jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yash Raj Singh | Darshil B. Shah | Dilip Maheshwari | Jignesh S. Shah | Shreeraj Shah
Source: Critical Reviews in Analytical Chemistry (review, 2023-09-06)
Field: Chemistry — Spectroscopy

Advances in AI-Driven Retention Prediction for Different Chromatographic Techniques: Unraveling the Complexity Retention prediction through Artificial intelligence (AI)-based techniques has gained exponential growth due to their abilities to process complex sets of data and ease the crucial task of identification and separation of compounds in most employed chromatographic techniques. Numerous approaches were reported for retention prediction in different chromatographic techniques, and consistent results demonstrated that the accuracy and effectiveness of deep learning models outclassed the linear machine learning models, mainly in liquid and gas chromatography, as ML algorithms use fewer complex data to train and predict information. Support Vector machine-based neural networks were found to be most utilized for the prediction of retention factors of different compounds in thin-layer chromatography. Cheminformatics, chemometrics, and hybrid approaches were also employed for the modeling and were more reliable in retention prediction over conventional models. Quantitative Structure Retention Relationship (QSRR) was also a potential method for predicting retention in different chromatographic techniques and determining the separation method for analytes. These techniques demonstrated the aids of incorporating QSRR with AI-driven techniques acquiring more precise retention predictions. This review aims at recent exploration of different AI-driven approaches employed for retention prediction in different chromatographic techniques, and due to the lack of summarized literature, it also aims at providing a comprehensive literature that will be highly useful for the society of scientists exploring the field of AI in analytical chemistry. Chemometrics ; Artificial intelligence ; Computer science ; Artificial neural network ; Machine learning ; Retention time ; Chromatographic separation ; Support vector machine ; Process (computing) ; Chromatography ; Chemistry ; High-performance liquid chromatography Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Sergio Arroyo Armida | Dariush Ebrahimibagha | Mallar Ray | Shubhabrata Datta
Source: Advanced Composite Materials (article, 2023-10-04)
Field: Materials Science — Materials Chemistry

Assessing thermoelectric performance of quasi 0D carbon and polyaniline nanocomposites using machine learning AbstractThermoelectric materials have been widely recognized as a simple approach to harness green energy by converting thermal gradients into electrical energy. However, the intricate interplay between electrical conductivity, Seebeck coefficient, and thermal conductivity in thermoelectric materials presents a challenge to improving their efficiency. Traditional experimental methods and calculation methods have troublesome steps and long cycles for predicting new thermoelectric materials. In this work we present materials informatics-based approach, where statistical and machine learning models like correlation matrix, multiple linear regression, principal component analysis and artificial neural network were employed to find the relationship between features and thermoelectric performance. Furthermore, artificial neural network was used to analyze the roles of several compositional and microstructural features along with temperature on electrical conductivity, thermal conductivity, Seebeck coefficient and thermoelectric figure of merit (ZT) for PANI and quasi 0D carbon-based composites.Keywords: Nanocompositespolyanilinecarbon nanostructuresmaterials informaticsthermoelectric performancemachine learningdata analytics AcknowledgmentsMR acknowledges the support of "University of Alberta-Tecnologico de Monterrey Seed Fund, 2022-2023" and SNI fellowship awarded by CONACyT (Grant ID 1047863).Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementThe data can be requested and accessed from the authors. Materials science ; Polyaniline ; Nanocomposite ; Composite material ; Thermoelectric effect ; Carbon fibers ; Composite number ; Polymer Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Kulpavee Jitapunkul | Krittapong Deshsorn | Krittamate Payakkachon | Tanapat Chaisrithong | Luckhana Lawtrakul | Pawin Iamprasertkun
Source: The Journal of Physical Chemistry C (article, 2023-08-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Insights into Heteroatom-Doped Graphene Supercapacitor Data through Manual Data Separation and Statistical Analysis The integration of data science with graphene-based supercapacitors has emerged as a promising approach for optimizing their performance. Graphene-based electrodes exhibit unique pseudo-capacitive properties, but optimizing their supercapacitors requires balancing multiple variables, including electrode features, electrolyte composition, and operating conditions. This is because the pseudo-capacitive effect can create different charge-storage mechanisms via surface redox reactions during charging/discharging. In this paper, we discuss the use of statistical data analysis for gaining insights into the complex interplay between material properties and electrochemical performance. We shed more light on the analysis of large datasets generated from experiments of heteroatom-doped graphene supercapacitors using data preprocessing techniques, such as scattered interpolation–extrapolation and Pearson correlation, instead of traditional machine learning models. We investigate the effects of various electrochemical features on capacitive performance and the influence of heteroatom doping, such as nitrogen, sulfur, and oxygen. Our analysis provides valuable insights into the essential electrochemical features and heteroatom doping that relate to capacitive-boosting performance, serving as a guideline for accelerating the development of energy storage, particularly for graphene-based supercapacitors, in both experimental and machine learning aspects. Heteroatom ; Supercapacitor ; Graphene ; Materials science ; Capacitive sensing ; Nanotechnology ; Electrochemistry ; Computer science ; Electrochemical energy conversion ; Doping ; Electrode ; Optoelectronics ; Chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Woosuck Shin | Yuki Yamaguchi | Mirano Horie | Hiroyuki Shimada | Katsuhiro Nomura | Hirofumi Sumi
Source: Ceramics International (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Machine learning based analysis of metal support co-sintering process for solid oxide fuel cells Porous metals are promising substrate supports of solid oxide fuel cell for the applications of mobile and high-power density. The process conditions for a suitable mixture of pore former and stainless-steel particles, and also for the co-sintering with oxide layers of SOFC are investigated by machine learning (ML) methods. Half-cell metal supported cell (MSC) process with ScSZ electrolyte and NiO-ScSZ anode layer coated on the metal substrate of SUS430 was predicted by two ML methods of random forest (RF) and neural network (NN) were investigated. RF of multi output methods predicted the target variables, such as shrinkage, bend and pore structures with the better accuracy compared to NN, revealing that not only sintering temperature but also pore former and degreasing temperature of metal support influenced the process by feature importance analysis. The power generation performance of MSC tested from 550 °C to750 °C, and the relation to the microstructure of MSC is discussed. Materials science ; Sintering ; Solid oxide fuel cell ; Oxide ; Microstructure ; Anode ; Electrolyte ; Porosity ; Substrate (aquarium) ; Chemical engineering ; Shrinkage ; Metal ; Metallurgy ; Layer (electronics) ; Composite material ; Electrode Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Lina Zhang | Yi-Fan Hou | Fuchun Ge | Pavlo O. Dral
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Energy-conserving molecular dynamics is not energy conserving Molecular dynamics (MD) is a widely-used tool for simulating molecular and materials properties. It is common wisdom that molecular dynamics simulations should obey physical laws and, hence, lots of effort is put into ensuring that molecular dynamics simulations are energy conserving. The emergence of machine learning (ML) potentials for MD leads to a growing realization that monitoring conservation of energy during simulations is of low utility because the dynamics is often unphysically dissociative. Other ML methods for MD are not based on a potential and provide only forces or trajectories which are reasonable but not necessarily energy-conserving. Here we propose to clearly distinguish between the simulation-energy and true-energy conservation and highlight that the simulations should focus on decreasing the degree of true-energy non-conservation. We introduce very simple, new criteria for evaluating the quality of molecular dynamics by estimating the degree of true-energy non-conservation and we demonstrate their practical utility on an example of infrared spectra simulations. These criteria are more important and intuitive than simply evaluating the quality of the ML potential energies and forces as is commonly done and can be applied universally, <i>e.g.</i>, even for trajectories with unknown or discontinuous potential energy. Such an approach introduces new standards for evaluating MD by focusing on the true-energy conservation and can help in developing more accurate methods for simulating molecular and materials properties. Molecular dynamics ; Energy conservation ; Energy (signal processing) ; Potential energy ; Computer science ; Statistical physics ; Realization (probability) ; Conservation of energy ; Quality (philosophy) ; Simple (philosophy) ; Physics ; Classical mechanics ; Mathematics ; Ecology ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Daegun You | Orcun Koray Celebi | Ahmed Sameer Khan Mohammed | Diab Abueidda | Seid Korić | Hüseyin Şehitoğlu
Source: International Journal of Plasticity (article, 2023-01-13)
Field: Materials Science — Metals and Alloys

CRSS determination combining ab-initio framework and Surrogate Neural Networks Materials science ; Stacking-fault energy ; Anisotropy ; Ab initio ; Cubic crystal system ; Dislocation ; Critical resolved shear stress ; Artificial neural network ; Lattice constant ; Work (physics) ; Statistical physics ; Condensed matter physics ; Thermodynamics ; Artificial intelligence ; Computer science ; Physics ; Composite material ; Optics Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and mechanical properties ; Machine Learning in Materials Science
Authors: Joshua L. Lansford | Klavs F. Jensen | Brian C. Barnes
Source: Propellants Explosives Pyrotechnics (article, 2023-02-04)
Field: Materials Science — Materials Chemistry

Physics‐informed Transfer Learning for Out‐of‐sample Vapor Pressure Predictions Abstract Recent advances have enabled machine learning methodologies developed for large datasets to be applied to the small experimental datasets typically available for chemical systems. Such advances typically involve a data‐based approach to transfer learning, where a portion of the experimental data for the property of interest is used to fine‐tune a model that is pre‐trained on computationally generated data. This transfer learning approach does not work for very small experimental datasets, where there are only enough data for model validation. Here, we develop a physics‐informed transfer learning strategy to train a directed‐message passing neural network (D‐MPNN) model, enabling extrapolation outside of the training domain. We demonstrate this approach by training a D‐MPNN model on interpolated vapor pressures and validate the model on an out‐of‐sample test set of energetic molecule vapor pressures, achieving accuracy comparable to those of experiments. Extrapolation ; Transfer of learning ; Artificial neural network ; Computer science ; Machine learning ; Sample (material) ; Test set ; Set (abstract data type) ; Artificial intelligence ; Experimental data ; Applicability domain ; Data set ; Physics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Guillaume Deffrennes | Kei Terayama | Taichi Abe | Etsuko Ogamino | Ryo Tamura
Source: Materials & Design (article, 2023-06-24)
Field: Materials Science — Materials Chemistry

A framework to predict binary liquidus by combining machine learning and CALPHAD assessments Knowledge of the liquidus is important for the design and processing of many materials. For instance, deep eutectics are important for the design of metallic glasses, and recently multi-principal element alloys have been designed based on eutectic compositions or melting temperatures extrapolated from binary liquidus data. In this study, we provide a general framework for predicting binary liquidus only from the properties of the pure elements and thermodynamic properties calculated by Miedema’s model. Our framework combines three machine learning models that are trained and evaluated on liquidus data collected from 466 CALPHAD assessments of binary phase diagrams. The first model predicts the formation of liquid miscibility gaps with a prediction accuracy of 95.3%, outperforming the empirical Mott model. The second and third models predict the equilibrium onset temperature of solidification and the critical temperature of liquid miscibility gaps, respectively. An important feature of our models is that they can give indications of the presence of congruent melting phases and eutectics. Using our framework, we predict the liquidus in 1563 binary systems not included in our CALPHAD dataset, many of which are unknown. By collecting more data, our framework will continue to grow towards better liquidus prediction. Liquidus ; Eutectic system ; CALPHAD ; Thermodynamics ; Binary number ; Materials science ; Phase diagram ; Spinodal decomposition ; Phase (matter) ; Metallurgy ; Mathematics ; Chemistry ; Alloy ; Physics Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; nanoparticles nucleation surface interactions
Authors: Fereshteh Ramezani | Sheikh Parvez | J. Pierce Fix | Arthur Battaglin | Seamus Whyte | Nicholas J. Borys | Bradley M. Whitaker
Source: Scientific Reports (article, 2023-01-28)
Field: Materials Science — Materials Chemistry

Automatic detection of multilayer hexagonal boron nitride in optical images using deep learning-based computer vision Computer vision algorithms can quickly analyze numerous images and identify useful information with high accuracy. Recently, computer vision has been used to identify 2D materials in microscope images. 2D materials have important fundamental properties allowing for their use in many potential applications, including many in quantum information science and engineering. One such material is hexagonal boron nitride (hBN), an isomorph of graphene with a very indistinguishable layered structure. In order to use these materials for research and product development, the most effective method is mechanical exfoliation where single-layer 2D crystallites must be prepared through an exfoliation procedure and then identified using reflected light optical microscopy. Performing these searches manually is a time-consuming and tedious task. Deploying deep learning-based computer vision algorithms for 2D material search can automate the flake detection task with minimal need for human intervention. In this work, we have implemented a new deep learning pipeline to classify crystallites of hBN based on coarse thickness classifications in reflected-light optical micrographs. We have used DetectoRS as the object detector and trained it on 177 images containing hexagonal boron nitride (hBN) flakes of varying thickness. The trained model achieved a high detection accuracy for the rare category of thin flakes ([Formula: see text] atomic layers thick). Further analysis shows that our proposed pipeline could be generalized to various microscope settings and is robust against changes in color or substrate background. Computer science ; Artificial intelligence ; Pipeline (software) ; Hexagonal boron nitride ; Exfoliation joint ; Materials science ; Microscope ; Boron nitride ; Crystallite ; Optical microscope ; Computer vision ; Graphene ; Optics ; Nanotechnology ; Physics ; Composite material 2D Materials and Applications ; Advanced Neural Network Applications ; Graphene research and applications
Authors: Zhao Liu | Biao Wang
Source: Materials Chemistry and Physics (article, 2023-02-09)
Field: Materials Science — Materials Chemistry

Prediction of ideal strength by machine learning Ideal (ethics) ; Reliability (semiconductor) ; Computer science ; Ideal solution ; Materials science ; Physics ; Thermodynamics ; Power (physics) Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Computational Drug Discovery Methods
Authors: Arafath Nihar | Thomas G. Ciardi | Rounak Chawla | Olatunde Akanbi | Vipin Chaudhary | Yinghui Wu | Roger H. French
Source:  (article, 2023-12-18)
Field: Materials Science — Materials Chemistry

Accelerating Time to Science using CRADLE: A Framework for Materials Data Science Modern materials science research problems present a challenge to data science and analytics as experiments generate Petabyte-scale spatiotemporal datasets that span a number of modalities and formats. Creating computing infrastructure and frameworks that support the scale and diversity of materials science data while remaining accessible for materials scientists to use is a non-trivial task. We have developed the Common Research Analytics and Data Lifecycle Environment (CRADLE) to solve the challenges of materials data science through a scalable research computing framework and cyber infrastructure that can (1) handle large-scale, heterogeneous datasets (2) provide a flexible toolbox for building machine learning pipelines that span from ingestion to model deployment (3) be accessible to research scientists with limited to extensive computational backgrounds and (4) utilize a myriad of low performance to high performance computer systems. CRADLE is a framework that integrates distributed systems like Hadoop and High-Performance Computing (HPC) infras-tructure to handle materials data at scale. This all enables the general materials data scientist to query Petabytes of data and train thousands of models in a parallel, distributed environment. We demonstrate three use cases for CRADLE to benchmark its capability to ingest and analyze spatiotemporal materials data at scale. These tasks span three data modalities: transforming 2.6 billion Photovoltaic time-series power measurements, training hundreds of deep learning models on Atomic Force Microscopy images, and ingesting 27 billion geospatial data points. CRADLE exemplifies an overarching framework that accelerates time to science, extends to other domains with similar challenges, and expands the horizon of data science and research. Computer science ; Data science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Kajjana Boonpalit | Jiramet Kinchagawat | Chanatkran Prommin | Sarana Nutanong | Supawadee Namuangruk‬
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Efficient exploration of transition-metal decorated MXene for carbon monoxide sensing using integrated active learning and density functional theory The urgent demand for chemical safety necessitates the real-time detection of carbon monoxide (CO), a highly toxic gas. MXene, a 2D material, has shown potential for gas sensing applications (<i>e.g.</i>, NH<sub>3</sub>, NO, SO<sub>2</sub>, CO<sub>2</sub>) due to its high surface accessibility, electrical conductivity, stability, and flexibility in surface functionalization. However, the pristine MXene generally exhibits poor interaction with CO; still, transition metal decoration can strengthen the interaction between CO and MXene. This study presents a high-throughput screening of 450 combinations of transition-metal (TM) decorated MXene (TM@MXene) for CO sensing applications using an integrated active learning (AL) and density functional theory (DFT) screening pipeline. Our AL pipeline, adopting a crystal graph convolutional neural network (CGCNN) as a surrogate model, successfully accelerates the screening of CO sensor candidates with minimal computational resources. This study identifies Sc@Zr<sub>3</sub>C<sub>2</sub>O<sub>2</sub> and Y@Zr<sub>3</sub>C<sub>2</sub>O<sub>2</sub> as the optimal TM@MXene candidates with promising CO sensing performance regarding the screening criteria of recovery time, surface stability, charge transfer, and sensitivity to CO. The proposed AL framework can be extended for property finetuning in the combinatorial chemical space. Carbon monoxide ; Density functional theory ; Transition metal ; Materials science ; Metal ; Carbon fibers ; Pipeline (software) ; Active carbon ; Chemical engineering ; Nanotechnology ; Chemistry ; Computer science ; Computational chemistry ; Composite number ; Organic chemistry ; Catalysis ; Composite material ; Engineering ; Metallurgy ; Waste management MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: James Chapman | Tim Hsu | Xiao Chen | Tae Wook Heo | Brandon C. Wood
Source: Nature Communications (article, 2023-07-07)
Field: Materials Science — Materials Chemistry

Quantifying disorder one atom at a time using an interpretable graph neural network paradigm Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder, called SODAS. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this methodology to four prototypical examples with varying levels of disorder: (1) grain boundaries, (2) solid-liquid interfaces, (3) polycrystalline microstructures, and (4) tensile failure/fracture. We also compare SODAS to several commonly used methods. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides a simple and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena. Computer science ; Artificial neural network ; Graph ; Artificial intelligence ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Thermal properties of materials ; Block Copolymer Self-Assembly