jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xin Li | Zijian Wei | Shuang Wang | Peifu Han | Xue Li | Tao Song
Source:  (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Semi-Template Retrosynthesis Prediction with 3D Spatial Structures and Reaction Center Disconnection Rules Retrosynthesis constructs rational synthetic pathways by predicting reactants from the target product. Previous research utilizing Graph Neural Networks often relies on two-dimensional molecular representations. The two-dimensional representations of functional groups of the same type are identical, and ignoring differences in their three-dimensional(3D) structures can cause confusion in identifying reaction centers. Additionally, they disconnect the reaction centers, hindering the accurate identification of chemical transformation rules. To address these issues, we propose the Retro3D model based on a semi-template-based method, integrating 3D structural information to enhance molecular representation. Due to the discrepancies in bond lengths and bond angles of the same type of functional groups in different 3D spatial structures, our method improves the accuracy of identifying reaction centers in the target product. Furthermore, our method determines whether reaction centers are disconnected, differentiating between types of reaction centers to obtain two types of chemical transformation rules. Given the known reaction class, experiments demonstrate that our model achieves superior accuracy in predicting reaction centers and final reactants compared to previous benchmark models. In summary, our methodology aligns with established chemical principles, enhancing its interpretability and broad applicability across diverse synthetic challenges. Disconnection ; Retrosynthetic analysis ; Computer science ; Center (category theory) ; Artificial intelligence Machine Learning in Materials Science
Authors: Dhruv Menon | David Fairen‐Jiménez
Source: ChemRxiv (preprint, 2024-06-27)
Field: Chemistry — Inorganic Chemistry

Guiding the rational design of biocompatible metal-organic frameworks for drug delivery using machine learning In drug delivery, metal-organic frameworks (MOFs) have emerged as an interesting paradigm owing to their tuneable porosity, structural diversity, and ease of external surface functionalization. Compared to other drug delivery systems, state-of-the-art MOFs can encapsulate up to 50 times higher quantities of drugs per unit of mass while showing very long release times. Nevertheless, for their translation into clinical applications, concerns regarding MOF biocompatibility necessitate comprehensive mitigation. Unfortunately, experiments are resource and time-intensive, while modeling approaches fail to capture the behavior of MOFs in intricate biological systems. Herein, we report a novel computational pipeline guided by machine learning (ML) for probing the biocompatibility of MOFs. The pipeline is designed to expedite the assessment of potential MOF toxicity based on the chemical properties of their precursors. Interpretable ML models were built on a database of over 35,000 organic molecules, predicting the potential toxicity of MOF linkers with 83% accuracy. Additionally, we established a comprehensive database cataloging the toxicity of MOF metallic centers. Leveraging these, we have screened the Cambridge Structural Database (CSD) containing 86,000 non-disordered MOFs, identifying existing and future promising candidates with minimal toxicity profiles for drug delivery applications. Beyond high-throughput screening, the developed models shed light on the chemical landscape associated with high biocompatibility precursor molecules, enabling the derivation of guidelines for the rational design of biocompatible MOFs. This framework thus expedites the identification of biocompatible materials, while facilitating deeper insights into their underlying chemistry. Biocompatible material ; Drug delivery ; Rational design ; Metal-organic framework ; Drug ; Computer science ; Nanotechnology ; Systems engineering ; Materials science ; Engineering ; Biomedical engineering ; Medicine ; Chemistry ; Pharmacology Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Dendrimers and Hyperbranched Polymers
Authors: Neda Helali Khiavi | Sevinch Rahimi Moghaddam | Sait Alp | Bülent Çakmak | Güven Turgut
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Explainable Artificial Intelligence-Driven Insights into 2d Hexagonal Boron Nitride Cvd Synthesis Hexagonal boron nitride ; Materials science ; Boron nitride ; Boron ; Nitride ; Hexagonal crystal system ; Nanotechnology ; Artificial intelligence ; Computer science ; Crystallography ; Chemistry ; Organic chemistry ; Graphene Machine Learning in Materials Science ; Semiconductor materials and devices ; Boron and Carbon Nanomaterials Research
Authors: Qin Gao | Liqiang Zhang | Fei Wang | Wei Xiong | Haibin Cui | Xinrong Wu | Wenyuan Wang | Lihong Zhang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Detection of Polyester Content of Recyclable Waste Textiles Based on Hyperspectral Imaging and Deep Learning Model Hyperspectral imaging ; Polyester ; Deep learning ; Artificial intelligence ; Materials science ; Environmental science ; Process engineering ; Waste management ; Computer science ; Engineering ; Composite material Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection
Authors: Qi Gao | Michihiko Sugawara | Paul D. Nation | Takao Kobayashi | Yu‐ya Ohnishi | Hiroyuki Tezuka | Naoki Yamamoto
Source: Intelligent Computing (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A Quantum-Classical Method Applied to Material Design: Photochromic Materials Optimization for Photopharmacology Applications The integration of quantum chemistry, machine learning, and optimization calculations is expected to accelerate materials discovery by making large chemical spaces amenable to computational study, a challenging task for classical computers. In this study, we develop a quantum-classical computing scheme involving the computational-basis variational quantum deflation (cVQD) method for calculating the excited states of a general classical Hamiltonian, such as an Ising Hamiltonian. We apply this scheme to the practical use case of generating photochromic diarylethene (DAE) derivatives for photopharmacology applications. Using a dataset of 384 DAE derivatives from quantum chemistry calculation results, we show that machine learning can accurately predict the wavelength of maximum absorbance, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mi>λ</mml:mi> <mml:mi>max</mml:mi> </mml:msub> </mml:math> , of 4,096 DAE derivatives. After screening over 4,096 molecules using the computing scheme, we identified 5 DAE candidates that have important applications in photopharmacology. In detail, a 12-qubit cVQD calculation provides the ground state and 4 excited states of an Ising Hamiltonian corresponding to DAE candidates possessing large <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mi>λ</mml:mi> <mml:mi>max</mml:mi> </mml:msub> </mml:math> . On a quantum simulator, results are found to be in excellent agreement with those obtained by an exact eigensolver. Utilizing error suppression and mitigation techniques, cVQD on a real quantum device produces results with accuracy comparable to calculations on the simulator. Finally, we show that quantum chemistry calculations for the DAE candidates provides a path to achieving large <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mi>λ</mml:mi> <mml:mi>max</mml:mi> </mml:msub> </mml:math> and oscillator strengths by means of the molecular engineering of DAE derivatives. Photochromism ; Quantum ; Materials science ; Nanotechnology ; Computer science ; Physics ; Quantum mechanics Photochromic and Fluorescence Chemistry ; Chemistry and Chemical Engineering ; Various Chemistry Research Topics
Authors: Vusumuzi Malele | Ashley Phala
Source: Indonesian Journal of Data and Science (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Probabilistic Graphical Models for Predicting Properties of New Materials Based on Their Composition and Structure Probabilistic graphical model (PGMs) offer a powerful framework for modeling complex relationships between different components. By integrating information on the element composition and structural features, these models enable the inference of materials properties with a probabilistic perspective. This approach holds promising efforts towards accelerating materials discovery design, as it facilitates the predication of diverse materials characteristics, ranging from electronic and mechanical properties to thermal and optical behavior. The use of PGMs in materials science represents a sophisticated methodology for harnessing data-driven insights to guide the exploration of innovative materials with tailored functionalities. The purpose of this paper is to investigate literature for the exploitation of the data science concepts, big data and machine learning that yields computational intelligence. A literature review approach to understand the exploitation and use of computational intelligence in the leading-edge research and innovation of materials science. The findings illustrate that machine learning can be used to intricate chemical problems that otherwise would not be tractable. Leveraging PGMs presents a promising avenue for predicting the properties of new materials based on their composition and structure. Computer science ; Probabilistic logic ; Graphical model ; Inference ; Perspective (graphical) ; Data science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yuchen Zhu | Changwen Liang | Yihang Huang | Lingxiao Zhu
Source: Journal of Physics Conference Series (article, 2024-11-01)
Field: Chemistry — Spectroscopy

Intelligent recognition of atomic saturated absorption spectra based on 1D Convolution Neural Network Abstract Laser frequency locking technology is widely used in quantum precision measurement, quantum simulation, quantum information, etc. The conventional frequency locking process based on the saturated absorption spectrum needs to find the target peak corresponding to a specific frequency manually. However, due to the complexity of atomic energy level structure and drifts caused by the parameters of semiconductor lasers in different environments, it is usually time-consuming and related to the experience of operators. To solve the above problems, a new approach based on machine learning has been proposed. Using the one-dimensional convolution neural network combined with the gradient analysis method, the recognition accuracy of 0.9784 and high precision of feature extraction have been achieved. The approach presented in this paper combines laser frequency locking in the quantum field with AI and addresses the bottleneck issue in intelligent frequency locking of semiconductor lasers, which holds significant reference value. Convolution (computer science) ; Artificial neural network ; Atomic absorption spectroscopy ; Spectral line ; Absorption (acoustics) ; Absorption spectroscopy ; Materials science ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Acoustics ; Physics ; Optics ; Astronomy ; Quantum mechanics Spectroscopy and Laser Applications ; Water Quality Monitoring and Analysis ; Analytical Chemistry and Sensors
Authors: Subhadarsi Nayak | Hrithwik Shalu | Joseph N. Stember
Source: arXiv (Cornell University) (preprint, 2024-04-12)
Field: Materials Science — Polymers and Plastics

Kinematics Modeling of Peroxy Free Radicals: A Deep Reinforcement Learning Approach Tropospheric ozone, known as a concerning air pollutant, has been associated with health issues including asthma, bronchitis, and impaired lung function. The rates at which peroxy radicals react with NO play a critical role in the overall formation and depletion of tropospheric ozone. However, obtaining comprehensive kinetic data for these reactions remains challenging. Traditional approaches to determine rate constants are costly and technically intricate. Fortunately, the emergence of machine learning-based models offers a less resource and time-intensive alternative for acquiring kinetics information. In this study, we leveraged deep reinforcement learning to predict ranges of rate constants (\textit{k}) with exceptional accuracy, achieving a testing set accuracy of 100%. To analyze reactivity trends based on the molecular structure of peroxy radicals, we employed 51 global descriptors as input parameters. These descriptors were derived from optimized minimum energy geometries of peroxy radicals using the quantum composite G3B3 method. Through the application of Integrated Gradients (IGs), we gained valuable insights into the significance of the various descriptors in relation to reaction rates. We successfully validated and contextualized our findings by conducting cross-comparisons with established trends in the existing literature. These results establish a solid foundation for pioneering advancements in chemistry, where computer analysis serves as an inspirational source driving innovation. Radical ; Reinforcement learning ; Kinematics ; Reinforcement ; Computer science ; Artificial intelligence ; Chemistry ; Engineering ; Physics ; Structural engineering ; Classical mechanics ; Organic chemistry Conducting polymers and applications ; Electron Spin Resonance Studies ; Analytical Chemistry and Sensors
Authors: Ashwini Kumar | Arunkumar Devalapura Thimmappa | Ritesh Kumar | Manali Gupta
Source: Proceedings on Engineering Sciences (article, 2024-03-20)
Field: Materials Science — Ceramics and Composites

PREDICTING ALUMINA COMPOSITES’ MECHANICAL CHARACTERISTICS USING A MACHINE LEARNING APPROACH Obtaining the requisite properties in alloys is crucial problem in the production of aluminium components, requiring great deal of time and effort for investigation and experimentation. In this study, machine-learning technique utilizing Bayesian-fine tuned Adaptive Gated Recurrent Unit (B-AGRU) to forecast the mechanical characteristics of aluminium alloys is presented.Training and testing are conducted on dataset, which has undergone comprehensive preparation process that includes cleaning and Zscore normalization.Principal Component Analysis (PCA) is used for feature extraction to increase algorithmic efficiency.The GRU approach, which is implemented in Python, hardness and yield strength, leading in more accurate findings.When compared to standard methodologies, process saves significant time and energy, as evidenced by metrics such as RMSE-20%, MAE-10% and R-squared-97%.This study reveals B-AGRU-based machine learning as a feasible strategy for enhancing efficiency and sustainability in forecasting mechanical properties of aluminium alloys, paving the way for wider application in industrial sector. Composite material ; Materials science ; Computer science Advanced ceramic materials synthesis ; Advanced machining processes and optimization ; Non-Destructive Testing Techniques
Authors: Saman Rais | Aminul Islam
Source: ChemistrySelect (article, 2024-01-30)
Field: Chemistry — Analytical Chemistry

Silica Grafted Imprinted Mesoporous Polymer for the Development of SPE Coupled FAAS Method of Nickel Determination, Removal, and Spent Polymer for Reduction of Permanganate: Multivariate Optimization Abstract A novel silica fabricated allyloxy ethanol based imprinted polymer is synthesised with surface area of 333 m 2 g −1 and average pore size of 6.5 nm by an environment friendly one pot surface imprinting process. It was employed for the selective solid‐phase extraction (SPE) of trace Ni(II) in real samples prior to FAAS determination. The factor settings produced via central composite design in response surface methodology (RSM) (pH: 3.6; initial concentration; 800 mg L −1 and time; 15 minute) provide adsorption capacity of 176 mg g −1 . Artificial neural network model employed on the RSM data, provided better predictability. Sips isotherm model and BSf kinetic model describes best the sorption phenomena of the homogenous binding terrain. The developed SPE‐coupled FAAS method demonstrate a detection limit of 1.62 μg L −1 , quantification limit of 4.17 μg L −1 , and Linear dynamic range of 20–8000 μg L −1 . It was successfully applied to determine nickel in food (Spinach, Radish, Green tea infusion, Black tea infusion, Coffee, Soya milk, Chocolate Soya milk, Tobacco) and wastewater samples (&gt;99 % removal). For five replicate determinations, the method‘s relative standard deviation was 2.3 %. The method was validated by analysing two certified reference materials, and the outcomes were well‐congruent with standard values. For the synthesised polymer Ni(II) exhibits higher distribution ratio than other competing ions (Cu(II), Pb(II), Cd(II), and Zn(II)) with the imprinting factor; β values of 5.73, 4.98, 6.80, and 5.04 respectively. The reusability experiments revealed that APS@Ni‐IIP retains a strong adsorption efficacy even after 15 cycles and after its exhaustion it was employed as catalyst for reducing permanganate ions, further reducing pollution. The rigid structure of mesoporous silica contributes to its acid resilience and reusability, which can lead to a relative increase in polymer performance in absorption rate and selectivity. Apart from the novelty in the synthesis, thorough examination of the method and material selectivity (in binary, ternary and multielement system), multi fold applications of determination, removal of Ni(II) and reduction of permanganate ions, makes APS@Ni‐IIP a promising candidate for environmental remediation. Permanganate ; Polymer ; Mesoporous material ; Molecularly imprinted polymer ; Nickel ; Mesoporous silica ; Materials science ; Chemical engineering ; Chemistry ; Inorganic chemistry ; Metallurgy ; Composite material ; Organic chemistry ; Catalysis Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Radioactive element chemistry and processing
Authors: Jung-Pin Lai | Wei‐Hsiu Hsu | Jerry Wang | Chih-Sheng Lin | Chih‐Yuan Shih | Yu-Po Wang | Ping‐Feng Pai
Source:  (article, 2024-10-22)
Field: Chemistry — Spectroscopy

Machine Learning Models with Synthetic Minority Oversampling in Predicting Substrate Warpage (IMPACT 2024) The performance of semiconductors is improved through advanced packaging design, and it is necessary to accurately calculate the specific values of the thermal and mechanical properties of semiconductor packages. For packaging structures, uneven warping often occurs on the packaging surface when the temperature changes. Packaging warpage is typically obtained through measurement characterization. Therefore, the impact of the volume and design of each material in the package on thermal effects becomes critical. This study employs machine learning (ML) models, namely Support Vector Regression, LightGBM, Random Forest and eXtreme Gradient Boosting, to forecast substrate warpage at both room temperature and high temperature. The input data include design parameters such as substrate thickness, die size, die thickness, core thickness, package size, and heat sink thickness. In addition, the synthetic minority oversampling technique (SMOTE) is used to deal with imbalanced data in this study. The mean absolute percentage error (MAPE) is used as a measurement for evaluating performances of machine learning models. Numerical results reveal that four machine learning models can obtain satisfied forecasting results and the eXtreme Gradient Boosting model generates the most accurate outcomes. Thus, machine learning models are feasible and promising alternative in forecasting substrate warpage. Oversampling ; Substrate (aquarium) ; Computer science ; Materials science ; Artificial intelligence ; Telecommunications ; Geology ; Bandwidth (computing) Analytical Chemistry and Chromatography ; Machine Learning in Materials Science
Authors: Lorena Alzate-Vargas | Kashi N. Subedi | Nicholas Lubbers | M. Cooper | Roxanne Tutchton | Tammie Gibson | Richard A. Messerly
Source: arXiv (Cornell University) (preprint, 2024-11-21)
Field: Materials Science — Materials Chemistry

Toward machine learning interatomic potentials for modeling uranium mononitride Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine learning interatomic potentials for reliable atomic-scale modeling of UN at finite temperatures. We constructed a training set using density functional theory (DFT) calculations that was enriched through an active learning procedure, and two neural network potentials were generated. Both potentials successfully reproduce key thermophysical properties of interest, such as temperature-dependent lattice parameter, specific heat capacity, and bulk modulus. We also evaluated the energy of stoichiometric defect reactions and defect migration barriers and found close agreement with DFT predictions, demonstrating that our potentials can be used for modeling defects in UN. Additional tests provide evidence that our potentials are reliable for simulating diffusion, noble gas impurities, and radiation damage. Uranium ; Interatomic potential ; Materials science ; Computer science ; Metallurgy ; Chemistry ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Nuclear Physics and Applications
Authors: Anke S. Ulrich | Sergey Kasatikov | Till König | Andrea Fantin | Johannes T. Margraf | Mathias C. Galetz
Source: Research Square (preprint, 2024-08-07)
Field: Materials Science — Surfaces, Coatings and Films

Decreased Metal Dusting Resistance of Ni-Cu Alloys by Fe and Mn Additions Materials science ; Alloy ; Oxide ; Metal ; Adsorption ; Analytical Chemistry (journal) ; Spectroscopy ; Scanning electron microscope ; Energy-dispersive X-ray spectroscopy ; Dissociation (chemistry) ; Density functional theory ; Metallurgy ; Physical chemistry ; Chemistry ; Computational chemistry ; Composite material Electron and X-Ray Spectroscopy Techniques ; High-Temperature Coating Behaviors ; Chemical and Physical Properties of Materials
Authors: Shai Pranesh | Shang Zhu | Venkat Viswanathan | Bharath Ramsundar
Source: arXiv (Cornell University) (preprint, 2024-01-16)
Field: Materials Science — Materials Chemistry

Open-Source Fermionic Neural Networks with Ionic Charge Initialization Finding accurate solutions to the electronic Schrödinger equation plays an important role in discovering important molecular and material energies and characteristics. Consequently, solving systems with large numbers of electrons has become increasingly important. Variational Monte Carlo (VMC) methods, especially those approximated through deep neural networks, are promising in this regard. In this paper, we aim to integrate one such model called the FermiNet, a post-Hartree-Fock (HF) Deep Neural Network (DNN) model, into a standard and widely used open source library, DeepChem. We also propose novel initialization techniques to overcome the difficulties associated with the assignment of excess or lack of electrons for ions. Initialization ; Artificial neural network ; Electron ; Statistical physics ; Computer science ; Ionic bonding ; Monte Carlo method ; Charge (physics) ; Ion ; Deep neural networks ; Open source ; Artificial intelligence ; Physics ; Algorithm ; Computational physics ; Software ; Quantum mechanics ; Mathematics ; Statistics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Nuclear Physics and Applications