jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fulvio Perrella | Federico Coppola | Nadia Rega | Alessio Petrone
Source: Molecules (article, 2023-04-12)
Field: Materials Science — Materials Chemistry

An Expedited Route to Optical and Electronic Properties at Finite Temperature via Unsupervised Learning Electronic properties and absorption spectra are the grounds to investigate molecular electronic states and their interactions with the environment. Modeling and computations are required for the molecular understanding and design strategies of photo-active materials and sensors. However, the interpretation of such properties demands expensive computations and dealing with the interplay of electronic excited states with the conformational freedom of the chromophores in complex matrices (i.e., solvents, biomolecules, crystals) at finite temperature. Computational protocols combining time dependent density functional theory and ab initio molecular dynamics (MD) have become very powerful in this field, although they require still a large number of computations for a detailed reproduction of electronic properties, such as band shapes. Besides the ongoing research in more traditional computational chemistry fields, data analysis and machine learning methods have been increasingly employed as complementary approaches for efficient data exploration, prediction and model development, starting from the data resulting from MD simulations and electronic structure calculations. In this work, dataset reduction capabilities by unsupervised clustering techniques applied to MD trajectories are proposed and tested for the ab initio modeling of electronic absorption spectra of two challenging case studies: a non-covalent charge-transfer dimer and a ruthenium complex in solution at room temperature. The K-medoids clustering technique is applied and is proven to be able to reduce by ∼100 times the total cost of excited state calculations on an MD sampling with no loss in the accuracy and it also provides an easier understanding of the representative structures (medoids) to be analyzed on the molecular scale. Electronic structure ; Ab initio ; Computer science ; Cluster analysis ; Excited state ; Chemistry ; Computational chemistry ; Statistical physics ; Artificial intelligence ; Physics ; Atomic physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Roya Mohammadzadeh kakhki | Mojtaba Mohammadpoor
Source: Inorganic Chemistry Communications (article, 2023-12-11)
Field: Materials Science — Materials Chemistry

Machine learning-driven approaches for synthesizing carbon dots and their applications in photoelectrochemical sensors Nanotechnology ; Computer science ; Photocurrent ; Biocompatibility ; Materials science ; Machine learning ; Optoelectronics Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Quantum Dots Synthesis And Properties
Authors: Rajat Kushwaha | Mayank K. Singh | Sarathkumar Krishnan | K. Dhirendra
Source: Journal of Materials Science (article, 2023-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning enabled property prediction of carbon-based electrodes for supercapacitors Supercapacitor ; Random forest ; Artificial neural network ; Materials science ; Carbon fibers ; Capacitance ; Artificial intelligence ; Machine learning ; Computer science ; Electrode ; Composite material Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Jiale Shi | Fahed Albreiki | Yamil J. Colón | Samanvaya Srivastava | Jonathan K. Whitmer
Source: Journal of Chemical Theory and Computation (article, 2023-04-17)
Field: Materials Science — Materials Chemistry

Transfer Learning Facilitates the Prediction of Polymer–Surface Adhesion Strength Machine learning (ML) accelerates the exploration of material properties and their links to the structure of the underlying molecules. In previous work [Shi et al. <i>ACS Applied Materials & Interfaces</i> 2022, 14, 37161-37169.], ML models were applied to predict the adhesive free energy of polymer-surface interactions with high accuracy from the knowledge of the sequence data, demonstrating successes in inverse-design of polymer sequence for known surface compositions. While the method was shown to be successful in designing polymers for a known surface, extensive data sets were needed for each specific surface in order to train the surrogate models. Ideally, one should be able to infer information about similar surfaces without having to regenerate a full complement of adhesion data for each new case. In the current work, we demonstrate a transfer learning (TL) technique using a deep neural network to improve the accuracy of ML models trained on small data sets by pretraining on a larger database from a related system and fine-tuning the weights of all layers with a small amount of additional data. The shared knowledge from the pretrained model facilitates the prediction accuracy significantly on small data sets. We also explore the limits of database size on accuracy and the optimal tuning of network architecture and parameters for our learning tasks. While applied to a relatively simple coarse-grained (CG) polymer model, the general lessons of this study apply to detailed modeling studies and the broader problems of inverse materials design. Computer science ; Sequence (biology) ; Artificial neural network ; Transfer of learning ; Complement (music) ; Artificial intelligence ; Machine learning ; Inverse ; Surface (topology) ; Deep learning ; Data mining ; Biological system ; Chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Software Engineering Research
Authors: Min Li | Liya Fu | Liyan Deng | Yingming Hu | Yue Yuan | Changyong Wu
Source: Environmental Science and Ecotechnology (article, 2023-02-04)
Field: Materials Science — Materials Chemistry

A tailored and rapid approach for ozonation catalyst design Catalytic ozonation is widely employed in advanced wastewater treatment owing to its high mineralization of refractory organics. The key to high mineralization is the compatibility between catalyst formulation and wastewater quality. Machine learning can greatly improve experimental efficiency, while fluorescence data can provide additional wastewater quality information on the composition and concentration of organics, which is conducive to optimizing catalyst formulation. In this study, machine learning combined with fluorescence spectroscopy was applied to develop ozonation catalysts (Mn/γ-Al<sub>2</sub>O<sub>3</sub> catalyst was used as an example). Based on the data collected from 52 different catalysts, a machine-learning model was established to predict catalyst performance. The correlation coefficient between the experimental and model-predicted values was 0.9659, demonstrating the robustness and good generalization ability of the model. The range of the catalyst formulations was preliminarily screened by fluorescence spectroscopy. When the wastewater was dominated by tryptophan-like and soluble microbial products, the impregnation concentration and time of Mn(NO<sub>3</sub>)<sub>2</sub> were less than 0.3 mol L<sup>-1</sup> and 10 h, respectively. Furthermore, the optimized Mn/γ-Al<sub>2</sub>O<sub>3</sub> formulation obtained by the model was impregnation with 0.155 mol L<sup>-1</sup> Mn(NO<sub>3</sub>)<sub>2</sub> solution for 8.5 h and calcination at 600 °C for 3.5 h. The model-predicted and experimental values for total organic carbon removal were 54.48% and 53.96%, respectively. Finally, the improved catalytic performance was attributed to the synergistic effect of oxidation (•OH and <sup>1</sup>O<sub>2</sub>) and the Mn/γ-Al<sub>2</sub>O<sub>3</sub> catalyst. This study provides a rapid approach to catalyst design based on the characteristics of wastewater quality using machine learning combined with fluorescence spectroscopy. Catalysis ; Process engineering ; Environmental science ; Computer science ; Chemical engineering ; Chemistry ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Advanced oxidation water treatment ; Industrial Gas Emission Control
Authors: Aagam Shah | Joshua A. Schiller | Isiah Ramos | James Serrano | Darren Adams | Sameh Tawfick | Elif Ertekin
Source: Materials Today Communications (article, 2023-05-09)
Field: Materials Science — Surfaces, Coatings and Films

Automated image segmentation of scanning electron microscopy images of graphene using U-Net Neural Network Scanning electron microscopy (SEM) is one of the most common approaches to the characterization of synthesized 2D materials such as graphene. Images from SEM contain detailed information about crystalline properties, domain size, and nucleation density, but typically are analyzed through a laborious, serial process that relies on the trained eye of synthesis experts. In this work, we demonstrate an image segmentation neural network that automatically distinguishes between pixels in SEM images that correspond to regions where graphene is and is not present. We utilize the U-Net architecture to learn on a training data set of more than 90 pre-labeled images coupled with moderate image augmentation. Comparing the performance of models trained on smaller high fidelity data set to those trained on larger low fidelity data sets, we find that higher quality is more valuable than higher quantity for achieving good performance. When neural network hyperparameters such as batch size and learning rate are properly tuned, the learned model shows an accuracy for classification of over 90% and an F1 score over 80%. The neural network trained on SEM images of graphene shows reasonable performance when directly applied to other 2D materials, suggesting the possibility of use in transfer learning. Detailed analysis of the inner workings of the model reveals that the domain edges are most critical for making classifications when segmenting the image. We also show the use of a post processing technique to estimate the graphene domain size using segmented masks. This demonstration shows the potential for SEM image segmentation at scale using deep learning approaches and gives insights into best practices for improving model performance. Artificial neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Segmentation ; Pixel ; Scanning electron microscope ; Computer science ; Data set ; Image segmentation ; Image (mathematics) ; Graphene ; Image processing ; Computer vision ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Advanced Neural Network Applications ; Non-Destructive Testing Techniques
Authors: Ryan Jacobs | Takuya Yamamoto | G.R. Odette | Dane Morgan
Source: Materials & Design (article, 2023-11-15)
Field: Materials Science — Metals and Alloys

Predictions and uncertainty estimates of reactor pressure vessel steel embrittlement using Machine learning An essential aspect of extending safe operation of the world’s active nuclear reactors is understanding and predicting the embrittlement that occurs in the steels that make up the Reactor pressure vessel (RPV). In this work we integrate state of the art machine learning methods using ensembles of neural networks with unprecedented data collection and integration to develop a new model for RPV steel embrittlement. The new model has multiple improvements over previous machine learning and hand-tuned efforts, including greater accuracy (e.g., at high-fluence relevant for extending the life of present reactors), wider domain of applicability (e.g., including a wide-range of compositions), uncertainty quantification, and online accessibility for easy use by the community. These improvements provide a model with significant new capabilities, including the ability to easily and accurately explore compositions, flux, and fluence effects on RPV steel embrittlement for the first time. Furthermore, our detailed comparisons show our approach improves on the leading American Society for Testing and Materials (ASTM) E900-15 standard model for RPV embrittlement on every metric we assessed, demonstrating the efficacy of machine learning approaches for this type of highly demanding materials property prediction. Embrittlement ; Reactor pressure vessel ; Materials science ; Metric (unit) ; Nuclear engineering ; Neutron flux ; Pressure vessel ; Machine learning ; Artificial neural network ; Fluence ; Artificial intelligence ; Computer science ; Metallurgy ; Irradiation ; Engineering ; Composite material ; Nuclear physics ; Neutron Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Machine Learning in Materials Science
Authors: Zihan Liu | Jiaqi Wang | Yun Luo | Shuang Zhao | Wenbin Li | Stan Z. Li
Source: Briefings in Bioinformatics (article, 2023-09-22)
Field: Materials Science — Biomaterials

Efficient prediction of peptide self-assembly through sequential and graphical encoding In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the lack of systematic analysis of the peptide encoding, which is essential for artificial intelligence-assisted peptide-related tasks, makes it an urgent problem to be solved for the improvement of prediction accuracy. To address this issue, we first collect a high-quality, colossal simulation dataset of peptide self-assembly containing over 62 000 samples generated by coarse-grained molecular dynamics. Then, we systematically investigate the effect of peptide encoding of amino acids into sequences and molecular graphs using state-of-the-art sequential (i.e. recurrent neural network, long short-term memory and Transformer) and structural deep learning models (i.e. graph convolutional network, graph attention network and GraphSAGE), on the accuracy of peptide self-assembly prediction, an essential physiochemical process prior to any peptide-related applications. Extensive benchmarking studies have proven Transformer to be the most powerful sequence-encoding-based deep learning model, pushing the limit of peptide self-assembly prediction to decapeptides. In summary, this work provides a comprehensive benchmark analysis of peptide encoding with advanced deep learning models, serving as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc. Deep learning ; Computer science ; Artificial intelligence ; Peptide ; Encoding (memory) ; Benchmark (surveying) ; Graph ; Artificial neural network ; Convolutional neural network ; Benchmarking ; Machine learning ; Theoretical computer science ; Chemistry ; Biochemistry Supramolecular Self-Assembly in Materials ; Machine Learning in Bioinformatics ; Chemical Synthesis and Analysis
Authors: Jie‐Jie Chen
Source: Environmental Science & Technology (review, 2023-12-12)
Field: Chemistry — Electrochemistry

Interfacial Electron Transfer in Chemical and Biological Transformation of Pollutants in Environmental Catalysis Interfacial electron transfer (IET) is essential for chemical and biological transformation of pollutants, operative across diverse lengths and time scales. This Perspective presents an array of multiscale molecular simulation methodologies, supplemented by in situ monitoring and imaging techniques, serving as robust tools to decode IET enhancement mechanisms such as interface molecular modification, catalyst coordination mode, and atomic composition regulation. In addition, three IET-based pollutant transformation systems, an electrocatalytic oxidation system, a bioelectrochemical spatial coupling system, and an enzyme-inspired electrocatalytic system, were developed, demonstrating a high effect in transforming and degrading pollutants. To improve the effectiveness and scalability of IET-based strategies, the refinement of these systems is necessitated through rigorous research and theoretical exploration, particularly in the context of practical wastewater treatment scenarios. Future endeavors aim to elucidate the synergy between biological and chemical modules, edit the environmental functional microorganisms, and harness machine learning for designing advanced environmental catalysts to boost efficiency. This Perspective highlights the powerful potential of IET-focused environmental remediation strategies, emphasizing the critical role of interdisciplinary research in addressing the urgent global challenge of water pollution. Pollutant ; Biochemical engineering ; Environmental remediation ; Context (archaeology) ; Scalability ; Computer science ; Electron transfer ; Environmental pollution ; Transformation (genetics) ; Nanotechnology ; Environmental science ; Chemistry ; Materials science ; Engineering ; Contamination ; Ecology ; Organic chemistry ; Biology ; Environmental protection Electrochemical Analysis and Applications ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Zhihong Wu | Wen‐Jin Yin | Bo Wen | Dongwei Ma | Yuandong Niu
Source: The Journal of Physical Chemistry Letters (article, 2023-02-22)
Field: Materials Science — Materials Chemistry

Oxygen Vacancy Diffusion in Rutile TiO<sub>2</sub>: Insight from Deep Neural Network Potential Simulations Defects play a crucial role in the surface reactivity and electronic engineering of titanium dioxide (TiO<sub>2</sub>). In this work, we have used an active learning method to train deep neural network potentials from the <i>ab initio</i> data of a defective TiO<sub>2</sub> surface. Validations show a good consistency between the deep potentials (DPs) and density functional theory (DFT) results. Therefore, the DPs were further applied on the extended surface and executed for nanoseconds. The results show that the oxygen vacancy at various sites are very stable under 330 K. However, some unstable defect sites will convert to the most favorable ones after tens or hundreds of picoseconds, while the temperature was elevated to 500 K. The DP predicated barriers of oxygen vacancy diffusion were similar to those of DFT. These results show that machine-learning trained DPs could accelerate the molecular dynamics with a DFT-level accuracy and promote people's understanding of the microscopic mechanism of fundamental reactions. Density functional theory ; Vacancy defect ; Diffusion ; Materials science ; Chemical physics ; Rutile ; Nanosecond ; Oxygen ; Ab initio ; Molecular dynamics ; Electronic structure ; Computational chemistry ; Chemistry ; Thermodynamics ; Physics ; Crystallography Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electrochemical Analysis and Applications
Authors: Fang Wu | Stan Z. Li
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations Molecular dynamics (MD) has long been the de facto choice for simulating complex atomistic systems from first principles. Recently deep learning models become a popular way to accelerate MD. Notwithstanding, existing models depend on intermediate variables such as the potential energy or force fields to update atomic positions, which requires additional computations to perform back-propagation. To waive this requirement, we propose a novel model called DiffMD by directly estimating the gradient of the log density of molecular conformations. DiffMD relies on a score-based denoising diffusion generative model that perturbs the molecular structure with a conditional noise depending on atomic accelerations and treats conformations at previous timeframes as the prior distribution for sampling. Another challenge of modeling such a conformation generation process is that a molecule is kinetic instead of static, which no prior works have strictly studied. To solve this challenge, we propose an equivariant geometric Transformer as the score function in the diffusion process to calculate corresponding gradients. It incorporates the directions and velocities of atomic motions via 3D spherical Fourier-Bessel representations. With multiple architectural improvements, we outperform state-of-the-art baselines on MD17 and isomers of C7O2H10 datasets. This work contributes to accelerating material and drug discovery. Molecular dynamics ; Computer science ; Statistical physics ; Force field (fiction) ; Computation ; Diffusion process ; Algorithm ; Physics ; Artificial intelligence ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced NMR Techniques and Applications
Authors: Michael Chigaev | Justin S. Smith | Steven Anaya | Benjamin Nebgen | Matthew Tyler Bettencourt | Kipton Barros | Nicholas Lubbers
Source: The Journal of Chemical Physics (article, 2023-05-09)
Field: Materials Science — Materials Chemistry

Lightweight and effective tensor sensitivity for atomistic neural networks Atomistic machine learning focuses on the creation of models that obey fundamental symmetries of atomistic configurations, such as permutation, translation, and rotation invariances. In many of these schemes, translation and rotation invariance are achieved by building on scalar invariants, e.g., distances between atom pairs. There is growing interest in molecular representations that work internally with higher rank rotational tensors, e.g., vector displacements between atoms, and tensor products thereof. Here, we present a framework for extending the Hierarchically Interacting Particle Neural Network (HIP-NN) with Tensor Sensitivity information (HIP-NN-TS) from each local atomic environment. Crucially, the method employs a weight tying strategy that allows direct incorporation of many-body information while adding very few model parameters. We show that HIP-NN-TS is more accurate than HIP-NN, with negligible increase in parameter count, for several datasets and network sizes. As the dataset becomes more complex, tensor sensitivities provide greater improvements to model accuracy. In particular, HIP-NN-TS achieves a record mean absolute error of 0.927 kcalmol for conformational energy variation on the challenging COMP6 benchmark, which includes a broad set of organic molecules. We also compare the computational performance of HIP-NN-TS to HIP-NN and other models in the literature. Tensor (intrinsic definition) ; Sensitivity (control systems) ; Translation (biology) ; Artificial neural network ; Computer science ; Benchmark (surveying) ; Rotation (mathematics) ; Algorithm ; Scalar (mathematics) ; Artificial intelligence ; Homogeneous space ; Statistical physics ; Mathematics ; Physics ; Chemistry ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods