jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chloé Leseigneur | Isabelle Radgen‐Morvant | César Metzger | Pierre Esseiva | Daniel Croll | Pascal Miéville
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-11-15)
Field: Chemistry — Analytical Chemistry

Rapid and In-situ Detection of Biological Agents via Near-Infrared Spectroscopy and Cloud-Powered Neural Networks ABSTRACT The contagious nature of certain biological agents and the difficulty in treating infections render them a significant threat to public health and safety. In situations involving chemical, biological, radiological, and nuclear (CBRN) agents, effective detection is of paramount importance to prevent the undetected spread of these agents and enable swift and targeted responses tailored to the specific threat. Although portable detection tools are effective for chemical and nuclear detection, current biological detection methods face several challenges, including limited mobility, extended processing times, and varying accuracy. In the context of biological threats, where agents such as anthrax can be rapidly dispersed due to environmental factors and human activities, rapid detection is of paramount importance. It is imperative to develop field-applicable detection devices that are highly selective, capable of differentiating between biological and non-biological agents, as well as benign and harmful microorganisms. This study examines the potential of near-infrared (NIR) spectroscopy in conjunction with machine learning as a rapid in-situ biological detection method. The objective is to distinguish between biological agents and common white powders that are used as confounding agents in suspect letters. The non-pathogenic surrogates employed are safe and representative of typical biological warfare agents. The near-infrared (NIR) spectra of lyophilized bacterial and fungal surrogates, along with common white powders, were subjected to analysis and processing through the application of principal component analysis (PCA) and hierarchical clustering analysis (HCA). This resulted in the successful classification of the samples into distinct groups. The classification model demonstrated high accuracy in its prediction, thereby emphasizing the potential of the method for field detection of solid biological agents. These promising results suggest that NIR spectroscopy combined with machine learning could be further investigated as a rapid in-situ tool for biological detection in CBRN contexts. In situ ; Cloud computing ; Spectroscopy ; Infrared ; Artificial neural network ; Infrared spectroscopy ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Physics ; Artificial intelligence ; Optics ; Astronomy ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: P. Goswami | Asif Ahmmed | Ashraful Al Sabid | Md. Abul Hashem
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

A Non-Destructive Technique to Discriminate and Classify Fresh and Frozen Mutton Using Nirs and Machine Learning Algorithms Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Algorithm ; Machine learning Spectroscopy and Chemometric Analyses
Authors: Jiahao Pan | Huiqun Cheng | Gaosheng Yan | Lei Zhang | Wenshan Yu | Shengping Shen
Source: arXiv (Cornell University) (preprint, 2024-07-01)
Field: Materials Science — General Materials Science

Atomic cluster expansion interatomic potential for defects and thermodynamics of Cu-W system The unique properties exhibited in immiscible metals, such as excellent strength, hardness, and radiation-damage tolerance, have stimulated the interest of many researchers. As a typical immiscible metal system, the Cu-W nano-multilayers combine the plasticity of copper and the strength of tungsten, making it a suitable candidate for applications in aerospace, nuclear fusion engineering, and electronic packaging etc. To understand the atomistic origin of the defects and thermodynamics of the Cu-W immiscible system, we have developed an accurate machine learning interatomic potential (ML-IAP) for Cu-W based on the atomic cluster expansion (ACE) method. The Cu-W ACE potential can faithfully reproduce the fundamental properties of Cu and W predicted by density functional theory (DFT). Moreover, the thermodynamical properties, such as the melting point, coefficient of thermal expansion, diffusion coefficient, and equation of the state curve of the Cu-W solid solution, are calculated and compared against DFT and experiments. Monte Carlo Molecular Dynamics (MC-MD) simulations performed with the Cu-W ACE potential predict the experimentally observed phase separation and uphill diffusion phenomena. Our findings not only provide an accurate ACE potential for describing the Cu-W immiscible system, but also shed light on understanding the atomistic mechanism during the Cu-W nano-multilayers formation process. Cluster expansion ; Interatomic potential ; Cluster (spacecraft) ; Thermodynamics ; Materials science ; Physics ; Statistical physics ; Chemistry ; Molecular dynamics ; Computational chemistry ; Computer science Metallurgical and Alloy Processes ; Intermetallics and Advanced Alloy Properties ; Advanced Materials Characterization Techniques
Authors: Yechen Deng | Yixin An | Xiaodong Zhan | Yangzhihong Xiao | Bingfeng Wang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

3d Processing Map Construction and Dynamic Recrystallization of an In-Situ Tib Particle Reinforced Ti-5al-5mo-5v-1cr-1fe (B) Titanium Alloy Dynamic recrystallization ; Materials science ; Recrystallization (geology) ; In situ ; Alloy ; Metallurgy ; Titanium ; Particle (ecology) ; Hot working ; Physics ; Geology Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Metal and Thin Film Mechanics
Authors: Xinxing Li | Shu Li | Diankun Ma | Buwen Liang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Detection Method of Salmon Foreign Object Residues Based on Hyperspectral Image Synthesis Color Image Computer science ; Hyperspectral imaging ; Download ; Image (mathematics) ; Object (grammar) ; Artificial intelligence ; Computer vision ; World Wide Web Spectroscopy and Chemometric Analyses
Authors: Al R. Alexis | Ryan Bischoff | M. Amir Khan
Source:  (article, 2024-04-22)
Field: Chemistry — Spectroscopy

Discerning molecular transitions using adaptive Savitzy-Golay filtering and ML-based classification in mid-infrared sensing Mid-infrared sensing in the broadband spectral region of 5 – 11 𝜇m is suitable for detecting and quantifying multiple trace species. However, the challenge in detection is precise discrimination due to the broad linewidth of molecular transitions of species like methane, nitrous oxide, and other volatile organic compounds. In addition, isotopic transitions are generally weaker, with significant overlap with the neighboring abundant molecular transitions. This paper shows broadband detection of multiple species using an external cavity laser operation in 6 to 11 𝜇m spectral region. We use a combination of Savitzy-Golay filtering and machine learning-based classification to discern weaker rotational vibrational transitions. The proposed scheme is used to denoise and discriminate molecular transition in mid-infrared absorption spectroscopy. We show that an optimized S-G framework can be used by choosing a selected frame length determined by the adaptive learning outcome with low loss. We show that an ML-based adaptive SV filter can effectively suppress mod-hop (or any other instrumental-related effects and drifts). This is achieved by appropriately training the absorption spectroscopy signals with a calibrated reference in a (gaussian or thermal) noisy environment. Binary Golay code ; Infrared ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Algorithm ; Physics ; Optics Spectroscopy and Laser Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Mechanical and Optical Resonators
Authors: Swathikrishna Punnoli Valayanad | Lukas Arnold
Source: Journal of Physics Conference Series (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

AI-Based Material Parameter Prediction from Cone Calorimeter Measurements Abstract In pyrolysis simulation, methods such as inverse modelling are used to determine the material parameters from small-scale experiments. This often requires significant computational resources due to its iterative nature. This study investigates an artificial intelligence (AI)-based alternative approach, that can give instantaneous predictions for material parameters once trained. A dataset based on Fire Dynamics Simulator (FDS) simulation of a cone calorimeter experiment is used for training these AI models. Different AI models are trained to predict polymethyl methacrylate’s thermo-physical parameters using heat release rate (HRR) curves as input. AI models including Random Forest, 1D-convolution, and Recurrent Neural Networks showed the ability to predict the material parameters accurately with low mean squared error on the test dataset. These models were also able to recreate the HRR curves in FDS using their predictions, following the trend of the experimental HRR curve closely. Expanding the dataset to include materials with different behaviours and modelling different experiments could give these AI models broader applicability. However, FDS version dependence is a limitation for the AI models explored here because they were trained on a simulation-based dataset. Looking at the results, AI models in general can be used to predict material parameters required for pyrolysis modelling, potentially saving time and effort or, at the very least, used to complement the existing inverse modelling approaches. Cone calorimeter ; Calorimeter (particle physics) ; Cone (formal languages) ; Materials science ; Computer science ; Physics ; Optics ; Algorithm ; Engineering ; Detector ; Chemical engineering Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Neural Networks and Applications
Authors: Adrien Teurtrie | Nathanaël Perraudin | Thomas Holvoet | Hui Chen | Duncan T. L. Alexander | Guillaume Obozinski | C. Hébert
Source: arXiv (Cornell University) (preprint, 2024-04-26)
Field: Chemistry — Spectroscopy

From STEM-EDXS data to phase separation and quantification using physics-guided NMF We present the development of a new algorithm which combines state-of-the-art energy-dispersive X-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron microscope EDX spectrum images. The algorithm is based on non-negative matrix factorization (NMF) incorporating a physics-guided factorization model. It optimizes a Poisson likelihood, under additional simplex constraint together with user-chosen sparsity-inducing and smoothing regularizations, and is based on iterative multiplicative updates. The fluorescence of X-rays is fully modeled thanks to state-of-the-art theoretical work. It is shown that the output of the algorithm can be used for a direct chemical quantification. With this approach, it is straightforward to include a priori knowledge on the specimen such as the presence or absence of certain chemical elements in some of its phases. This work is implemented within two open-source Python packages, espm and emtables, which are used here for data simulation, data analysis and quantification. Using simulated data, we demonstrate that incorporating physical modeling in the decomposition helps retrieve meaningful components from spatially and spectrally mixed phases, even when the data are very noisy. For synthetic data with a higher signal, the regularizations yield a tenfold increase in the quality of the reconstructed abundance maps compared to standard NMF. Our approach is further validated on experimental data with a known ground truth, where state-of-the art results are achieved by using prior knowledge about the sample. Our model can be generalized to any other scanning spectroscopy techniques where underlying physical modeling can be linearized. Separation (statistics) ; Non-negative matrix factorization ; Phase (matter) ; Physics ; Computer science ; Matrix decomposition ; Machine learning ; Quantum mechanics Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Nuclear Physics and Applications
Authors: Manchev, Yulian | Popelier, Paul; id_orcid 0000-0001-9053-1363
Source: Research Explorer (The University of Manchester) (article, 2024-01-25)
Field: Materials Science — Materials Chemistry

FFLUX Molecular Simulations Driven by Atomic Gaussian Process Regression Models Machine learning (ML) force fields are revolutionizing molecular dynamics (MD) simulations as they bypass the computational cost associated with ab initio methods but do not sacrifice accuracy in the process. In this work, the GPyTorch library is used to create Gaussian process regression (GPR) models that are interfaced with the next-generation ML force field FFLUX. These models predict atomic properties of different molecular configurations that appear in a progressing MD simulation. An improved kernel function is utilized to correctly capture the periodicity of the input descriptors. The first FFLUX molecular simulations of ammonia, methanol, and malondialdehyde with the updated kernel are performed. Geometry optimizations with the GPR models result in highly accurate final structures with a maximum root-mean-squared deviation of 0.064 Å and sub-kJ mol-1 total energy predictions. Additionally, the models are tested in 298 K MD simulations with FFLUX to benchmark for robustness. The resulting energy and force predictions throughout the simulation are in excellent agreement with ab initio data for ammonia and methanol but decrease in quality for malondialdehyde due to the increased system complexity. GPR model improvements are discussed, which will ensure the future scalability to larger systems. Kriging ; Molecular dynamics ; Force field (fiction) ; Gaussian process ; Ab initio ; Benchmark (surveying) ; Kernel (algebra) ; Process (computing) ; Atomic model Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Chemical Physics Studies
Authors: Yanis Zirem | Léa Ledoux | Nina Ogrinc | Roland P. Bourette | Chann Lagadec | Paul Chaillou | Michel Salzet | Isabelle Fournier
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Spectroscopy

Development of Molecular Digital Twins Based on Ambient Ionization Mass Spectrometry Imaging for Real-Time Application in Oncological Surgery Pipeline (software) ; Population ; Pixel ; Mass spectrometry imaging ; Mass spectrometry ; Computer science ; Artificial intelligence ; Medicine ; Chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Materials Science
Authors: Daniel A. Heller | Mijin Kim | Dana Goerzen | Jaina Wollowitz | Stanislav Piletsky
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

(Invited) Quantum Well Nanotube Emission for Cancer Research and Diagnostics Quantum well defects (QWDs) on single-walled carbon nanotubes (SWCNTs) enable expanded analyte sensitization and functionalization for biosensing applications. We are using these capabilities to develop new research and diagnostic methods for cancers, infectious disease, and other indications. For instance, we are addressing issues with serum biomarker measurements, which are widely used for diagnostics, but these markers often fail to result in a clear diagnosis of complex disease indications. We developed a method using QWD-modified SWCNTs (QWNTs) to identify a “spectral fingerprint” through the collection of large data sets of molecular binding interactions to an array of QWNTs. We found that a library of QWNTs exhibit differentiated spectral variations in response to an ensemble of molecular binding events in patient serum. Via machine learning algorithms, we built prediction models of nanosensor responses that can reliably identify ovarian cancer, and we have expanded this approach to other indications without known biomarkers, providing a general method to identify clinical indications. Nanotechnology ; Nanotube ; Cancer ; Materials science ; Carbon nanotube ; Physics ; Medicine ; Internal medicine Carbon Nanotubes in Composites ; Nanotechnology research and applications
Authors: chengcheng Peng | Guosheng Chen | Cailiu Yin | Xinjiang Zhang | Wenchuang Yang | Xinyuan Han | Chunqiang Yi | Wenbo Zhu | Xinghao Fan
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Application of Machine Learning to Identify Key Features Affecting Conductivity in Cnt/ Pedot:Pss Nanocomposites PEDOT:PSS ; Key (lock) ; Conductivity ; Nanocomposite ; Materials science ; Conductive polymer ; Nanotechnology ; Composite material ; Computer science ; Polymer ; Chemistry ; Computer security Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Recycling and Waste Management Techniques
Authors: Sobhan Naderian | Naveen Kumar | Vihar Georgiev
Source:  (article, 2024-11-25)
Field: Chemistry — Spectroscopy

Peptide Sequencing Using Neural Machine Translation Based on Sequence-2-Sequence Architecture and Long Short-Term Memory Networks Mass spectrometry is the most reliable and accurate approach for analyzing a complex biological sample and identifying its protein content, which is time-consuming and reasonably expensive. One possible option to overcome such limitations is to use potentiometric sensors based on transistors. However, for such technology to work, a protein database that contains information on billions of small peptides and amino acids (AA) is required. The only practical way to build such a database is to use machine learning, and this study is going to show the initial steps towards achieving this aim. This study sheds light on the possibility of a new approach for peptide sequencing combining analytical simulations with large language models (LLM) based on Sequence-2-Sequence (Seq-2-Seq) architecture built by long short-term memory (LSTM) networks. A total of 11,573 tokenized data points (voltage and capacitance cross-over points) with a vocabulary size of 504 are fed into the model; 80% of the data are used for training and validation, and 20% are used for testing. The model is tested on unseen data and the accuracy during the test is 71.74%, which is significant if compared to expensive and time-consuming conventional methods, i.e., spectrometry. In conclusion, the output results of this study show that the proposed Seq-2-Seq LLM architecture could be used to build a material database for a potentiometric sensor to replace the mass spectrometry method. Sequence (biology) ; Computer science ; Machine translation ; Artificial neural network ; Architecture ; Translation (biology) ; Term (time) ; Artificial intelligence ; Computer architecture ; Biology ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Bidhya Thapa | Narayan Prasad Adhikari
Source: Bibechana (article, 2024-10-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Characterization of the molecular interactions between Kaiso and CTCF using AlphaFold2 and molecular dynamics simulations CCCTC-binding factor (CTCF) is an architectural protein that plays a crucial role in global chromatin organization and remodeling via its interaction with several protein partners. The interaction between Kaiso and CTCF regulates the enhancer-blocking function of CTCF. Despite the important biological function of their interactions, structural characterization of the Kaiso-CTCF complex has yet to be carried out. In this work, we have employed modeling and molecular dynamics (MD) simulation to predict the complex between Kaiso and CTCF and investigate its structural features. We have employed the neural network-based modeling method Alphafold2 to predict the complex between Kaiso and CTCF, and the MD simulation was used to explore the detailed dynamics of the interactions involved in complex formation and stabilization. We predicted the key residues and their role in the binding of Kaiso to CTCF. Our results show that the hydrophobic interactions between the inter-facial residues play a significant role in forming the Kaiso-CTCF complex. In addition, several non-covalent interactions, such as hydrogen bonds and electrostatic and van der Waals interactions, stabilize the complex. The significant value of hydrophobic contact area and binding free energy signifies the stability of the predicted Kaiso-CTCF complex. Molecular dynamics ; Characterization (materials science) ; Chemistry ; Environmental science ; Nanotechnology ; Materials science ; Computational chemistry Magnetism in coordination complexes
Authors: Giacomo Nardi | Matheus Santos Sano | Margaux Bilay | Anne Brelot | Jean-Christophe Olivo-Marin | Thibault Lagache
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-12-20)
Field: Materials Science — Surfaces, Coatings and Films

Characterizing particle dynamics in live imaging through stochastic physical models and machine learning Abstract Particle dynamics determine the orchestration of molecular signaling in cellular processes. A wide range of subdiffusive motions has been described at the cell interior and membrane, corresponding to different environmental constraints. However, the standard methods for motion analysis, embedded in a diffusion-based framework, lack robustness for capturing the complexity of stochastic dynamics. This work develops a classification method to detect the five main stochastic laws modeling particle dynamics accurately. The method builds on machine-learning techniques that use features properly designed to capture the intrinsic geometric properties of trajectories governed by the different processes. This guarantees the accurate classification of observed dynamics in an interpretable and explainable framework. The main asset of this approach is its capability to distinguish different subdiffusive behaviors making it a privileged tool for biological investigations. The robustness to localization error and motion composition is proven, ensuring its reliability on experimental data. Moreover, the classification of composed trajectories is investigated, showing that the method can uncover the path’s mono-vs bi-dynamics nature. The method is used to study the dynamics of membrane receptors CCR5, involved in HIV infection. Comparing the basal state to an agonist-bound state which displays potent anti-HIV-1 activity, we show that the latter affects the natural dynamic state of receptors, thus clarifying the link between movement and receptor activation. Dynamics (music) ; Particle dynamics ; Stochastic dynamics ; Statistical physics ; Computer science ; Particle (ecology) ; Artificial intelligence ; Physics ; Geology ; Quantum mechanics ; Molecular dynamics ; Acoustics Electron and X-Ray Spectroscopy Techniques ; Electrical and Bioimpedance Tomography