jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anirban Mondal | Dina Kussainova | Shuwen Yue | Athanassios Z. Panagiotopoulos
Source: Journal of Chemical Theory and Computation (article, 2022-10-14)
Field: Materials Science — Materials Chemistry

Modeling Chemical Reactions in Alkali Carbonate–Hydroxide Electrolytes with Deep Learning Potentials We developed a deep potential machine learning model for simulations of chemical reactions in molten alkali carbonate-hydroxide electrolyte containing dissolved CO<sub>2</sub>, using an active learning procedure. We tested the deep neural network (DNN) potential and training procedure against reaction kinetics, chemical composition, and diffusion coefficients obtained from density functional theory (DFT) molecular dynamics calculations. The DNN potential was found to match DFT results for the structural, transport, and short-time chemical reactions in the melt. Using the DNN potential, we extended the time scales of observation to 2 ns in systems containing thousands of atoms, while preserving quantum chemical accuracy. This allowed us to reach chemical equilibrium with respect to several chemical species in the melt. The approach can be generalized for a broad spectrum of chemically reactive systems. Electrolyte ; Hydroxide ; Chemical reaction ; Carbonate ; Diffusion ; Alkali metal ; Density functional theory ; Chemical species ; Chemistry ; Molecular dynamics ; Chemical equilibrium ; Computational chemistry ; Materials science ; Chemical physics ; Thermodynamics ; Physical chemistry ; Inorganic chemistry ; Organic chemistry ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Process Optimization and Integration
Authors: Bin Ma | Lisheng Zhang | Wentao Wang | Hanqing Yu | Xianbin Yang | Siyan Chen | Huizhi Wang | Xinhua Liu
Source: Green Energy & Environment (article, 2022-10-13)
Field: Materials Science — Materials Chemistry

Application of deep learning for informatics aided design of electrode materials in metal-ion batteries To develop emerging electrode materials and improve the performances of batteries, the machine learning techniques can provide insights to discover, design and develop battery new materials in high-throughput way. In this paper, two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage, specific capacity and specific energy. The deep learning models are trained with the multilayer perceptron as the core. The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models. Based on 10 types of ion batteries, the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V, respectively. The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms. Besides, the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries. This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design. Battery (electricity) ; Computer science ; Throughput ; Deep learning ; Artificial intelligence ; Bayesian optimization ; Multilayer perceptron ; Artificial neural network ; Machine learning ; Voltage ; Materials science ; Electrical engineering ; Engineering ; Wireless Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Zhilong Liang | Zhiwei Li | Shuo Zhou | Yiwen Sun | Jinying Yuan | Changshui Zhang
Source: Cell Reports Physical Science (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

Machine-learning exploration of polymer compatibility Prediction of material property is a key problem because of its significance to material design and screening. Here, we present a general machine-learning method for polymer compatibility. Specifically, we mine data from related literature to build a specific database and give a prediction based on the basic molecular structures of blending polymers and, as auxiliary, the blending composition. Our model obtains at least 75% accuracy on the dataset consisting of thousands of entries. We demonstrate that the relationship between structure and properties can be learned and simulated by a machine-learning method. Compatibility (geochemistry) ; Computer science ; Polymer ; Artificial intelligence ; Machine learning ; Materials science ; Engineering ; Chemical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Xiaowei Zang | Xiang Zhou | Haitao Bian | Weiping Jin | Xuhai Pan | Juncheng Jiang | M. Yu. Koroleva | Ruiqi Shen
Source: Molecules (review, 2022-12-31)
Field: Materials Science — Materials Chemistry

Prediction and Construction of Energetic Materials Based on Machine Learning Methods Energetic materials (EMs) are the core materials of weapons and equipment. Achieving precise molecular design and efficient green synthesis of EMs has long been one of the primary concerns of researchers around the world. Traditionally, advanced materials were discovered through a trial-and-error processes, which required long research and development (R&D) cycles and high costs. In recent years, the machine learning (ML) method has matured into a tool that compliments and aids experimental studies for predicting and designing advanced EMs. This paper reviews the critical process of ML methods to discover and predict EMs, including data preparation, feature extraction, model construction, and model performance evaluation. The main ideas and basic steps of applying ML methods are analyzed and outlined. The state-of-the-art research about ML applications in property prediction and inverse material design of EMs is further summarized. Finally, the existing challenges and the strategies for coping with challenges in the further applications of the ML methods are proposed. Machine learning ; Computer science ; Artificial intelligence ; Engineering Machine Learning in Materials Science
Authors: Zhuoran Zhang | Zeren Jiao | Ruiqing Shen | Pingan Song | Qingsheng Wang
Source: ACS Applied Engineering Materials (article, 2022-11-07)
Field: Materials Science — Polymers and Plastics

Accelerated Design of Flame Retardant Polymeric Nanocomposites via Machine Learning Prediction Improving the flame retardancy of polymeric materials used in engineering applications is an increasingly important strategy for limiting fire hazards. However, the wide variety of flame retardant polymeric nanocomposite compositions prevents quick identification of the optimal design for a specific application. In this study, we built a flame retardancy database of more than 800 polymeric nanocomposites, including information from polymer flammability, thermal stability, and nanofiller properties. Then, we applied five machine learning algorithms to predict the flame retardancy index for different types of flame retardant polymeric nanocomposites. Among them, extreme gradient boosting regression gives the best prediction with a coefficient of determination (R2) of 0.94 and a root-mean-square error of 0.17. In addition, we studied how the physical features of polymeric nanocomposites affected flame retardancy using the correlation matrix and feature importance plot, which in turn was used to guide the design of polymeric nanocomposites for flame retardant applications. Following the guidelines, a high-performance flame retardant polymeric nanocomposite was designed and synthesized, and the experimental FRI result was compared with the machine learning prediction (6% prediction error). This result demonstrated a fast identification of flame retardancy of polymeric nanocomposite without large-scale fire tests, which could accelerate the design of functional polymeric nanocomposites in the flame retardant field. Fire retardant ; Nanocomposite ; Flammability ; Materials science ; Limiting oxygen index ; Composite material ; Thermal stability ; Fire performance ; Polymer ; Combustion ; Chemical engineering ; Organic chemistry ; Chemistry ; Engineering Flame retardant materials and properties ; Fire dynamics and safety research ; Polymer Nanocomposites and Properties
Authors: Hao Wang | Mingqi Chen | Yimin Sun | Lian Xu | Fei Li | Jinsong Han
Source: Analytical Chemistry (article, 2022-01-27)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Pattern Recognition of Amyloid Beta Aggregates with Fluorescent Conjugated Polymers and Graphite Oxide Electrostatic Complexes Five fluorescent positively charged poly(<i>para</i>-aryleneethynylene) (<b>P1</b>-<b>P5</b>) were designed to construct electrostatic complexes <b>C1</b>-<b>C5</b> with negatively charged graphene oxide (<b>GO</b>). The fluorescence of conjugated polymers was quenched by the quencher <b>GO</b>. Three electrostatic complexes were enough to distinguish between 12 proteins with 100% accuracy. Furthermore, using these sensor arrays, we could identify the levels of <i>A</i>β40 and Aβ42 aggregates (monomers, oligomers, and fibrils) <i>via</i> employing machine learning algorithms, making it an attractive strategy for early diagnosis of Alzheimer's disease. Chemistry ; Conjugated system ; Fluorescence ; Graphene ; Polymer ; Oxide ; Monomer ; Graphite oxide ; Amyloid beta ; Electrostatics ; Graphite ; Nanotechnology ; Photochemistry ; Combinatorial chemistry ; Organic chemistry ; Peptide ; Biochemistry ; Physical chemistry Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Conducting polymers and applications
Authors: Yong‐Bin Zhuang | Ruihao Bi | Jun Cheng
Source: The Journal of Chemical Physics (article, 2022-10-07)
Field: Materials Science — Materials Chemistry

Resolving the odd–even oscillation of water dissociation at rutile TiO2(110)–water interface by machine learning accelerated molecular dynamics Aqueous rutile TiO2(110) is the most widely studied water–oxide interface, and yet questions about water dissociation are still controversial. Theoretical studies have systematically investigated the influence of the slab thickness on water dissociation energy (Ediss) at 1 monolayer coverage using static density functional theory calculation and found that Ediss exhibits odd–even oscillation with respect to the TiO2 slab thickness. However, less studies have accounted for the full solvation of an aqueous phase using ab initio molecular dynamics due to high computational costs in which only three, four, and five trilayer models of rutile(110)–water interfaces have been simulated. Here, we report Machine Learning accelerated Molecular Dynamics (MLMD) simulations of defect-free rutile(110)–water interfaces, which allows for a systematic study of the slab thickness ranging from 3 to 17 trilayers with much lower costs while keeping ab initio accuracy. Our MLMD simulations show that the dissociation degree of surface water (α) oscillates with the slab thickness and converges to ∼2% as the TiO2 slab becomes thicker. Converting α into dissociation free energy (ΔAdiss) and comparing with dissociation total energy Ediss calculated with a single monolayer of water, we find that the full solvation of the interfaces suppresses surface water from dissociating. It is interesting to note that the machine learning potential trained from the dataset containing exclusively the five trilayer TiO2 model exhibits excellent transferability to other slab thicknesses and further captures the oscillating behavior of surface water dissociation. Detailed analyses indicate that the central plane in odd trilayer slabs modulates the interaction between double trilayers and, thus, the bonding strength between terminal Ti and water, which affects pKa of surface water and water dissociation degree. Rutile ; Dissociation (chemistry) ; Oscillation (cell signaling) ; Molecular dynamics ; Interface (matter) ; Chemical physics ; Materials science ; Computer science ; Chemistry ; Chemical engineering ; Physical chemistry ; Computational chemistry ; Engineering ; Aqueous solution Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Spectroscopy and Quantum Chemical Studies
Authors: Reshma Beeram | Dipanjan Banerjee | Linga Murthy Narlagiri | S. Venugopal Rao
Source: Analytical Methods (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning for rapid quantification of trace analyte molecules using SERS and flexible plasmonic paper substrates Given the intrinsic nature of low reproducibility and signal blinking in the surface enhanced Raman scattering (SERS) technique, especially while detecting trace/ultra-trace amounts, it remains a major challenge to quantify the analyte under study. Here we present a simple and economically viable, flexible hydrophobic plasmonic filter paper-based SERS substrate for the quantification of two trace analytes [crystal violet (CV) and picric acid (PA)] using machine learning techniques and SERS data. The wettability of the substrate was modified with an easy and low-cost technique of coating it with silicone oil. Gold nanoparticles were synthesized using a femtosecond laser ablation in water technique. The prepared nanoparticles were characterized using UV, TEM, and SEM techniques and subsequently loaded onto filter papers before using them for SERS studies. We have considered the SERS intensities of the analytes at different concentrations with over 900 spectra to train the model. Principal component analysis (PCA) was used to reduce the dimensionality and, hence, the complexity of the model. Furthermore, support vector regression was used to quantify the analyte molecules and we achieved an <i>R</i><sup>2</sup> error of 0.9629 for CV and 0.9472 for PA. In conjunction with a portable Raman spectrometer and a computation time of less than <10 s, we believe that this is an affordable and rapid method for quantification of analytes using the SERS technique. Analyte ; Plasmon ; TRACE (psycholinguistics) ; Substrate (aquarium) ; Molecule ; Nanotechnology ; Chemistry ; Materials science ; Computer science ; Chromatography ; Optoelectronics ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Pin Chen | Rui Jiao | Jinyu Liu | Yang Liu | Yutong Lu
Source: Journal of Chemical Information and Modeling (article, 2022-11-01)
Field: Chemistry — Inorganic Chemistry

Interpretable Graph Transformer Network for Predicting Adsorption Isotherms of Metal–Organic Frameworks Predicting interactions between metal-organic frameworks (MOFs) and their adsorbates based on structures is critical to design high-performance porous materials. Many gas uptake prediction models have been proposed, but adsorption isotherm prediction is still challenging for most existing models. Here, we report a deep learning approach (MOFNet) that can predict adsorption isotherms for MOFs based on hierarchical representation and pressure adaptive mechanism. We elaborately design a hierarchical representation to encode the MOF structures. We adopt a graph transformer network to capture atomic-level information, which can help learn chemical features required under low-pressure conditions. A pressure adaptive mechanism is employed to interpolate and extrapolate the given limited data points by transfer learning, which can predict adsorption isotherms on a wider pressure range by only one model. We demonstrate that our predictor outperformed other traditional machine learning as well as graph neural network models on the challenging benchmarks and also achieves high performance on the real-world experimental observed adsorption isotherms. Finally, we interpret the models to discover and present potential structure-property relationships using the self-attention mechanism in the network. The proof-of-concept applications, such as disordered MOF predictions and missing data imputation of gas adsorption isotherms, showcase the generality and usability of our model to improve MOF material design. Computer science ; Adsorption ; Generality ; Metal-organic framework ; Artificial neural network ; Artificial intelligence ; Graph ; Machine learning ; Theoretical computer science ; Chemistry ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Shuo Liu | Xiuguo Chen | Tianjuan Yang | Chunfu Guo | Jiahao Zhang | Jianyuan Ma | Chao Chen | Cai Wang | Chuanwei Zhang | Shiyuan Liu
Source: Measurement (article, 2022-02-01)
Field: Materials Science — Surfaces, Coatings and Films

Machine learning aided solution to the inverse problem in optical scatterometry Robustness (evolution) ; Dimensionality reduction ; Solver ; Computer science ; Artificial neural network ; Inverse problem ; Curse of dimensionality ; Semiconductor device fabrication ; Artificial intelligence ; Machine learning ; Engineering ; Mathematics Optical Coatings and Gratings ; Near-Field Optical Microscopy ; Surface Roughness and Optical Measurements
Authors: Prottay Malakar | Md Shajedul Hoque Thakur | Shahriar Muhammad Nahid | Md Mahbubul Islam
Source: ACS Applied Nano Materials (article, 2022-10-26)
Field: Materials Science — Materials Chemistry

Data-Driven Machine Learning to Predict Mechanical Properties of Monolayer Transition-Metal Dichalcogenides for Applications in Flexible Electronics Understanding of the material properties of layered transition-metal dichalcogenides (TMDs) is critical for their applications in flexible electronics. Data-driven machine learning (ML)-based approaches are being developed in contrast to the traditional experimental or computational methods to predict and understand material properties under varied operating conditions. In this study, we used two ML algorithms, namely, long short-term memory (LSTM) and feed forward neural network (FFNN), combined with molecular dynamics (MD) simulations to predict the mechanical properties of MX2 (M = Mo, W and X = S, Se) TMDs. The LSTM model is found to be capable of predicting the entire stress–strain response, whereas the FFNN is used to predict material properties such as fracture stress, fracture strain, and Young’s modulus. The effects of operating temperature, chiral orientation, and pre-existing crack size on the mechanical properties are thoroughly investigated. We carried out 1440 MD simulations to produce the input dataset for the neural network models. Our results indicate that both LSTM and FFNN are capable of predicting the mechanical response of monolayer TMDs under different conditions with more than 95% accuracy. The FFNN model exhibits lower computational cost than LSTM; however, the capability of the LSTM model to predict the entire stress–strain curve is advantageous for assessing material properties. The study paves the pathway toward extending this approach to predict other important properties, such as optical, electrical, and magnetic properties of TMDs. Monolayer ; Electronics ; Transition metal ; Materials science ; Nanotechnology ; Flexible electronics ; Engineering physics ; Engineering ; Electrical engineering ; Chemistry ; Catalysis 2D Materials and Applications ; MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Wei Li | Zigeng Wang | Xia Xiao | Zhiqiang Zhang | Anderson Janotti | Sanguthevar Rajasekaran | Bharat Medasani
Source: Physical review. B./Physical review. B (article, 2022-10-28)
Field: Materials Science — Materials Chemistry

Predicting band gaps and band-edge positions of oxide perovskites using density functional theory and machine learning Density functional theory (DFT) within the local or semilocal density approximations, i.e., the local density approximation (LDA) or generalized gradient approximation (GGA), has become a workhorse in the electronic structure theory of solids, being extremely fast and reliable for energetics and structural properties, yet remaining highly inaccurate for predicting band gaps of semiconductors and insulators. The accurate prediction of band gaps using first-principles methods is time consuming, requiring hybrid functionals, quasiparticle GW, or quantum Monte Carlo methods. Efficiently correcting DFT-LDA/GGA band gaps and unveiling the main chemical and structural factors involved in this correction is desirable for discovering novel materials in high-throughput calculations. In this direction, we use DFT and machine learning techniques to correct band gaps and band-edge positions of a representative subset of $AB{\mathrm{O}}_{3}$ perovskite oxides. Relying on the results of HSE06 hybrid functional calculations as target values of band gaps, we find a systematic band-gap correction of $\ensuremath{\sim}1.5$ eV for this class of materials, where $\ensuremath{\sim}1$ eV comes from downward shifting the valence band and $\ensuremath{\sim}0.5$ eV from uplifting the conduction band. The main chemical and structural factors determining the band-gap correction are determined through a feature selection procedure. Density functional theory ; Band gap ; Hybrid functional ; Local-density approximation ; Electronic band structure ; Semimetal ; Quasiparticle ; Electronic structure ; Quasi Fermi level ; Condensed matter physics ; Physics ; Materials science ; Statistical physics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Ruihao Yuan | Deqing Xue | Yangyang Xu | Dezhen Xue | Jinshan Li
Source: Journal of Alloys and Compounds (article, 2022-03-12)
Field: Materials Science — Materials Chemistry

Machine learning combined with feature engineering to search for BaTiO3 based ceramics with large piezoelectric constant Dielectric ; Feature (linguistics) ; Piezoelectricity ; Computer science ; Permittivity ; Constant (computer programming) ; Limit (mathematics) ; Algorithm ; Feature vector ; Set (abstract data type) ; Artificial intelligence ; Materials science ; Mathematics ; Mathematical analysis ; Composite material ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices