jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bo Su | Guannan Qian | Ruoyang Gao | Fen Tao | Ling Zhang | Guohao Du | Biao Deng | P. Pianetta | Yijin Liu
Source: Journal of Synchrotron Radiation (article, 2024-02-01)
Field: Materials Science — Surfaces, Coatings and Films

Image registration for <i>in situ</i> X-ray nano-imaging of a composite battery cathode with deformation The structural and chemical evolution of battery electrodes at the nanoscale plays an important role in affecting the cell performance. Nano-resolution X-ray microscopy has been demonstrated as a powerful technique for characterizing the evolution of battery electrodes under operating conditions with sensitivity to their morphology, compositional distribution and redox heterogeneity. In real-world batteries, the electrode could deform upon battery operation, causing challenges for the image registration which is necessary for several experimental modalities, e.g. XANES imaging. To address this challenge, this work develops a deep-learning-based method for automatic particle identification and tracking. This approach was not only able to facilitate image registration with good robustness but also allowed quantification of the degree of sample deformation. The effectiveness of the method was first demonstrated using synthetic datasets with known ground truth. The method was then applied to an experimental dataset collected on an operating lithium battery cell, revealing a high degree of intra- and interparticle chemical complexity in operating batteries. Battery (electricity) ; Cathode ; Electrode ; Robustness (evolution) ; Computer science ; Materials science ; Artificial intelligence ; Nanoscopic scale ; Computer vision ; Nanotechnology ; Electrical engineering ; Physics ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advancements in Photolithography Techniques
Authors: Jianping Xiao | Yang Li | Shuqun Wang | Z. Y. He
Source: Computational Materials Science (article, 2024-06-30)
Field: Chemistry — Spectroscopy

Accurate prediction of second harmonic generation coefficients using graph neural networks Artificial neural network ; Graph ; Computer science ; Statistical physics ; Algorithm ; Artificial intelligence ; Physics ; Theoretical computer science Spectroscopy and Laser Applications ; GaN-based semiconductor devices and materials ; Thermography and Photoacoustic Techniques
Authors: Khadijah Mohammedsaleh Katubi | Muhammad Saqib | Muhammad Sulaman | Z.A. Alrowaili | M.S. Al-Buriahi
Source: Chemical Physics (article, 2024-04-11)
Field: Materials Science — Polymers and Plastics

Designing high-efficiency organic semi-conductors for organic photodetectors assisted by machine learning and property prediction Organic solar cell ; Electrical conductor ; Photodetector ; Artificial intelligence ; Gradient boosting ; Organic semiconductor ; Computer science ; Photovoltaic system ; Random forest ; Machine learning ; Cluster analysis ; Boosting (machine learning) ; Optoelectronics ; Materials science ; Electrical engineering ; Engineering Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: E. O. Khazieva | N. M. Chtchelkatchev | R. E. Ryltsev
Source: The Journal of Chemical Physics (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Transfer learning for accurate description of atomic transport in Al–Cu melts Machine learning interatomic potentials (MLIPs) provide an optimal balance between accuracy and computational efficiency and allow studying problems that are hardly solvable by traditional methods. For metallic alloys, MLIPs are typically developed based on density functional theory with generalized gradient approximation (GGA) for the exchange-correlation functional. However, recent studies have shown that this standard protocol can be inaccurate for calculating the transport properties or phase diagrams of some metallic alloys. Thus, optimization of the choice of exchange-correlation functional and specific calculation parameters is needed. In this study, we address this issue for Al-Cu alloys, in which standard Perdew-Burke-Ernzerhof (PBE)-based MLIPs cannot accurately calculate the viscosity and melting temperatures at Cu-rich compositions. We have built MLIPs based on different exchange-correlation functionals, including meta-GGA, using a transfer learning strategy, which allows us to reduce the amount of training data by an order of magnitude compared to a standard approach. We show that r2SCAN- and PBEsol-based MLIPs provide much better accuracy in describing thermodynamic and transport properties of Al-Cu alloys. In particular, r2SCAN-based deep machine learning potential allows us to quantitatively reproduce the concentration dependence of dynamic viscosity. Our findings contribute to the development of MLIPs that provide quantum chemical accuracy, which is one of the most challenging problems in modern computational materials science. Density functional theory ; Viscosity ; Statistical physics ; Thermodynamics ; Computer science ; Quantum ; Phase (matter) ; Materials science ; Algorithm ; Chemistry ; Machine learning ; Physics ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Materials Characterization Techniques
Authors: Grigory V. Karsakov | Vladimir P. Shirobokov | Alena N. Kulakova | Valentin A. Milichko
Source: The Journal of Physical Chemistry Letters (article, 2024-03-12)
Field: Chemistry — Inorganic Chemistry

Prediction of Metal–Organic Frameworks with Phase Transition via Machine Learning Metal-organic frameworks (MOFs) possess a virtually unlimited number of potential structures. Although the latter enables an efficient route to control the structure-related functional properties of MOFs, it still complicates the prediction and searching for an optimal structure for specific application. Next to prediction of the MOFs for gas sorption/separation and catalysis via machine learning (ML), we report on ML to find MOFs demonstrating a phase transition (PT). On the basis of an available QMOF database (7463 frameworks), we create and train the autoencoder followed by training the classifier of MOFs from a unique database with experimentally confirmed PT. This makes it possible to identify MOFs with a high potential for PT and evaluate the most likely stimulus for it (guest molecules or temperature/pressure). The formed list of available MOFs for PT allows us to discuss their structural features and opens an opportunity to search for phase change MOFs for diverse physical/chemical application. Metal-organic framework ; Phase transition ; Phase (matter) ; Materials science ; Computer science ; Artificial intelligence ; Chemistry ; Condensed matter physics ; Physics ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Marie Stiefel | Martin Müller | Björn-Ivo Bachmann | María Agustina Guitar | U.P. Nayak | Frank Mücklich
Source: MRS Communications (article, 2024-04-10)
Field: Materials Science — Materials Chemistry

Enhancing machine learning classification of microstructures: A workflow study on joining image data and metadata in CNN Abstract In view of the paradigm shift toward data-driven research in materials science and engineering, handling large amounts of data becomes increasingly important. The application of FAIR (findable, accessible, interoperable, reusable) data principles emphasizes the importance of metadata describing datasets. We propose a novel data processing and machine learning (ML) pipeline to extract metadata from micrograph image files, then combine image data and their metadata for microstructure classification with a deep learning approach compared to a classic ML approach. The ML model attained excellent performances with and without metadata and bears potential for performance improvement of further use cases within the community. Graphical abstract Metadata ; Workflow ; Pipeline (software) ; Data element ; Computer science ; Interoperability ; Metadata modeling ; Metadata repository ; Artificial intelligence ; Information retrieval ; Data mining ; Database ; World Wide Web Machine Learning in Materials Science ; Mineral Processing and Grinding ; Research Data Management Practices
Authors: Ruslan Kotlyarov | Konstantinos Papachristos | Geoffrey P. F. Wood | Jonathan M. Goodman
Source: Journal of Chemical Information and Modeling (article, 2024-05-06)
Field: Chemistry — Inorganic Chemistry

Leveraging Language Model Multitasking To Predict C–H Borylation Selectivity C-H borylation is a high-value transformation in the synthesis of lead candidates for the pharmaceutical industry because a wide array of downstream coupling reactions is available. However, predicting its regioselectivity, especially in drug-like molecules that may contain multiple heterocycles, is not a trivial task. Using a data set of borylation reactions from Reaxys, we explored how a language model originally trained on USPTO_500_MT, a broad-scope set of patent data, can be used to predict the C-H borylation reaction product in different modes: product generation and site reactivity classification. Our fine-tuned T5Chem multitask language model can generate the correct product in 79% of cases. It can also classify the reactive aromatic C-H bonds with 95% accuracy and 88% positive predictive value, exceeding purpose-developed graph-based neural networks. Borylation ; Computer science ; Regioselectivity ; Set (abstract data type) ; Product (mathematics) ; Chemistry ; Programming language ; Mathematics ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis ; Organoboron and organosilicon chemistry
Authors: Patricia Fernández-López | Sofia A. Alves | Aleksey B. Rogov | Aleksey Yerokhin | Iban Quintana | Aitor Duo | Aitor Aguirre-Ortuzar
Source: Coatings (article, 2024-08-03)
Field: Materials Science — Materials Chemistry

Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights PEO constitutes a promising surface technology for the development of protective and functional ceramic coatings on lightweight alloys. Despite its interesting advantages, including enhanced wear and corrosion resistances and eco-friendliness, the industrial implementation of PEO technology is limited by its relatively high energy consumption. This study explores the development and optimization of novel PEO processes by means of machine learning (ML) to improve the coating thickness. For this purpose, ML models random forest and XGBoost were employed to predict the thickness of the developed PEO coatings based on the key process variables (frequency, current density, and electrolyte composition). The predictive performance was significantly improved by including the composition of the used electrolyte in the models. Furthermore, Shapley values identified the pulse frequency and the TiO2 concentration in the electrolyte as the most influential variables, with higher values leading to increased coating thickness. The residual analysis revealed a certain heteroscedasticity, which suggests the need for additional samples with high thickness to improve the accuracy of the model. This study reveals the potential of artificial intelligence (AI)-driven optimization in PEO processes, which could pave the way for more efficient and cost-effective industrial applications. The findings achieved further emphasize the significance of integrating interactions between variables, such as frequency and TiO2 concentration, into the design of processing operations. Plasma electrolytic oxidation ; Electrolyte ; Plasma ; Materials science ; Artificial intelligence ; Computer science ; Chemical engineering ; Chemistry ; Engineering ; Electrode ; Physics Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Electrodeposition and Electroless Coatings
Authors: Mitchell Falgoust | Peter Kroll
Source: Journal of the American Ceramic Society (article, 2024-08-06)
Field: Materials Science — Materials Chemistry

Machine‐learning interatomic potentials for pyrolysis of polysiloxanes and properties of SiCO ceramics Abstract We present machine‐learning interatomic potentials (MLIPs) for simulations of Si–C–O–H compounds. The MLIPs are constructed from moment tensor potentials (MTPs) and were trained to a library of configurations that included polysiloxane structures, hypothetical crystalline and amorphous SiCOH structures, and trajectories of Si–C–O–H systems obtained via ab initio molecular dynamic (aiMD) simulations at elevated temperatures. Passive, active, and hybrid learning strategies were implemented to develop the MLIPs. The MLIPs reproduce vibrational properties of polymers and SiCOH structures obtained from aiMD simulations, thus providing a tool to identify chemical units and distinct structural characteristics through their vibrational properties. Simulations of the polymer‐to‐ceramic transformation show the development of mixed tetrahedra in SiCO ceramics and align with experimental observations. Million‐atom simulations for several nanoseconds highlight the precipitation of graphitic nanosheets from a carbon‐rich SiCO precursor. Atomistic simulations with the MLIPs deliver details of chemical reaction mechanisms during the pyrolysis of polysiloxanes, including methane abstraction and Kumada‐like rearrangements that transform the siloxane backbone. While the MLIPs still leave room for systematic improvement, they deliver simulations with “density functional theory (DFT)‐like” quality at low and high temperatures. Ceramic ; Materials science ; Amorphous solid ; Ab initio ; Molecular dynamics ; Density functional theory ; Chemical physics ; Computational chemistry ; Chemistry ; Composite material ; Crystallography ; Organic chemistry Machine Learning in Materials Science ; High-pressure geophysics and materials ; Advanced NMR Techniques and Applications
Authors: Behnam Ahmadikia | Adolph L. Beyerlein | Jonathan M. Hestroffer | M. Arul Kumar | Irene J. Beyerlein
Source: Journal of Materials Science Materials Theory (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Designing Ti-6Al-4V microstructure for strain delocalization using neural networks Abstract The deformation behavior of Ti-6Al-4V titanium alloy is significantly influenced by slip localized within crystallographic slip bands. Experimental observations reveal that intense slip bands in Ti-6Al-4V form at strains well below the macroscopic yield strain and may serially propagate across grain boundaries, resulting in long-range localization that percolates through the microstructure. These connected, localized slip bands serve as potential sites for crack initiation. Although slip localization in Ti-6Al-4V is known to be influenced by various factors, an investigation of optimal microstructures that limit localization remains lacking. In this work, we develop a novel strategy that integrates an explicit slip band crystal plasticity technique, graph networks, and neural network models to identify Ti-6Al-4V microstructures that reduce the propensity for strain localization. Simulations are conducted on a dataset of 3D polycrystals, each represented as a graph to account for grain neighborhood and connectivity. The results are then used to train neural network surrogate models that accurately predict localization-based properties of a polycrystal, given its microstructure. These properties include the ratio of slip accumulated in the band to that in the matrix, fraction of total applied strain accommodated by slip bands, and spatial connectivity of slip bands throughout the microstructure. The initial dataset is enriched by synthetic data generated by the surrogate models, and a grid search optimization is subsequently performed to find optimal microstructures. Describing a 3D polycrystal with only a few features and a combination of graph and neural network models offer robustness compared to the alternative approaches without compromising accuracy. We show that while each material property is optimized through a unique microstructure solution, elongated grain shape emerges as a recurring feature among all optimal microstructures. This finding suggests that designing microstructures with elongated grains could potentially mitigate strain localization without compromising strength. Microstructure ; Slip (aerodynamics) ; Materials science ; Lüders band ; Plasticity ; Titanium alloy ; Artificial neural network ; Composite material ; Artificial intelligence ; Computer science ; Alloy ; Physics ; Thermodynamics Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Corrosion Behavior and Inhibition
Authors: Keming Zhu | Zhibin Zhang
Source: Computational Materials Science (article, 2024-09-17)
Field: Materials Science — Materials Chemistry

Equivariance is essential, local representation is a need: A comprehensive and critical study of machine learning potentials for tobermorite phases Tobermorite ; Representation (politics) ; Materials science ; Computer science ; Statistical physics ; Chemistry ; Physics ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Marvin Alberts | Oliver Schilter | Federico Zipoli | Nina Hartrampf | Teodoro Laino
Source: arXiv (Cornell University) (preprint, 2024-07-04)
Field: Chemistry — Analytical Chemistry

Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry Spectroscopic techniques are essential tools for determining the structure of molecules. Different spectroscopic techniques, such as Nuclear magnetic resonance (NMR), Infrared spectroscopy, and Mass Spectrometry, provide insight into the molecular structure, including the presence or absence of functional groups. Chemists leverage the complementary nature of the different methods to their advantage. However, the lack of a comprehensive multimodal dataset, containing spectra from a variety of spectroscopic techniques, has limited machine-learning approaches mostly to single-modality tasks for predicting molecular structures from spectra. Here we introduce a dataset comprising simulated $^1$H-NMR, $^{13}$C-NMR, HSQC-NMR, Infrared, and Mass spectra (positive and negative ion modes) for 790k molecules extracted from chemical reactions in patent data. This dataset enables the development of foundation models for integrating information from multiple spectroscopic modalities, emulating the approach employed by human experts. Additionally, we provide benchmarks for evaluating single-modality tasks such as structure elucidation, predicting the spectra for a target molecule, and functional group predictions. This dataset has the potential automate structure elucidation, streamlining the molecular discovery pipeline from synthesis to structure determination. The dataset and code for the benchmarks can be found at https://rxn4chemistry.github.io/multimodal-spectroscopic-dataset. Chemistry ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Jin‐Hoon Yang | Habin Kang | Hyuk Jin Kim | Taeho Kim | Heonsu Ahn | Tae Gyu Rhee | Yeong Gwang Khim | Byoung Ki Choi | Moon‐Ho Jo | Hyunju Chang | Jonghwan Kim | Young Jun Chang | Yea‐Lee Lee
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

https://2DMat.ChemDX.org: Experimental data platform for 2D materials from synthesis to physical properties https://2DMat.ChemDX.org is a comprehensive data platform tailored for 2D materials research, emphasizing the handling and analysis of experimental data through specialized data management, visualization, and machine learning tools. Visualization ; Computer science ; Experimental data ; Data management ; Data science ; Research data ; Data mining ; Database ; Information retrieval ; Data curation Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: Guangyu Hu | Marat I. Latypov
Source: Computational Materials Science (article, 2024-05-28)
Field: Materials Science — Materials Chemistry

AnisoGNN: Graph neural networks generalizing to anisotropic properties of polycrystals Anisotropy ; Artificial neural network ; Graph ; Materials science ; Statistical physics ; Computer science ; Condensed matter physics ; Physics ; Theoretical computer science ; Artificial intelligence ; Optics Machine Learning in Materials Science ; Handwritten Text Recognition Techniques ; Welding Techniques and Residual Stresses
Authors: Majid Bagheri | Shai McKenney | Julie Gabriella Ware | Nakisa Farshforoush
Source: Journal of Hazardous Materials (article, 2024-09-20)
Field: Chemistry — Analytical Chemistry

Deep learning models for predicting plant uptake of emerging contaminants by including the role of plant macromolecular compositions Contamination ; Biochemical engineering ; Chemistry ; Environmental science ; Environmental chemistry ; Biotechnology ; Biology ; Engineering ; Ecology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Statistical and Computational Modeling
Authors: Da‐Jiang Liu | Jie Zhang | Long Qi | James W. Evans
Source: Carbon (article, 2024-10-16)
Field: Materials Science — Materials Chemistry

Molecular simulation using transfer-learned potentials for the disordered nanoscale structure of nitrogen-doped nanoporous carbons Nanoporous ; Nanoscopic scale ; Nitrogen ; Materials science ; Molecular dynamics ; Doping ; Chemical physics ; Nanotechnology ; Chemical engineering ; Chemistry ; Computational chemistry ; Organic chemistry ; Optoelectronics ; Engineering Machine Learning in Materials Science ; Graphene research and applications ; Boron and Carbon Nanomaterials Research
Authors: Shuang Jiang | Xijun Wang | Yuanyuan Chong | Yan Huang | Wei Hu | Pieter E. S. Smith | Jun Jiang | Shuo Feng
Source: The Journal of Physical Chemistry Letters (article, 2024-02-23)
Field: Materials Science — Materials Chemistry

Spectra-Based Machine Learning for Predicting the Statistical Interaction Properties of CO Adsorbates on Surface Theoretical analyses of small-molecule adsorption on heterogeneous catalyst surfaces often rely on simplified models of molecular adsorption with the most favorable configuration. Given that real-world experimental tests frequently entail multiple molecules interacting with the surface, there is a pressing need for a comprehensive multimolecule adsorption model to bridge the gap between theory and experiment. Using machine learning, we predict the average values of important adsorption properties from conformationally averaged, calculated infrared and Raman spectra and compare these values to those theoretically derived from the conformationally averaged ensemble. Remarkably, our approach yields excellent predictions even when faced with large and indeterminate numbers of surface molecules. These quantitative spectra-averaged property relationships provide a theoretical framework for extracting key interaction properties from the spectra of real chemical environments. Adsorption ; Molecule ; Spectral line ; Surface (topology) ; Raman spectroscopy ; Chemical physics ; Chemistry ; Statistical physics ; Materials science ; Computational chemistry ; Physical chemistry ; Physics ; Mathematics ; Quantum mechanics ; Organic chemistry ; Geometry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science