jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eli Chien | Mufei Li | Anthony Aportela | Kerr Ding | Shuyi Jia | Supriyo Maji | Zhongyuan Zhao | J. Duarte | Victor Fung | Cong Hao | Yunan Luo | Olgica Milenković | David Z. Pan | Santiago Segarra | Pan Li
Source: Nature Reviews Electrical Engineering (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Opportunities and challenges of graph neural networks in electrical engineering Computer science ; Inference ; Artificial intelligence ; Data science ; Graph ; Artificial neural network ; Science and engineering ; Machine learning ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Memory and Neural Computing
Authors: Kevin M. Roccapriore | Riccardo Torsi | Joshua A. Robinson | Sergei V. Kalinin | Maxim Ziatdinov
Source: Science Advances (article, 2024-07-17)
Field: Materials Science — Surfaces, Coatings and Films

Dynamic STEM-EELS for single-atom and defect measurement during electron beam transformations This study introduces the integration of dynamic computer vision-enabled imaging with electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). This approach involves real-time discovery and analysis of atomic structures as they form, allowing us to observe the evolution of material properties at the atomic level, capturing transient states traditional techniques often miss. Rapid object detection and action system enhances the efficiency and accuracy of STEM-EELS by autonomously identifying and targeting only areas of interest. This machine learning (ML)-based approach differs from classical ML in that it must be executed on the fly, not using static data. We apply this technology to V-doped MoS<sub>2</sub>, uncovering insights into defect formation and evolution under electron beam exposure. This approach opens uncharted avenues for exploring and characterizing materials in dynamic states, offering a pathway to increase our understanding of dynamic phenomena in materials under thermal, chemical, and beam stimuli. Scanning transmission electron microscopy ; Cathode ray ; Electron ; Nanotechnology ; Atom (system on chip) ; Computer science ; Materials science ; Beam (structure) ; Biological system ; Transmission electron microscopy ; Physics ; Optics ; Biology Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Tathagata Pradhan | Ojasvi Gupta | Gita Chawla
Source: ChemistrySelect (article, 2024-03-27)
Field: Materials Science — Materials Chemistry

The Future of ChatGPT in Medicinal Chemistry: Harnessing AI for Accelerated Drug Discovery Abstract Artificial Intelligence (AI) has emerged as a transformative tool in various fields, including medicinal chemistry and drug discovery. The integration of artificial intelligence (AI) and machine learning (ML) technologies in medicinal chemistry has revolutionized drug discovery processes. Among AI models, ChatGPT (Generative Pre‐trained Transformer) has shown remarkable capabilities in natural language understanding and generation. This review article delves into the potential applications and prospects of ChatGPT in the domain of medicinal chemistry, focusing on its contributions to drug discovery, compound design, virtual screening, target identification, and optimization of pharmacokinetic properties. ChatGPT′s ability to generate contextually relevant text makes it a valuable tool for data mining and analysis of vast chemical and biological information repositories. In this review article, the authors have discussed the challenges and ethical considerations associated with the implementation of ChatGPT 3.5 in medicinal chemistry research. Authors also extend their acknowledgment to ChatGPT (version 3.5) for its contribution in furnishing responses to the prompt, thereby shedding light on the applications of ChatGPT in the field of medicinal chemistry. The authors have analyzed the responses of ChatGPT. Drug discovery ; Drug ; Data science ; Chemistry ; Computer science ; Nanotechnology ; Pharmacology ; Medicine ; Materials science ; Biochemistry Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Computational Drug Discovery Methods
Authors: Wenjun Yan | Yun Liu | Yan Bai | Yulong Chen | Houpan Zhou | Waqar Ahmad
Source: ACS Applied Materials & Interfaces (article, 2024-09-04)
Field: Materials Science — Materials Chemistry

Intelligent MEMS Sensor Based on an Oxidized Medium-Entropy Alloy (FeCoNi) for H<sub>2</sub> and CO Recognition In this paper, we present the design and evaluation of an intelligent MEMS sensor employing the oxidized medium-entropy alloy (O-MEA) of FeCoNi as the gas-sensing material. Due to the specific catalytic exothermic reaction of the O-MEA on H<sub>2</sub>/CO, the sensor shows great selectivity for H<sub>2</sub> and CO at 150 °C of the integrated microheater in the MEMS device, with the theoretical detection limit of 0.3 ppm for H<sub>2</sub> and 0.29 ppm for CO. The MEMS heater, capable of instantaneous temperature changes in pulse operation mode, offers a novel approach for thermal modulation of the sensor, which is crucial for the adsorption and reaction of H<sub>2</sub>/CO molecules on the sensing layer surface. Consequently, we investigate the gas-sensing capabilities of the sensor under pulse heating modes (PHMs) of the MEMS heater and then propose the gas-sensing mechanism. The results indicate that PHMs significantly not only reduce the operating temperature and power consumption but also enhance the sensor's functionality by providing multivariable response signals, paving the way for intelligent gas detection. Based on the high selectivity to H<sub>2</sub> and CO, transforming the transient sensing curves into two-dimensional images via Gramian Angular Field (GAF) model and subsequent modeling using a convolutional neural network (CNN) algorithm, we have realized efficient and intelligent recognition of H<sub>2</sub> and CO. This work provides insight for the development of low-power, high-performance MEMS gas sensors and further intelligence of individual MEMS sensors. Materials science ; Exothermic reaction ; Microelectromechanical systems ; Alloy ; Entropy (arrow of time) ; Catalysis ; Chemical engineering ; Nanotechnology ; Composite material ; Thermodynamics ; Organic chemistry Advanced Thermoelectric Materials and Devices ; Transition Metal Oxide Nanomaterials ; High Entropy Alloys Studies
Authors: Ryo Tamura | Kenji Nagata | Keitaro Sodeyama | K Nakamura | Toshiki Tokuhira | Satoshi Shibata | Kazuki Hammura | Hiroki Sugisawa | Masaya Kawamura | Teruki Tsurimoto | Masanobu Naito | Masahiko Demura | Takashi Nakanishi
Source: Science and Technology of Advanced Materials (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis Predicting the mechanical properties of polymer materials using machine learning is essential for the design of next-generation of polymers. However, the strong relationship between the higher-order structure of polymers and their mechanical properties hinders the mechanical property predictions based on their primary structures. To incorporate information on higher-order structures into the prediction model, X-ray diffraction (XRD) can be used. This study proposes a strategy to generate appropriate descriptors from the XRD analysis of the injection-molded polypropylene samples, which were prepared under almost the same injection molding conditions. To this end, first, Bayesian spectral deconvolution is used to automatically create high-dimensional descriptors. Second, informative descriptors are selected to achieve highly accurate predictions by implementing the black-box optimization method using Ising machine. This approach was applied to custom-built polymer datasets containing data on homo- polypropylene and derived composite polymers with the addition of elastomers. Results show that reasonable accuracy of predictions for seven mechanical properties can be achieved using only XRD. Polypropylene ; Materials science ; Polymer ; Molding (decorative) ; Composite material ; Diffraction ; Elastomer ; Computer science ; Biological system ; Optics ; Physics Machine Learning in Materials Science ; Injection Molding Process and Properties ; Computational Drug Discovery Methods
Authors: Mohammad Ullah | Md. Munirul Hasan | Rasidi Roslan | Rajan Jose | Izan Izwan Misnon
Source: Journal of Electroanalytical Chemistry (article, 2024-08-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sustainable graphitic carbon derived from oil palm frond biomass for supercapacitor application: Effect of redox additive and artificial neural network‑based modeling approach Chemistry ; Frond ; Supercapacitor ; Palm oil ; Biomass (ecology) ; Redox ; Carbon fibers ; Artificial neural network ; Activated carbon ; Electrochemistry ; Pulp and paper industry ; Chemical engineering ; Electrode ; Organic chemistry ; Botany ; Artificial intelligence ; Food science ; Agronomy ; Composite material ; Composite number ; Adsorption Supercapacitor Materials and Fabrication ; Biodiesel Production and Applications ; Electrospun Nanofibers in Biomedical Applications
Authors: Muhammad Ali Yousif Al Janabi | Rima Nour Elhouda Tiri | Ali Chérif | Elif Esra Altuner | Chul‐Jin Lee | Fatih Şen | Elena-Niculina Drăgoi | Fatemeh Karimi | Shankramma Kalikeri
Source: Topics in Catalysis (article, 2024-02-20)
Field: Chemistry — Organic Chemistry

Hydrogen Generation by Methanolysis of NaBH4 via Efficient CuFe2O4 Nanoparticle Catalyst: A Kinetic Study and DNN Model Catalysis ; Nanoparticle ; Materials science ; Hydrothermal circulation ; Reusability ; Nanomaterials ; Chemical engineering ; Activation energy ; Kinetic energy ; Nanotechnology ; Biological system ; Chemistry ; Computer science ; Physical chemistry ; Organic chemistry ; Physics Nanomaterials for catalytic reactions ; Hydrogen Storage and Materials ; Environmental remediation with nanomaterials
Authors: Song Jin | Zhenwei Lan | Guangze Yang | Xinyu Li | Qinfeng Shi | Yun Liu | Chun‐Xia Zhao
Source: Aggregate (article, 2024-06-03)
Field: Materials Science — Biomaterials

Computationally guided design and synthesis of dual‐drug loaded polymeric nanoparticles for combination therapy Abstract Single‐drug therapies or monotherapies are often inadequate, particularly in the case of life‐threatening diseases like cancer. Consequently, combination therapies emerge as an attractive strategy. Cancer nanomedicines have many benefits in addressing the challenges faced by small molecule therapeutic drugs, such as low water solubility and bioavailability, high toxicity, etc. However, it remains a significant challenge in encapsulating two drugs in a nanoparticle. To address this issue, computational methodologies are employed to guide the rational design and synthesis of dual‐drug‐loaded polymer nanoparticles while achieving precise control over drug loading. Based on the sequential nanoprecipitation technology, five factors are identified that affect the formulation of drug candidates into dual‐drug loaded nanoparticles, and then screened 176 formulations under different experimental conditions. Based on these experimental data, machine learning methods are applied to pin down the key factors. The implementation of this methodology holds the potential to significantly mitigate the complexities associated with the synthesis of dual‐drug loaded nanoparticles, and the co‐assembly of these compounds into nanoparticulate systems demonstrates a promising avenue for combination therapy. This approach provides a new strategy for enabling the streamlined, high‐throughput screening and synthesis of new nanoscale drug‐loaded entities. Nanoparticle ; Dual (grammatical number) ; Materials science ; Drug ; Nanotechnology ; Computer science ; Medicine ; Pharmacology Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: Ganchang Lei | Xiaoyun Lin | Hongping Yan | Lijuan Shen | Shiping Wang | Shijing Liang | Zhi‐Jian Zhao | Fujian Liu | Yingying Zhan | Lilong Jiang
Source: ACS Catalysis (article, 2024-11-06)
Field: Chemistry — Organic Chemistry

Valence Electron and Coordination Structure Guided Metal Active Site Design for Hydrolytic Cleavage of Carbon–Sulfide Double Bonds The catalytic cleavage of carbon–sulfur (C═S) double bonds on the metal sites without deactivation has aroused great interest in both fundamental catalytic research and industrial chemistry. Herein, activity descriptors are developed via machine learning and density functional theory (DFT) calculations to screen transition-metal single-site catalysts, which quantify the effect of both atomic electronic properties and coordination configuration on the hydrolysis of C═S double bonds. The valence electron number and electronegativity of active sites are found to be well related to C═S activation and sulfur poisoning, where Fe demonstrates high catalytic potential among a series of metal centers. On the other hand, the isolated Fe1 and Fe2 sites favor carbonyl sulfide (COS) adsorption and activation, while the COS easily dissociates into *S and *CO on Fe3 hollow site, thus resulting in the formation of robust Fe–S bonds and catalyst deactivation. As anticipated, the as-designed Fe1–N4 site achieves a COS conversion of ca. 96% at 100 °C, slightly better than the Fe2–N4 site, approximately 8 times higher than that of the Fe/C, which is also better than those of other monatomic catalysts (such as Co-NC, Ni-NC, Sn-NC, and Bi-NC). The combination of in situ characterizations and theoretical calculations suggests that *COS and *H2O/*OH have a competitive adsorption relationship on Fe–N4 sites, and two Fe–N4 sites can synergistically catalyze the COS hydrolysis through the spilled H and OH. Sulfide ; Catalysis ; Chemistry ; Metal ; Cleavage (geology) ; Valence (chemistry) ; Active site ; Hydrolysis ; Valence electron ; Electron ; Photochemistry ; Inorganic chemistry ; Materials science ; Organic chemistry Chemical Synthesis and Reactions ; Advanced Photocatalysis Techniques ; Sulfur-Based Synthesis Techniques
Authors: Hannes H. Loeffler | Shunzhou Wan | Marco Klähn | Agastya P. Bhati | Peter V. Coveney
Source: Journal of Chemical Theory and Computation (article, 2024-09-03)
Field: Materials Science — Materials Chemistry

Optimal Molecular Design: Generative Active Learning Combining REINVENT with Precise Binding Free Energy Ranking Simulations Active learning (AL) is a specific instance of sequential experimental design and uses machine learning to intelligently choose the next data point or batch of molecular structures to be evaluated. In this sense, it closely mimics the iterative design-make-test-analysis cycle of laboratory experiments to find optimized compounds for a given design task. Here, we describe an AL protocol which combines generative molecular AI, using REINVENT, and physics-based absolute binding free energy molecular dynamics simulation, using ESMACS, to discover new ligands for two different target proteins, 3CL<sup>pro</sup> and TNKS2. We have deployed our generative active learning (GAL) protocol on Frontier, the world's only exa-scale machine. We show that the protocol can find higher-scoring molecules compared to the baseline, a surrogate ML docking model for 3CL<sup>pro</sup> and compounds with experimentally determined binding affinities for TNKS2. The ligands found are also chemically diverse and occupy a different chemical space than the baseline. We vary the batch sizes that are put forward for free energy assessment in each GAL cycle to assess the impact on their efficiency on the GAL protocol and recommend their optimal values in different scenarios. Overall, we demonstrate a powerful capability of the combination of physics-based and AI methods which yields effective chemical space sampling at an unprecedented scale and is of immediate and direct relevance to modern, data-driven drug discovery. Ranking (information retrieval) ; Computer science ; Generative grammar ; Generative model ; Energy (signal processing) ; Molecular binding ; Machine learning ; Human–computer interaction ; Artificial intelligence ; Chemistry ; Physics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Tian Ren | Yao Lin | Yubin Su | Simin Ye | Chengbin Zheng
Source: Analytical Chemistry (article, 2024-03-21)
Field: Chemistry — Analytical Chemistry

Machine Learning-Assisted Portable Microplasma Optical Emission Spectrometer for Food Safety Monitoring To meet the needs of food safety for simple, rapid, and low-cost analytical methods, a portable device based on a point discharge microplasma optical emission spectrometer (μPD-OES) was combined with machine learning to enable on-site food freshness evaluation and detection of adulteration. The device was integrated with two modular injection units (i.e., headspace solid-phase microextraction and headspace purge) for the examination of various samples. Aromas from meat and coffee were first introduced to the portable device. The aroma molecules were excited to specific atomic and molecular fragments at excited states by room temperature and atmospheric pressure microplasma due to their different atoms and molecular structures. Subsequently, different aromatic molecules obtained their own specific molecular and atomic emission spectra. With the help of machine learning, the portable device was successfully applied to the assessment of meat freshness with accuracies of 96.0, 98.7, and 94.7% for beef, pork, and chicken meat, respectively, through optical emission patterns of the aroma at different storage times. Furthermore, the developed procedures can identify beef samples containing different amounts of duck meat with an accuracy of 99.5% and classify two coffee species without errors, demonstrating the great potential of their application in the discrimination of food adulteration. The combination of machine learning and μPD-OES provides a simple, portable, and cost-effective strategy for food aroma analysis, potentially addressing field monitoring of food safety. Microplasma ; Chemistry ; Aroma ; Spectrometer ; Food safety ; Analytical Chemistry (journal) ; Chromatography ; Food science ; Plasma Analytical chemistry methods development ; Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies
Authors: Weijie Liao | Ruihao Yuan | Xiangyi Xue | Jun Wang | Jinshan Li | Turab Lookman
Source: npj Computational Materials (article, 2024-08-04)
Field: Materials Science — Materials Chemistry

Unsupervised learning-aided extrapolation for accelerated design of superalloys Machine learning has been widely used to guide the search for new materials by learning the patterns underlying available data. However, the pure prediction-oriented search is often biased to interpolation due to the limited data in a large unexplored space. Here we present a sampling framework towards extrapolation, that integrates unsupervised clustering, interpretable analysis, and similarity evaluation to sample target candidates with improved properties from a vast search space. Using the design of superalloys with improved $${\gamma }^{{\prime} }$$ -phase solvus temperature ( $${T}_{{\gamma }^{{\prime} }}$$ ) as a model case, we start with sparse data, and by a few experiments, we find nine new superalloys with chemistries distinct to those in the training data. Three of them show improved $${T}_{{\gamma }^{{\prime} }}$$ by about 50 °C, a large enhancement for superalloys. Moreover, we find two features characterizing mismatch in atomic size and mixing enthalpy linearly effect. This work demonstrates the capability of unsupervised learning to search for new materials when limited data is available. Extrapolation ; Cluster analysis ; Unsupervised learning ; Interpolation (computer graphics) ; Computer science ; Prime (order theory) ; Superalloy ; Artificial intelligence ; Machine learning ; Data mining ; Materials science ; Mathematics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Daniel Wines | Kamal Choudhary
Source: Materials Futures (article, 2024-05-13)
Field: Materials Science — Materials Chemistry

Data-driven design of high pressure hydride superconductors using DFT and deep learning The observation of superconductivity in hydride-based materials under ultrahigh pressures (for example, H<sub>3</sub>S and LaH<sub>10</sub>) has fueled the interest in a more data-driven approach to discovering new high-pressure hydride superconductors. In this work, we performed density functional theory (DFT) calculations to predict the critical temperature (<i>T</i><sub><i>c</i></sub>) of over 900 hydride materials under a pressure range of (0 to 500) GPa, where we found 122 dynamically stable structures with a <i>T</i><sub><i>c</i></sub> above MgB<sub>2</sub> (39 K). To accelerate screening, we trained a graph neural network (GNN) model to predict <i>T</i><sub><i>c</i></sub> and demonstrated that a universal machine learned force-field can be used to relax hydride structures under arbitrary pressures, with significantly reduced cost. By combining DFT and GNNs, we can establish a more complete map of hydrides under pressure. Hydride ; Superconductivity ; Computer science ; Materials science ; Condensed matter physics ; Physics ; Metallurgy Nuclear Materials and Properties ; Superconducting Materials and Applications ; Nuclear Physics and Applications
Authors: Liqun Lyu | Chunlin Song | Yanghe Wang | Dirui Wu | Yingli Zhang | Shengyao Su | Boyuan Huang | Changjian Li | Mengkang Xu | Jiangyu Li
Source: Advanced Materials (article, 2024-11-02)
Field: Materials Science — Materials Chemistry

Anomalously High Apparent Young's Modulus of Ultrathin Freestanding PZT Films Revealed by Machine Learning Empowered Nanoindentation The mechanical properties at small length scales are not only significant for understanding the intriguing size-dependent behaviors but also critical for device applications. Nanoindentation via atomic force microscopy is widely used for small-scale mechanical testing, yet determining the Young's modulus of quasi-2D films from freestanding force-displacement curve of nanoindentation remains challenging, complicated by both bending and stretching that are highly nonlinear. To overcome these difficulties, a machine learning model is developed based on the back propagation (BP) neural network and finite element training to accurately determine the Young's modulus, pretension, and thickness of freestanding films from nanoindentation force-displacement curves simultaneously, improving the computational efficiency by two orders of magnitude over conventional brute force curve fitting. Using this technique, anomalously high apparent Young's modulus is discovered of 2.8 nm thick PbZr<sub>0.2</sub>Ti<sub>0.8</sub>O<sub>3</sub> (PZT) film as large as 229.4 ± 12.1 GPa, much higher than the bulk value. The enhancement can be attributed to the strain gradient-induced flexoelectric effect, and the corresponding flexoelectric coefficient is estimated to be ≈200 nC m<sup>-1</sup>. The method is developed to enable an artificial intelligence (AI) system to study the mechanical properties of a wide range of low-dimensional materials. Nanoindentation ; Materials science ; Modulus ; Elastic modulus ; Composite material ; Length scale ; Displacement (psychology) ; Young's modulus ; Bending ; Mechanics Nonlocal and gradient elasticity in micro/nano structures ; Force Microscopy Techniques and Applications ; Ferroelectric and Piezoelectric Materials