jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Faiza Qayyum | Murad Ali Khan | Do‐Hyeun Kim | Hyunseok Ko | Ga-Ae Ryu
Source: Materials (article, 2023-11-24)
Field: Materials Science — Materials Chemistry

Explainable AI for Material Property Prediction Based on Energy Cloud: A Shapley-Driven Approach The scientific community has raised increasing apprehensions over the transparency and interpretability of machine learning models employed in various domains, particularly in the field of materials science. The intrinsic intricacy of these models frequently results in their characterization as "black boxes", which poses a difficulty in emphasizing the significance of producing lucid and readily understandable model outputs. In addition, the assessment of model performance requires careful deliberation of several essential factors. The objective of this study is to utilize a deep learning framework called TabNet to predict lead zirconate titanate (PZT) ceramics' dielectric constant property by employing their components and processes. By recognizing the crucial importance of predicting PZT properties, this research seeks to enhance the comprehension of the results generated by the model and gain insights into the association between the model and predictor variables using various input parameters. To achieve this, we undertake a thorough analysis with Shapley additive explanations (SHAP). In order to enhance the reliability of the prediction model, a variety of cross-validation procedures are utilized. The study demonstrates that the TabNet model significantly outperforms traditional machine learning models in predicting ceramic characteristics of PZT components, achieving a mean squared error (MSE) of 0.047 and a mean absolute error (MAE) of 0.042. Key contributing factors, such as d33, tangent loss, and chemical formula, are identified using SHAP plots, highlighting their importance in predictive analysis. Interestingly, process time is less effective in predicting the dielectric constant. This research holds considerable potential for advancing materials discovery and predictive systems in PZT ceramics, offering deep insights into the roles of various parameters. Interpretability ; Computer science ; Machine learning ; Predictive modelling ; Property (philosophy) ; Artificial intelligence ; Mean squared error ; Mathematics ; Statistics Machine Learning in Materials Science
Authors: Samrendra Singh | Abhishek T. Sose | Fangxi Wang | Karteek K. Bejagam | Sanket A. Deshmukh
Source: Journal of Chemical Theory and Computation (article, 2023-09-27)
Field: Chemistry — Inorganic Chemistry

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption Hydrogen gas (H<sub>2</sub>) is a clean and renewable energy source, but the lack of efficient and cost-effective storage materials is a challenge to its widespread use. Metal-organic frameworks (MOFs), a class of porous materials, have been extensively studied for H<sub>2</sub> storage due to their tunable structural and chemical features. However, the large design space offered by MOFs makes it challenging to select or design appropriate MOFs with a high H<sub>2</sub> storage capacity. To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H<sub>2</sub> storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H<sub>2</sub> uptake. This automated, data driven framework adds appropriate functional groups to IRMOF-10 to improve its H<sub>2</sub> adsorption capacity. A detailed analysis of the top selected MOFs, their adsorption isotherms, and MOF design rules to enhance H<sub>2</sub> adsorption are presented. We found a functionalized IRMOF-10 with an enhanced H<sub>2</sub> adsorption increased by ∼6 times compared to that of pure IRMOF-10 at 1 bar and 77 K. Furthermore, this study also utilizes machine learning and deep learning techniques to analyze a large data set of MOF structures and properties, in order to identify the key factors that influence hydrogen adsorption. The proof-of-concept that uses a machine learning/deep learning approach to predict hydrogen adsorption based on the identified structural and chemical properties of the MOF is demonstrated. Adsorption ; Hydrogen storage ; Metal-organic framework ; Computer science ; Materials science ; Hydrogen ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Industrial Gas Emission Control
Authors: Surajit Nandi | Tejs Vegge | Arghya Bhowmik
Source: Scientific Data (article, 2023-11-08)
Field: Materials Science — Materials Chemistry

MultiXC-QM9: Large dataset of molecular and reaction energies from multi-level quantum chemical methods Well curated extensive datasets have helped spur intense molecular machine learning (ML) method development activities over the last few years, encouraging nonchemists to be part of the effort as well. QM9 dataset is one of the benchmark databases for small molecules with molecular energies based on B3LYP functional. G4MP2 based energies of these molecules were published later. To enable a wide variety of ML tasks like transfer learning, delta learning, multitask learning, etc. with QM9 molecules, in this article, we introduce a new dataset with QM9 molecule energies estimated with 76 different DFT functionals and three different basis sets (228 energy numbers for each molecule). We additionally enumerated all possible A ↔ B monomolecular interconversions within the QM9 dataset and provided the reaction energies based on these 76 functionals, and basis sets. Lastly, we also provide the bond changes for all the 162 million reactions with the dataset to enable structure- and bond-based reaction energy prediction tools based on ML. Quantum chemical ; Computational chemistry ; Quantum ; Computer science ; Chemistry ; Physics ; Molecule ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Xiaohang Li | Hongda Wang | Meiting Jiang | Mengxiang Ding | Xiaoyan Xu | Bei Xu | Yadan Zou | Yuetong Yu | Wenzhi Yang
Source: Molecules (review, 2023-05-12)
Field: Chemistry — Spectroscopy

Collision Cross Section Prediction Based on Machine Learning Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected. Ion-mobility spectrometry ; Characterization (materials science) ; Computer science ; Mass spectrometry ; Chemical space ; Metabolome ; Collision ; Quantum chemical ; Metabolomics ; Separation (statistics) ; Chemistry ; Nanotechnology ; Biological system ; Biochemical engineering ; Machine learning ; Materials science ; Chromatography ; Engineering ; Molecule Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Renato Belli Strozi | Matthew Witman | Vitalie Stavila | Jakub Čı́žek | Kouji Sakaki | Hyunjeong Kim | Oksana Melikhova | Loïc Perrière | Akihiko Machida | Yuki Nakahira | Guilherme Zepon | Walter José Botta Filho | Claudia Zlotea
Source: ACS Applied Materials & Interfaces (article, 2023-08-04)
Field: Materials Science — Materials Chemistry

Elucidating Primary Degradation Mechanisms in High-Cycling-Capacity, Compositionally Tunable High-Entropy Hydrides The hydrogen sorption properties of single-phase bcc (TiVNb)<sub>100-<i>x</i></sub>Cr<i><sub>x</sub></i> alloys (<i>x</i> = 0-35) are reported. All alloys absorb hydrogen quickly at 25 °C, forming fcc hydrides with storage capacity depending on the Cr content. A thermodynamic destabilization of the fcc hydride is observed with increasing Cr concentration, which agrees well with previous compositional machine learning models for metal hydride thermodynamics. The steric effect or repulsive interactions between Cr-H might be responsible for this behavior. The cycling performances of the TiVNbCr alloy show an initial decrease in capacity, which cannot be explained by a structural change. Pair distribution function analysis of the total X-ray scattering on the first and last cycled hydrides demonstrated an average random fcc structure without lattice distortion at short-range order. If the as-cast alloy contains a very low density of defects, the first hydrogen absorption introduces dislocations and vacancies that cumulate into small vacancy clusters, as revealed by positron annihilation spectroscopy. Finally, the main reason for the capacity drop seems to be due to dislocations formed during cycling, while the presence of vacancy clusters might be related to the lattice relaxation. Having identified the major contribution to the capacity loss, compositional modifications to the TiVNbCr system can now be explored that minimize defect formation and maximize material cycling performance. Materials science ; Alloy ; Hydride ; Hydrogen storage ; Configuration entropy ; Vacancy defect ; Hydrogen ; Chemical physics ; Solid solution ; Thermodynamics ; Crystallography ; Metal ; Metallurgy Nuclear Materials and Properties ; Hydrogen Storage and Materials ; Metal and Thin Film Mechanics
Authors: Tianyi Wang | Ying Tan | Yu Chen | Chunyan Tan
Source: Journal of Chemical Information and Modeling (article, 2023-08-02)
Field: Chemistry — Analytical Chemistry

Infrared Spectral Analysis for Prediction of Functional Groups Based on Feature-Aggregated Deep Learning Infrared (IR) spectroscopy is a powerful and versatile tool for analyzing functional groups in organic compounds. A complex and time-consuming interpretation of massive unknown spectra usually requires knowledge of chemistry and spectroscopy. This paper presents a new deep learning method for transforming IR spectral features into intuitive imagelike feature maps and prediction of major functional groups. We obtained 8272 gas-phase IR spectra from the NIST Chemistry WebBook. Feature maps are constructed using the intrinsic correlation of spectral data, and prediction models are developed based on convolutional neural networks. Twenty-one major functional groups for each molecule are successfully identified using binary and multilabel models without expert guidance and feature selection. The multilabel classification model can produce all prediction results simultaneously for rapid characterization. Further analysis of the detailed substructures indicates that our model is capable of obtaining abundant structural information from IR spectra for a comprehensive investigation. The interpretation of our model reveals that the peaks of most interest are similar to those often considered by spectroscopists. In addition to demonstrating great potential for spectral identification, our method may contribute to the development of automated analyses in many fields. Artificial intelligence ; Pattern recognition (psychology) ; Feature (linguistics) ; Computer science ; Feature selection ; Convolutional neural network ; Infrared spectroscopy ; Machine learning ; Biological system ; Data mining ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Zhiwei Jiang | Ke Jin | Lingjiao Zhong | Ying Zheng | Qingsong Shao | Ailian Zhang
Source: Industrial Crops and Products (article, 2023-03-20)
Field: Chemistry — Analytical Chemistry

Near-infrared spectroscopy combined with machine learning for rapid identification of Atractylodis rhizoma decoction pieces Decoction ; Atractylodes ; Artificial intelligence ; Computer science ; Mathematics ; Traditional medicine ; Pattern recognition (psychology) ; Biological system ; Botany ; Biology ; Medicine Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Saba Ranjbar | Amir Hesam Salavati | Negar Ashari Astani | Naimeh Naseri | Navid Davar | Mohammad Reza Ejtehadi
Source: ACS Sensors (article, 2023-11-14)
Field: Materials Science — Polymers and Plastics

Electrochromic Sensor Augmented with Machine Learning for Enzyme-Free Analysis of Antioxidants Our study presents an electrochromic sensor that operates without the need for enzymes or multiple oxidant reagents. This sensor is augmented with machine learning algorithms, enabling the identification, classification, and prediction of six different antioxidants with high accuracy. We utilized polyaniline (PANI), Prussian blue (PB), and copper-Prussian blue analogues (Cu-PBA) at their respective oxidation states as electrochromic materials (ECMs). By designing three readout channels with these materials, we were able to achieve visual detection of antioxidants without relying on traditional "lock and key" specific interactions. Our sensing approach is based on the direct electrochemical reactions between oxidized electrochromic materials (ECMs<sub>ox</sub>) as electron acceptors and various antioxidants, which act as electron donors. This interaction generates unique fingerprint patterns by switching the ECMs<sub>ox</sub> to reduced electrochromic materials (ECMs<sub>red</sub>), causing their colors to change. Through the application of density functional theory (DFT), we demonstrated the molecular-level basis for the distinct multicolor patterns. Additionally, machine learning algorithms were employed to correlate the optical patterns with RGB data, enabling complex data analysis and the prediction of unknown samples. To demonstrate the practical applications of our design, we successfully used the EC sensor to diagnose antioxidants in serum samples, indicating its potential for the on-site monitoring of antioxidant-related diseases. This advancement holds promise for various applications, including the real-time monitoring of antioxidant levels in biological samples, the early diagnosis of antioxidant-related diseases, and personalized medicine. Furthermore, the success of our electrochromic sensor design highlights the potential for exploring similar strategies in the development of sensors for diverse analytes, showcasing the versatility and adaptability of this approach. Electrochromism ; Prussian blue ; Computer science ; Fingerprint (computing) ; Nanotechnology ; Antioxidant ; Materials science ; Polyaniline ; Combinatorial chemistry ; Artificial intelligence ; Chemistry ; Electrochemistry ; Biochemistry Conducting polymers and applications ; Electrochemical sensors and biosensors ; Electrochemical Analysis and Applications
Authors: Norah A. M. Alsaif | M. S. Shams | Adel M. El-Refaey | Y. S. Rammah | Mousmi A. Chaurasia | Nazima Siddiqui | R. A. Elsad | Shaik Kareem Ahmmad
Source: Optik (article, 2023-04-03)
Field: Materials Science — Ceramics and Composites

On cobalt zinc boro sodium fluoride glasses doped with Y2O3: Synthesis, artificial intelligence density prediction and dielectric spectroscopy Materials science ; Dielectric ; Spectroscopy ; Linear regression ; Analytical Chemistry (journal) ; Machine learning ; Decision tree ; Artificial neural network ; Artificial intelligence ; Computer science ; Chemistry ; Physics Glass properties and applications ; Luminescence Properties of Advanced Materials ; Microwave Dielectric Ceramics Synthesis
Authors: Renfeng Guo | Youjia Zhang | Yuxuan Liao | Qiong Yang | Ting Xie | Xiaqiong Fan | Zhonglong Lin | Yi Chen | Hongmei Lü | Zhimin Zhang
Source: Communications Chemistry (article, 2023-07-04)
Field: Chemistry — Spectroscopy

Highly accurate and large-scale collision cross sections prediction with graph neural networks The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values. It achieved a coefficient of determination of 0.9945 and a median relative error of 1.1751% on the test set. The model-agnostic interpretation method and the visualization of the learned representations were used to investigate the chemical rationality of SigmaCCS. An in-silico database with 282 million CCS values was generated for three different adduct types of 94 million compounds. Its source code is publicly available at https://github.com/zmzhang/SigmaCCS . Altogether, SigmaCCS is an accurate, rational, and off-the-shelf method to directly predict CCS values from molecular structures. Graph ; Computer science ; Visualization ; Collision ; Source code ; Set (abstract data type) ; Artificial neural network ; Adduct ; Algorithm ; Artificial intelligence ; Data mining ; Theoretical computer science ; Chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Honggang Zhang | Xiaokun Gu | Zheyong Fan | Hua Bao
Source: Physical review. B./Physical review. B (article, 2023-07-28)
Field: Materials Science — Materials Chemistry

Vibrational anharmonicity results in decreased thermal conductivity of amorphous <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>HfO</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> at high temperature While the high-temperature thermal transport in crystalline materials has been recently carefully addressed, it is much less explored for amorphous materials. Most of the existing studies have focused on the low-/mid-temperature range and have generally found the increasing trend of thermal conductivity with temperature and converging to a constant value, mainly due to the temperature dependence of heat capacity. In this work, we investigate the temperature-dependent thermal conductivity of amorphous ${\mathrm{HfO}}_{2}$ with three different methods, including molecular dynamics, the Allen-Feldman theory, and the quasiharmonic Green-Kubo method, with the forces extracted from a machine-learning potential parametrized from first principles. While the Allen-Feldman theory and the quasiharmonic Green-Kubo method show the same temperature dependence trend as the previous expectation even at high temperatures, molecular dynamics simulations show a clear decreasing trend of thermal conductivity at high temperatures. By comparing the results from these approaches, we identify that two anharmonic effects, i.e., thermal expansion and vibrational mode softening, are the mechanisms of the decreased thermal conductivity of amorphous ${\mathrm{HfO}}_{2}$ at high temperatures. Anharmonicity ; Thermal conductivity ; Amorphous solid ; Thermodynamics ; Materials science ; Atmospheric temperature range ; Work (physics) ; Condensed matter physics ; Phonon ; Heat capacity ; Softening ; Machine learning ; Physics ; Chemistry ; Crystallography ; Computer science ; Composite material Thermal properties of materials ; Diamond and Carbon-based Materials Research ; Semiconductor materials and devices
Authors: Etienne Palos | Saswata Dasgupta | Eleftherios Lambros | Francesco Paesani
Source: Chemical Physics Reviews (article, 2023-01-10)
Field: Materials Science — Materials Chemistry

Data-driven many-body potentials from density functional theory for aqueous phase chemistry Density functional theory (DFT) has been applied to modeling molecular interactions in water for over three decades. The ubiquity of water in chemical and biological processes demands a unified understanding of its physics, from the single molecule to the thermodynamic limit and everything in between. Recent advances in the development of data-driven and machine-learning potentials have accelerated simulation of water and aqueous systems with DFT accuracy. However, anomalous properties of water in the condensed phase, where a rigorous treatment of both local and non-local many-body (MB) interactions is in order, are often unsatisfactory or partially missing in DFT models of water. In this review, we discuss the modeling of water and aqueous systems based on DFT and provide a comprehensive description of a general theoretical/computational framework for the development of data-driven many-body potentials from DFT reference data. This framework, coined MB-DFT, readily enables efficient many-body molecular dynamics (MD) simulations of small molecules, in both gas and condensed phases, while preserving the accuracy of the underlying DFT model. Theoretical considerations are emphasized, including the role that the delocalization error plays in MB-DFT potentials of water and the possibility to elevate DFT and MB-DFT to near-chemical-accuracy through a density-corrected formalism. The development of the MB-DFT framework is described in detail, along with its application in MB-MD simulations and recent extension to the modeling of reactive processes in solution within a quantum mechanics/MB molecular mechanics (QM/MB-MM) scheme, using water as a prototypical solvent. Finally, we identify open challenges and discuss future directions for MB-DFT and QM/MB-MM simulations in condensed phases. Density functional theory ; Delocalized electron ; Molecular dynamics ; Statistical physics ; Aqueous solution ; Molecule ; Computational chemistry ; Formalism (music) ; Chemistry ; Computer science ; Chemical physics ; Physics ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics