jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maryam Nurhuda | Yusuf Hafidh | Carole C. Perry | Matthew A. Addicoat
Source: ChemRxiv (preprint, 2022-07-20)
Field: Chemistry — Inorganic Chemistry

Functional Group Pair Distance based Descriptor for Isomerization in Porous Molecular Framework Materials Molecular Framework Materials (MFMs), including Metal Organic Frameworks (MOFs), Covalent Organic Frameworks (COFs) and their discrete equivalents, Metal Organic Polyhedra (MOPs) and Porous Organic Cages (POCs) are porous materials, composed of molecular fragments, bound in one of many topologies. MFMs have a wide variety of potential and realised adsorption applications. In order to design an ideal framework material for a particular application, the composition of molecular fragments is not the only factor, but the arrangement of the those fragments is also important, especially when the fragments (molecular building blocks) are chemically functionalised and lack symmetry. As has been observed in Metal Organic Frameworks, the flexibility and absorption properties may differ greatly when altering the orientation of the building units or changing the position of functional groups. However, although the position of functional group has a great influence on a targeted property, studies on functional group arrangements have only been performed on a small set of MOF structures. In this contribution, we develop a fingerprint/descriptor for optimising functionalised molecular framework structures using machine learning. We begin from the perspective of a molecular framework structure described as a collection of discrete pore shapes. To describe the chemical environment of the pore, we derive a fingerprint based on the occurrence of pairwise distances between functional groups in each pore. We present the possibilities of functional group arrangements in the 14 most common pore shapes, created by ditopic (2-connected) linkers are presented. The method to enumerate and identify possible isomers is explained. Finally the fingerprints of the 14 pore shapes are presented. Metal-organic framework ; Functional group ; Molecular recognition ; Chemistry ; Group (periodic table) ; Molecule ; Flexibility (engineering) ; Materials science ; Topology (electrical circuits) ; Nanotechnology ; Adsorption ; Mathematics ; Polymer ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Molecular Sensors and Ion Detection
Authors: Shuyan Zhang | Jie Gong | Sharon Lynn Chu | Daniel Z. Xiao | B. Reeja‐Jayan | Alan J. H. McGaughey
Source: arXiv (Cornell University) (preprint, 2022-10-13)
Field: Materials Science — Materials Chemistry

Pair distribution function analysis for oxide defect identification through feature extraction and supervised learning Feature extraction and a neural network model are applied to predict the defect types and concentrations in experimental TiO$_2$ samples. A dataset of TiO$_2$ structures with vacancies and interstitials of oxygen and titanium is built and the structures are relaxed using energy minimization. The features of the calculated pair distribution functions (PDFs) of these defected structures are extracted using linear methods (principal component analysis, non-negative matrix factorization) and non-linear methods (autoencoder, convolutional neural network). The extracted features are used as the inputs to a neural network that maps the feature weights to the concentration of each defect type. The performance of this machine learning pipeline is validated by predicting the defect concentrations based on experimentally-measured TiO$_2$ PDFs and comparing the results to brute-force predictions. A physics-based initialization of the autoencoder has the highest accuracy in predicting the defect concentrations. This model incorporates physical interpretability and predictability of material properties, enabling a more efficient material characterization process with scattering data. Convolutional neural network ; Artificial neural network ; Autoencoder ; Feature extraction ; Principal component analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Initialization ; Feature (linguistics) ; Interpretability ; Materials science ; Biological system Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Geochemistry and Geologic Mapping
Authors: Yi Chen | Jingyu Wang | Ben Hoar | Shengtao Lu | Xun Guan | Chong Liu
Source: ChemRxiv (preprint, 2022-03-15)
Field: Chemistry — Electrochemistry

AI-based inverse design for electrochemically controlled microscopic gradients of O2 and H2O2 A fundamental understanding of the extracellular microenvironments of O2 and reactive oxygen species (ROS) such as H2O2, ubiquitous in microbiology, demands high-throughput methods of mimicking, controlling, and perturbing gradients of O2 and H2O2 at microscopic scale with high spatiotemporal precision. However, there is a paucity for a high-throughput strategy of microenvironment design and it remains challenging to achieve O2 and H2O2 heterogeneities with the microbiologically desirable spatiotemporal resolutions. Here we report machine-learning-based inverse design of electrochemically generated microscopic O2 and H2O2 profiles that are relevant for microbiology. Microwire arrays with suitably designed electrochemical catalysts enable the independent control of O2 and H2O2 profiles with spatial resolution of ~101 μm and temporal resolution of ~100 sec. Neural networks aided by data augmentation inversely design the experimental conditions needed for targeted O2 and H2O2 microenvironments while being order-of-magnitude faster. Integrating artificial intelligence with electrochemically controlled concentration heterogeneity creates a viable fast-response platform towards better understanding the extracellular space with desirable spatiotemporal control. Throughput ; Nanotechnology ; Computer science ; Biological system ; Inverse ; Materials science ; Chemistry ; Biochemical engineering ; Biology ; Engineering ; Wireless ; Mathematics Electrochemical Analysis and Applications ; Photoacoustic and Ultrasonic Imaging ; Photoreceptor and optogenetics research
Authors: Surendra Kumar
Source: International Journal of Humanities and Information Technology (article, 2022-03-25)
Field: Materials Science — Materials Chemistry

Machine Learning Regression Techniques for Modeling Complex Industrial Systems: A Comprehensive Summary Machine learning regression techniques have become essential for modeling complex industrial systems in the industry 4.0 era. This summary reviews key regression methods—from linear models to deep learning—and examines their applications across manufacturing, energy, chemical processing, and predictive maintenance. We discuss how different techniques balance accuracy, interpretability, and computational requirements, and highlight real-world implementations that have achieved substantial efficiency gains. The review also addresses critical challenges including data quality, model interpretability, and generalization, while exploring future directions such as physics-informed models, federated learning, and explainable AI. Key (lock) ; Implementation ; Regression analysis ; Regression ; Linear regression ; Deep learning Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Machine Learning and ELM
Authors: Sophia Haussener
Source: ECS Meeting Abstracts (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

(Invited, Digital Presentation) Transport Characterization in Nano and Micron-Sized Multi-Component and Multi-Functional Materials Porous and heterogeneous materials are core components in energy conversion and storage devices such as batteries, fuel cells and electrolyzers, or photoelectrochemical fuel generators. The heterogeneity and structural complexity of: i) the multi-functional nature of the applications requiring the presence of various functional materials in close vicinity, ii) nano- and micron-scale structuring of the material required to overcome the bulk material transport limitations, and iii) cheap and simple synthesis methods resulting in stochastic and complex morphologies. Understanding of the multi-physical transport phenomena and optimization of the component for enhanced performance, requires an accurate modelling and prediction of the transport properties, which heavily rely on the complex nano to micron-scale morphology. In this presentation, I will show how tomography-based direct numerical simulation scan be used for the accurate numerical characterization of the heterogeneous components’ transport properties. We will use X-ray micro-tomography for the characterization of the (thermal) transport in partially saturated gas diffusion layers/electrodes or in porous thermochemical reactors, and FIB-SEM nano-tomography for the multi-physical transport characterization of photoelectordes for water splitting and catalyst layers of CO 2 reducing gas diffusion electrodes. I will show how we have built up a digital library of (photo)electrodes. Furthermore, I will show how machine learning approaches can be used to guide the design of optimized structures. Characterization (materials science) ; Materials science ; Component (thermodynamics) ; Transport phenomena ; Nanotechnology ; Nano- ; Porosity ; Porous medium ; Computer science ; Mechanics ; Physics Machine Learning in Materials Science
Authors: Yi Chen | Jingyu Wang | Ben Hoar | Shengtao Lu | Xun Guan | Chong Liu
Source: ChemRxiv (preprint, 2022-03-14)
Field: Chemistry — Electrochemistry

AI-based inverse design for electrochemically controlled microscopic gradients of O2 and H2O2 A fundamental understanding of the extracellular microenvironments of O2 and reactive oxygen species (ROS) such as H2O2, ubiquitous in microbiology, demands high-throughput methods of mimicking, controlling, and perturbing gradients of O2 and H2O2 at microscopic scale with high spatiotemporal precision. However, there is a paucity for a high-throughput strategy of microenvironment design and it remains challenging to achieve O2 and H2O2 heterogeneities with the microbiologically desirable spatiotemporal resolutions. Here we report machine-learning-based inverse design of electrochemically generated microscopic O2 and H2O2 profiles that are relevant for microbiology. Microwire arrays with suitably designed electrochemical catalysts enable the independent control of O2 and H2O2 profiles with spatial resolution of ~101 μm and temporal resolution of ~100 sec. Neural networks aided by data augmentation inversely design the experimental conditions needed for targeted O2 and H2O2 microenvironments while being order-of-magnitude faster. Integrating artificial intelligence with electrochemically controlled concentration heterogeneity creates a viable fast-response platform towards better understanding the extracellular space with desirable spatiotemporal control. Throughput ; Nanotechnology ; Inverse ; Biological system ; Computer science ; Materials science ; Chemistry ; Biology ; Wireless ; Mathematics Electrochemical Analysis and Applications ; Photoacoustic and Ultrasonic Imaging ; Photoreceptor and optogenetics research
Authors: Isabel Pietka | Ralf Drautz | Thomas Hammerschmidt
Source: The Journal of Open Source Software (article, 2022-11-28)
Field: Materials Science — Materials Chemistry

strucscan: A lightweight Python-based framework forhigh-throughput material simulation The development of new materials by computational materials science relies to a large degree on the prediction of material properties by simulation at different time and length scales. A common challenge at the atomic scale is the need for large numbers of calculations in order to sample, e.g., different chemical compositions, different crystal structures or different simulation settings. Typical examples of the required high-throughput calculations are (i) the sampling of the combinatorial space of structure and composition for determining the most stable structure of a mixture of chemical elements, (ii) the generation of a data set for constructing an interatomic interaction model or (iii) the generation of a data set for inferring properties by machine learning. Depending on the problem at hand, the number of required calculations may range from hundreds to millions. Python (programming language) ; Computer science ; Programming language ; Computational science ; Throughput ; Parallel computing ; Computer architecture ; Operating system Machine Learning in Materials Science ; Scientific Computing and Data Management ; Parallel Computing and Optimization Techniques
Authors: Abhijit Gupta
Source: arXiv (Cornell University) (preprint, 2022-08-22)
Field: Materials Science — Materials Chemistry

Prediction of good reaction coordinates and future evolution of MD trajectories using Regularized Sparse Autoencoders: A novel deep learning approach Identifying reaction coordinates(RCs) is an active area of research, given the crucial role RCs play in determining the progress of a chemical reaction. The choice of the reaction coordinate is often based on heuristic knowledge. However, an essential criterion for the choice is that the coordinate should capture both the reactant and product states unequivocally. Also, the coordinate should be the slowest one so that all the other degrees of freedom can easily equilibrate along the reaction coordinate. Also, the coordinate should be the slowest one so that all the other degrees of freedom can easily equilibrate along the reaction coordinate. We used a regularised sparse autoencoder, an energy-based model, to discover a crucial set of reaction coordinates. Along with discovering reaction coordinates, our model also predicts the evolution of a molecular dynamics(MD) trajectory. We showcased that including sparsity enforcing regularisation helps in choosing a small but important set of reaction coordinates. We used two model systems to demonstrate our approach: alanine dipeptide system and proflavine and DNA system, which exhibited intercalation of proflavine into DNA minor groove in an aqueous environment. We model MD trajectory as a multivariate time series, and our latent variable model performs the task of multi-step time series prediction. This idea is inspired by the popular sparse coding approach - to represent each input sample as a linear combination of few elements taken from a set of representative patterns. Reaction coordinate ; Computer science ; Coordinate system ; Algorithm ; Artificial intelligence ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; RNA and protein synthesis mechanisms
Authors: Oliver Lerstøl Hebnes | Marianne Etzelmüller Bathen | Øyvind Sigmundson Schøyen | Sebastian G. Winther Larsen | Lasse Vines | M. Hjorth‐Jensen
Source: arXiv (Cornell University) (preprint, 2022-03-30)
Field: Materials Science — Materials Chemistry

Predicting Solid State Material Platforms for Quantum Technologies Semiconductor materials provide a compelling platform for quantum technologies (QT), and the properties of a vast amount of materials can be found in databases containing information from both experimental and theoretical explorations. However, searching these databases to find promising candidate materials for quantum technology applications is a major challenge. Therefore, we have developed a framework for the automated discovery of semiconductor host platforms for QT using material informatics and machine learning methods, resulting in a dataset consisting of over $25.000$ materials and nearly $5000$ physics-informed features. Three approaches were devised, named the Ferrenti, extended Ferrenti and the empirical approach, to label data for the supervised machine learning (ML) methods logistic regression, decision trees, random forests and gradient boosting. We find that of the three, the empirical approach relying exclusively on findings from the literature predicted substantially fewer candidates than the other two approaches with a clear distinction between suitable and unsuitable candidates when comparing the two largest eigenvalues in the covariance matrix. In contrast to expectations from the literature and that found for the Ferrenti and extended Ferrenti approaches focusing on band gap and ionic character, the ML methods from the empirical approach highlighted features related to symmetry and crystal structure, including bond length, orientation and radial distribution, as influential when predicting a material as suitable for QT. All three approaches and all four ML methods agreed on a subset of $47$ eligible candidates %(to a probability of $&gt;50 \ \%$) of $8$ elemental, $29$ binary, and $10$ tertiary compounds, and provide a basis for further material explorations towards quantum technology. Solid-state ; Computer science ; Engineering physics ; Physics Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Cloud Computing and Resource Management
Authors: yuxuanliao
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-04-29)
Field: Chemistry — Spectroscopy

pSCNN Nuclear magnetic resonance (NMR) spectroscopy provides us a powerful tool to analyze mixtures consisting of small molecules but is difficult to identify compounds in mixtures because of chemical shift variation between samples and peak overlapping among molecules. We presented a pseudo Siamese convolutional neural network method (pSCNN) to solve the problems of compound identification in NMR spectra of mixtures. This is the model and some NMR data for the paper "Deep Learning-based Method for Compound Identification in NMR Spectra of Mixtures". Computer science Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Jindal K. Shah
Source: ECS Meeting Abstracts (article, 2022-10-09)
Field: Materials Science — Materials Chemistry

(Invited) Using Machine Learning to Guide Ionic Liquid Design Ionic liquids can be considered as nonaqueous, highly concentrated electrolytes composed entirely of ions with melting points typically less than 100 0 C. Many ionic liquids are derived from an organic cation and an organic/inorganic anion, at least one of them possessing low charge density, rendering them liquids near ambient conditions. Properties such as negligible vapor pressure, nonflammability, and high thermal stability are primary drivers for using ionic liquids as potential electrolytes in the next-generation batteries and energy storage devices. Despite the favorable attributes, challenges surrounding low ionic conductivity and sometimes low electrochemical stability need to be surmounted. Although ionic liquid properties can be tuned by changing the cation, anion, and/or substituents on the ions, the chemical space is too vast to be navigated efficiently either experimentally or using atomistic simulation techniques. In this presentation, our efforts at addressing these challenges with machine learning techniques will be described. Ionic liquid ; Ion ; Electrolyte ; Ionic conductivity ; Thermal stability ; Electrochemistry ; Ionic bonding ; Materials science ; Melting point ; Vapor pressure ; Chemical physics ; Nanotechnology ; Chemistry ; Physical chemistry ; Organic chemistry ; Electrode ; Composite material Machine Learning in Materials Science ; Ionic liquids properties and applications ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Pengru Huang | Ruslan luckin | Maxim Faleev | N. Kazeev | Abdalaziz Rashid Al-Maeeni | Daria V. Andreeva | A. Ustyuzhanin | Alexander Tormasov | A. H. Castro Neto | Kostya S. Novoselov
Source: arXiv (Cornell University) (preprint, 2022-12-05)
Field: Materials Science — Materials Chemistry

Unveiling the complex structure-property correlation of defects in 2D materials based on high throughput datasets Modification of physical properties of materials and design of materials with on-demand characteristics is at the heart of modern technology. Rare application relies on pure materials--most devices and technologies require careful design of materials properties through alloying, creating heterostructures of composites or controllable introduction of defects. At the same time, such designer materials are notoriously difficult for modelling. Thus, it is very tempting to apply machine learning methods for such systems. Unfortunately, there is only a handful of machine learning-friendly material databases available these days. We develop a platform for easy implementation of machine learning techniques to materials design and populate it with datasets on pristine and defected materials. Here we describe datasets of defects in represented 2D materials such as MoS2, WSe2, hBN, GaSe, InSe, and black phosphorous, calculated using DFT. Our study provides a data-driven physical understanding of complex behaviors of defect properties in 2D materials, holding promise for a guide to the development of efficient machine learning models. In addition, with the increasing enrollment of datasets, our database could provide a platform for designing of materials with predetermined properties. Computer science ; Throughput ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Materials science ; Wireless Machine Learning in Materials Science ; 2D Materials and Applications ; Boron and Carbon Nanomaterials Research
Authors: Lily Clough | Brett A. McKinney | Bethany Theiling | Victoria Da Poian | Jingyi Chen | Jonathan D. Major | Lauren Seyler
Source: Abstracts with programs - Geological Society of America (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

NEAREST-NEIGHBOR MACHINE LEARNING FEATURE SELECTION FOR INTERPRETATION OF MICROBIAL MOLECULAR SIGNATURES FROM ISOTOPE RATIO MASS SPECTROMETRY DATA Feature selection ; Mass spectrometry ; Computer science ; k-nearest neighbors algorithm ; Isotope-ratio mass spectrometry ; Artificial intelligence ; Pattern recognition (psychology) ; Feature (linguistics) ; Isotope ; Interpretation (philosophy) ; Selection (genetic algorithm) ; Chemistry ; Chromatography ; Physics ; Nuclear physics Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Industrial Technology and Control Systems