesian optimization machine learning approach. Further, we constrain the optimization to ensure that the substrate temperature never exceeds 50 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∘</sup>C so that this technique can be applied on delicate and sensitive substrates. Overall sintering performance is benchmarked against thermal sintering of identically fabricated ITO films. Materials science ; Indium tin oxide ; Plasma ; Sintering ; Bayesian optimization ; Tin ; Indium ; Jet (fluid) ; Oxide ; Thermal ; Thin film ; Composite material ; Optoelectronics ; Computer science ; Metallurgy ; Nanotechnology ; Aerospace engineering ; Physics ; Engineering ; Thermodynamics ; Artificial intelligence ; Nuclear physics ZnO doping and properties
Authors: Alice Carlotto | Alessandro Chiasera | Maurizio Ferrari | Stefano Varas | Giacomo Zanetti | Osman Saygıner | Matteo Bonomo | Simone Galliano | Claudia Barolo | Andrea Farina | Silvia Maria Pietralunga
Source:  (article, 2023-06-12)
Field: Materials Science — Surfaces, Coatings and Films

Multi-Cavity Dielectric Mirrors for Spectral-Splitting Photovoltaic Applications Dielectric optical mirrors are designed, fabricated and tested, to be used as spectral-splitting mirrors in four-terminal photovoltaic modules. Mirrors are conceived as 1D-Photonics Bandgap Materials and designed as coupled multiple Bragg reflectors. Spectral tunability, transition steepness and in-band flatness are set by tuning a whole set of parameters, namely: the number and thickness of layers for the single cavity, the number of cavities and their sequence and the adequate refractive index contrast. The final design is optimized by machine-learning. In the present case, the final structure is made of the cascade of 5 Bragg reflectors, each one formed by 4 pairs of alternated layers of SiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> /TiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , or SiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> /HfO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> as a whole summing up to 41 layers. Multilayers fabrication is by Radio-Frequency sputtering deposition. Theoretical and experimental spectral curves well match. Preliminary results confirm the suitability of the fabrication technique to perform on flexible substrates, both of thin silica glass and of polyurethane resist. Dielectric ; Fabrication ; Materials science ; Photonics ; Refractive index ; Optoelectronics ; Optics ; Physics Optical Coatings and Gratings ; Photonic and Optical Devices ; Nanowire Synthesis and Applications
Authors: Akhil Shajan | Madushanka Manathunga | Andreas Goetz | Kenneth M. Merz
Source: ChemRxiv (preprint, 2023-08-22)
Field: Materials Science — Materials Chemistry

Geometry Optimization: A Comparison of Different Open-Source Geometry Optimizers Based on a series of energy minimizations with starting structures obtained from the Baker test set of 30 organic molecules, a comparison is made between various open-source geometry optimization codes that are interfaced with the open-source QUantum Interaction Computational Kernel (QUICK) program for gradient and energy calculations. The findings demonstrate how the choice of the coordinate system influences the optimization process to reach an equilibrium structure. With fewer steps, internal coordinates outperform Cartesian coordinates while the choice of the initial Hessian and Hessian update method in quasi-Newton approaches made by different optimization algorithms also contributes to the rate of convergence. Furthermore, an available open-source machine learning method based on Gaussian Process Regression (GPR) was evaluated for energy minimizations over surrogate potential energy surfaces with both Cartesian and internal coordinates, with internal coordinates outperforming Cartesian. Overall, geomeTRIC and DL-FIND with their default optimization method as well as with GPR-based model using Hartree--Fock theory with the 6-31G** basis set, needed a comparable number of geometry optimization steps to the approach of Baker using a unit matrix as the initial Hessian to reach the optimized geometry. On the other hand, the Berny and Sella offerings in ASE outperformed the other algorithms. Based on this we recommend using the file-based approaches, ASE/Berny and ASE/Sella, for large-scale optimization efforts, while if using a single executable is preferable, we now distribute QUICK integrated with DL-FIND. Hessian matrix ; Cartesian coordinate system ; Computer science ; Energy minimization ; Geometry ; Algorithm ; Curvilinear coordinates ; Coordinate descent ; Optimization problem ; Mathematical optimization ; Mathematics ; Applied mathematics ; Physics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Gaussian Processes and Bayesian Inference