jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jawad Chowdhury | Charles Fricke | Olajide H. Bamidele | Mubarak Bello | Wenqiang Yang | Andreas Heyden | Gabriel Terejanu
Source: Journal of Chemical Information and Modeling (article, 2024-01-10)
Field: Materials Science — Materials Chemistry

Invariant Molecular Representations for Heterogeneous Catalysis Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface. Catalysis ; Adsorption ; Invariant (physics) ; Density functional theory ; Robustness (evolution) ; Computation ; Molecular descriptor ; Chemistry ; Artificial neural network ; Heterogeneous catalysis ; Computational chemistry ; Computer science ; Physical chemistry ; Artificial intelligence ; Algorithm ; Quantum mechanics ; Physics ; Organic chemistry ; Stereochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Anubhab Haldar | Quentin Clark | Marios Zacharias | Feliciano Giustino | Sahar Sharifzadeh
Source: Physical Review Materials (article, 2024-10-11)
Field: Materials Science — Materials Chemistry

Machine learning electron-phonon interactions in two-dimensional semiconducting materials: The case of zero-point renormalization We utilize first-principles theory to investigate the role of electron-phonon interactions within a dataset of monolayer materials. Using density functional theory to describe excited-state transitions and the special displacement method to describe the role of phonons, we analyze the relationship between simple physical observables and electron-phonon coupling strength. For over 100 materials, we compute the band gap renormalization due to zero-point vibrational (ZPR) motion as a measure of electron-phonon interactions and train a machine learning model based on physical parameters. We demonstrate that the strength of electron-phonon interactions is highly dependent on the band gap, dielectric constant, and degree of ionicity, all of which can be physically justified. We then apply this model to 1302 2D materials, predicting the ZPR, which for five randomly selected materials tested agree well with the first-principles predictions. This work provides an approach for quantitatively predicting the ZPR as a measure of electron-phonon interactions in 2D materials. Phonon ; Condensed matter physics ; Materials science ; Renormalization ; Measure (data warehouse) ; Electron ; Dielectric ; Coupling (piping) ; Band gap ; Physics ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ga2O3 and related materials
Authors: Jae Min Jeong | Moonsoo Ra | Jinha Jeong | Woong Lee
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Performance of a deep convolutional neural network to classify crystal structures using selected area electron beam diffraction patterns containing lattice defect information A deep convolutional neural network (DCNN) architecture ResNet has been tested to verify its ability to handle selected area electron diffraction pattern (SADP) datasets carrying information on lattice defects including strains, thermal lattice vibrations, point defects, dislocations, and twin boundaries. The disordered states of the crystal lattices in the presence of these defects were predicted by <i>ab initio</i> molecular dynamics simulations, first principles geometry optimizations, and lattice manipulation operations in an effort to establish a possible dataset augmentation strategy for the improvement of classification performance of the ResNet. Using the disordered lattice information originating from the defects, test dataset SADPs were generated by simulating electron diffraction in transmission electron microscopy. The ResNet, pre-trained using SADPs from defect-free materials, showed decreasing but acceptable classification accuracies with increasing degrees of lattice disorder regarding the lattice vibrations and point defects. When tested using the diffraction patterns for strained lattices, the ResNet responded to the changing lattice symmetry when strain levels are relatively high suggesting that it has capability to discern different symmetries induced by large strains. However, the ResNet failed to recognize lattice structure when dislocations and twin boundaries were considered. It is suggested that DCNN architectures be trained over various scenarios including changes in the image feature characteristics in the diffraction patterns related to defects in future developments for improved general classification performances. Convolutional neural network ; Diffraction ; Lattice (music) ; Crystal structure ; Materials science ; Electron diffraction ; Artificial neural network ; Cathode ray ; Computer science ; Crystallography ; Artificial intelligence ; Pattern recognition (psychology) ; Electron ; Optics ; Physics ; Chemistry ; Acoustics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Welding Techniques and Residual Stresses
Authors: Yuting Wang | Yanling Jiang | Yu Zhou | He Huai | Jincao Tang | Anqing Luo | Zeng Liu | Chi Ma | Qin Xiao | Tianbing Guan | Chuanyun Dai
Source: AAPS PharmSciTech (article, 2024-06-11)
Field: Chemistry — Physical and Theoretical Chemistry

Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface Cocrystal ; Nifedipine ; Solubility ; Computer science ; Artificial neural network ; Materials science ; Chemistry ; Machine learning ; Calcium ; Molecule ; Hydrogen bond ; Organic chemistry Crystallography and molecular interactions ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Hang Che | Tian Lu | Shumin Cai | Minjie Li | Wencong Lu
Source: Materials (article, 2024-07-22)
Field: Materials Science — Materials Chemistry

Inverse Design of Low-Resistivity Ternary Gold Alloys via Interpretable Machine Learning and Proactive Search Progress Ternary gold alloys (TGAs) are highly regarded for their excellent electrical properties. Electrical resistivity is a crucial indicator for evaluating the electrical performance of TGAs. To explore new promising TGAs with lower resistivity, we developed a reverse design approach integrating machine learning techniques and proactive searching progress (PSP) method. Compared with other models, the support vector regression (SVR) was determined to be the most optimal model for resistivity prediction. The training and test sets yielded R<sup>2</sup> values of 0.73 and 0.77, respectively. The model interpretation indicated that lower electrical resistivity was associated with the following conditions: a van der Waals Radius (<i>V</i><sub>rt</sub>) of 0, a <i>V</i><sub>r</sub> (another van der Waals Radius) of less than 217, and a mass attenuation coefficient of MoKα (<i>M</i><sub>acm</sub>) greater than 77.5 cm<sup>2</sup>g<sup>-1</sup>. Applying the PSP method, we successfully identified eight candidates whose resistivity was lower than that of the sample with the lowest resistivity in the dataset by more than 53-60%, e.g., Au<sub>1.000</sub>Cu<sub>4.406</sub>Pt<sub>1.833</sub> and Au<sub>1.000</sub>Pt<sub>2.232</sub>In<sub>1.502</sub>. Finally, the candidates were validated to possess low resistivity through the pattern recognition method. Electrical resistivity and conductivity ; Ternary operation ; Support vector machine ; Inverse ; Materials science ; Artificial intelligence ; Computer science ; Machine learning ; Mathematics ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Electrodeposition and Electroless Coatings
Authors: Mohammad Rezasefat | James D. Hogan
Source: Computational Materials Today (article, 2024-08-23)
Field: Materials Science — Ceramics and Composites

Data-driven integration of synthetic representative volume elements and machine learning for improved microstructure-property linkage and material performance in ceramics Ceramic materials, characterized by their heat resistance, dielectric properties, and mechanical performance, are often compromised by brittleness due to microstructural inclusions. These inclusions, such as defects, secondary phases, and pores, are critically analyzed for their impact on material strength and fracture properties. In this study, the linkage between microstructure and properties in ceramic materials is explored through a methodological approach that combines experimental observations with physics-based and machine learning models. A data-driven approach has been employed, utilizing synthetic Representative Volume Elements (RVEs) derived from X-ray computed tomography scans of ceramics. The methodology involves an automated finite element (FE) simulation process for progressive failure analysis under uniaxial compression and tension. The analysis is conducted using statistical, data-driven, and machine learning techniques, including principal component analysis and k -means clustering, to assess the microstructural features' impact on material performance. The study focuses on the use of RVEs for accurately capturing essential microstructural characteristics and addresses the challenges in developing synthetic models and the limitations of simulation capabilities. The study's findings reveal that less uniform inclusion distribution and a higher standard deviation in inclusion size correlate to lower mechanical performance. By applying data-driven methods, this research contributes to the optimization of material performance and the establishment of structure-property relationships, with a particular emphasis on the influence of inclusions and defects on the mechanical behavior of ceramic materials. Linkage (software) ; Microstructure ; Property (philosophy) ; Ceramic ; Volume (thermodynamics) ; Materials science ; Artificial intelligence ; Computer science ; Metallurgy ; Biology ; Physics ; Genetics ; Thermodynamics Advanced ceramic materials synthesis ; X-ray Diffraction in Crystallography ; Advanced materials and composites
Authors: Jonathan P. Mailoa | Xin Li | Shengyu Zhang
Source: Nature Communications (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

3T-VASP: fast ab-initio electrochemical reactor via multi-scale gradient energy minimization Ab-initio methods such as density functional theory (DFT) is useful for fundamental atomistic-level study and is widely used across many scientific fields, including for the discovery of electrochemical reaction byproducts. However, many DFT steps may be needed to discover rare electrochemical reaction byproducts, which limits DFT's scalability. In this work, we demonstrate that it is possible to generate many elementary electrochemical reaction byproducts in-silico using just a small number of ab-initio energy minimization steps if it is done in a multi-scale manner, such as via previously reported tiered tensor transform (3T) method. We first demonstrate the algorithm through a simple example of a complex floppy organic molecule passivator binding onto perovskite solar cell surface defect site. We then demonstrate more complex examples by generating hundreds of electrochemical reaction byproducts in lithium-ion battery liquid electrolyte (many are verified in previous experimental studies), with most trajectories completed within 50-100 DFT steps as opposed to more than 10,000 steps typically utilized in an ab-initio molecular dynamics trajectory. This approach requires no machine learning training data generation and can be directly applied on any new chemistries, making it suitable for ab-initio elementary chemical reaction byproduct investigation when temperature dependence is not required. Ab initio ; Density functional theory ; Electrochemistry ; Computational chemistry ; Chemistry ; Lithium (medication) ; Materials science ; Chemical physics ; Physical chemistry ; Electrode ; Organic chemistry Machine Learning in Materials Science ; Advanced battery technologies research ; Advancements in Battery Materials
Authors: Aniwat Kesorn | Rutchapon Hunkao | Cheewawut Na Talang | Chanaprom Cholsuk | Asawin Sinsarp | Tobias Vogl | Sujin Suwanna | Suraphong Yuma
Source: Machine Learning Science and Technology (article, 2024-07-23)
Field: Materials Science — Materials Chemistry

Formation energy prediction of neutral single-atom impurities in 2D materials using tree-based machine learning Abstract We applied tree-based machine learning algorithms to predict the formation energy of impurities in 2D materials, where adsorbates and interstitial defects are investigated. Regression models based on random forest, gradient boosting regression, histogram-based gradient-boosting regression, and light gradient-boosting machine algorithms are employed for training, testing, cross validation, and blind testing. We utilized chemical features from fundamental properties of atoms and supplemented them with structural features from the interaction of the added chemical element with its neighboring host atoms via the Jacobi–Legendre (JL) polynomials. Overall, the prediction accuracy yields optimal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mtext>MAE</mml:mtext> <mml:mo>≈</mml:mo> <mml:mn>0.518</mml:mn> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mtext>RMSE</mml:mtext> <mml:mo>≈</mml:mo> <mml:mn>1.14</mml:mn> </mml:mrow> </mml:math> , and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>≈</mml:mo> <mml:mn>0.855</mml:mn> </mml:mrow> </mml:math> . When trained separately, we obtained lower residual errors RMSE and MAE, and higher R 2 value for predicting the formation energy in the adsorbates than in the interstitial defects. In both cases, the inclusion of the structural features via the JL polynomials improves the prediction accuracy of the formation energy in terms of decreasing RMSE and MAE, and increasing R 2 . This work demonstrates the potential and application of physically meaningful features to obtain physical properties of impurities in 2D materials that otherwise would require higher computational cost. Impurity ; Atom (system on chip) ; Energetic neutral atom ; Energy (signal processing) ; Computer science ; Machine learning ; Materials science ; Artificial intelligence ; Atomic physics ; Physics ; Nuclear physics ; Plasma ; Quantum mechanics ; Parallel computing Machine Learning in Materials Science ; Nuclear Materials and Properties ; Electronic and Structural Properties of Oxides
Authors: Gabriel Gemadzie | Baosen Zhang | Aliaksei Boika
Source: The Analyst (article, 2024-01-01)
Field: Chemistry — Electrochemistry

Polymer bead size revealed <i>via</i> neural network analysis of single-entity electrochemical data Single-entity electrochemistry methods for detecting polymer microbeads offer a promising approach to analyzing microplastics. However, conventional methods for determining microparticle size face challenges due to non-uniform current distribution across the surface of a sensing disk microelectrode. In this study, we demonstrate the utility of neural network (NN) analysis for extracting the size information from single-entity electrochemical data (current steps). We developed fully connected regression NN models capable of predicting microparticle radii based on experimental parameters and current-time data. Once trained, the models provide near-real-time predictions with good accuracy for microparticles of the same size, as well as the average size of two different-sized microparticles in solution. Potential future applications include analyzing various bioparticles, such as viruses and bacteria of different sizes and shapes. Artificial neural network ; Bead ; Electrochemistry ; Polymer ; Particle size ; Materials science ; Computer science ; Chemical engineering ; Artificial intelligence ; Chemistry ; Electrode ; Composite material ; Engineering Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: Atsuto Seko
Source: Physical review. B./Physical review. B (article, 2024-12-02)
Field: Materials Science — Materials Chemistry

Globally stable and metastable crystal structure enumeration using polynomial machine learning potentials for elemental As, Bi, Ga, In, La, P, Sb, Sn, and Te Machine learning potentials (MLPs) have become indispensable for conducting accurate large-scale atomistic simulations and for the efficient prediction of crystal structures. Polynomial MLPs, defined by polynomial rotational invariants, have been systematically developed for a wide range of elemental, alloy, and ionic systems. This study introduces a highly efficient and robust methodology for enumerating globally stable and metastable structures utilizing polynomial MLPs. To develop MLPs that are sufficiently robust for structure enumeration, an iterative process involving random structure searches and subsequent updates to the MLPs is employed. This methodology has been systematically applied to elemental systems such as As, Bi, Ga, In, La, P, Sb, Sn, and Te, where numerous local minima present significant competition with the global minimum in terms of energy. The proposed approach facilitates robust global structure searches and structure enumerations by markedly accelerating the search process and expanding the search space. Enumeration ; Metastability ; Crystal structure ; Polynomial ; Mathematics ; Combinatorics ; Physics ; Crystallography ; Materials science ; Discrete mathematics ; Computer science ; Chemistry ; Mathematical analysis ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Jiaxin Xu | Xiaoyu Ye | Zongchao Lv | Yi‐Hung Chen | Xiang Simon Wang
Source: Chemistry - A European Journal (article, 2024-02-26)
Field: Chemistry — Organic Chemistry

The Role of Base in Reaction Performance of Photochemical Synthesis of Thiazoles: An Integrated Theoretical and Experimental Study Artificial intelligence (AI)/machine learning (ML) is emerging as pivotal in synthetic chemistry, offering revolutionary potential in retrosynthetic analysis, reaction conditions and reaction prediction. We have combined chemical descriptors, primarily based on Density Functional Theory (DFT) calculations, with various AI/ML tools such as Multi-Layer Perceptron (MLP) and Random Forest (RF), to predict the synthesis of 2-arylbenzothiazole in photoredox reactions. Significantly, our models underscore the critical role of the molecular structure and physicochemical characteristics of the base, especially the total atomic polarizabilities, in the rate-determining steps involving cyclohexyl and phenethyl moieties of the substrate. Moreover, we validated our findings in articles through experimental studies. It showcases the power of AI/ML and quantum chemistry in shaping the future of organic chemistry. Base (topology) ; Chemistry ; Photochemistry ; Combinatorial chemistry ; Computational chemistry ; Mathematics Sulfur-Based Synthesis Techniques ; Radical Photochemical Reactions ; Chemical Synthesis and Reactions
Authors: Yixin Song | Linhua Yuan | Houshan Mei
Source: Physica Scripta (article, 2024-08-15)
Field: Materials Science — Polymers and Plastics

Machine learning aids in detrapping of V<sub>2</sub>O<sub>5</sub> electrochromic materials from ion-traps Abstract The rapid decay of electrochromic performance of V 2 O 5 limits its widespread application, which has been proven to be attributed to the presence of ion traps. detrapping operation is an effective strategy to overcome ion traps and restore the electrochromic performance of V 2 O 5 . This process frees the ions embedded in V 2 O 5 from shallow defects, but the effect and mechanism of action are still unclear. Therefore, this study attempts to fit this issue using data-driven machine learning (ML) methods, predicting the varying performance regeneration levels of V 2 O 5 electrochromic materials due to different electrical parameters. Six different machine learning methods were employed in the experiment, and the models were trained using five-fold cross-validation. The results showed that the Random Forest (RF) method had the highest coefficient of determination score (R 2 = 0.9) and lower root mean square error (MSE = 0.0054) for predicting material performance recovery, indicating its effectiveness in predicting the degree of material performance recovery. Furthermore, data-driven sensitivity analysis indicates that the extracted charge amount during constant detrapping procedure is a crucial factor determining the restoration effect. These results can serve as a reference for research in the field of electrochromism. Electrochromism ; Ion ; Materials science ; Sensitivity (control systems) ; Electric field ; Mean squared error ; Computer science ; Analytical Chemistry (journal) ; Statistics ; Physics ; Mathematics ; Electronic engineering ; Chemistry ; Electrode ; Physical chemistry Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; Water Quality Monitoring and Analysis