jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sophia Li | Emma Wang | Leia Pei | S. K. Deb | Prashanth Prabhala | Sai Hruday Reddy Nara | Raina Panda | Shiven Eltepu | Marx Akl | Larry McMahan | Edward Njoo
Source: ChemRxiv (preprint, 2024-07-31)
Field: Chemistry — Spectroscopy

Evaluation of machine learning models for the accelerated prediction of Density Functional Theory calculated 19F chemical shifts based on local atomic environments The introduction of fluorine in compounds plays a crucial role in drug development as it greatly influences their final pharmacokinetic and dynamic properties. Due to the increasing prevalence of fluorine in FDA-approved drugs in recent years, identifying the underlying mechanisms driving their chemical transformations has become crucial in the drug discovery landscape. 19F NMR spectroscopy is a powerful analytical technique that allows for the examination of fluorine-containing compounds, offering valuable information about their structure, dynamics, and reactivity. Consequently, this technique has become a cornerstone in the mechanistic evaluation of fluorine-containing chemical transformations. NMR spectra can be interpreted through the leveraging of Density Functional Theory (DFT), an ab initio modeling method that can be harnessed for the prediction of NMR chemical shifts. However, the screening of compounds and discovery of feasible drug candidates is severely limited due to the computational cost of DFT. Here, we present a machine learning approach to accelerate the prediction of DFT-calculated 19F NMR chemical shifts. The fluorine atoms’ features in the models were derived from their local three-dimensional structural environments, representing their neighboring atoms within a radius of n Å away from the given fluorine atom in the compound. A comparative analysis of thirteen regression models was conducted using features extracted from 501 fluorinated compounds in our laboratory’s chemical inventory. The target chemical shift values were calculated using DFT with the quantum chemistry software ORCA. Among the models evaluated, Gradient Boosting Regression (GBR) exhibited the highest performance, achieving a mean absolute error of 2.89 ppm - 3.73 ppm with a local environment radius of 3 Å. This demonstrates a comparable accuracy to DFT calculations while significantly reducing computational time per compound from several hundred seconds to milliseconds. 3 Å was also found to be the most optimal radius across all models when encoding features for local atomic environments. Chemical shift ; Density functional theory ; Fluorine ; Chemistry ; Computational chemistry ; Drug discovery ; Atom (system on chip) ; Machine learning ; Computer science ; Physical chemistry ; Organic chemistry Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Gan Li | Qunbo Fan | Guoju Li | Lin Yang | Haichao Gong | Meiqin Li | Shun Xu | Xingwang Cheng
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Unveiling the Quantitative Relationship between Microstructural Features and Quasi-Static Tensile Properties in Dual-Phase Titanium Alloys Based on Data-Driven Neural Networks Ultimate tensile strength ; Materials science ; Dual (grammatical number) ; Phase (matter) ; Artificial neural network ; Titanium ; Titanium alloy ; Composite material ; Metallurgy ; Computer science ; Artificial intelligence ; Physics ; Alloy Titanium Alloys Microstructure and Properties ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Toshiaki Taniike | Felicia Daniela Cannavacciuolo | Mostafa Khoshsefat | Diego De Canditiis | Giuseppe Antinucci | Patchanee Chammingkwan | Roberta Cipullo | Vincenzo Busico
Source: ChemRxiv (preprint, 2024-03-12)
Field: Chemistry — Inorganic Chemistry

End-to-End High-Throughput Approach for Data-Driven Internal Donor Development in Heterogeneous Ziegler-Natta Propylene Polymerization Internal donors (IDs) play a decisive role in shaping the structure and performance of Ziegler-Natta catalyst formulations for the isotactic polypropylene production. Unfortunately, their diverse and intricate functions remain elusive, and rational ID discovery therefore is still problematic. Exploitation of artificial intelligence methods such as machine learning, in turn, has been hindered by the lack of training datasets with adequate quality and size. This study proposes an integrated high-throughput workflow encompassing catalyst synthesis, propylene polymerization, and polypropylene characterization. Its application to an ID library of 35 molecules generated a robust and consistent dataset, which highlighted important and intriguing quantitative structure-properties relations (QSPRs). Furthermore, by fingerprinting ID molecular structure in combination with feature selection, a black-box QSPR model correlating ID molecular structure and catalytic performance was successfully implemented. This study demonstrates that the combination of high-throughput experimentation and machine learning is a promising asset for accelerating the research and development of Ziegler-Natta catalysts. Natta ; Throughput ; End milling ; Polymerization ; Materials science ; Computer science ; Polymer ; Composite material ; Operating system ; Metallurgy ; Machining Inorganic Chemistry and Materials
Authors: Hanieh Mardani | Ata Herizchi | Hossein Roghani‐Mamaqani
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Stimuli-Induced Modulation of White Light Emission in Colloidal Polymer Particles: Design of Chemosensors and Dynamic Chromism Toward Unclonable Patterning and its Classification Based on Machine Learning Materials science ; Methacrylate ; Polymer ; Nanotechnology ; Optoelectronics ; Light-emitting diode ; Chemical engineering ; Polymerization ; Composite material Photochromic and Fluorescence Chemistry ; bioluminescence and chemiluminescence research ; Luminescence and Fluorescent Materials
Authors: Eman A. Mostafa | Mohammad Abdul Azim | Asmaa A. El‐Zaher | Ehab F. Elkady | Marwa Mohammed Fouad | Fatma H. Ghazy | Esraa A. Radi | Mahmoud Abo El Makarim Saleh | Ahmed M. El Kerdawy
Source: Scientific Reports (article, 2024-08-13)
Field: Chemistry — Spectroscopy

Correlating physico-chemical properties of analytes with Hansen solubility parameters of solvents using machine learning algorithm for predicting suitable extraction solvent Artificial neural networks (ANNs) are biologically inspired algorithms designed to simulate the way in which the human brain processes information. In sample preparation for bioanalysis, liquid-liquid extraction (LLE) represents an important step with the extraction solvent selection is the key laborious step. In the current work, a robust and reliable ANNs model for LLE solvent prediction was generated which could predict the suitable solvent for analyte extraction. The developed ANNs model takes a set of chosen descriptors for the cited analyte as an input and predicts the corresponding Hansen solubility parameters of the suitable extraction solvent as a model output. Then, from the solvent combination's appendix, the analyst can identify the proposed extraction solvents' combination for the cited analyte easily and efficiently. For the experimental validation of the model prediction capabilities, twenty structurally diverse drugs belonging to different pharmacological classes were extracted from human plasma. The extraction process was performed using the predicted extraction solvent combination for each drug and quantitively estimated by HPLC/UV methods to assess their extraction recovery. The developed LLE solvent prediction model is in- line with the global trend towards green chemistry since it limits the consumption of organic solvents. Solubility ; Solvent ; Hildebrand solubility parameter ; Extraction (chemistry) ; Solvent extraction ; Analyte ; Computer science ; Chromatography ; Chemistry ; Materials science ; Organic chemistry Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods
Authors: Rania Loukil | Wejdene GAZEHI | Mongi Besbes
Source:  (preprint, 2024-04-14)
Field: Materials Science — Materials Chemistry

Improvement the classification of a nanocomposite using nanoparticules based on a meta-analysis study, Recurrent Neural Network and Recurrent Neural Network Monte-Carlo algorithms This paper may be the first meta-analysis that presents a comprehensive synthesis of scientific works spanning the last five years, focusing on methodologies and results related to the analysis of nanocomposite using nanoparticules. The primary objective is to identify the optimal algorithm using software information and leading to better classification methodology. Specifically, this study come up with the advantages and the drawbacks of the most used algorithms and proposes an enhancement and performance of Recurrent Neural Networks based Long Short Term Memory (LSTM) neurons. Besides, a comparaison of Deep Learning methods for the classification of polymeric nanoparticles, with polypropylene serving as a case study will be implemented. Experiment comparison were conducted to assess with one physical property, later expanded to four properties and finally to eight properties. Neural networks, including Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Recurrent Neural Networks-Monte Carlo, were employed for simulations. The evaluation criteria encompassed accuracy, calculation time, mean square error (MSE) and other metrics. The findings contribute to the selection of an optimal algorithm for the analysis of polymeric nanoparticles, emphasizing the potential of Deep Learning methodologies, particularly Recurrent Neural Networks Monte Carlo, in advancing classification accuracy and efficiency. Recurrent neural network ; Artificial neural network ; Computer science ; Monte Carlo method ; Artificial intelligence ; Deep learning ; Machine learning ; Mean squared error ; Algorithm ; Mathematics Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Shun Muroga | Yasuaki Miki | Kenji Hata
Source: Seikei-Kakou (article, 2024-01-20)
Field: Materials Science — Materials Chemistry

Multimodal Deep Learning for Digital Transformation of Polymer Composites Materials science ; Polymer science ; Engineering physics ; Engineering ethics ; Engineering Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Mechanical Behavior of Composites
Authors: Ulrik Friis-Jensen | Frederik L. Johansen | Andy S. Anker | Erik B. Dam | Kirsten M. Ø. Jensen | Raghavendra Selvan
Source:  (preprint, 2024-08-24)
Field: Materials Science — Materials Chemistry

CHILI: <u>Ch</u> emically- <u>I</u> nformed <u>L</u> arge-scale <u>I</u> norganic Nanomaterials Dataset for Advancing Graph Machine Learning Advances in graph machine learning (ML) have been driven by applications in chemistry as graphs have remained the most expressive representations of molecules. While early graph ML methods focused primarily on small organic molecules, recently, the scope of graph ML has expanded to include inorganic materials. Modelling the periodicity and symmetry of inorganic crystalline materials poses unique challenges, which existing graph ML methods are unable to address. Moving to inorganic nanomaterials increases complexity as the scale of number of nodes within each graph can be broad ($10$ to $10^5$). The bulk of existing graph ML focuses on characterising molecules and materials by predicting target properties with graphs as input. However, the most exciting applications of graph ML will be in their generative capabilities, which is currently not at par with other domains such as images or text. We invite the graph ML community to address these open challenges by presenting two new chemically-informed large-scale inorganic (CHILI) nanomaterials datasets: A medium-scale dataset (with overall &gt;6M nodes, &gt;49M edges) of mono-metallic oxide nanomaterials generated from 12 selected crystal types (CHILI-3K) and a large-scale dataset (with overall &gt;183M nodes, &gt;1.2B edges) of nanomaterials generated from experimentally determined crystal structures (CHILI-100K). We define 11 property prediction tasks and 6 structure prediction tasks, which are of special interest for nanomaterial research. We benchmark the performance of a wide array of baseline methods and use these benchmarking results to highlight areas which need future work. To the best of our knowledge, CHILI-3K and CHILI-100K are the first open-source nanomaterial datasets of this scale -- both on the individual graph level and of the dataset as a whole -- and the only nanomaterials datasets with high structural and elemental diversity. Scale (ratio) ; Nanomaterials ; Graph ; Computer science ; Data science ; Nanotechnology ; Machine learning ; Materials science ; Theoretical computer science ; Geography ; Cartography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Chanseok Kim | Mina Yoon | Jun Hee Lee
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Oer Activity for Pnma Perovskite Via Minimal Structure Relaxation Relaxation (psychology) ; Perovskite (structure) ; Materials science ; Computer science ; Artificial intelligence ; Crystallography ; Psychology ; Chemistry ; Social psychology Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Perovskite Materials and Applications
Authors: Majid Rezaei | Sung Sakong | Axel Groß
Source: ChemRxiv (preprint, 2024-01-25)
Field: Materials Science — Materials Chemistry

Sodium Triflate Water-in-Salt Electrolyte in Advanced Battery Applications: A First-principles Based Molecular Dynamics Study Offering a compelling combination of safety and cost-effectiveness, Water-in-Salt (WiS) electrolytes have emerged as promising frontiers in energy storage technology. Still, there is a strong demand for research and development efforts to make these electrolytes ripe for commercialization. Here we present a first-principles based molecular dynamics study addressing in detail the properties of a sodium triflate WiS electrolyte for Na-ion batteries. We have developed a workflow based on a machine learning (ML) potential derived from ab initio molecular dynamics simulations. As ML potentials are typically restricted to the interpolation of the data points of the training set and have hardly any predictive properties, we subsequently optimize a classical force field based on physics principles to ensure broad applicability and high performance. Performing and analysing detailed molecular dynamics simulations, we identify several very promising properties of the sodium triflate as a WiS electrolyte, but also indicate some potential stability challenges associated with its use as a battery electrolyte. Trifluoromethanesulfonate ; Electrolyte ; Battery (electricity) ; Salt (chemistry) ; Sodium ; Salt water ; Molecular dynamics ; Dynamics (music) ; Sodium salt ; Materials science ; Computer science ; Chemistry ; Chemical engineering ; Inorganic chemistry ; Environmental science ; Thermodynamics ; Engineering ; Physics ; Physical chemistry ; Computational chemistry ; Environmental engineering ; Organic chemistry ; Metallurgy ; Catalysis ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Materials and Technologies
Authors: M. B. Yunusov | R. M. Khusnutdinoff
Source: High Energy Chemistry (article, 2024-11-23)
Field: Materials Science — Materials Chemistry

Neural Network Model for Predicting the Atomization Energy of Multi-Atomic Molecules Based on Sorted Coulomb Matrices This paper develops a method for predicting the atomization energy Eat of multi-atomic compounds by representing the molecular configuration as a sorted Coulomb matrix M(Ri, Zi). A dataset of 7165 molecules with H, C, N, O, S content is analyzed and the input data for training is generated. The optimal layer configuration and hyperparameters values were found for the fully-connected neural network model. The minimum mean absolute error MAE ≈ 7.66 kcal/mol was achieved in predicting the atomization energy Eat. Chemistry ; Coulomb ; Artificial neural network ; Molecule ; Energy (signal processing) ; Computational chemistry ; Statistical physics ; Artificial intelligence ; Quantum mechanics ; Organic chemistry ; Physics ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Guan-Ming Chen | Zihao Ye | Zhiming Li | Junliang Zhang
Source: ChemRxiv (preprint, 2024-03-01)
Field: Materials Science — Materials Chemistry

Universal Descriptors of 'Quasi Transition States' for Small-Data-Driven Asymmetric Catalysis Prediction in Machine Learning Model In contemporary enantioselectivity prediction models, the demand for numerous descriptors and extensive datasets poses a substantial challenge. Descriptor selection, fraught with uncertainty, compounds the issue while amassing requisite data remains a daunting task. The introduction of descriptors derived from quasi-transition-states (qTS) offers a promising avenue to alleviate this burden. However, the challenge of descriptor selection persists. Herein, a novel small-data-driven model based on universal descriptors (UD-qTS) is proposed. Key differentiating properties between diastereomeric qTSs, encompassing energies, frontier orbital energies, Cartesian forces and charges of core atoms, are proposed as UD. The model's efficacy is validated through its application to the asymmetric aldol reaction, utilizing 3 experimental variables and merely 9 UDs, and fewer than 150 training samples. Moreover, a novel method is presented using Cartesian forces to rectify discrepancies between qTS and true TS. This UD-qTS strategy circumvents tedious large-scale descriptor exploration and screening, offering an efficient choice for small-data-driven enantioselectivity prediction. Diastereomer ; Task (project management) ; Cartesian coordinate system ; Computer science ; Selection (genetic algorithm) ; Artificial intelligence ; Transition (genetics) ; Machine learning ; Chemistry ; Mathematics ; Stereochemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Guan-Ming Chen | Zihao Ye | Zhiming Li | Junliang Zhang
Source: ChemRxiv (preprint, 2024-02-29)
Field: Materials Science — Materials Chemistry

Universal Descriptors of 'Quasi Transition States' for Small-Data-Driven Asymmetric Catalysis Prediction in Machine Learning Model In contemporary enantioselectivity prediction models, the demand for numerous descriptors and extensive datasets poses a substantial challenge. Descriptor selection, fraught with uncertainty, compounds the issue while amassing requisite data remains a daunting task. The introduction of descriptors derived from quasi-transition-states (qTS) offers a promising avenue to alleviate this burden. However, the challenge of descriptor selection persists. Herein, a novel small-data-driven model based on universal descriptors (UD-qTS) is proposed. Key differentiating properties between diastereomeric qTSs, encompassing energies, frontier orbital energies, Cartesian forces and charges of core atoms, are proposed as UD. The model's efficacy is validated through its application to the asymmetric aldol reaction, utilizing 3 experimental variables and merely 9 UDs, and fewer than 150 training samples. Moreover, a novel method is presented using Cartesian forces to rectify discrepancies between qTS and true TS. This UD-qTS strategy circumvents tedious large-scale descriptor exploration and screening, offering an efficient choice for small-data-driven enantioselectivity prediction. Diastereomer ; Selection (genetic algorithm) ; Cartesian coordinate system ; Task (project management) ; Artificial intelligence ; Computer science ; Transition (genetics) ; Machine learning ; Mathematics ; Chemistry ; Stereochemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Zemeng Feng | Ziyi Hu | Tong Yu | Panpan Lai | Rui Ge | Hua Shao | Dashan Shang | Zhiqiang Li | Kui Xu | Junjie Li | Rui Chen | Ling Li
Source:  (article, 2024-12-07)
Field: Materials Science — Materials Chemistry

First Large-Scale (68 × 25 × 5 nm<sup>3</sup>) Atomistic Modeling for Accurate and Efficient Etching Process Based on Machine Learning Molecular Dynamics (MLMD) Revealing the reaction mechanisms in manufacturing advanced devices is crucial for enhancing performance at nodes below 3 nm. In this study, we introduce the first large-scale atomistic model utilizing a machine learning molecular dynamics (MLMD) framework. This model simulates the lateral isotropic selective etching process of SiO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>/Si stack structures in Gate-all-around field-effect transistors (GAAFETs) using F* radical plasma. The dataset for this model is generated using active learning strategies based on search for F* radical reacting with Si(100) substrate. Our simulation spans <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$68\times 25\times 5\ \text{nm}^{3}$</tex>, involving over 600,000 atoms, and demonstrates a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4\times$</tex> improvement in model error for etching reaction trends compared to the classic molecular dynamics (CMD) method. Additionally, the model achieves a typical runtime of 16.7 hours, representing a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 44.9\times$</tex> speedup over CMD. Moreover, the simulation provides detailed reaction path and uncovers the mechanisms behind lateral etching. This framework paves the way for potential atom-to-device multi-scale process modeling, contributing to Design Technology Co-optimization (DTCO). Process (computing) ; Molecular dynamics ; Scale (ratio) ; Computer science ; Etching (microfabrication) ; Computational science ; Materials science ; Nanotechnology ; Physics ; Computational chemistry ; Chemistry ; Programming language Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Fuel Cells and Related Materials