jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andreia Duarte Menezes | Edilberto Pereira Teixeira | José Roberto Delalíbera Finzer | Rafael Bonacin de Oliveira
Source: Research Society and Development (article, 2022-07-05)
Field: Materials Science — Ceramics and Composites

Machine learning-driven development of niobium-containing optical glasses High refractive index glasses are essential for old and new optical systems, such as microscopes, telescopes and novel augmented reality lenses and micro projectors. However, a fair portion of these glasses use toxic components, such as PbO, BaO, As2O3, and TeO2, which lead to high refractive indexes and facilitate the melting operation, but are harmful for human beings and the environment. On the other hand, it is known that niobium significantly increases the refractive index and is a non-toxic element. The objective of this paper was to develop new optical glass compositions containing Nb2O5 with a relatively high refractive index (nd &gt; 1.65), intermediate Abbe number (35 &lt; Vd &lt; 55) and fair glass transition temperature, Tg. To this end, we used a machine learning algorithm titled GLAS, which was recently developed at DEMA-UFSCar to produce new optical glasses composition. After running the algorithm 13 times, two of the most promising compositions were chosen and tested for their glass forming ability and other properties. The best composition was analyzed in respect to the refractive index, glass transition temperature and chemical durability. A comparison between the laboratory results and predictions of the artificial neural network indicates that the GLAS algorithm provides adequate formulations and can be immediately used for accelerating the design of new glasses, substantially reducing the laboratory testing effort. Also, the results indicate that niobium glasses might offer some advantages over its main competitor (La2O3). Niobium ; Refractive index ; High-refractive-index polymer ; Optical glass ; Glass transition ; Materials science ; Block (permutation group theory) ; Durability ; Index (typography) ; Optics ; Mineralogy ; Optoelectronics ; Composite material ; Metallurgy ; Computer science ; Chemistry ; Polymer ; Physics ; Mathematics Glass properties and applications
Authors: Meiting Zhao | Erxiao Wu | Dongyang Li | Junfei Luo | Xin Zhang | Zhuquan Wang | Qing Huang | Shiyu Du | Yiming Zhang
Source: Journal of Materials Research and Technology (article, 2022-12-17)
Field: Materials Science — Materials Chemistry

Development of a baseline model for MAX/MXene synthesis recipes extraction via pre-trained model with domain knowledge Due to their unique combination of metallic- and ceramic-like properties, MAX phases have attracted a lot of attentions. By selectively etching A-site atoms, MXenes with unique two-dimensional structures can be potentially generated. Due to their extraordinary properties, MXenes have currently made their way to the forefront of various research areas including electronics, photonics and catalysis. Therefore, the development of novel synthesis strategies for MAX/MXene is a key issue for the further development of MAX/MXene. Distilling insights from scientific literatures could accelerate the exploration of novel synthesis recipes; however, manually extracting scattered information from thousands of journal articles is laborious. In this study, we present an annotated corpus incorporating domain knowledge about MAX/MXene synthesis processes, deriving from experimental sections within 110 papers on MAX/MXene research; and based on that, a baseline model (including named entity recognition (NER) and relation extraction (RE) parts) is proposed for distilling information about MAX/MXene synthesis conditions from literatures using pre-trained natural language processing (NLP) models. We also demonstrate the efficacy of the proposed pipeline owning to the joint effort of domain knowledge (about MAX/MXene) and machine learning; where the entity recognition model possessing optimized setting could detect the entities with F1 score of 0.8452, and for relation extraction model with F1 score of 0.8476. It is hoped that the current work would provide an auxiliary for the future research and development of novel MAX/MXenes. In addition, the developed model could serve as a pre-trained model of MAX/MXenes synthesis routes extraction for future data augment. MXenes ; Artificial intelligence ; Computer science ; Baseline (sea) ; Machine learning ; Pipeline (software) ; Relationship extraction ; MAX phases ; Domain (mathematical analysis) ; Relation (database) ; Key (lock) ; Information extraction ; Nanotechnology ; Materials science ; Data mining ; Ceramic ; Programming language MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Tobias Greisager Rehfeldt | Ralf Gabriels | Robbin Bouwmeester | Siegfried Gessulat | Benjamin A. Neely | Magnus Palmblad | Yasset Pérez‐Riverol | Tobias Schmidt | Juan Antonio Vizcaíno | Eric W. Deutsch
Source: ChemRxiv (preprint, 2022-10-05)
Field: Chemistry — Spectroscopy

ProteomicsML: An Online Platform for Community-Curated Datasets and Tutorials for Machine Learning in Proteomics Dataset acquisition and curation are often the hardest and most time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based LC-IM-MS datasets, due to the high-throughput data structure with high levels of noise and complexity between raw and machine learning-ready formats. While predictive proteomics is a field on the rise, when predicting peptide behavior in LC-IM-MS setups, each lab often uses unique and complex data processing pipelines in order to maximize performance, at the cost of accessibility and reproducibility. For this reason we introduce ProteomicsML, an online resource for proteomics-based datasets and tutorials across most of the currently explored physicochemical peptide properties. This community-driven resource makes it simple to access data in easy-to-process formats, and contains easy-to-follow tutorials that allow new users to interact with even the most advanced algorithms in the field. ProteomicsML provides datasets that are useful for comparing state-of-the-art (SOTA) machine learning algorithms, as well as providing introductory material for teachers and newcomers to the field alike. The platform is freely available on https://www.proteomicsml.org/ and we welcome the entire proteomics community to contribute to the project at https://github.com/proteomicsml/. Computer science ; Field (mathematics) ; Resource (disambiguation) ; Pipeline (software) ; Process (computing) ; Machine learning ; Proteomics ; Data science ; Artificial intelligence ; Raw data Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Jingjin He | Xiaopo Su | Changxin Wang | Junjie Li | Yuxuan Hou | Zhonghua Li | Chuanbao Liu | Dezhen Xue | Jiangli Cao | Yanjing Su | Lijie Qiao | Turab Lookman | Yang Bai
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Predictions of Multi-Component Phase Diagrams and Fine Boundary Information Component (thermodynamics) ; Boundary (topology) ; Computer science ; Phase (matter) ; Artificial intelligence ; Phase diagram ; Machine learning ; Mathematics ; Physics ; Thermodynamics ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; High-pressure geophysics and materials
Authors: Carl Poelking | Felix A. Faber | Bingqing Cheng
Source: Machine Learning Science and Technology (article, 2022-01-19)
Field: Materials Science — Materials Chemistry

BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale Abstract We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules. The guiding principle underlying the benchmarking approach is to evaluate raw descriptor performance by limiting model complexity to simple regression schemes while enforcing best ML practices, allowing for unbiased hyperparameter optimization, and assessing learning progress through learning curves along series of synchronized train-test splits. The resulting models are intended as baselines that can inform future method development, in addition to indicating how easily a given dataset can be learnt. Through a comparative analysis of the training outcome across a diverse set of physicochemical, topological and geometric representations, we glean insight into the relative merits of these representations as well as their interrelatedness. Benchmarking ; Computer science ; Hyperparameter ; Machine learning ; Set (abstract data type) ; Simple (philosophy) ; Limiting ; Artificial intelligence ; Scale (ratio) ; Regression ; Theoretical computer science ; Data science ; Mathematics ; Programming language ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yinan Huang | Xingang Peng | Jianzhu Ma | Muhan Zhang
Source: arXiv (Cornell University) (preprint, 2022-10-22)
Field: Materials Science — Materials Chemistry

Boosting the Cycle Counting Power of Graph Neural Networks with I$^2$-GNNs Message Passing Neural Networks (MPNNs) are a widely used class of Graph Neural Networks (GNNs). The limited representational power of MPNNs inspires the study of provably powerful GNN architectures. However, knowing one model is more powerful than another gives little insight about what functions they can or cannot express. It is still unclear whether these models are able to approximate specific functions such as counting certain graph substructures, which is essential for applications in biology, chemistry and social network analysis. Motivated by this, we propose to study the counting power of Subgraph MPNNs, a recent and popular class of powerful GNN models that extract rooted subgraphs for each node, assign the root node a unique identifier and encode the root node's representation within its rooted subgraph. Specifically, we prove that Subgraph MPNNs fail to count more-than-4-cycles at node level, implying that node representations cannot correctly encode the surrounding substructures like ring systems with more than four atoms. To overcome this limitation, we propose I$^2$-GNNs to extend Subgraph MPNNs by assigning different identifiers for the root node and its neighbors in each subgraph. I$^2$-GNNs' discriminative power is shown to be strictly stronger than Subgraph MPNNs and partially stronger than the 3-WL test. More importantly, I$^2$-GNNs are proven capable of counting all 3, 4, 5 and 6-cycles, covering common substructures like benzene rings in organic chemistry, while still keeping linear complexity. To the best of our knowledge, it is the first linear-time GNN model that can count 6-cycles with theoretical guarantees. We validate its counting power in cycle counting tasks and demonstrate its competitive performance in molecular prediction benchmarks. Identifier ; Computer science ; Discriminative model ; ENCODE ; Theoretical computer science ; Boosting (machine learning) ; Graph ; Node (physics) ; Class (philosophy) ; Subgraph isomorphism problem ; Artificial intelligence ; Computer network ; Gene ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Source: Nature Computational Science (article, 2022-06-23)
Field: Materials Science — Materials Chemistry

Improving the efficiency of ab initio electronic-structure calculations by deep learning Ab initio ; Electronic structure ; Materials science ; Ab initio quantum chemistry methods ; Computational chemistry ; Chemical physics ; Computer science ; Artificial intelligence ; Chemistry ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Mohammed Wasay Mudassir | Sriram Goverapet Srinivasan | Mahesh Mynam | Beena Rai
Source: The Journal of Physical Chemistry A (article, 2022-10-27)
Field: Materials Science — Materials Chemistry

Systematic Identification of Atom-Centered Symmetry Functions for the Development of Neural Network Potentials Neural network potentials are emerging as promising classical force fields that can enable long-time and large-length scale simulations at close to <i>ab initio</i> accuracies. They learn the underlying potential energy surface by mapping the Cartesian coordinates of atoms to system energies using elemental neural networks. To ensure invariance with respect to system translation, rotation, and atom index permutations, in the Behler-Parrinnello type of neural network potential (BP-NNP), the Cartesian coordinates of atoms are transformed into "structural fingerprints" using atom-centered symmetry functions (ACSFs). Development of an accurate BP-NNP for any chemical system critically relies on the choice of these ACSFs. In this work, we have proposed a systematic framework for the identification of an optimal set of ACSFs for any target system, which not only considers the diverse atomic environments present in the training dataset but also inter-ACSF correlations. Our method is applicable to different kinds of ACSFs and across diverse chemical systems. We demonstrate this by building accurate BP-NNPs for water and Cu<sub>2</sub>S systems. Identification (biology) ; Symmetry (geometry) ; Artificial neural network ; Atom (system on chip) ; Physics ; Computer science ; Artificial intelligence ; Mathematics ; Biology ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Qiao Dong | Wenqi Wang | Xinyi Cao | Yibo Xiao | Xiaohan Guo | Jingxuan Ma | Lianhui Wang | Li Gao
Source: Chinese Optics Letters (article, 2022-11-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic nanostructure characterized by deep-neural-network-assisted spectroscopy [Invited] The lateral geometry and material property of plasmonic nanostructures are critical parameters for tailoring their optical resonance for sensing applications. While lateral geometry can be easily observed by a scanning electron microscope or an atomic force microscope, characterizing materials properties of plasmonic devices is not straightforward and requires delicate examination of material composition, cross-sectional thickness, and refractive index. In this study, a deep neural network is adopted to characterize these parameters of unknown plasmonic nanostructures through simple transmission spectra. The network architecture is established based on simulated data to achieve accurate identification of both geometric and material parameters. We then demonstrate that the network training by a mixture of simulated and experimental data can result in correct material property recognition. Our work may indicate a simple and intelligent characterization approach to plasmonic nanostructures by spectroscopic techniques. Plasmon ; Nanostructure ; Materials science ; Characterization (materials science) ; Refractive index ; Artificial neural network ; Optics ; Spectroscopy ; Nanotechnology ; Optoelectronics ; Computer science ; Artificial intelligence ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Water Quality Monitoring and Analysis ; Plasmonic and Surface Plasmon Research
Authors: Devyani Jivani | Olga Wodo
Source: Computational Materials Science (article, 2022-08-10)
Field: Materials Science — Materials Chemistry

Skeletal-based microstructure representation and featurization through descriptors Microstructure ; Representation (politics) ; Computer science ; Materials science ; Natural language processing ; Artificial intelligence ; Chemistry ; Crystallography Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Graph Theory and Algorithms
Authors: Keisuke Takahashi | Itsuki Miyazato | Satoshi Maeda | Lauren Takahashi
Source: PLoS ONE (article, 2022-05-17)
Field: Materials Science — Materials Chemistry

Designing transformer oil immersion cooling servers for machine learning and first principle calculations A transfomer oil immersion cooling server is designed and constructed for machine learning applications and first principle calculations that are carried out for materials-related research. CPU, motherboard, random access memory, hard disk drive, solid state drive, graphic card, and the power supply unit are submerged into the transformer oil in order to cool the entire system. Benchmark tests reveal that overall performance is improved while performance times for multicore calculations are dramatically improved. Furthermore, calculation times for machine learning with large data sets and density functional theory calculations are shortened during single core calculations. Thus, a transformer oil immersion cooling server is proposed to be an alternative cooling system used for improving the performance of first principle calculations and machine learning. Transformer ; Computer science ; Immersion (mathematics) ; Xeon ; Central processing unit ; Server ; Embedded system ; Nuclear engineering ; Simulation ; Computer hardware ; Operating system ; Electrical engineering ; Engineering ; Mathematics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Heat Transfer and Boiling Studies
Authors: Boris Kozinsky | Yu Xie | Jonathan Vandermause | Senja Ramakers | Nakib H. Protik | Anders Johansson
Source: Research Square (preprint, 2022-06-02)
Field: Materials Science — Materials Chemistry

Uncertainty-aware molecular dynamics from Bayesian active learning: Phase Transformations and Thermal Transport in SiC Abstract Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomic level processes. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present an efficient Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. As an application, we train a model for silicon carbide (SiC), a wide-gap semiconductor with complex polymorphic structure and diverse technological applications in power electronics, nuclear physics and astronomy. We show that the high pressure phase transformation is accurately captured by the autonomous active learning workflow. The trained force field shows excellent agreement with both \textit{ab initio} calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow readily generalizes to a wide range of systems and accelerates computational understanding and design. Molecular dynamics ; Dynamics (music) ; Bayesian probability ; Phase (matter) ; Thermal ; Computer science ; Statistical physics ; Materials science ; Artificial intelligence ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Semiconductor materials and devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: Eugene B. Postnikov | M. Bartoszek | Justyna Polak | Mirosław Chorążewski
Source: International Journal of Molecular Sciences (article, 2022-10-03)
Field: Chemistry — Organic Chemistry

Combination of Machine Learning and Analytical Correlations for Establishing Quantitative Compliance between the Trolox Equivalent Antioxidant Capacity Values Obtained via Electron Paramagnetic Resonance and Ultraviolet–Visible Spectroscopies Recent interest in the antioxidant capacity of foods and beverages is based on the established medical knowledge that antioxidants play an essential role in counteracting the damaging effects of free radicals, preventing human neurodegenerative diseases, cardiovascular disorders, and even cancer. At the same time, there is no "the method" that uniquely defines the antioxidant capacity of substances; moreover, the question of interrelation between results obtained by different experimental techniques is still open. In this work, we consider the trolox equivalent antioxidant capacity (TEAC) values obtained by electron paramagnetic resonance (EPR) spectroscopy and ultraviolet-visible (UV-vis) spectroscopy using the classic objects for such studies as an example: red, rosé, and white wine samples. Based on entirely different physical principles, these two methods give values that are not so simply interrelated; this creates a demand for machine learning as a suitable tool for revealing quantitative correspondence between them. The consideration consists of an approximate correlation-based analytical model for the key argument (i.e., TEACEPR) with subsequent adjustment by machine learning-based processing utilizing the CatBoost algorithm with the usage of auxiliary chemical data, such as the total phenolic content and color index, which cannot be accurately described by analytical expressions. Trolox ; Antioxidant capacity ; Ultraviolet ; Electron paramagnetic resonance spectroscopy ; Compliance (psychology) ; Electron paramagnetic resonance ; Paramagnetism ; Electron ; Antioxidant ; Chemistry ; Nuclear magnetic resonance ; Analytical Chemistry (journal) ; Materials science ; Physics ; Environmental chemistry ; Biochemistry ; Nuclear physics ; Condensed matter physics ; Optoelectronics Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Divy Patel | Dhairya Shah | Param Modi | Mohendra Roy
Source: Materials Today Proceedings (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence powered material search engine Computer science ; Interpretability ; Artificial neural network ; Search engine ; Random forest ; Machine learning ; Metadata ; Artificial intelligence ; Ensemble learning ; Bayesian network ; Data mining ; Information retrieval Machine Learning in Materials Science ; Rough Sets and Fuzzy Logic ; Geochemistry and Geologic Mapping