jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: S. Laref | Fouzi Harrou | Ying Sun | Xin Gao | Takashi Gojobori
Source: International Journal of Molecular Sciences (article, 2024-04-30)
Field: Materials Science — Materials Chemistry

Exploring Antiviral Drugs on Monolayer Black Phosphorene: Atomistic Theory and Explainable Machine Learning-Assisted Platform Favipiravir (FP) and ebselen (EB) belong to a diverse class of antiviral drugs known for their significant efficacy in treating various viral infections. Utilizing molecular dynamics (MD) simulations, machine learning, and van der Waals density functional theory, we accurately elucidate the binding properties of these antiviral drugs on a phosphorene single-layer. To further investigate these characteristics, this study employs four distinct machine learning models-Random Forest, Gradient Boosting, XGBoost, and CatBoost. The Hamiltonian of antiviral molecules within a monolayer of phosphorene is appropriately trained. The key aspect of utilizing machine learning (ML) in drug design revolves around training models that are efficient and precise in approximating density functional theory (DFT). Furthermore, the study employs SHAP (SHapley Additive exPlanations) to elucidate model predictions, providing insights into the contribution of each feature. To explore the interaction characteristics and thermodynamic properties of the hybrid drug, we employ molecular dynamics and DFT calculations in a vacuum interface. Our findings suggest that this functionalized 2D complex exhibits robust thermostability, indicating its potential as an effective and enabled entity. The observed variations in free energy at different surface charges and temperatures suggest the adsorption potential of FP and EB molecules from the surrounding environment. Phosphorene ; Monolayer ; Computer science ; Nanotechnology ; Materials science Machine Learning in Materials Science ; MXene and MAX Phase Materials ; 2D Materials and Applications
Authors: Massina Abderrahmane | Hamza Tajmouati | Vinícius Barros Ribeiro da Silva | Quentin Perron
Source: ChemRxiv (preprint, 2024-02-07)
Field: Materials Science — Materials Chemistry

Predicting the price of molecules using their predicted synthetic pathways Currently, numerous metrics allow chemists and computational chemists to refine and filter libraries of virtual molecules in order to prioritize their synthesis. Some of the most commonly used metrics and models are QSAR models, docking scores, diverse druggability metrics, and synthetic feasibility scores to name only a few. Among the known metrics, a function which estimates the price of a novel virtual molecule and which takes into account the availability and price of starting materials has never been considered before. Being able to make such a prediction could improve and accelerate the decision-making process related to the cost-of-goods. Taking advantage of recent advances in the field of Computer Aided Synthetic Planning (CASP), we decided to investigate if the predicted retrosynthetic pathways of a given molecule and the prices of its associated starting materials could be good features to predict the price of that compound. In this work, we present a deep learning model, RetroPriceNet, that predicts the price of molecules using their predicted synthetic pathways. On a holdout test set, the model achieves better performance than the state-of-the-art model. The developed approach takes into account the synthetic feasibility of molecules and the availability and prices of the starting materials. Economics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Paulina Portales Picazo | Alexander Gray | Navid Zobeiry
Source: Composites Part A Applied Science and Manufacturing (article, 2024-12-13)
Field: Materials Science — Materials Chemistry

Efficient characterization and optimization of pyrolysis in carbon-carbon composites through machine learning Materials science ; Composite material ; Pyrolysis ; Characterization (materials science) ; Carbon fibers ; Reinforced carbon–carbon ; Composite number ; Chemical engineering ; Nanotechnology Thermal and Kinetic Analysis ; Fiber-reinforced polymer composites ; Epoxy Resin Curing Processes
Authors: Beatriz G. del Rio | L. González
Source: Journal of Chemical Theory and Computation (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Exploring Challenging Properties of Liquid Metallic Systems through Machine Learning: Liquid La and Li<sub>4</sub>Pb Systems In this machine learning (ML) study, we delved into the unique properties of liquid lanthanum and the Li<sub>4</sub>Pb alloy, revealing some unexpected features and also firmly establishing some of the debated characteristics. Leveraging interatomic potentials derived from ab initio calculations, our investigation achieved a level of precision comparable to first-principles methods while at the same time entering the hydrodynamic regime. We compared the structure factors and pair distribution functions to experimental data and unearthed distinctive collective excitations with intriguing features. Liquid lanthanum unveiled two transverse collective excitation branches, each closely tied to specific peaks in the velocity autocorrelation function spectrum. Furthermore, the analysis of the generalized specific heat ratio in the hydrodynamic regime investigated with the ML molecular dynamics simulations uncovered a peculiar behavior, impossible to discern with only ab initio simulations. Liquid Li<sub>4</sub>Pb, on the other hand, challenged existing claims by showcasing a rich array of branches in its longitudinal dispersion relation, including a high-frequency LiLi mode with a nonhydrodynamic optical character that maintains a finite value as <i>q</i> → 0. Additionally, we conducted an in-depth analysis of various transport coefficients, expanding our understanding of these liquid metallic systems. In summary, our ML approach yielded precise results, offering new and captivating insights into the structural and dynamic aspects of these materials. Ab initio ; Autocorrelation ; Excitation ; Lanthanum ; Dispersion (optics) ; Physics ; Statistical physics ; Character (mathematics) ; Function (biology) ; Materials science ; Computer science ; Chemical physics ; Optics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Chemical Thermodynamics and Molecular Structure
Authors: Andrei Manolache | Dragos Tantaru | Mathias Niepert
Source: arXiv (Cornell University) (preprint, 2024-10-10)
Field: Chemistry — Physical and Theoretical Chemistry

MolMix: A Simple Yet Effective Baseline for Multimodal Molecular Representation Learning In this work, we propose a simple transformer-based baseline for multimodal molecular representation learning, integrating three distinct modalities: SMILES strings, 2D graph representations, and 3D conformers of molecules. A key aspect of our approach is the aggregation of 3D conformers, allowing the model to account for the fact that molecules can adopt multiple conformations-an important factor for accurate molecular representation. The tokens for each modality are extracted using modality-specific encoders: a transformer for SMILES strings, a message-passing neural network for 2D graphs, and an equivariant neural network for 3D conformers. The flexibility and modularity of this framework enable easy adaptation and replacement of these encoders, making the model highly versatile for different molecular tasks. The extracted tokens are then combined into a unified multimodal sequence, which is processed by a downstream transformer for prediction tasks. To efficiently scale our model for large multimodal datasets, we utilize Flash Attention 2 and bfloat16 precision. Despite its simplicity, our approach achieves state-of-the-art results across multiple datasets, demonstrating its effectiveness as a strong baseline for multimodal molecular representation learning. Simple (philosophy) ; Baseline (sea) ; Representation (politics) ; Computer science ; Artificial intelligence ; Epistemology ; Political science ; Philosophy Various Chemistry Research Topics ; Machine Learning in Materials Science ; Machine Learning in Bioinformatics
Authors: Ying Fang | Hezhu Shao
Source: Materials (article, 2024-05-08)
Field: Materials Science — Materials Chemistry

Wenzhou TE: A First-Principle-Calculated Thermoelectric Materials Database Since the implementation of the Materials Genome Project by the Obama administration in the United States, the development of various computational materials' databases has fundamentally expanded the choice of industries such as materials and energy. In the field of thermoelectric materials, the thermoelectric figure of merit (ZT) quantifies the performance of the material. From the viewpoint of calculations for vast materials, the ZT values are not easily obtained due to their computational complexity. Here, we show how to build a database of thermoelectric materials based on first-principle calculations for the electronic and heat transport of materials. Firstly, the initial structures are classified according to the values of bandgap and other basic properties using the clustering algorithm K-means in machine learning, and high-throughput first principle calculations are carried out for narrow-bandgap semiconductors which exhibit a potential thermoelectric application. The present framework of calculations mainly includes a deformation potential module, an electrical transport performance module, a mechanical and a thermodynamic properties module. We have also set up a search webpage for the calculated database of thermoelectric materials, providing search facilities and the ability to view the related physical properties of materials. Our work may inspire the construction of more computational databases of first-principle thermoelectric materials and accelerate research progress in the field of thermoelectrics. Thermoelectric materials ; Thermoelectric effect ; Materials science ; Field (mathematics) ; Engineering physics ; Work (physics) ; Computer science ; Mechanical engineering ; Database ; Engineering ; Physics ; Thermodynamics ; Mathematics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; X-ray Diffraction in Crystallography
Authors: Yingbo Shao | Zhiyuan Ren | Zhihui Han | Li Chen | Yao Li | Xiao‐Song Xue
Source: Beilstein Journal of Organic Chemistry (article, 2024-06-28)
Field: Chemistry — Organic Chemistry

Predicting bond dissociation energies of cyclic hypervalent halogen reagents using DFT calculations and graph attention network model Although hypervalent iodine(III) reagents have become staples in organic chemistry, the exploration of their isoelectronic counterparts, namely hypervalent bromine(III) and chlorine(III) reagents, has been relatively limited, partly due to challenges in synthesizing and stabilizing these compounds. In this study, we conduct a thorough examination of both homolytic and heterolytic bond dissociation energies (BDEs) critical for assessing the chemical stability and functional group transfer capability of cyclic hypervalent halogen compounds using density functional theory (DFT) analysis. A moderate linear correlation was observed between the homolytic BDEs across different halogen centers, while a strong linear correlation was noted among the heterolytic BDEs across these centers. Furthermore, we developed a predictive model for both homolytic and heterolytic BDEs of cyclic hypervalent halogen compounds using machine learning algorithms. The results of this study could aid in estimating the chemical stability and functional group transfer capabilities of hypervalent bromine(III) and chlorine(III) reagents, thereby facilitating their development. Hypervalent molecule ; Heterolysis ; Chemistry ; Homolysis ; Density functional theory ; Halogen bond ; Reagent ; Computational chemistry ; Halogen ; Bromine ; Bond-dissociation energy ; Dissociation (chemistry) ; Combinatorial chemistry ; Photochemistry ; Organic chemistry ; Radical ; Alkyl ; Catalysis Oxidative Organic Chemistry Reactions ; Catalytic C–H Functionalization Methods ; Synthesis and Catalytic Reactions
Authors: Timothy Liao | Weiyi Xia | Masahiro Sakurai | Chao Zhang | Huaijun Sun | Renhai Wang | Kai‐Ming Ho | Cai‐Zhuang Wang | James R. Chelikowsky
Source: Physical Review Materials (article, 2024-10-11)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated discovery of iron cobalt phosphides as rare-earth-free magnets The discovery of rare-earth-free permanent magnets has been a goal of scientists for decades. The absence of rare-earth elements will alleviate a pressing concern about the availability of rare-earth elements used in permanent magnets. These magnets are crucial for applications such as wind turbines, electric cars, and memory devices. Rare-earth magnets are special owing to a large magnetic anisotropy energy (${K}_{1}$). In contrast, iron cobalt phosphides hold promise since doping P into cubic FeCo can induce anisotropy, leading to a large coercivity, without introducing rare-earth elements. We present a comprehensive search over the Fe-Co-P ternary space for magnets, utilizing recently developed adaptive machine learning feedback to efficiently screen over 850 000 structures. We focus on machine learning acceleration as a paradigm for materials design. Further adaptive genetic algorithm searches and first-principles calculations aid in the identification of 16 new structures below the known convex hull. Five of them possess high magnetic polarization (${J}_{s}\phantom{\rule{4pt}{0ex}}&gt;$ 1 T). The structures with desirable magnetic properties center on ${(\mathrm{Fe},\mathrm{Co})}_{2}\mathrm{P}$. This supports conventional wisdom, which focuses on the mixture of the two known end compounds: ${\mathrm{Fe}}_{2}\mathrm{P}$ and ${\mathrm{Co}}_{2}\mathrm{P}$. Our work provides guidance for synthesis. We find ${\mathrm{Fe}}_{7}{\mathrm{CoP}}_{4}$ shows the most promise (${J}_{s}=1.03\phantom{\rule{0.16em}{0ex}}\mathrm{T}$ and ${K}_{1}=0.83\phantom{\rule{0.16em}{0ex}}\mathrm{MJ}/{\mathrm{m}}^{3}$). Magnet ; Materials science ; Coercivity ; Rare earth ; Cobalt ; Anisotropy ; Condensed matter physics ; Crystallography ; Nanotechnology ; Mechanical engineering ; Physics ; Metallurgy ; Chemistry Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Ferroelectric and Negative Capacitance Devices
Authors: Tomoya Horide | Akira Iyo | Yusuke Ichino
Source: IEEJ Transactions on Fundamentals and Materials (article, 2024-08-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data Science, Simulation, and Experimental Methods for Exploration and Design of Superconducting Materials The discovery of new superconductors has a significant impact on the scientific and engineering communities, unraveling interesting physical phenomena and providing unique applications in energy and devices. Superconductors with a high critical temperature are limited to a few families, such as cuprates, iron-based compounds, and hydrides under ultra-high pressure. In traditional studies, the exploration of new superconductors relies on theories, experiments, and simulations. However, recent advances in data science have made machine learning available in a variety of fields, including materials informatics. Utilizing superconductor databases and various regression methods, machine learning has proposed several new superconductors. The chemical descriptors are widely used, and the descriptor of the crystalline structure is being developed for more accurate prediction. In this review, the theoretical and experimental studies for the discovery of new superconductors are explained. The available database and data-driven studies are also shown. Furthermore, after reviewing the recent machine learning studies for the discovery of new superconductors and other materials, future aspects in this field are discussed. Superconductivity ; Field (mathematics) ; Computer science ; Cuprate ; High-temperature superconductivity ; Data science ; Informatics ; Artificial intelligence ; Machine learning ; Nanotechnology ; Engineering physics ; Materials science ; Physics ; Condensed matter physics ; Engineering ; Electrical engineering ; Mathematics Iron-based superconductors research ; Machine Learning in Materials Science ; Superconductivity in MgB2 and Alloys
Authors: X.B. He | Juan Zuo | Wenhui Zhang | Andrei Ionut Mardare | Chaohui Guan | Tianzhen Han | Dewei Zhao
Source: Computational Materials Science (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Utilizing deep learning for swift analysis of high-throughput spectroscopic ellipsometry data on anodized oxides of valve metals Anodizing ; Ellipsometry ; Throughput ; Materials science ; Metallurgy ; Nanotechnology ; Computer science ; Thin film ; Telecommunications ; Wireless Corrosion Behavior and Inhibition ; Electrochemical Analysis and Applications ; Non-Destructive Testing Techniques
Authors: William Bro‐Jørgensen | Joseph Hamill | Gréta Mezei | Brent Lawson | Umar Rashid | András Halbritter | Maria Kamenetska | Veerabhadrarao Kaliginedi | Gemma C. Solomon
Source: ACS Nanoscience Au (article, 2024-06-06)
Field: Chemistry — Spectroscopy

Making the Most of Nothing: One-Class Classification for Single-Molecule Transport Studies Single-molecule experiments offer a unique means to probe molecular properties of individual molecules-yet they rest upon the successful control of background noise and irrelevant signals. In single-molecule transport studies, large amounts of data that probe a wide range of physical and chemical behaviors are often generated. However, due to the stochasticity of these experiments, a substantial fraction of the data may consist of blank traces where no molecular signal is evident. One-class (OC) classification is a machine learning technique to identify a specific class in a data set that potentially consists of a wide variety of classes. Here, we examine the utility of two different types of OC classification models on four diverse data sets from three different laboratories. Two of these data sets were measured at cryogenic temperatures and two at room temperature. By training the models solely on traces from a blank experiment, we demonstrate the efficacy of OC classification as a powerful and reliable method for filtering out blank traces from a molecular experiment in all four data sets. On a labeled 4,4'-bipyridine data set measured at 4.2 K, we achieve an accuracy of 96.9 ± 0.3 and an area under the receiver operating characteristic curve of 99.5 ± 0.3 as validated over a fivefold cross-validation. Given the wide range of physical and chemical properties that can be probed in single-molecule experiments, the successful application of OC classification to filter out blank traces is a major step forward in our ability to understand and manipulate molecular properties. Nothing ; Class (philosophy) ; Computer science ; Epistemology ; Artificial intelligence ; Philosophy Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Joaquín Hernández‐Fernández | Jose Martinez-Trespalacios | Edgar Márquez
Source: Foods (article, 2024-04-15)
Field: Chemistry — Analytical Chemistry

Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging Sorbitol derivatives and other additives are commonly used in various products, such as packaging or food packaging, to improve their mechanical, physical, and optical properties. To accurately and precisely evaluate the efficacy of adding sorbitol-type nucleating agents to these articles, their quantitative determination is essential. This study systematically investigated the quantification of sorbitol-type nucleating agents in food packaging made from impact copolymers of polypropylene (PP) and polyethylene (PE) using attenuated total reflectance infrared spectroscopy (ATR-FTIR) together with analysis of principal components (PCA) and machine learning algorithms. The absorption spectra revealed characteristic bands corresponding to the C-O-C bond and hydroxyl groups attached to the cyclohexane ring of the molecular structure of sorbitol, providing crucial information for identifying and quantifying sorbitol derivatives. PCA analysis showed that with the selected FTIR spectrum range and only the first two components, 99.5% of the variance could be explained. The resulting score plot showed a clear pattern distinguishing different concentrations of the nucleating agent, affirming the predictability of concentrations based on an impact copolymer. The study then employed machine learning algorithms (NN, SVR) to establish prediction models, evaluating their quality using metrics such as RMSE, R<sup>2</sup>, and RMSECV. Hyperparameter optimization was performed, and SVR showed superior performance, achieving near-perfect predictions (R<sup>2</sup> = 0.9999) with an RMSE of 0.100 for both calibration and prediction. The chosen SVR model features two hidden layers with 15 neurons each and uses the Adam algorithm, balanced precision, and computational efficiency. The innovative ATR-FTIR coupled SVR model presented a novel and rapid approach to accurately quantify sorbitol-type nucleating agents in polymer production processes for polymer research and in the analysis of nucleating agent derivatives. The analytical performance of this method surpassed traditional methods (PCR, NN). Attenuated total reflection ; Principal component analysis ; Sorbitol ; Chemistry ; Fourier transform infrared spectroscopy ; Infrared spectroscopy ; Artificial intelligence ; Machine learning ; Analytical Chemistry (journal) ; Materials science ; Biological system ; Chromatography ; Computer science ; Organic chemistry ; Chemical engineering Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Chandan Mukherjee | Suraj Sunil Chothe | Sudipto Mukhopadhyay
Source: Thermal Science and Engineering Progress (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

Predicting effective thermal conductivity of HGM composite using ML Composite number ; Thermal conductivity ; Materials science ; Composite material Thermal properties of materials ; Numerical methods in engineering ; Advanced ceramic materials synthesis