jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: John Findley | Janice A. Steckel
Source:  (article, 2024-08-06)
Field: Chemistry — Inorganic Chemistry

Machine Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture Direct air capture (DAC) is a method for removing CO2 directly from air. Metal organic frameworks (MOFs) have been studied as DAC sorbent materials because of their structural and chemical diversity. Thermodynamic calculations using classical force fields are often used to evaluate MOFs for their performance in separations such as CO2 capture. Machine-learned force fields (MLFFs) can use machine learning to form quantitative relationships between a material’s chemical structure and the forces and energies predicted by more accurate quantum mechanical calculations, such as dispersion-corrected density functional theory (DFT). These descriptions of forces and energies can be used to improve the accuracy of adsorption calculations. In this work, classical models were used to pre-screen MOFs for CO2 capture. DFT calculations were then used to examine the adsorption mechanism. Next, MLFF models were developed for MOFs to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption. These methods were parametrized based on thousands of DFT calculations of CO2 in flexible MOFs and used to predict MOF structural properties as well as CO2 adsorption properties. Metal-organic framework ; Field (mathematics) ; Computer science ; Environmental science ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Control Systems Optimization
Authors: Rabia Korkmaz Tan | Hakan Çanta | Reşat MUTLU
Source: Trakya Üniversitesi Mühendislik Bilimleri Dergisi (article, 2024-12-23)
Field: Materials Science — Materials Chemistry

ARTIFICIAL NEURAL NETWORK MODELS OF CROSS-LINKED POLYETHYLENE Cross-linked polyethylene (XLPE) is the most widely used insulator material in high-power cables. The complex electrical permittivity of the XLPE layer mostly determines the leakage admittance of the cable and the propagation speed of the signal. The complex electrical permittivity of XLPE depends on not only operating frequency but also temperature. In this study, Artificial neural networks (ANNs) are used to model the complex electrical permittivity parts of the XLPE. The structure of the ANNs is optimized. It has been found that the optimized ANN can predict the behavior of the XLPE with an R2 value of 0.99. Cross-linked polyethylene ; Materials science ; Permittivity ; Polyethylene ; Artificial neural network ; Power network ; Electronic engineering ; Dielectric ; SIGNAL (programming language) ; Insulator (electricity) ; Admittance ; Partial discharge ; Composite material ; Electrical engineering ; Acoustics ; Power (physics) ; Electric power system ; Engineering ; Computer science ; Optoelectronics ; Voltage ; Electrical impedance ; Physics ; Artificial intelligence High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Transport Systems and Technology
Authors: Tomasz J. Wąsowicz | M. Jurkowski | Allison Harris | Ivan Ljubić
Source: The Journal of Chemical Physics (article, 2024-08-09)
Field: Chemistry — Spectroscopy

Unveiling the electron-induced ionization cross sections and fragmentation mechanisms of 3,4-dihydro-2H-pyran The interactions of electrons with molecular systems under various conditions are essential to interdisciplinary research fields extending over the fundamental and applied sciences. In particular, investigating electron-induced ionization and dissociation of molecules may shed light on the radiation damage to living cells, the physicochemical processes in interstellar environments, and reaction mechanisms occurring in combustion or plasma. We have, therefore, studied electron-induced ionization and dissociation of the gas phase 3,4-dihydro-2H-pyran (DHP), a cyclic ether appearing to be a viable moiety for developing efficient clinical pharmacokinetics and revealing the mechanisms of biofuel combustion. The mass spectra in the m/z = 10-90 mass range were measured at several different energies of the ionizing electron beam using mass spectrometry. The mass spectra of DHP at the same energies were simulated using on-the-fly semi-classical molecular dynamics (MD) within the framework of the QCxMS formalism. The MD settings were suitably adjusted until a good agreement with the experimental mass spectra intensities was achieved, thus enabling a reliable assignment of cations and unraveling the plausible fragmentation channels. Based on the measurement of the absolute total ionization cross section of DHP (18.1 ± 0.9) × 10-16 cm2 at 100 eV energy, the absolute total and partial ionization cross sections of DHP were determined in the 5-140 eV electron energy. Moreover, a machine learning algorithm that was trained with measured cross sections from 25 different molecules was used to predict the total ionization cross section for DHP. Comparison of the machine learning simulation with the measured data showed acceptable agreement, similar to that achieved in past predictions of the algorithm. Electron ionization ; Ionization ; Fragmentation (computing) ; Chemistry ; Mass spectrometry ; Dissociation (chemistry) ; Mass spectrum ; Atomic physics ; Electron ; Ion ; Molecule ; Analytical Chemistry (journal) ; Physics ; Physical chemistry ; Organic chemistry ; Nuclear physics ; Chromatography Mass Spectrometry Techniques and Applications ; Free Radicals and Antioxidants ; Advanced Chemical Physics Studies
Authors: Hengkai Wang | Zengtao Lv | Santosh Kumar | Qinglin Wang
Source: APL Machine Learning (review, 2024-11-27)
Field: Materials Science — Materials Chemistry

Applications of machine learning to high temperature and high pressure environments: A literature review In recent years, machine learning as a new style of calculation has been developed quickly, and because it can obtain results that experiments cannot achieve, it has become a useful calculation tool in the field of high temperature and high pressure (HTHP). It can simulate and calculate the experimental results according to some calculation principles, such as first-principles, and execute prediction based on models created, such as Gaussian approximation potential, to obtain high-precision results. In addition, its simulation process is very fast, and the cost is not as expensive as that of density functional theory, so machine learning in the field of HTHP computing has aroused great research interest. The rapid development of machine learning makes it a powerful tool to predict some parameter or mechanism of materials and brings a new chance to simulate more complex experimental environments. In this paper, we review some of the most recent applications and insights into machine learning techniques in the fields of mechanics, thermology, electricity, and structural search under the demanding conditions of HTHP. High pressure ; Computer science ; Engineering physics ; Engineering Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Heat transfer and supercritical fluids
Authors: Z. Zhang | Zijie Li | Amir Barati Farimani
Source: AIP Advances (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Masked pretraining strategy for neural potentials We propose a masked pretraining method for Graph Neural Networks (GNNs) to improve their performance on fitting potential energy surfaces, particularly in water and small organic molecule systems. GNNs are pretrained by recovering the spatial information of masked-out atoms from molecules selected with certain ratios and then transferred and fine-tuned on atomic force fields. Through such pretraining, GNNs learn meaningful prior about the structural and underlying physical information of molecule systems that are useful for downstream tasks. With comprehensive experiments and ablation studies, we show that the proposed method improves both the accuracy and convergence speed of GNNs compared to their counterparts trained from scratch or with other pretraining techniques. This approach showcases its potential to enhance the performance and data efficiency of GNNs in fitting molecular force fields. Computer science ; Neuroscience ; Chemistry ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Force Microscopy Techniques and Applications
Authors: Pooja Sharma | Prahlad Roy Chowdhury | Amber Jain | G. Naresh Patwari
Source: The Journal of Physical Chemistry A (article, 2024-11-17)
Field: Materials Science — Materials Chemistry

Machine Learned Potential Enables Molecular Dynamics Simulation to Predict the Experimental Branching Ratios in the NO Release Channel of Nitroaromatic Compounds This study employs a machine learning (ML) model using the Gaussian process regression algorithm to generate potential energy surfaces (PES) from density functional theory calculations, facilitating the investigation of photodissociation dynamics of nitroaromatic compounds, resulting in NO release. The experimentally observed trends in the slow-to-fast branching ratios of the NO moiety were captured by estimating the branching ratio between the two distinct reaction pathways, viz., roaming and oxaziridine mechanisms, calculated from molecular dynamics simulations performed on a reduced two-dimensional T<sub>1</sub> surface. The qualitative agreement between the calculated and experimental results suggests that the mechanism dictating NO release is primarily governed by the dynamics on the T<sub>1</sub> surface. Branching (polymer chemistry) ; Molecular dynamics ; Channel (broadcasting) ; Statistical physics ; Biological system ; Computer science ; Chemical physics ; Chemistry ; Computational chemistry ; Physics ; Biology ; Telecommunications ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Catalytic Processes in Materials Science
Authors: Haonan Wang | Peipei Xu | Yi‐Ting Chen | Chuyi Wang | Shurui Chen | Junjie Ren | Yudong Lu | Jingbo Chen | Li Zhang | Yunzhen Liu | Ruiyun You
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Biomaterials

"Partner" Cellulose Gel with "Dialysis" Function: Achieve the Integration of Filtration-Enrichment-Sers Detection Cellulose ; Filtration (mathematics) ; Dialysis ; Function (biology) ; Chromatography ; Chemistry ; Medicine ; Mathematics ; Internal medicine ; Biochemistry ; Biology ; Statistics Advanced Cellulose Research Studies
Authors: Artem Zholus | Maksim Kuznetsov | Roman Schutski | Rim Shayakhmetov | Daniil Polykovskiy | Sarath Chandar | Alex Zhavoronkov
Source: arXiv (Cornell University) (preprint, 2024-06-06)
Field: Materials Science — Materials Chemistry

BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning Generating novel active molecules for a given protein is an extremely challenging task for generative models that requires an understanding of the complex physical interactions between the molecule and its environment. In this paper, we present a novel generative model, BindGPT which uses a conceptually simple but powerful approach to create 3D molecules within the protein's binding site. Our model produces molecular graphs and conformations jointly, eliminating the need for an extra graph reconstruction step. We pretrain BindGPT on a large-scale dataset and fine-tune it with reinforcement learning using scores from external simulation software. We demonstrate how a single pretrained language model can serve at the same time as a 3D molecular generative model, conformer generator conditioned on the molecular graph, and a pocket-conditioned 3D molecule generator. Notably, the model does not make any representational equivariance assumptions about the domain of generation. We show how such simple conceptual approach combined with pretraining and scaling can perform on par or better than the current best specialized diffusion models, language models, and graph neural networks while being two orders of magnitude cheaper to sample. Reinforcement learning ; Scalability ; Computer science ; Reinforcement ; Human–computer interaction ; Cognitive science ; Artificial intelligence ; Psychology ; Materials science ; Database ; Composite material Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Modular Robots and Swarm Intelligence
Authors: Margus Kodu | Martin Lind | V. Kiisk | Indrek Renge | Raivo Jaaniso
Source:  (article, 2024-04-08)
Field: Materials Science — Materials Chemistry

Selective Detection of Toxic Gases by Arrays of Single-Layer Graphene Sensors Functionalized with Nanolayers of Different Oxides Graphene provides an ideal platform for chemiresistive gas sensors as the material is fully exposed to the surrounding environment. For practical use in an ambient atmosphere, its sensitivity and selectivity should be evoked by functionalization by defects and dopants or by decoration with nanophases of metals or metal oxides. Here, we demonstrate a few successful cases of selectivity enhancement by functionalizing the graphene with different oxide layers and applying machine learning to the resulting sensor array. Graphene ; Materials science ; Layer (electronics) ; Nanotechnology ; Toxic gas ; Optoelectronics ; Chemical engineering ; Chemistry ; Environmental chemistry Graphene research and applications ; Gas Sensing Nanomaterials and Sensors ; Advanced biosensing and bioanalysis techniques
Authors: Dhruv Menon | David Fairen‐Jiménez
Source: ChemRxiv (preprint, 2024-05-31)
Field: Chemistry — Inorganic Chemistry

Guiding the rational design of biocompatible metal-organic frameworks for drug delivery using machine learning The poor prognosis of cancer and other diseases has often been linked to challenges pertaining to drug accessibility and delivery. To combat these challenges, nanotechnology has witnessed decades of intensive research, resulting in the development of innovative nanoscale strategies for improving therapies and minimizing the side effects of existing drugs. Notably, multiple nanocarriers have obtained clinical approval as proficient drug delivery systems. 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. To overcome these barriers, we review here recent advances on biocompatibility and propose a novel computational pipeline guided by machine learning (ML). This 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. The protocols developed here will be easily transferred to other systems, accelerating associated research timescales. Biocompatible material ; Drug delivery ; Rational design ; Metal-organic framework ; Nanotechnology ; Delivery system ; Computer science ; Medicine ; Biomedical engineering ; Business ; Engineering management ; Materials science ; Engineering ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Dendrimers and Hyperbranched Polymers
Authors: Yoshiki Nishiya | Takahiro Hosokawa | Yu Hirabayashi | Haruka Iga | Shinnosuke Tokuta | Yusuke Shimada | Akiyasu Yamamoto
Source: IEEJ Transactions on Fundamentals and Materials (article, 2024-08-31)
Field: Materials Science — Materials Chemistry

Deep Learning Segmentation of Polycrystalline Superconductors with Different Compositions Image analysis to identify the phases from microstructural images is an important issue for understanding the mechanism associated with the microstructures of functional polycrystalline materials. In this study, the segmentation ability of the deep learning model and the effect of data augmentation were investigated when applied to ceramic superconducting materials with different compositions than the trained materials. Crystallite ; Superconductivity ; Ceramic ; Segmentation ; Materials science ; Microstructure ; Artificial intelligence ; Deep learning ; Mineralogy ; Computer science ; Condensed matter physics ; Metallurgy ; Chemistry ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Source: Jordan Journal of Mechanical and Industrial Engineering (article, 2024-08-28)
Field: Materials Science — Ceramics and Composites

Glass-Reinforced Aluminum Matrix Composite: Synthesizes, Analysis, and Hardness and Porosity Modeling Using Artificial Neural Networks This study aims to investigate the predictive capabilities of artificial neural networks (ANNs) for Al-glass composites, specifically in exploring the effect of glass particle size and content on hardness, porosity, and microstructure in Al-glass composites.Contents between 0-15 wt.% glass particles, with two size ranges-less than 53m, and between 53-75m-were incorporated within the pure aluminum matrix.Powder metallurgy was employed to produce the composite specimens.Pressing at 400 MPa was applied to the powders to produce the green compacts.The sintering temperature was 550 C.Three sintering periods were used: one, two, and four hours.The results indicate that the most significant factors affecting the hardness and porosity were glass percentage and sintering time.The highest hardness value of 27.50 HRB was obtained in specimen with 10% glass content sintered for 4 hours, with glass grain size of 53-75 m.Whereas the highest porosity percentage of 5.4% was recorded for specimen with 15% glass content sintered for 1 hour, with glass grain size of 53-75 m.For ANN, three inputs and one output were established, where the Levenberg-Marquardt training algorithm neural network had the highest accuracy of prediction.With highest value of R2= 99.96% and 99.99%, and RMSE=0.06855and 0.007141 for hardness and porosity, respectively.As such a high prediction accuracy was obtained using the ANNs, this study proves that ANN is a significant tools for the prediction of nonlinear relationships.The novelty of this study lies in the combination of glass with aluminum as a new composite material, alongside the high predictive accuracy of the model with very small error margins, demonstrating the potential of ANNs to effectively handle nonlinear relationships in composite materials.Additionally, the ANN approach significantly saves time and costs associated with experimental testing and helps in finding the optimal combination with the best values of the mechanical properties, streamlining the development process for new composite materials. Porosity ; Materials science ; Composite number ; Artificial neural network ; Aluminium ; Matrix (chemical analysis) ; Composite material ; Computer science ; Artificial intelligence Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Advanced Surface Polishing Techniques
Authors: Daniel Rothchild | Andrew Rosen | Eric Taw | Connie Robinson | Joseph E. Gonzalez | Aditi S. Krishnapriyan
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Investigating the behavior of diffusion models for accelerating electronic structure calculations We present an investigation of diffusion models for molecular generation, with the aim of better understanding how their predictions compare to the results of physics-based calculations. The investigation into these models is driven by their potential to significantly accelerate electronic structure calculations using machine learning, without requiring expensive first-principles datasets for training interatomic potentials. We find that the inference process of a popular diffusion model for <i>de novo</i> molecular generation is divided into an exploration phase, where the model chooses the atomic species, and a relaxation phase, where it adjusts the atomic coordinates to find a low-energy geometry. As training proceeds, we show that the model initially learns about the first-order structure of the potential energy surface, and then later learns about higher-order structure. We also find that the relaxation phase of the diffusion model can be re-purposed to sample the Boltzmann distribution over conformations and to carry out structure relaxations. For structure relaxations, the model finds geometries with ∼10× lower energy than those produced by a classical force field for small organic molecules. Initializing a density functional theory (DFT) relaxation at the diffusion-produced structures yields a >2× speedup to the DFT relaxation when compared to initializing at structures relaxed with a classical force field. Diffusion ; Statistical physics ; Electronic structure ; Chemical physics ; Materials science ; Computer science ; Computational chemistry ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies