jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jinzhe Zeng | Duo Zhang | Denghui Lu | Pinghui Mo | Zeyu Li | Yixiao Chen | Marián Rynik | Li'ang Huang | Ziyao Li | Shaochen Shi | Yingze Wang | Haotian Ye | Ping Tuo | Jiabin Yang | Ye Ding | Yifan Li | Davide Tisi | Qiyu Zeng | Han Bao | Yu Xia | Jiameng Huang | Koki Muraoka | Yibo Wang | Junhan Chang | Fengbo Yuan | Sigbjørn Løland Bore | Chun Cai | Yin‐Nian Lin | Bo Wang | Jiayan Xu | Jiaxin Zhu | Chenxing Luo | Yuzhi Zhang | Rhys E. A. Goodall | Wenshuo Liang | Anurag Kumar Singh | Sikai Yao | Jingchao Zhang | Renata M. Wentzcovitch | Jiequn Han | Jie Liu | Weile Jia | Darrin M. York | E Weinan | Roberto Car | Linfeng Zhang | Handong Wang
Source: arXiv (Cornell University) (preprint, 2023-04-19)
Field: Materials Science — Materials Chemistry

DeePMD-kit v2: A software package for Deep Potential models DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. Computer science ; Usability ; Software ; Documentation ; Embedding ; Computational science ; Deep learning ; Graphical user interface ; Software package ; Range (aeronautics) ; Software engineering ; Artificial intelligence ; Programming language ; Human–computer interaction ; Engineering Machine Learning in Materials Science ; Nuclear Physics and Applications ; Protein Structure and Dynamics
Authors: Ishan D. Khurjekar | Bryan Conry | Michael S. Kesler | Michael Tonks | Amanda R. Krause | Joel B. Harley
Source: Frontiers in Materials (article, 2023-01-27)
Field: Materials Science — Materials Chemistry

Automated, high-accuracy classification of textured microstructures using a convolutional neural network Crystallographic texture is an important descriptor of material properties but requires time-intensive electron backscatter diffraction (EBSD) for identifying grain orientations. While some metrics such as grain size or grain aspect ratio can distinguish textured microstructures from untextured microstructures after significant grain growth, such morphological differences are not always visually observable. This paper explores the use of deep learning to classify experimentally measured textured microstructures without knowledge of crystallographic orientation. A deep convolutional neural network is used to extract high-order morphological features from binary images to distinguish textured microstructures from untextured microstructures. The convolutional neural network results are compared with a statistical Kolmogorov–Smirnov tests with traditional morphological metrics for describing microstructures. Results show that the convolutional neural network achieves a significantly improved classification accuracy, particularly at early stages of grain growth, highlighting the capability of deep learning to identify the subtle morphological patterns resulting from texture. The results demonstrate the potential of a convolutional neural network as a tool for reliable and automated microstructure classification with minimal preprocessing. Convolutional neural network ; Artificial intelligence ; Electron backscatter diffraction ; Pattern recognition (psychology) ; Microstructure ; Texture (cosmology) ; Materials science ; Deep learning ; Preprocessor ; Computer science ; Artificial neural network ; Image (mathematics) ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ali Davariashtiyani | Sara Kadkhodaei
Source: Communications Materials (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Formation energy prediction of crystalline compounds using deep convolutional network learning on voxel image representation Abstract Emerging machine-learned models have enabled efficient and accurate prediction of compound formation energy, with the most prevalent models relying on graph structures for representing crystalline materials. Here, we introduce an alternative approach based on sparse voxel images of crystals. By developing a sophisticated network architecture, we showcase the ability to learn the underlying features of structural and chemical arrangements in inorganic compounds from visual image representations, subsequently correlating these features with the compounds’ formation energy. Our model achieves accurate formation energy prediction by utilizing skip connections in a deep convolutional network and incorporating augmentation of rotated crystal samples during training, performing on par with state-of-the-art methods. By adopting visual images as an alternative representation for crystal compounds and harnessing the capabilities of deep convolutional networks, this study extends the frontier of machine learning for accelerated materials discovery and optimization. In a comprehensive evaluation, we analyse the predicted convex hulls for 3115 binary systems and introduce error metrics beyond formation energy error. This evaluation offers valuable insights into the impact of formation energy error on the performance of the predicted convex hulls. Computer science ; Deep learning ; Convolutional neural network ; Artificial intelligence ; Representation (politics) ; Convex hull ; Binary number ; Graph ; Energy (signal processing) ; Regular polygon ; Pattern recognition (psychology) ; Machine learning ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Lixiang Xing | Zijian Chen | Wei Chen | Paul K. Chu | Xue‐Feng Yu | Haitao Zhao
Source: Chemical Engineering Journal (article, 2023-04-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Robotic platform for accelerating the high-throughput study of silver nanocrystals in sensitive/selective Hg2+ detection Detection limit ; Throughput ; Materials science ; Linear range ; Aqueous solution ; Nanotechnology ; Scalability ; Nanoscopic scale ; Computer science ; Chemistry ; Database ; Chromatography Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques
Authors: Wenxuan Li | Yizhen Situ | Lifeng Ding | Yanling Chen | Qingyuan Yang
Source: ACS Applied Materials & Interfaces (article, 2023-12-12)
Field: Chemistry — Inorganic Chemistry

MOF-GRU: A MOFid-Aided Deep Learning Model for Predicting the Gas Separation Performance of Metal–Organic Frameworks The remarkable versatility of metal-organic frameworks (MOFs) stems from their rich chemical information, leading to numerous successful applications. However, identifying optimal MOFs for specific tasks necessitates a thorough assessment of their chemical attributes. Conventional machine learning approaches for MOF prediction have relied on intricate chemical and structural details, hampering rapid evaluations. Drawing inspiration from recent advancements exemplified by Snurr et al., wherein a text string was used to represent a MOF (MOFid), we introduce a MOFid-aided deep learning model, named the MOF-GRU model. This model, founded on natural language processing principles and utilizing the gated recurrent unit architecture, leverages the serialized text string representation of metal-organic frameworks (MOFs) to forecast gas separation performance. Through a focused study on CH<sub>4</sub>/N<sub>2</sub> separation, we substantiate the efficacy of this approach. Comparative assessments against traditional machine learning techniques underscore our model's superior predictive accuracy and its capacity to handle extensive data sets adeptly. The MOF-GRU model remarkably uncovers latent structure-performance relationships with only MOF sequences, obviating the necessity for intricate three-dimensional (3D) structural information. Overall, this model's judicious design empowers efficient data utilization, thereby hastening the discovery of high-performance materials tailored for gas separation applications. Metal-organic framework ; Deep learning ; Computer science ; Artificial intelligence ; Representation (politics) ; String (physics) ; Machine learning ; Architecture ; Nanotechnology ; Materials science ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Jin Da Tan | Balamurugan Ramalingam | Swee Liang Wong | Jayce Jian Wei Cheng | Yee‐Fun Lim | Vijila Chellappan | Saif A. Khan | Jatin Kumar | Kedar Hippalgaonkar
Source: Journal of Chemical Information and Modeling (article, 2023-07-11)
Field: Materials Science — Materials Chemistry

Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization The skew and shape of the molecular weight distribution (MWD) of polymers have a significant impact on polymer physical properties. Standard summary metrics statistically derived from the MWD only provide an incomplete picture of the polymer MWD. Machine learning (ML) methods coupled with high-throughput experimentation (HTE) could potentially allow for the prediction of the entire polymer MWD without information loss. In our work, we demonstrate a computer-controlled HTE platform that is able to run up to 8 unique variable conditions in parallel for the free radical polymerization of styrene. The segmented-flow HTE system was equipped with an inline Raman spectrometer and offline size exclusion chromatography (SEC) to obtain time-dependent conversion and MWD, respectively. Using ML forward models, we first predict monomer conversion, intrinsically learning varying polymerization kinetics that change for each experimental condition. In addition, we predict entire MWDs including the skew and shape as well as SHAP analysis to interpret the dependence on reagent concentrations and reaction time. We then used a transfer learning approach to use the data from our high-throughput flow reactor to predict batch polymerization MWDs with only three additional data points. Overall, we demonstrate that the combination of HTE and ML provides a high level of predictive accuracy in determining polymerization outcomes. Transfer learning can allow exploration outside existing parameter spaces efficiently, providing polymer chemists with the ability to target the synthesis of polymers with desired properties. Polymerization ; Polymer ; Computer science ; Molar mass distribution ; Monomer ; Biological system ; Throughput ; Chain transfer ; Materials science ; Radical polymerization ; Chemical engineering ; Process engineering ; Chemistry ; Composite material Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Longen Gui | Botong Wang | Renye Cai | Zexin Yu | Meimei Liu | Qixin Zhu | Yingchun Xie | Shaowu Liu | Andreas Killinger
Source: Materials (article, 2023-09-19)
Field: Materials Science — Materials Chemistry

Prediction of In-Flight Particle Properties and Mechanical Performances of HVOF-Sprayed NiCr–Cr3C2 Coatings Based on a Hierarchical Neural Network High-velocity oxygen fuel (HVOF) spraying is a promising technique for depositing protective coatings. The performances of HVOF-sprayed coatings are affected by in-flight particle properties, such as temperature and velocity, that are controlled by the spraying parameters. However, obtaining the desired coatings through experimental methods alone is challenging, owing to the complex physical and chemical processes involved in the HVOF approach. Compared with traditional experimental methods, a novel method for optimizing and predicting coating performance is presented herein; this method involves combining machine learning techniques with thermal spray technology. Herein, we firstly introduce physics-informed neural networks (PINNs) and convolutional neural networks (CNNs) to address the overfitting problem in small-sample algorithms and then apply the algorithms to HVOF processes and HVOF-sprayed coatings. We proposed the PINN and CNN hierarchical neural network to establish prediction models for the in-flight particle properties and performances of NiCr-Cr<sub>3</sub>C<sub>2</sub> coatings (e.g., porosity, microhardness, and wear rate). Additionally, a random forest model is used to evaluate the relative importance of the effect of the spraying parameters on the properties of in-flight particles and coating performance. We find that the particle temperature and velocity as well as the coating performances (porosity, wear resistance, and microhardness) can be predicted with up to 99% accuracy and that the spraying distance and velocity of in-flight particles exert the most substantial effects on the in-flight particle properties and coating performance, respectively. This study can serve as a theoretical reference for the development of intelligent HVOF systems in the future. Thermal spraying ; Coating ; Materials science ; Porosity ; Particle (ecology) ; Artificial neural network ; Indentation hardness ; Composite material ; Nichrome ; Mechanical engineering ; Biological system ; Metallurgy ; Computer science ; Engineering ; Artificial intelligence ; Microstructure Nuclear Materials and Properties ; Heat transfer and supercritical fluids
Authors: Sergei Manzhos | Tucker Carrington | Manabu Ihara
Source: Artificial Intelligence Chemistry (article, 2023-07-17)
Field: Materials Science — Materials Chemistry

Orders of coupling representations as a versatile framework for machine learning from sparse data in high-dimensional spaces Machine learning (ML) techniques are already widely and increasingly used in diverse applications in science and technology, including computational chemistry. Specifically in computational chemistry, neural networks (NN) and kernel methods such as Gaussian process regressions (GPR) have been increasingly used for the construction of potential functions and functionals for density functional theory. While ML techniques have a number of advantages vs intuition-based models, notably their generality and black-box nature, they are still challenged when faced with high dimensionality of the feature space or low and uneven data density – in part because of their general nature. We review recent works using methods such as NNs and GPR as building blocks of composite methods in the framework of an expansion over orders of coupling. We introduce models using NN or GPR-based components as part of HDMR (high-dimensional model representations)-based structures. HDMR is a formalization of orders-of-coupling representations that include the many-body and N-mode representations well known in computational chemistry and allows, in particular, building all terms from one dataset of arbitrarily distributed data. The resulting HDMR-NN and HDMR-GPR combinations and NN with HDMR-GPR derived neuron activation functions not requiring non-linear optimization enhance machine learning capabilities in high dimensional spaces and or with sparse data. Computer science ; Generality ; Kriging ; Curse of dimensionality ; Artificial intelligence ; Ground-penetrating radar ; Artificial neural network ; Interpretability ; Gaussian process ; Machine learning ; Feature vector ; Kernel (algebra) ; Gaussian ; Theoretical computer science ; Algorithm ; Mathematics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Attilio Sbrana | Aline Gabriel de Almeida | Andre Mobaier de Oliveira | Henrique Seschin Neto | Joao Paulo Cesar Rimes | Maria Cristina Belli
Source: IEEE Sensors Letters (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Plastic Classification With NIR Hyperspectral Images and Deep Learning Because most plastics are incompatible and cannot be blended, polymers must be separated before recycling. We propose a sensor-based classifier that collects NIR wavelengths from polymers to estimate their composition. Unlike prior research, this work aims to apply machine learning to the difficult problem of categorizing low-reflectance black polymers in NIR and without complimentary sensory data. We collected 1491 hyperspectral pictures of roughly 4500 plastic pieces from an industrial recycling operation. Using N-BEATS, an ensemble deep learning approach for waveform decomposition, we achieved an overall f1-score of 0.79 and accuracy of 0.79 for all plastics, 0.9 for colored plastics, and 0.67 for black plastics. The method achieves noteworthy performance results, as black polymer differentiation in the NIR has been deemed unfeasible by the majority of the existing literature. Hyperspectral imaging ; Classifier (UML) ; Computer science ; Polymer ; Artificial intelligence ; Colored ; Pattern recognition (psychology) ; Reflectivity ; Spectral signature ; Deep learning ; Materials science ; Remote sensing ; Composite material ; Optics ; Geology ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Industrial Vision Systems and Defect Detection
Authors: Zheng Tan | Yan Li | Xin Wu | Ziying Zhang | Weimei Shi | Shiqing Yang | Wanli Zhang
Source: RSC Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

<i>De novo</i> creation of fluorescent molecules <i>via</i> adversarial generative modeling The development of AI for fluorescent materials design is technologically demanding due to the issue of accurately forecasting fluorescent properties. Besides the huge efforts made in predicting the photoluminescent properties of organic dyes in terms of machine learning techniques, this article aims to introduce an adversarial generation paradigm for the rational design of fluorescent molecules. Molecular SMILES is employed as the input of a GRU based autoencoder, where the encoding and decoding of the string information are processed. A generative adversarial network is applied on the latent space with a generator to generate samples to mimic the latent space, and a discriminator to distinguish samples from the latent space. It is found that the excited state property distributions of generated molecules fully match those of the original samples, with the molecular synthesizability being accessible as well. Further screening of the generated samples delivers a remarkable luminescence efficiency of molecules epitomized by the significant oscillator strength and charge transfer characteristics, demonstrating the great potential of the adversarial model in enriching the fluorescent library. Autoencoder ; Discriminator ; Fluorescence ; String (physics) ; Encoding (memory) ; Computer science ; Generator (circuit theory) ; Space (punctuation) ; Decoding methods ; Artificial intelligence ; Excited state ; Molecule ; Biological system ; Nanotechnology ; Materials science ; Artificial neural network ; Algorithm ; Physics ; Telecommunications ; Quantum mechanics Machine Learning in Materials Science ; Nanofabrication and Lithography Techniques ; Organic Electronics and Photovoltaics
Authors: Abhishek Futane | Pramod Jadhav | Abu Hasnat Mustafa | Arthi Srinivasan | Vigneswaran Narayanamurthy
Source: Biotechnology Letters (article, 2023-12-28)
Field: Chemistry — Inorganic Chemistry

Aptamer-functionalized MOFs and AI-driven strategies for early cancer diagnosis and therapeutics Nanotechnology ; Aptamer ; Metal-organic framework ; Materials science ; Cancer therapy ; Cancer treatment ; Cancer ; Chemistry ; Medicine ; Biology Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Machine Learning in Materials Science
Authors: Jaekak Yoo | Youngwoo Cho | Byeonggeun Jeong | Soo Ho Choi | Ki Kang Kim | Seong Chu Lim | Seung Mi Lee | Jaegul Choo | Mun Seok Jeong
Source: Advanced Intelligent Systems (article, 2023-03-29)
Field: Materials Science — Materials Chemistry

Explainable Artificial Intelligence Approach to Identify the Origin of Phonon‐Assisted Emission in WSe<sub>2</sub> Monolayer The application of explainable artificial intelligence in nanomaterial research has emerged in the past few years, which has facilitated the discovery of novel physical findings. However, a fundamental question arises concerning the physical insights presented by deep neural networks; the model interpretation results have not been carefully evaluated. Herein, explainable artificial intelligence and quantum mechanical calculations is bridged to investigate the correlation between light scattering and emission in a WSe 2 monolayer. Convolutional neural networks using light scattering and emission data are first trained, while expecting the networks to determine the relationships between them. The trained models are interpreted and the specific phonon contribution during the exciton relaxation process is derived. Finally, the findings are independently evaluated through quantum mechanical calculations, such as the Born–Oppenheimer molecular dynamics simulation and density functional perturbation theory. The study provides reliable fundamental physical insight by evaluating the results of neural networks and suggests a novel methodology that can be applied in materials science. Phonon ; Artificial neural network ; Artificial intelligence ; Quantum ; Convolutional neural network ; Computer science ; Exciton ; Statistical physics ; Monolayer ; Relaxation (psychology) ; Physics ; Nanotechnology ; Materials science ; Quantum mechanics ; Psychology 2D Materials and Applications ; Machine Learning in Materials Science ; Perovskite Materials and Applications