jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yanbing Zhu | Evan R. Antoniuk | Dylan Wright | Fariborz Kargar | Nicholas Sesing | Austin D. Sendek | Tina T. Salguero | Ludwig Bartels | Alexander A. Balandin | Evan J. Reed | Felipe H. da Jornada
Source: The Journal of Physical Chemistry C (article, 2023-10-27)
Field: Materials Science — Materials Chemistry

Machine-Learning-Driven Expansion of the 1D van der Waals Materials Space One-dimensional (1D) van der Waals (vdW) materials display electronic and magnetic transport properties that make them uniquely suited as interconnect materials and for low-dimensional optoelectronic applications. However, there are only around 700 1D vdW structures in general materials databases, making database curation approaches ineffective for 1D discovery. Here, we utilize machine-learning techniques to discover 1D vdW compositions that have not yet been synthesized. Our techniques go beyond discovery efforts involving elemental substitutions and instead start with a composition space of 4741 binary and 392,342 ternary formulas. We predict up to 3000 binary and 10,000 ternary 1D compounds and further classify them by expected magnetic and electronic properties. Our model identifies MoI3, a material we experimentally confirm to exist with wire-like subcomponents and exotic magnetic properties. More broadly, we find several chalcogen-, halogen-, and pnictogen-containing compounds expected to be synthesizable using chemical vapor deposition and chemical vapor transport. Pnictogen ; van der Waals force ; Ternary operation ; Chalcogen ; Space (punctuation) ; Binary number ; Chemical space ; Materials science ; Chemical physics ; Nanotechnology ; Computer science ; Chemistry ; Condensed matter physics ; Physics ; Crystallography ; Superconductivity ; Molecule ; Quantum mechanics ; Drug discovery ; Mathematics Machine Learning in Materials Science ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Minh Tuan Dau | Mohamed Al Khalfioui | Adrien Michon | Antoine Reserbat‐Plantey | S. Vézian | P. Boucaud
Source: Scientific Reports (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

Descriptor engineering in machine learning regression of electronic structure properties for 2D materials We build new material descriptors to predict the band gap and the work function of 2D materials by tree-based machine-learning models. The descriptor's construction is based on vectorizing property matrices and on empirical property function, leading to mixing features that require low-resource computations. Combined with database-based features, the mixing features significantly improve the training and prediction of the models. We find R[Formula: see text] greater than 0.9 and mean absolute errors (MAE) smaller than 0.23 eV both for the training and prediction. The highest R[Formula: see text] of 0.95, 0.98 and the smallest MAE of 0.16 eV and 0.10 eV were obtained by using extreme gradient boosting for the bandgap and work-function predictions, respectively. These metrics were greatly improved as compared to those of database features-based predictions. We also find that the hybrid features slightly reduce the overfitting despite a small scale of the dataset. The relevance of the descriptor-based method was assessed by predicting and comparing the electronic properties of several 2D materials belonging to new classes (oxides, nitrides, carbides) with those of conventional computations. Our work provides a guideline to efficiently engineer descriptors by using vectorized property matrices and hybrid features for predicting 2D materials properties via ensemble models. Computer science ; Regression ; Machine learning ; Regression analysis ; Artificial intelligence ; Statistics ; Mathematics Machine Learning in Materials Science ; Graphene research and applications ; 2D Materials and Applications
Authors: Sampreeti Bhattacharya | Arkaprava Roy
Source: Computational Materials Science (article, 2023-10-26)
Field: Materials Science — Materials Chemistry

Linking stability with molecular geometries of perovskites and lanthanide richness using machine learning methods Principal component analysis ; Photovoltaics ; Robustness (evolution) ; Computer science ; Nonparametric statistics ; Perovskite (structure) ; Lanthanide ; Oxide ; Materials science ; Support vector machine ; Machine learning ; Chemistry ; Photovoltaic system ; Artificial intelligence ; Chemical engineering ; Mathematics ; Econometrics ; Engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Amaury Coste | Ema Slejko | Julija Zavadlav | Matej Praprotnik
Source: Journal of Chemical Theory and Computation (article, 2023-12-20)
Field: Materials Science — Materials Chemistry

Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media Molecular dynamics (MD) simulations of biophysical systems require accurate modeling of their native environment, i.e., aqueous ionic solution, as it critically impacts the structure and function of biomolecules. On the other hand, the models should be computationally efficient to enable simulations of large spatiotemporal scales. Here, we present the deep implicit solvation model for sodium chloride solutions that satisfies both requirements. Owing to the use of the neural network potential, the model can capture the many-body potential of mean force, while the implicit water treatment renders the model inexpensive. We demonstrate our approach first for pure ionic solutions with concentrations ranging from physiological to 2 M. We then extend the model to capture the effective ion interactions in the vicinity and far away from a DNA molecule. In both cases, the structural properties are in good agreement with all-atom MD, showcasing a general methodology for the efficient and accurate modeling of ionic media. Solvation ; Ionic bonding ; Molecular dynamics ; Computer science ; Water model ; Chemical physics ; Aqueous solution ; Ion ; Implicit solvation ; Molecule ; Statistical physics ; Biological system ; Chemistry ; Computational chemistry ; Physics ; Physical chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Xiaoyu Liu | Dali Ji | Xiaoheng Jin | Vanesa Quintano | Rakesh Joshi
Source: Carbon (article, 2023-07-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning assisted chemical characterization to investigate the temperature-dependent supercapacitance using Co-rGO electrodes X-ray photoelectron spectroscopy ; Graphene ; Materials science ; Cyclic voltammetry ; Supercapacitor ; Capacitance ; Oxide ; Electrode ; Analytical Chemistry (journal) ; Computer science ; Nanotechnology ; Chemical engineering ; Electrochemistry ; Chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advancements in Battery Materials
Authors: Sibei Guo | Jun Jiang | Hao Ren | Song Wang
Source: The Journal of Physical Chemistry Letters (article, 2023-08-14)
Field: Chemistry — Spectroscopy

Fusion of Multiple Spectra for Investigating Chemical Bonding Properties via Machine Learning Chemical bonding properties are crucial to understanding the chemical behavior of molecules. Spectroscopy is a versatile technical tool to study various microscopic properties, but its interpretation suffers from human biases and the loss of high-dimensional information. Here, we present a machine learning approach to predict diverse bonding properties, including the bond dissociation energy, bond length, and α-C connectivity of hydroxyls in organic molecules, by fusing multiple spectra with different physical mechanisms. Combining nuclear magnetic resonance and vibrational spectroscopy exhibits higher prediction accuracy than what they did separately. On the hold-out test data set, the models achieve a mean absolute error of 1.243 kcal/mol and 1.041 × 10<sup>-4</sup> Å for BDE and bond length and an accuracy of 95.09% for hydroxyl α-C connectivity. Our models demonstrate strong extrapolation capabilities when they are transferred to different molecules, external electric fields, and solvation environments. These end-to-end models pave the way to investigating chemical bonding properties by using spectroscopic observables. Extrapolation ; Solvation ; Chemical bond ; Molecule ; Dissociation (chemistry) ; Spectroscopy ; Infrared spectroscopy ; Bond-dissociation energy ; Spectral line ; Chemical physics ; Observable ; Chemistry ; Computational chemistry ; Materials science ; Computer science ; Physical chemistry ; Physics ; Organic chemistry ; Mathematics Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Shinji Wakimoto | Yoshiya Matsukawa | Yui Numazawa | Yohsuke Matsushita | Hideyuki Aoki
Source: Fuel (article, 2023-02-26)
Field: Materials Science — Materials Chemistry

Neural network estimation of kinetic parameters in distributed activation energy model (DAEM) without a priori assumptions for parallel reaction system Kinetic energy ; Artificial neural network ; Gaussian ; Limit (mathematics) ; A priori and a posteriori ; Feedforward neural network ; Biological system ; Computation ; Computer science ; Activation energy ; Applied mathematics ; Thermodynamics ; Algorithm ; Mathematics ; Chemistry ; Physics ; Physical chemistry ; Mathematical analysis ; Computational chemistry ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Xiaohan Yu | Dai Tang | Jia Yuan Chng | David S. Sholl
Source: The Journal of Physical Chemistry C (article, 2023-09-14)
Field: Chemistry — Inorganic Chemistry

Efficient Exploration of Adsorption Space for Separations in Metal–Organic Frameworks Combining the Use of Molecular Simulations, Machine Learning, and Ideal Adsorbed Solution Theory Adsorption-based separations using metal-organic frameworks (MOFs) are promising candidates for replacing common energy-intensive separation processes. The so-called adsorption space formed by the combination of billions of possible molecules and thousands of reported MOFs is vast. It is very challenging to comprehensively evaluate the performance of MOFs for chemical separation through experiments. Molecular simulations and machine learning (ML) have been widely applied to make predictions for adsorption-based separations. Previous ML approaches to these issues were typically limited to smaller molecules and often had poor accuracy in the dilute limit. To enable exploration of a wider adsorption space, we carefully selected a diverse set of 45 molecules and 335 MOFs and generated single-component isotherms of 15,075 MOF-molecule pairs by grand canonical Monte Carlo. Using this database, we successfully developed accurate (<i>r</i><sup>2</sup> > 0.9) machine learning models predicting adsorption isotherms of diverse molecules in large libraries of MOFs. With this approach, we can efficiently make predictions of large collections of MOFs for arbitrary mixture separations. By combining molecular simulation data and ML predictions with Ideal Adsorbed Solution Theory, we tested the ability of these approaches to make predictions of adsorption selectivity and loading for challenging near-azeotropic mixtures. Adsorption ; Chemical space ; Molecule ; Metal-organic framework ; Ideal (ethics) ; Space (punctuation) ; Component (thermodynamics) ; Monte Carlo method ; Materials science ; Computer science ; Chemistry ; Thermodynamics ; Physics ; Physical chemistry ; Organic chemistry ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Qi Kong | Yasushi Shibuta
Source: MATERIALS TRANSACTIONS (article, 2023-03-30)
Field: Materials Science — Materials Chemistry

High-Precision Prediction of Thermal Conductivity of Metals by Molecular Dynamics Simulation in Combination with Machine Learning Approach Molecular dynamics (MD) simulation is a powerful tool to estimate materials properties from atomistic viewpoint. However, the scope of application of MD simulations is limited to problems where the Newton’s equation of motion for atoms is dominant. Therefore, it is inherently insufficient to estimate thermal conductivity of metallic materials, which consists of phononic and electronic components. In this study, machine learning (ML)-based regression model is employed to predict thermal conductivity of metals with high accuracy using deficient results from MD simulations. A regression analysis with the least absolute shrinkage and selection operator (Lasso) including electrical conductivity as predictor variables successfully predict the thermal conductivity of metals with negative temperature dependence, which indicates a significant contribution of electrons to thermal conduction in metals. It should be stressed that our prediction is better than the conventional estimation from the Wiedemann–Franz law. This study shows us new possibilities of new ML approach for improving the accuracy of physical properties obtained from MD simulations. Thermal conductivity ; Molecular dynamics ; Materials science ; Thermal conduction ; Lasso (programming language) ; Electron ; Thermal ; Statistical physics ; Shrinkage ; Conductivity ; Thermodynamics ; Machine learning ; Computer science ; Physics ; Computational chemistry ; Physical chemistry ; Composite material ; Chemistry Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Hasan Can Gülbalkan | Alper Uzun | Seda Keskın
Source: ACS Applied Materials & Interfaces (review, 2023-12-11)
Field: Chemistry — Inorganic Chemistry

Evaluating CH<sub>4</sub>/N<sub>2</sub> Separation Performances of Hundreds of Thousands of Real and Hypothetical MOFs by Harnessing Molecular Modeling and Machine Learning Considering the large abundance and diversity of metal-organic frameworks (MOFs), evaluating the gas adsorption and separation performance of the entire MOF material space using solely experimental techniques or brute-force computer simulations is impractical. In this study, we integrated high-throughput molecular simulations with machine learning (ML) to explore the potential of both synthesized, the real MOFs, and computer-generated, the hypothetical MOFs (hypoMOFs), for adsorption-based CH<sub>4</sub>/N<sub>2</sub> separation. CH<sub>4</sub>/N<sub>2</sub> mixture adsorption data obtained from molecular simulations were used to train the ML models that could accurately predict gas uptakes of 4612 real MOFs. These models were then transferred to two distinct databases consisting of 98 601 hypoMOFs and 587 anion-pillared hypoMOFs to examine their CH<sub>4</sub>/N<sub>2</sub> mixture separation performances using various adsorbent evaluation metrics. The top adsorbents were identified for vacuum swing adsorption (VSA) and pressure swing adsorption (PSA) conditions and examined in detail to gain molecular insights into their structural and chemical properties. Results revealed that the hypoMOFs offered high CH<sub>4</sub> selectivities, up to 14.8 and 13.6, and high working capacities, up to 3.1 and 5.8 mol/kg, at VSA and PSA conditions, respectively, and many of the hypoMOFs could outperform the real MOFs. Our approach offers a rapid and accurate assessment of the mixture adsorption and separation properties of MOFs without the need for computationally demanding simulations. Our results for the best adsorbents will be useful in accelerating the experimental efforts for the design of novel MOFs that can achieve high-performance CH<sub>4</sub>/N<sub>2</sub> separation. Adsorption ; Pressure swing adsorption ; Materials science ; Metal-organic framework ; Separation (statistics) ; Computer science ; Chemical space ; Gas separation ; Process engineering ; Nanotechnology ; Machine learning ; Chemistry ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Membrane Separation and Gas Transport
Authors: Qidong Lin | Bin Jiang
Source: The Journal of Physical Chemistry Letters (article, 2023-08-15)
Field: Materials Science — Materials Chemistry

Modeling Equilibration Dynamics of Hyperthermal Products of Surface Reactions Using Scalable Neural Network Potential with First-Principles Accuracy Equilibration dynamics of hot oxygen atoms following the dissociation of O<sub>2</sub> on Pd(100) and Pd(111) surfaces are investigated by molecular dynamics simulations based on a scalable neural network potential enabling first-principles description of the interaction of O<sub>2</sub> and O interacting with variable Pd supercells. By analyzing hundreds of trajectories with appropriate initial sampling, the measured distance distribution of equilibrated atom pairs on Pd(111) is well reproduced. However, our results on Pd(100) suggest that the ballistic motion of hot atoms predicted previously is a rare event under ideal conditions, while initial molecular orientation and surface thermal fluctuation could significantly affect the overall postdissociation dynamics. On both surfaces, dissociated hyperthermal oxygen atoms primarily locate their nascent positions and experience a similar random walk motion nearby. Molecular dynamics ; Dissociation (chemistry) ; Chemistry ; Chemical physics ; Oxygen atom ; Atom (system on chip) ; Thermal ; Dynamics (music) ; Surface (topology) ; Statistical physics ; Molecular physics ; Physics ; Molecule ; Physical chemistry ; Computational chemistry ; Thermodynamics ; Computer science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalytic Processes in Materials Science
Authors: Yu Hong-Lei | Wang De-xi | Gong Chen | Yunlong Li | Xueyi Ma
Source: Journal of Molecular Liquids (article, 2023-12-19)
Field: Materials Science — Materials Chemistry

Insights into the diffusion coefficient and adsorption energy of NH3 in MgCl2 from molecular simulation, experiments, and machine learning Adsorption ; Diffusion ; Energy (signal processing) ; Materials science ; Effective diffusion coefficient ; Chemical physics ; Thermodynamics ; Chemistry ; Physical chemistry ; Physics Luminescence Properties of Advanced Materials ; Ammonia Synthesis and Nitrogen Reduction ; Hydrogen Storage and Materials
Authors: Jaehyun Kim | Ho Won Jang
Source: Korean Journal of Materials Research (article, 2023-05-27)
Field: Materials Science — Materials Chemistry

Can Artificial Intelligence Boost Developing Electrocatalysts for Efficient Water Splitting to Produce Green Hydrogen? Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen. Fossil fuel ; Renewable energy ; Electrolysis of water ; Biochemical engineering ; Catalysis ; Hydrogen production ; Process engineering ; Oxygen evolution ; Hydrogen fuel ; Hydrogen ; Computer science ; Nanotechnology ; Environmental science ; Electrolysis ; Materials science ; Chemistry ; Waste management ; Engineering ; Electrochemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Shaoxuan Yuan | Zhiwen Zhu | Jiayi Lu | Fengru Zheng | Hao Jiang | Qiang Sun
Source: Molecules (article, 2023-07-13)
Field: Materials Science — Materials Chemistry

Applying a Deep-Learning-Based Keypoint Detection in Analyzing Surface Nanostructures Scanning tunneling microscopy (STM) imaging has been routinely applied in studying surface nanostructures owing to its capability of acquiring high-resolution molecule-level images of surface nanostructures. However, the image analysis still heavily relies on manual analysis, which is often laborious and lacks uniform criteria. Recently, machine learning has emerged as a powerful tool in material science research for the automatic analysis and processing of image data. In this paper, we propose a method for analyzing molecular STM images using computer vision techniques. We develop a lightweight deep learning framework based on the YOLO algorithm by labeling molecules with its keypoints. Our framework achieves high efficiency while maintaining accuracy, enabling the recognitions of molecules and further statistical analysis. In addition, the usefulness of this model is exemplified by exploring the length of polyphenylene chains fabricated from on-surface synthesis. We foresee that computer vision methods will be frequently used in analyzing image data in the field of surface chemistry. Computer science ; Nanostructure ; Deep learning ; Surface (topology) ; Artificial intelligence ; Field (mathematics) ; Scanning tunneling microscope ; Nanotechnology ; Image (mathematics) ; High resolution ; Image processing ; Computer vision ; Materials science ; Remote sensing ; Mathematics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques