jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fayza Yulia | Noval Pajri | Nasruddin Nasruddin | Byan Wahyu Riyandwita | Yose Fachmi Buys | Sri Hastuty | Arie Sukma Jaya | Nonni Soraya Sambudi | Sylvia Ayu Pradanawati
Source: Evergreen (article, 2024-09-01)
Field: Materials Science — Polymers and Plastics

Predicting Cooling Load and Energy Consumption of Coconut Oil as Phase Change Material for Thermal Management in a Residential Building using Artificial Neural Network Reducing energy consumption in buildings is crucial for minimizing environmental impacts and improving energy efficiency.In the building industry, air-conditioning systems account for most of the energy consumption.The passive use of thermal energy storage (TES) incorporated into building envelopes has been proven to reduce energy consumption.Therefore, this research examines the application of TES in residential structures, employing coconut oil as the Phase Change Material (PCM).The assessment of coconut oil's effectiveness as a thermal storage medium in a residential building situated in tropical climates is conducted using EnergyPlus software.The thermal comfort, including the indoor temperature and relative humidity, were compared with and without PCM.The results demonstrate that using coconut oil can stabilize thermal comfort, thereby reducing both the cooling load and energy consumption.Finally, an Artificial Neural Network was employed to predict energy consumption based on daily fluctuations in outdoor temperature and relative humidity, resulting in a strong correlation.The modeling results found that the energy consumption and the cooling load reached a maximum of 2797,435 W and 28,009.4kJ.This research would be valuable in the early phases of building design when simulating the built environment using PCM at the tropical. Phase-change material ; Artificial neural network ; Cooling load ; Environmental science ; Coconut oil ; Energy consumption ; Thermal ; Phase change ; Consumption (sociology) ; Process engineering ; Architectural engineering ; Waste management ; Materials science ; Computer science ; Engineering ; Mechanical engineering ; Meteorology ; Artificial intelligence ; Chemistry ; Electrical engineering ; Engineering physics ; Food science ; Geography Textile materials and evaluations ; Building Energy and Comfort Optimization ; Phase Change Materials Research
Authors: Kai Riedmiller | Patrick Reiser | Elizaveta Bobkova | Kiril Maltsev | Ganna Gryn’ova | Pascal Friederich | Frauke Gräter
Source: ChemRxiv (preprint, 2024-01-09)
Field: Materials Science — Materials Chemistry

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17,000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of < 3 kcal/mol). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations. Context (archaeology) ; Hydrogen atom ; Density functional theory ; Monte Carlo method ; Inference ; Statistical physics ; Hydrogen ; Chemistry ; Molecular dynamics ; Atom (system on chip) ; Biological system ; Kinetic Monte Carlo ; Computer science ; Computational chemistry ; Chemical physics ; Physics ; Mathematics ; Quantum mechanics ; Artificial intelligence Corrosion Behavior and Inhibition ; Free Radicals and Antioxidants ; Collagen: Extraction and Characterization
Authors: Nissa Adiarifia | I Wayan Budiastra | Sutrisno Suro Mardjan
Source: Jurnal Keteknikan Pertanian (article, 2024-04-04)
Field: Chemistry — Analytical Chemistry

Non-Destructive Prediction of Chemical Content in Palm Oil Fruit Using Near-Infrared Spectroscopy and Artificial Neural Network Oil and water content are an important quality criteria of crude palm oil (CPO) resulted from palm oil fruit processing. Those contents are usually determined using chemical method in the laboratory. This method is time consuming, long procedure, and destructive. Some efforts had been carried out to determine oil and water content of palm oil fruit non-destructively using some methods including Near-Infrared Spectroscopy (NIRS), but the results had not been satisfied. This research aims to assess Artificial Neural Network (ANN) and NIRS method to predict oil and water content of palm oil fruit’s non-destructively. The samples were palm oil fruits with ten maturity levels harvested from plantation in Bogor. Sample’s reflectance was measured with spectrometer NIR-Flex 500 at wavelength of 1000-2500 nm. After that, oil and water content were determined using chemical method. Some pre-treatments of NIR spectra namely normalization, savitzky-golay first derivative, their combinations, and standard normal variate were applied. Multivariate analysis such as PLS were carried out and the results of Factor Component (FC) were input for ANN model. The result showed the best method to predict oil content was combination savitzky-golay first derivative and normalization pre-treatment using PLS-ANN with 20 FC (R2=0.99; SEC=0,58%, RPD = 29.89; CV = 2.47%). For water content, the best prediction was standard normal variate pre-treatment using PLS-ANN with 20 FC (R2=0.99; SEC=1,07%, RPD=20.68; CV=1,73%). The result shows that developed ANN and NIRS can predict oil and water content of palm oil fruit non-destructively. Palm oil ; Artificial neural network ; Infrared spectroscopy ; Infrared ; Palm ; Content (measure theory) ; Spectroscopy ; Biological system ; Artificial intelligence ; Materials science ; Computer science ; Chemistry ; Food science ; Biology ; Mathematics ; Organic chemistry ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Vaishali Ingale | Saniya Devale | Anushka Khedekar | Manasi Gaikwad | Dnyaneshwari More
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Extensive Review Paper on Computational Catalyst Discovery using Machine Learning Algorithms Abstract: As the world continues to grapple with energy scarcity and climate change, the future of our energy supply faces increasing challenges. These technologies offer the opportunity to develop efficient, carbon-neutral ways to store and produce energy. But doing so requires discovering efficient and economical materials that can speed up chemical processes. One way to find effective catalysts is to use molecular experiments. Specifically, each simulation simulates the interaction of the catalyst surface with molecules typically found in electrochemical reactions. By accurately predicting these interactions, the effect of the catalyst on the overall rate of the chemical reaction can be predicted Scarcity ; Catalysis ; Computer science ; Energy (signal processing) ; Biochemical engineering ; Process engineering ; Chemistry ; Engineering ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Topic Modeling
Authors: Riccardo Alessandri | Cheng-Han Li | Sheila M. Keating | Khirabdhi T. Mohanty | Aaron Peng | Jodie L. Lutkenhaus | Stuart J. Rowan | Daniel P. Tabor | Juan Pablo
Source: ChemRxiv (preprint, 2024-03-21)
Field: Materials Science — Polymers and Plastics

Structural, Ionic, and Electronic Properties of Solid-State Phthalimide-Containing Polymers for All-Organic Batteries Redox-active polymers serving as the active materials in solid-state electrodes offer a promising path towards realizing all-organic batteries. While both cathodic and an- odic redox-active polymers are needed, the diversity of the available anodic materials is limited. Here, we predict solid-state structural, ionic, and electronic properties of anodic, phthalimide-containing polymers using a multiscale approach that combines atomistic molecular dynamics, electronic structure calculations, and machine learning surrogate models. Importantly, by combining information from each of these scales, we are able to bridge the gap between bottom-up molecular characteristics and macro- scopic properties such as apparent diffusion coefficients of electron transport ($D_{app}$). We investigate the impact of different polymer backbones and of two critical factors during battery operation: state of charge and polymer swelling. Our findings reveal that the state of charge significantly influences solid-state packing and the thermophys- ical properties of the polymers, which, in turn, affect ionic and electronic transport. A combination of molecular-level properties (such as the reorganization energy) and condensed-phase properties (such as effective electron hopping distances) determine the predicted ranking of electron transport capabilities of the polymers. We predict Dapp for the phthalimide-based polymers and for a reference nitroxide radical-based polymer, finding a three orders of magnitude increase in $D_{app}$ ($\approx 10^{−6}$ cm$^2$ s$^{−1}$) with respect to the reference. This study underscores the promise of phthalimide-containing polymers as highly capable redox-active polymers for anodic materials in all-organic batteries, due to their exceptional predicted electron transport capabilities. Phthalimide ; Solid-state ; Ionic bonding ; Polymer ; Materials science ; Chemical engineering ; Polymer chemistry ; Ion ; Inorganic chemistry ; Chemistry ; Organic chemistry ; Physical chemistry ; Engineering Conducting polymers and applications ; Ionic liquids properties and applications ; Polyoxometalates: Synthesis and Applications
Authors: Stephen T. Schaefer | Davi Fébba | Kingsley Egbo | Glenn Teeter | Andriy Zakutayev | M. Brooks Tellekamp
Source: arXiv (Cornell University) (preprint, 2024-01-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Rapid screening of molecular beam epitaxy conditions for monoclinic (InxGa1-x)2O3 alloys Molecular beam epitaxy is one of the highest quality growth methods, capable of achieving theoretical material property limits and unprecedented device performance. However, such ultimate quality usually comes at the cost of painstaking optimization of synthesis conditions and slow experimental iteration rates. Here we report on high-throughput molecular beam epitaxy with rapid screening of synthesis conditions using a novel cyclical growth and in-situ etch method. This novel approach leverages sub-oxide desorption present during molecular beam epitaxy and as such should be broadly applicable to other material systems. As a proof of concept, this method is applied to rapidly investigate the growth space for the ternary alloy (InxGa1-x)2O3 on (010) oriented beta-Ga2O3 substrates using in-situ reflection high energy electron diffraction measurements. Two distinct growth regimes are identified and analyzed using machine learning image recognition algorithms, the first stabilizing a streaky 2x surface reconstruction typical of In-catalyzed beta-Ga2O3 growth, and the second exhibiting a spotty/faceted pattern typical of phase separation. Targeted growth of (InxGa1-x)2O3 is performed under conditions near the boundary of the two regimes resulting in a 980 nm thick epitaxial layer with In mole fraction up to 5.6%. The cyclical growth/etch method retains the ~1 nm surface roughness of the single crystal substrate, increases experimental throughput approximately 6x, and improves single crystal substrate utilization by &gt;40x. The high-throughput MBE method enables rapid discovery of growth regimes for ultra-wide bandgap oxide alloys for power conversion devices operating with high efficiency at high voltages and temperatures, as well as optical devices such as ultraviolet photodetectors. Molecular beam epitaxy ; Materials science ; Epitaxy ; Substrate (aquarium) ; Optoelectronics ; Crystal growth ; Monoclinic crystal system ; Crystal (programming language) ; Nanotechnology ; Crystallography ; Crystal structure ; Layer (electronics) ; Chemistry ; Computer science Ga2O3 and related materials ; ZnO doping and properties ; Advanced Photocatalysis Techniques
Authors: Vittoriano Muttillo | Vincenzo Stoico | Marco Santic | Giacomo Valente | Luigi Pomante | Daniele Frigioni
Source:  (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

SLIDE-x-ML: System-Level Infrastructure for Dataset E-xtraction and Machine Learning Framework for High-Level Synthesis Estimations Electronic Design Automation (EDA) is a crucial research area related to the development of electronic systems. In particular, High-Level Synthesis (HLS) simplifies HW design by automatically translating C/C++/System C specifications into HW description languages. However, HLS for large systems can be time-consuming. In recent years, Machine Learning (ML) has emerged as a prominent topic in EDA, with numerous studies demonstrating its potential to enhance EDA methods covering nearly all phases of the HW design flow. In such a context, this work presents an approach and related frameworks to collect datasets (i.e., SLIDE-x) useful for performing HLS timing and resource estimation through ML techniques (i.e., SLIDE-x-ML), introducing a data-driven component for feature creation that enhances predictions through various input representations and ML methods. Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science
Authors: Sergei Manzhos
Source: Institutional Repositories DataBase (IRDB) (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

At the Intersection of Material Informatics and Machine Learning Method Development Intersection (aeronautics) ; Development (topology) ; Informatics ; Materials informatics ; Active learning (machine learning) Machine Learning in Materials Science ; Big Data and Digital Economy ; Digital Transformation in Industry
Authors: Paul Katzberger | Laurent Hauswirth | Antonia S. Kuhn | Gregory A. Landrum | Sereina Riniker
Source: ChemRxiv (preprint, 2024-12-12)
Field: Chemistry — Spectroscopy

Rapid Access to Small Molecule Conformational Ensembles in Organic Solvents Enabled by Graph Neural Network Based Implicit Solvent Model Understanding and manipulating the conformational behavior of a molecule in different solvent environments is of great interest in the fields of drug discovery and organic synthesis. Molecular dynamics (MD) simulations with solvent molecules explicitly present are the gold standard to compute such conformational ensembles (within the accuracy of the underlying force field), complementing experimental findings and supporting their interpretation. However, conventional methods often face challenges related to computational cost (explicit solvent) or accuracy (implicit solvent). Here, we showcase how our graph neural network (GNN)-based implicit solvent (GNNIS) approach can be used to rapidly compute small molecule conformational ensembles in 39 common organic solvents with high accuracy compared to explicit-solvent simulations. We validate this approach using nuclear magnetic resonance (NMR) measurements, thus identifying the conformers contributing most to the experimental observable. The method allows the time required to accurately predict conformational ensembles to be reduced from days to minutes while achieving results within one kBT of the experimental values. Conformational isomerism ; Solvent ; Molecular dynamics ; Molecule ; Observable ; Artificial neural network ; Chemistry ; Computational chemistry ; Solvent models ; Graph ; Force field (fiction) ; Computer science ; Chemical physics ; Biological system ; Solvation ; Theoretical computer science ; Artificial intelligence ; Physics ; Organic chemistry ; Quantum mechanics Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Elizaveta I. Yakovenko | Iurii M. Nevolin | Anatoliy Chasovskikh | Artem Mitrofanov | Vadim Korolev
Source: arXiv (Cornell University) (preprint, 2024-08-20)
Field: Materials Science — Materials Chemistry

Data-driven prediction of structure of metal-organic frameworks Crystal structure prediction (CSP) has proven to be a highly effective route for discovering new materials. Substantial advancements have been made in CSP of inorganic and molecular crystals, while hybrid materials, including metal-organic frameworks (MOFs), have been unfairly overlooked. The ab initio techniques adopted for the CSP of MOFs cannot be scaled to a high-throughput regime, which is required for efficient exploration of the immense chemical space. Here, we propose a data-driven method to tackle current needs of computational MOF discovery. By examining CSP through the lens of reticular chemistry, coarse-grained neural networks were implemented to predict underlying net topology of crystal graphs. The models showed satisfactory performance, which was next enhanced by limiting the applicability domain. Flue gas separation was used as an illustrative example to validate the proposed CSP approach. Several hundred in silico-generated systems revealed a notable discrepancy in adsorption capacity among competing polymorphs. Metal-organic framework ; Metal ; Materials science ; Chemistry ; Metallurgy ; Organic chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Hiromoto SHIMAZU
Source: Kobunshi (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Recent Advances of AI and Machine Learning in Materials Research 近年、科学研究への人工知能（AI）・機械学習の活用が注目を集めている。データ駆動型の研究を進める上で、データのサイズと品質を担保することが重要であり、材料研究では特性評価ツールが将来の開発の焦点となる。そして、これらの自動化（ハイスループット化）を推進するロボティクスが、自律的発見のパラダイムを現実のものにするための鍵の一つと考えられている。いままさに新しい時代の科学研究のあり方への課題が提示されており、どういう材料を開発するかだけでなく、新しい時代に対応した人材、環境、システムを含む科学研究の方法の変革が重要になっている。 Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: 康伸 安藤 | Yasunobu Ando
Source: Institutional Repositories DataBase (IRDB) (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Machine Learning‐Assisted Survey on Charge Storage of MXenes in Aqueous Electrolytes MXenes ; Aqueous solution ; Electrolyte ; Charge (physics) ; Energy storage MXene and MAX Phase Materials ; Advancements in Battery Materials ; Machine Learning in Materials Science
Authors: Yashan Feng | Yafang Tian | Yongxin Yang | Yufang Zhang | Haiwei Guo | Jingan Li
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Analyze the Performance of Electroactive Anticorrosion Coating of Medical Magnesium Alloy Using Deep Learning Electroactive anticorrosion coatings are specialized surface treatments that prevent or minimize corrosion. The study employs strategic thermodynamic equilibrium calculations to pioneer a novel factor in corrosion protection. A first-time proposal, the total acidity (TA) potential of the hydrogen (pH) concept significantly shapes medical magnesium alloys. These coatings are meticulously designed for robust corrosion resistance, blending theoretical insights and practical applications to enhance our grasp of corrosion prevention mechanisms and establish a systematic approach to coating design. The groundbreaking significance of this study lies in its innovative integration of the TA/pH concept, which encompasses the TA/pH ratio of the chemical environment. This approach surpasses convention by acknowledging the intricate interplay between the acidity and pH levels within the coating formulation, thereby optimizing metal-phosphate-based conversion coatings and transforming corrosion mitigation strategies. To authenticate the TA/pH concept, the study comprehensively compares its findings with existing research, rigorously validating the theoretical framework and reinforcing the correlates among TA/pH values and observed corrosion resistance in the coatings. The influence of mutations that occur naturally in the detergent solution on persistent phosphorus changes is shown by empirical confirmation, which improves corrosion resistance. This realization advances the field of materials and the field’s knowledge of coated generation, particularly anticorrosion converter layers. Corrosion ; Coating ; Materials science ; GRASP ; Conversion coating ; Alloy ; Metallurgy ; Magnesium ; Computer science ; Nanotechnology Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Magnesium Alloys: Properties and Applications
Authors: Ruoyu Wang | Mingyu Guo | Yuxiang Gao | Xiao‐Xu Wang | Yuzhi Zhang | Bin Deng | Xin Chen | Mengchao Shi | Linfeng Zhang | Zhicheng Zhong
Source: arXiv (Cornell University) (preprint, 2024-06-26)
Field: Materials Science — Materials Chemistry

A Pre-trained Deep Potential Model for Sulfide Solid Electrolytes with Broad Coverage and High Accuracy Solid electrolytes with fast ion transport are one of the key challenges for solid state lithium metal batteries. To improve ion conductivity, chemical doping has been the most effective strategy, and atomistic simulation with machine-learning potential helps find optimized doping by predicting ion conductivity for arbitrary composition. Yet most existing machine-learning models are trained on narrow chemistry, and new model has to be trained for each system, wasting transferable knowledge and incurring significant cost. Here, we propose a pre-trained deep potential model purpose-built for sulfide electrolytes with attention mechanism, known as DPA-SSE. The training set encompasses 15 elements and consists of both equilibrium and extensive out-of-equilibrium configurations. DPA-SSE achieves a high energy resolution of less than 2 meV/atom for dynamical trajectories up to 1150 K, and reproduces experimental ion conductivity of sulfide electrolytes with remarkable accuracy. DPA-SSE exhibits good transferability, covering a range of complex electrolytes with mixes of cation and anion atoms. Highly efficient dynamical simulation with DPA-SSE can be realized by model distillation which generates a faster model for given systems. DPA-SSE also serves as a platform for continuous learning, and the model fine-tune requires only a portion of downstream data. These results demonstrate the possibility of a new pathway for AI-driven development of solid electrolytes with exceptional performance. Sulfide ; Electrolyte ; Computer science ; Environmental science ; Materials science ; Chemistry ; Metallurgy ; Physical chemistry ; Electrode Solid-state spectroscopy and crystallography ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Csaba Both | Subhro Das | Nima Dehmamy | Tommi Jaakkola | Jeet Mohapatra
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations Artificial neural network ; Feature (linguistics) ; Stability (learning theory) ; Minification ; Noise (video) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Protein Structure and Dynamics