jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dongbo Zhao | Yilin Zhao | Enhua Xu | Wenqi Liu | Paul W. Ayers | Shubin Liu | Dahua Chen
Source: Journal of Chemical Theory and Computation (article, 2024-03-05)
Field: Materials Science — Materials Chemistry

Fragment-Based Deep Learning for Simultaneous Prediction of Polarizabilities and NMR Shieldings of Macromolecules and Their Aggregates Simultaneous prediction of the molecular response properties, such as polarizability and the NMR shielding constant, at a low computational cost is an unresolved issue. We propose to combine a linear-scaling generalized energy-based fragmentation (GEBF) method and deep learning (DL) with both molecular and atomic information-theoretic approach (ITA) quantities as effective descriptors. In GEBF, the total molecular polarizability can be assembled as a linear combination of the corresponding quantities calculated from a set of small embedded subsystems in GEBF. In the new GEBF-DL(ITA) protocol, one can predict subsystem polarizabilities based on the corresponding molecular wave function (thus electron density and ITA quantities) and DL model rather than calculate them from the computationally intensive coupled-perturbed Hartree-Fock or Kohn-Sham equations and finally obtain the total molecular polarizability via a linear combination equation. As a proof-of-concept application, we predict the molecular polarizabilities of large proteins and protein aggregates. GEBF-DL(ITA) is shown to be as accurate enough as GEBF, with mean absolute percentage error <1%. For the largest protein aggregate (>4000 atoms), GEBF-DL(ITA) gains a speedup ratio of 3 compared with GEBF. It is anticipated that when more advanced electronic structure methods are used, this advantage will be more appealing. Moreover, one can also predict the NMR chemical shieldings of proteins with reasonably good accuracy. Overall, the cost-efficient GEBF-DL(ITA) protocol should be a robust theoretical tool for simultaneously predicting polarizabilities and NMR shieldings of large systems. Polarizability ; Chemistry ; Computational chemistry ; Speedup ; Scaling ; Statistical physics ; Molecule ; Molecular physics ; Computer science ; Physics ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Mass Spectrometry Techniques and Applications
Authors: Yafang Guan | Xiaoshan Huang | Fangyi Xu | Wenfei Wang | Huilin Li | Lingtao Gong | Yue Zhao | Shuya Guo | Hong Liang | Zhiwei Qiao
Source: Nanomaterials (article, 2024-06-24)
Field: Chemistry — Inorganic Chemistry

Data-Driven and Machine Learning to Screen Metal–Organic Frameworks for the Efficient Separation of Methane With the rapid growth of the economy, people are increasingly reliant on energy sources. However, in recent years, the energy crisis has gradually intensified. As a clean energy source, methane has garnered widespread attention for its development and utilization. This study employed both large-scale computational screening and machine learning to investigate the adsorption and diffusion properties of thousands of metal–organic frameworks (MOFs) in six gas binary mixtures of CH4 (H2/CH4, N2/CH4, O2/CH4, CO2/CH4, H2S/CH4, He/CH4) for methane purification. Firstly, a univariate analysis was conducted to discuss the relationships between the performance indicators of adsorbents and their characteristic descriptors. Subsequently, four machine learning methods were utilized to predict the diffusivity/selectivity of gas, with the light gradient boosting machine (LGBM) algorithm emerging as the optimal one, yielding R2 values of 0.954 for the diffusivity and 0.931 for the selectivity. Furthermore, the LGBM algorithm was combined with the SHapley Additive exPlanation (SHAP) technique to quantitatively analyze the relative importance of each MOF descriptor, revealing that the pore limiting diameter (PLD) was the most critical structural descriptor affecting molecular diffusivity. Finally, for each system of CH4 mixture, three high-performance MOFs were identified, and the commonalities among high-performance MOFs were analyzed, leading to the proposals of three design principles involving changes only to the metal centers, organic linkers, or topological structures. Thus, this work reveals microscopic insights into the separation mechanisms of CH4 from different binary mixtures in MOFs. Methane ; Metal-organic framework ; Thermal diffusivity ; Adsorption ; Selectivity ; Binary number ; Gas separation ; Computer science ; Work (physics) ; Univariate ; Materials science ; Artificial intelligence ; Machine learning ; Nanotechnology ; Thermodynamics ; Chemistry ; Mathematics ; Physical chemistry ; Physics ; Membrane ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Wei Xu | Rui Wang | Chunhai Hu | Guilin Wen | Junqi Cui | Longjiang Zheng | Zhen Sun | Yungang Zhang | Zhiguo Zhang
Source: npj Computational Materials (article, 2024-09-03)
Field: Materials Science — Materials Chemistry

Co-training machine learning enables interpretable discovery of near-infrared phosphors with high performance Near-infrared (NIR) phosphors based on Cr3+ doped garnets present great potential in the next generation of NIR light sources. Nevertheless, the huge searching space for the garnet composition makes the rapid discovery of NIR phosphors with high performance remain a great challenge for the scientific community. Herein, a generalizable machine learning (ML) strategy is designed to accelerate the exploration of innovative NIR phosphors via establishing the relationship between key parameters and emission peak wavelength (EPW). We propose a semi-supervised co-training model based on kernel ridge regression (KRR) and support vector regression (SVR), which successfully establishes an expanded dataset with unlabeled dataset (previously unidentified garnets), addressing the overfitting issue resulted from a small dataset and greatly improving the model generalization capability. The model is then interpreted to extract valuable insights into the contribution originated from different features. And a new type NIR luminescent material of Lu3Y2Ga3O12: Cr3+ (EPW~750 nm) is efficiently screened, which demonstrates a high internal (external) quantum efficiency of 97.1% (38.8%) and good thermal stability, particularly exhibiting promising application in the NIR phosphor-converted LEDs (pc-LED). These results suggest the strategy proposed in this work could provide new viewpoint and direction for developing NIR luminescence materials. Training (meteorology) ; Phosphor ; Computer science ; Infrared ; Artificial intelligence ; Training set ; Machine learning ; Materials science ; Optoelectronics ; Meteorology ; Physics ; Optics Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Advanced Chemical Sensor Technologies
Authors: Abhinav S. Raman | Annabella Selloni
Source: The Journal of Chemical Physics (article, 2024-06-28)
Field: Chemistry — Physical and Theoretical Chemistry

Insights into the structure and dynamics of K+ ions at the muscovite–water interface from machine learning potential simulations The surfaces of many minerals are covered by naturally occurring cations that become partially hydrated and can be replaced by hydronium or other cations when the surface is exposed to water or an aqueous solution. These ion exchange processes are relevant to various chemical and transport phenomena, yet elucidating their microscopic details is challenging for both experiments and simulations. In this work, we make a first step in this direction by investigating the behavior of the native K+ ions at the interface between neat water and the muscovite mica (001) surface with ab-initio-based machine learning molecular dynamics and enhanced sampling simulations. Our results show that the desorption of the surface K+ ions in pure ion-free water has a significant free energy barrier irrespective of their local surface arrangement. In contrast, facile K+ diffusion between mica's ditrigonal cavities characterized by different Al/Si orderings is observed. This behavior suggests that the K+ ions may favor a dynamic disordered surface arrangement rather than complete desorption when exposed to deionized water. Muscovite ; Hydronium ; Mica ; Ion ; Chemical physics ; Desorption ; Aqueous solution ; Chemistry ; Molecular dynamics ; Diffusion ; Materials science ; Adsorption ; Physical chemistry ; Computational chemistry ; Thermodynamics ; Physics ; Quartz Electrostatics and Colloid Interactions ; Nanopore and Nanochannel Transport Studies ; Geophysical and Geoelectrical Methods
Authors: Çağrı Çavdaroğlu | Nur Altug | Arda Serpen | Mecit Halil Öztop | Banu Özen
Source: Food Research International (article, 2024-12-30)
Field: Chemistry — Analytical Chemistry

Comparative performance of artificial neural networks and support vector Machines in detecting adulteration of apple juice concentrate using spectroscopy and time domain NMR Artificial neural network ; Fruit juice ; Nuclear magnetic resonance spectroscopy ; Domain (mathematical analysis) ; Biological system ; Food science ; Support vector machine ; Artificial intelligence ; Chemistry ; Computer science ; Pattern recognition (psychology) ; Mathematics ; Biology ; Organic chemistry Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Ronit Sarangi | Suman Maity | Atanu Acharya
Source: Journal of Chemical Theory and Computation (article, 2024-07-24)
Field: Materials Science — Materials Chemistry

Machine Learning Approach to Vertical Energy Gap in Redox Processes A straightforward approach to calculating the free energy change (Δ<i>G</i>) and reorganization energy of a redox process is linear response approximation (LRA). However, accurate prediction of redox properties is still challenging due to difficulties in conformational sampling and vertical energy-gap sampling. Expensive hybrid quantum mechanical/molecular mechanical (QM/MM) calculations are typically employed in sampling energy gaps using conformations from simulations. To alleviate the computational cost associated with the expensive QM method in the QM/MM calculation, we propose machine learning (ML) methods to predict the vertical energy gaps (VEGs). We tested several ML models to predict the VEGs and observed that simple models like linear regression show excellent performance (mean absolute error ∼0.1 eV) in predicting VEGs in all test systems, even when using features extracted from cheaper semiempirical methods. Our best ML model (extra trees regressor) shows a mean absolute error of around 0.1 eV while using features from the cheapest QM method. We anticipate our approach can be generalized to larger macromolecular systems with more complex redox centers. Redox ; Computer science ; Energy (signal processing) ; Data science ; Chemistry ; Physics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Fuel Cells and Related Materials
Authors: Georg Klepp
Source: Energy (article, 2024-07-04)
Field: Materials Science — Materials Chemistry

Modelling activated carbon hydrogen storage tanks using machine learning models Hydrogen storage ; Conservation of mass ; Hydrogen ; Energy conservation ; Conservation of energy ; Hydrogen fuel ; Process engineering ; Porous medium ; Limiting ; Adsorption ; Compressed hydrogen ; Computer science ; Chemistry ; Porosity ; Engineering ; Thermodynamics ; Mechanical engineering ; Physics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Spacecraft and Cryogenic Technologies
Authors: Abrar Hussain | Dmitri Goljandin | Vitali Podgursky | Can Rüstü Yörük | Fjodor Sergejev | Jakob Kübarsepp | H.S. Maurya | Ramin Rahmani
Source: European Polymer Journal (article, 2024-05-05)
Field: Materials Science — Materials Chemistry

Industrial sustainable Fabrication, SEM Characterization, mechanical Testing, ANOVA analysis of PP-PETF recycled Composites: Artificial intelligence and deep learning studies for nuclear shielding applications Characterization (materials science) ; Fabrication ; Materials science ; Electromagnetic shielding ; Composite material ; Nanotechnology Graphite, nuclear technology, radiation studies ; Radiation Shielding Materials Analysis ; Innovative concrete reinforcement materials
Authors: Qian Chen | Xin Zhang
Source: MRS Bulletin (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Nanoparticle self-assemblies with modern complexity Nanotechnology ; Nanoparticle ; Materials science ; Nanoengineering ; Characterization (materials science) ; Plasmonic nanoparticles ; Computer science ; Topology (electrical circuits) Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications ; Machine Learning in Materials Science
Authors: Bowen Zheng | Grace X. Gu | Carine Ribeiro dos Santos | Rodrigo Neumann Barros Ferreira | M. Steiner | Binquan Luan
Source: APL Machine Learning (article, 2024-05-07)
Field: Chemistry — Inorganic Chemistry

Simulating CO2 diffusivity in rigid and flexible Mg-MOF-74 with machine-learning force fields The flexibility of metal–organic frameworks (MOFs) affects their gas adsorption and diffusion properties. However, reliable force fields for simulating flexible MOFs are lacking. As a result, most atomistic simulations so far have been carried out assuming rigid MOFs, which inevitably overestimates the gas adsorption energy. Here, we show that this issue can be addressed by applying a machine-learning potential, trained on quantum chemistry data, to atomistic simulations. We find that inclusion of flexibility is particularly important for simulating CO2 chemisorption in MOFs with coordinatively unsaturated metal sites. Specifically, we demonstrate that the diffusion of CO2 in a flexible Mg-MOF-74 structure is about one order of magnitude faster than in a rigid one, challenging the rigid-MOF assumption in previous simulations. Thermal diffusivity ; Materials science ; Computer science ; Mechanics ; Physics ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Inorganic Fluorides and Related Compounds
Authors: Youkang Hu | Jing Xu | Jiyao Wang | Wei Xu
Source: IEEE Transactions on Power Electronics (article, 2024-05-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-Inspired Multimodal Feature Fusion Cascaded Networks for Data-Driven Magnetic Core Loss Modeling This article proposes a physics-inspired multimodal feature fusion cascaded network (PI-MFF-CN) for data-driven magnetic core loss modeling based on MagNet database. The proposed methodology consists of two cascaded sub-models: the physics-inspired network model and the multimodal feature fusion network model. Firstly, a network model inspired by physics and related micromagnetism, is developed based on the Landau-Lifshitz-Gilbert (LLG) equation. It provides new sequence information (HLLG (t)) for the next cascaded core loss prediction model. This addresses the limitation where H(t) waveforms are unable to participate in the actual prediction process. With embedded physical micromagenetic parameters (A, K, Ms) in the gradient learning process of the neural network, the trained physics-inspired network can be regarded as the inverse model (B(t)→HLLG(t)) of LLG Equation having physical interpretability. Then, in order to address a series of challenges in multimodal information learning, a multimodal feature fusion-based network model is proposed. This approach combines the advantages of convolutional neural network (CNN) and fully connected neural network (FCNN) to learn hybrid sequence-scale data. Specifically, it employs parallel CNN branches for sequence feature mappings, followed by concatenating these mappings with other scalar data into an FCNN for global learning. To validate the effectiveness of the proposed method, this article trains and optimizes the proposed models based on MagNet database, and then a series of experiments including extensive material validation (Ferroxcube-3C90, 3C94 & TDK-N27, N30, N49, N87, etc.) were carried out. A series of experimental outcomes demonstrate that the proposed PI-MFF-CN-based method is generalized and robust in accurately predicting magnetic core losses. Interpretability ; Feature (linguistics) ; Artificial intelligence ; Convolutional neural network ; Artificial neural network ; Deep learning ; Computer science ; Recurrent neural network ; Machine learning ; Physics Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Power Transformer Diagnostics and Insulation
Authors: Mikkel Baldtzer Liisberg | Tom Vosch
Source: Nano Letters (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

Fluorescence Screening of DNA-AgNCs with Pulsed White Light Excitation DNA-stabilized silver nanoclusters (DNA-AgNCs) are a class of fluorophores with interesting photophysical properties dominated by the choice of DNA sequence. Screening methods with ultraviolet excitation and steady state well plate readers have previously been used for deepening the understanding between DNA sequence and emission color of the resulting DNA-AgNCs. Here, we present a new method for screening DNA-AgNCs by using pulsed white light excitation (λ<sub>ex</sub> ≈ 490-900 nm). By subtraction and time gating we are able to circumvent the dominating scatter of the white excitation light and extract both temporally and spectrally resolved emission of DNA-AgNCs over the visible to near-infrared range. Additionally, we are able to identify weak long-lived emission, which is often buried underneath the intense nanosecond fluorescence. This new approach will be useful for future screening of DNA-AgNCs (or other novel emissive materials) and aid machine-learning models by providing a richer training data set. Fluorescence ; White light ; Excitation ; Materials science ; Chemistry ; Photochemistry ; Optoelectronics ; Optics ; Physics Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: R. A. Mohamed | M.M. El-Nahass | M. Y. El-Bakry | El‐Sayed A. El‐Dahshan | E. H. Aamer | D. M. Habashy
Source: The European Physical Journal Plus (article, 2024-05-03)
Field: Materials Science — Polymers and Plastics

Investigation of optical properties of molybdenum trioxide (MoO3) thin films using neural networks Abstract Nowadays, transition metal oxide, the semiconductor molybdenum trioxide (MoO 3 ), is a favorable choice because it has several industrial applications and alluring qualities. It has a significant role as an industrial catalyst due to its electrochromic properties. The optical behavior of the MoO 3 thin film onto various substrates of the powder of MoO 3 was investigated. x-ray diffraction analysis revealed an orthorhombic structure. Measurements of the average crystallite size and dislocation density were obtained, which were 132.8 nm and 5.7× 10 –4 nm −2 , respectively. Spectrophotometric measurements of transmittance ( T ) and reflectance ( R ) at normal incidence in the 300–1400 nm wavelength range of the film onto various substrates were examined. The dispersion and indirect optical transitions and nonlinear optical parameters were identified for glass and quartz substrates. An estimation of the optical behavior of the MoO 3 thin film using the artificial neural network (ANN) model was carried out. Experimental data were used as inputs. The optical characterization of ANN modeling outputs provides excellent results. Error values support the success of the modeling process, with a mean squared error value of &lt; 10 −1 . Moreover, the theoretical equation describing the experimental results was obtained depending on the ANN model, representing the relation between the inputs and outputs. According to the findings of this research, the ANN model can be utilized as an efficient tool to simulate and predict the optical parameters of the MoO 3 thin film. Additionally, it can establish strong connections between theoretical and experimental fields. Materials science ; Thin film ; Molybdenum ; Molybdenum trioxide ; Crystallite ; Artificial neural network ; Transmittance ; Orthorhombic crystal system ; Diffraction ; Optoelectronics ; Optics ; Nanotechnology ; Computer science ; Metallurgy ; Machine learning Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; Analytical Chemistry and Sensors
Authors: Mandi Fang | Shi Tang | Zheyong Fan | Yao Shi | Nan Xu | Yi He
Source: The Journal of Physical Chemistry A (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

Transferability of Machine Learning Models for Predicting Raman Spectra Theoretical prediction of vibrational Raman spectra enables a detailed interpretation of experimental spectra, and the advent of machine learning techniques makes it possible to predict Raman spectra while achieving a good balance between efficiency and accuracy. However, the transferability of machine learning models across different molecules remains poorly understood. This work proposed a new strategy whereby machine learning-based polarizability models were trained on similar but smaller alkane molecules to predict spectra of larger alkanes, avoiding extensive first-principles calculations on certain systems. Results showed that the developed polarizability model for alkanes with a maximum of nine carbon atoms can exhibit high accuracy in the predictions of polarizabilities and Raman spectra for the <i>n</i>-undecane molecule (11 carbon atoms), validating its reasonable extrapolation capability. Additionally, a descriptor space analysis method was further introduced to evaluate the transferability, demonstrating potentials for accurate and efficient Raman predictions of large molecules using limited training data labeled for smaller molecules. Polarizability ; Raman spectroscopy ; Extrapolation ; Transferability ; Spectral line ; Molecule ; Artificial intelligence ; Machine learning ; Raman scattering ; Chemistry ; Computational chemistry ; Computer science ; Physics ; Optics ; Mathematics ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses