jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tomislav Piskor | Peter Pinski | Thilo Mast | Vladimir V. Rybkin
Source: International Journal of Molecular Sciences (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Multi-Level Protocol for Mechanistic Reaction Studies Using Semi-Local Fitted Potential Energy Surfaces In this work, we propose a multi-level protocol for routine theoretical studies of chemical reaction mechanisms. The initial reaction paths of our investigated systems are sampled using the Nudged Elastic Band (NEB) method driven by a cheap electronic structure method. Forces recalculated at the more accurate electronic structure theory for a set of points on the path are fitted with a machine learning technique (in our case symmetric gradient domain machine learning or sGDML) to produce a semi-local reactive potential energy surface (PES), embracing reactants, products and transition state (TS) regions. This approach has been successfully applied to a unimolecular (Bergman cyclization of enediyne) and a bimolecular (S<sub>N</sub>2 substitution) reaction. In particular, we demonstrate that with only 50 to 150 energy-force evaluations with the accurate reference methods (here complete-active-space self-consistent field, CASSCF, and coupled-cluster singles and doubles, CCSD) it is possible to construct a semi-local PES giving qualitative agreement for stationary-point geometries, intrinsic reaction coordinates and barriers. Furthermore, we find a qualitative agreement in vibrational frequencies and reaction rate coefficients. The key aspect of the method's performance is its multi-level nature, which not only saves computational effort but also allows extracting meaningful information along the reaction path, characterized by zero gradients in all but one direction. Agnostic to the nature of the TS and computationally economic, the protocol can be readily automated and routinely used for mechanistic reaction studies. Potential energy surface ; Work (physics) ; Domain (mathematical analysis) ; Enediyne ; Protocol (science) ; Potential energy ; Electronic structure ; Path (computing) ; Reaction coordinate ; Set (abstract data type) ; Energy profile ; Surface hopping ; Computer science ; Chemistry ; Computational chemistry ; Energy (signal processing) ; Biological system ; Ab initio ; Physics ; Mathematics ; Atomic physics ; Quantum mechanics ; Molecular dynamics ; Stereochemistry ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Robert Martinis
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Enhancing Mass Spectrometry Imaging : A Deep Learning Based Approach Mass spectrometry (MS) is essential for determining molecular compositions by measuring mass-to-charge ratios. When combined with imaging, Mass Spectrometry Imaging (MSI) spatially maps these distributions, useful in fields like biomedical research. However, MSI often has lower spatial resolution and longer acquisition times than traditional microscopy. This study explores deep learning models to enhance MSI resolution and reduce acquisition times while maintaining image quality. We evaluated Fast Super-Resolution Convolutional Neural Network (FSRCNN), Efficient Sub-Pixel Convolutional Neural Network (ESPCN), and Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN). These models were trained on MSI and optical images from the Lanekoff Lab and public datasets. Metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) assessed image quality. Results showed that CNN-based models performed consistently, while Real-ESRGAN achieved higher PSNR but lower SSIM values due to artifacts. We also explored fusing MSI data with high-resolution optical images using U-net architectures and Spatial Transformer Networks (STNs) for alignment and fusion. The STN method outperformed traditional MATLAB-based alignment methods, achieving higher mutual information (MI), normalized mutual information (NMI), and lower mean square error (MSE). The U-net-based fusion effectively combined structural details from optical images with the chemical specificity of MSI data. Overall, deep learning techniques significantly enhance MSI image resolution and quality. Future work should optimize these models for better efficiency and explore hybrid approaches to further enhance MSI and optical image integration. Deep learning ; Convolutional neural network ; Pattern recognition (psychology) ; Generative adversarial network ; Similarity (geometry) ; Image resolution ; Artificial neural network ; Mean squared error Mass Spectrometry Techniques and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies
Authors: Dan Cheng | Zhizheng Zhuo | Jiang Du | Jinyuan Weng | Chengzhou Zhang | Yunyun Duan | Ting Sun | Minghao Wu | Min Guo | Tiantian Hua | Ying Jin | Boyang Peng | Zhaohui Li | Mingwang Zhu | Maliha Imami | Chetan Bettegowda | Haris I. Sair | Harrison X. Bai | Frederik Barkhof | Xing Liu | Yaou Liu
Source:  (preprint, 2024-01-05)
Field: Materials Science — Materials Chemistry

Supplementary Document S2 from A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images &lt;p&gt;Next-generation sequencing&lt;/p&gt; Posterior fossa ; Artificial intelligence ; Computer science ; Deep learning ; Natural language processing ; Pattern recognition (psychology) ; Medicine ; Anatomy Machine Learning in Materials Science ; Radiomics and Machine Learning in Medical Imaging
Authors: Yong Gyun Shin | Yoon‐Suk Chang | Belal Almomani
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Mechanical Properties Estimation and Structural Integrity Assessment of Snf Cladding Under Hypothetical Drop Accident Conditions Structural integrity ; Cladding (metalworking) ; Drop (telecommunication) ; Drop out ; Computer science ; Engineering ; Forensic engineering ; Reliability engineering ; Structural engineering ; Materials science ; Mechanical engineering ; Composite material ; Economics Nuclear Materials and Properties ; Nuclear and radioactivity studies ; High-Velocity Impact and Material Behavior
Authors: Yong Gyun Shin | Belal Almomani | Yoon-Suk Chang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Mechanical Properties Estimation and Structural Integrity Assessment of Snf Cladding Under Hypothetical Drop Accident Conditions Cladding (metalworking) ; Drop (telecommunication) ; Structural integrity ; Environmental science ; Drop out ; Computer science ; Forensic engineering ; Engineering ; Structural engineering ; Materials science ; Composite material ; Mechanical engineering ; Economics Nuclear Materials and Properties ; Recycling and Waste Management Techniques ; Metallurgical Processes and Thermodynamics
Authors: Xuefeng Liu | Chun Zhao | Yaxin Wang
Source:  (article, 2024-08-05)
Field: Chemistry — Analytical Chemistry

Modeling and Optimization of Organic Molecule Infrared Spectra Prediction Based on Semi-Empirical Structure Calculation High-precision and efficient molecular spectrum simulation is crucial for substance discovery and structural identification. Despite significant progress in quantum chemistry simulations for providing accurate spectrum predictions, the complexity of electronic structure calculations on a large number of molecules has resulted in substantial computational resource and time requirements, limiting its practicality in real-time spectrum recognition applications. Semi-empirical structural calculations offer a rapid advantage in molecular spectrum simulation, but their application is constrained by lower accuracy. This paper proposes a solution based on a neural network model, training the model to map low-precision spectra obtained from semi-empirical structural calculations to high-precision Density Functional Theory (DFT) infrared spectra. This approach addresses the accuracy limitations of semi-empirical structures, achieving fast and efficient predictions of organic molecular infrared spectra. Through a case study on the QM9 dataset, we utilize semi-empirical structural calculations for frequency determination, followed by Lorentzian broadening to obtain infrared spectra. The neural network method is then applied to fit these spectra onto the high-precision infrared spectra calculated using Density Functional Theory (DFT). This study aims to validate whether the proposed method can achieve rapid predictions of organic molecular infrared spectra while ensuring accuracy. The proposed approach provides an effective pathway for predicting organic molecular infrared spectra, offering a method to overcome the limitations of traditional quantum chemistry simulations in real-time spectrum recognition applications. Molecule ; Infrared spectroscopy ; Infrared ; Materials science ; Spectral line ; Computer science ; Organic molecules ; Chemistry ; Optics ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses
Authors: Tianzhong Luo | Geer Teng | Mengyu Bao | Xiangjun Xu | Kai Wei | Zhifang Zhao | Yongyue Zheng | Bingheng Lu | Qianqian Wang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Identification of Mutong Medicinal Materials Using Laser-Induced Breakdown Spectroscopy Laser-induced breakdown spectroscopy ; Identification (biology) ; Spectroscopy ; Materials science ; Laser ; Optics ; Physics ; Botany ; Astronomy ; Biology Heavy Metals in Plants
Authors: A N Dolgova | Bislan Kh. Goltakov | Galiya F. Gabdulsadykova
Source: EKONOMIKA I UPRAVLENIE PROBLEMY RESHENIYA (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

AI IN THE DEVELOPMENT OF NANOMATERIALS WITH THERMOELECTRIC PROPERTIES FOR CONVERTING WASTE HEAT INTO ELECTRIC ENERGY The article examines the role of artificial intelligence (AI) in the development of nanomaterials with thermoelectric properties capable of converting waste heat into electrical energy. The advantages of using AI are discussed, including accelerating research, increasing the accuracy of predictions, and optimizing development processes. Examples of using machine learning and deep learning algorithms for modeling material properties, creating new compounds, and managing production processes are described. It is shown that AI promotes an interdisciplinary approach and ensures environmental sustainability, making thermoelectric technologies more efficient and accessible. Prospects for further application of AI in energy research are considered. Nanomaterials ; Thermoelectric effect ; Thermoelectric materials ; Heat energy ; Waste heat ; Materials science ; Engineering physics ; Nanotechnology ; Mechanical engineering ; Engineering ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Chemical and Physical Properties of Materials
Authors: Yuancheng Sun | Kai Chen | Kang Liu | Qiwei Ye
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-09-14)
Field: Materials Science — Materials Chemistry

3D Molecular Pretraining via Localized Geometric Generation Abstract Self-supervised learning on 3D molecular structures is gaining importance in data-driven scientific research and applications due to the high costs of annotating bio-chemical data. However, the strategic selection of semantic units for modeling 3D molecular structures remains underexplored, despite its crucial role in effective pre-training—a concept well-established in language processing and computer vision. We introduce Localized Geometric Generation (LEGO), a novel approach that treats tetrahedrons within 3D molecular structures as fundamental building blocks, leveraging their geometric simplicity and widespread presence across chemical functional patterns. Inspired by masked modeling, LEGO perturbs tetrahedral local structures and learns to reconstruct them in a self-supervised manner. Experimental results demonstrate LEGO consistently enhances molecular representations across biochemistry and quantum property prediction benchmarks. Additionally, the tetrahedral modeling and pretraining generalize from small molecules to larger molecular systems, validating by protein-ligand affinity prediction. Our results highlight the potential of selecting semantic units to build more expressive and interpretable neural networks for scientific AI applications. Computer science ; Tetrahedron ; Artificial intelligence ; Simplicity ; Selection (genetic algorithm) ; Geometric modeling ; Machine learning ; Chemistry ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Dan Cheng | Zhizheng Zhuo | Jiang Du | Jinyuan Weng | Chengzhou Zhang | Yunyun Duan | Ting Sun | Minghao Wu | Min Guo | Tiantian Hua | Ying Jin | Boyang Peng | Zhaohui Li | Mingwang Zhu | Maliha Imami | Chetan Bettegowda | Haris I. Sair | Harrison X. Bai | Frederik Barkhof | Xing Liu | Yaou Liu
Source:  (preprint, 2024-01-05)
Field: Materials Science — Materials Chemistry

Supplementary Document S1 from A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images &lt;p&gt;DNA methylation profiling&lt;/p&gt; Posterior fossa ; Artificial intelligence ; Computer science ; Deep learning ; Ependymoma ; Pattern recognition (psychology) ; Natural language processing ; Radiology ; Medicine Machine Learning in Materials Science ; Radiomics and Machine Learning in Medical Imaging
Authors: Nicholas J. Williams | Lara Kabalan | Ljiljana Stojanović | Viktor Zólyomi | Edward O. Pyzer‐Knapp
Source: arXiv (Cornell University) (preprint, 2024-08-15)
Field: Chemistry — Physical and Theoretical Chemistry

Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents A significant challenge in computational chemistry is developing approximations that accelerate \emph{ab initio} methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potentials that can be transferred across different molecular and crystalline systems. Most MLIPs are trained only on energies and forces in vacuum, while an improved description of the potential energy surface could be achieved by including the curvature of the potential energy surface. We present Hessian QM9, the first database of equilibrium configurations and numerical Hessian matrices, consisting of 41,645 molecules from the QM9 dataset at the $ω$B97x/6-31G* level. Molecular Hessians were calculated in vacuum, as well as water, tetrahydrofuran, and toluene using an implicit solvation model. To demonstrate the utility of this dataset, we show that incorporating second derivatives of the potential energy surface into the loss function of a MLIP significantly improves the prediction of vibrational frequencies in all solvent environments, thus making this dataset extremely useful for studying organic molecules in realistic solvent environments for experimental characterization. Hessian matrix ; Quantum chemistry ; Quantum chemical ; Computational chemistry ; Chemistry ; Quantum ; Computer science ; Combinatorial chemistry ; Organic chemistry ; Reaction mechanism ; Physics ; Molecule ; Quantum mechanics ; Mathematics ; Applied mathematics ; Catalysis Various Chemistry Research Topics ; History and advancements in chemistry ; Inorganic and Organometallic Chemistry
Authors: Peder Lyngby | Casper Larsen | Karsten W. Jacobsen
Source: arXiv (Cornell University) (preprint, 2024-08-28)
Field: Materials Science — Materials Chemistry

Bayesian optimization of atomic structures with prior probabilities from universal interatomic potentials The optimization of atomic structures plays a pivotal role in understanding and designing materials with desired properties. However, conventional computational methods often struggle with the formidable task of navigating the vast potential energy surface, especially in high-dimensional spaces with numerous local minima. Recent advancements in machine learning-driven surrogate models offer a promising avenue for alleviating this computational burden. In this study, we propose a novel approach that combines the strengths of universal machine learning potentials with a Bayesian approach using Gaussian processes. By using the machine learning potentials as priors for the Gaussian process, the Gaussian process has to learn only the difference between the machine learning potential and the target energy surface calculated for example by density functional theory. This turns out to improve the speed by which the global optimal structure is identified across diverse systems for a well-behaved machine learning potential. The approach is tested on periodic bulk materials, surface structures, and a cluster. Interatomic potential ; Bayesian probability ; Bayesian optimization ; Statistical physics ; Computer science ; Physics ; Molecular dynamics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Xu Chen | Yuanjie Sun | Eugen Hruška | Vivek Dixit | Jinming Yang | Yu He | Yao Wang | Fang Liu
Source: arXiv (Cornell University) (preprint, 2024-06-06)
Field: Materials Science — Materials Chemistry

Detecting Thermodynamic Phase Transition via Explainable Machine Learning of Photoemission Spectroscopy Identifying thermodynamic signatures of electronic phases, such as superconductivity, is challenging in low-dimensional materials due to strong fluctuations and low probing volume. Spectroscopic methods are often used to identify new bulk phases, but their main measurable quantity -- electronic energy gaps -- is no longer an effective order parameter in low-dimensional and fluctuating systems. Combining angle-resolved photoemission with a domain-adversarial neural network, we report a data-driven method to identify thermodynamic phase transitions solely based on single-particle spectra. We demonstrate 97.6$\%$ accuracy in cuprate superconductor Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ with strong superconducting fluctuations. This model notably compensates for the scarcity of experimental data by leveraging virtually inexhaustible simulated data. Further, its explainability reveals the crucial role of in-gap spectral weight in detecting phase fluctuations and thermodynamic transitions. Our work pinpoints the spectroscopic signatures of fluctuating orders and enables using spectroscopy for machine-learning-assisted material discovery for low-dimensional and strong coupling systems. Superconductivity ; Identification (biology) ; Particle identification ; Particle (ecology) ; Computer science ; Physics ; Artificial intelligence ; Condensed matter physics ; Optics ; Geology ; Biology Machine Learning in Materials Science ; Computational Physics and Python Applications ; Topic Modeling
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Predicting hydrogen adsorption energies on platinum nanoparticles and surfaces with machine learning The growing interest in hydrogen gas as a fuel drives research into environmentally friendly hydrogen production methods.One viable approach of obtaining hydrogen is the electrocatalysis of water, which includes the hydrogen evolution reaction (HER) as one of the half-reactions.In the search of highly active catalysts for the HER, machine learning can be effectively utilized to develop models for calculating hydrogen adsorption energy, a key descriptor of catalytic activity.In this study, we learned models for predicting hydrogen adsorption energy on platinum.We used various machine-learning (ML) techniques on two datasets, one for extended surfaces and the other for nanoparticles.The respective results reveal that ML models for extended surfaces are more accurate than those for nanoparticles, and that the features describing the local environment are the most significant for the predictions.For surfaces, the coordination number is the most relevant feature, while the d-band center is the most important for nanoparticles.The ML models developed in this study lack sufficient accuracy to provide reliable results, highlighting the need for further investigation with additional features or larger datasets. Platinum ; Platinum nanoparticles ; Adsorption ; Nanoparticle ; Materials science ; Hydrogen ; Computer science ; Nanotechnology ; Chemistry ; Physical chemistry ; Catalysis Catalytic Processes in Materials Science ; Chemical and Physical Properties of Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Dan Cheng | Zhizheng Zhuo | Jiang Du | Jinyuan Weng | Chengzhou Zhang | Yunyun Duan | Ting Sun | Minghao Wu | Min Guo | Tiantian Hua | Ying Jin | Boyang Peng | Zhaohui Li | Mingwang Zhu | Maliha Imami | Chetan Bettegowda | Haris I. Sair | Harrison X. Bai | Frederik Barkhof | Xing Liu | Yaou Liu
Source:  (preprint, 2024-01-05)
Field: Materials Science — Materials Chemistry

Supplementary Document S1 from A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images &lt;p&gt;DNA methylation profiling&lt;/p&gt; Posterior fossa ; Artificial intelligence ; Computer science ; Natural language processing ; Deep learning ; Pattern recognition (psychology) ; Radiology ; Medicine Machine Learning in Materials Science ; Radiomics and Machine Learning in Medical Imaging
Authors: Gabriel Vogel | Jana M. Weber
Source: arXiv (Cornell University) (preprint, 2024-09-30)
Field: Chemistry — Organic Chemistry

Inverse Design of Copolymers Including Stoichiometry and Chain Architecture The demand for innovative synthetic polymers with improved properties is high, but their structural complexity and vast design space hinder rapid discovery. Machine learning-guided molecular design is a promising approach to accelerate polymer discovery. However, the scarcity of labeled polymer data and the complex hierarchical structure of synthetic polymers make generative design particularly challenging. We advance the current state-of-the-art approaches to generate not only repeating units, but monomer ensembles including their stoichiometry and chain architecture. We build upon a recent polymer representation that includes stoichiometries and chain architectures of monomer ensembles and develop a novel variational autoencoder (VAE) architecture encoding a graph and decoding a string. Using a semi-supervised setup, we enable the handling of partly labelled datasets which can be benefitial for domains with a small corpus of labelled data. Our model learns a continuous, well organized latent space (LS) that enables de-novo generation of copolymer structures including different monomer stoichiometries and chain architectures. In an inverse design case study, we demonstrate our model for in-silico discovery of novel conjugated copolymer photocatalysts for hydrogen production using optimization of the polymer's electron affinity and ionization potential in the latent space. Stoichiometry ; Copolymer ; Inverse ; Chain (unit) ; Architecture ; Computer science ; Materials science ; Chemical engineering ; Chemistry ; Mathematics ; Organic chemistry ; Engineering ; Physics ; Geography ; Polymer ; Geometry Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly ; Dendrimers and Hyperbranched Polymers