jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ikhlas Kachou | Kamel Saidi | Christian Hernández‐Álvarez | Mohamed Dammak | Inocencio R. Martín
Source: Materials Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Enhancing thermometric precision: modulating the temperature of maximum sensitivity <i>via</i> erbium dopant addition in Ba<sub>2</sub>GdV<sub>3</sub>O<sub>11</sub>:Tm<sup>3+</sup>/Yb<sup>3+</sup> nano phosphors Enhancing thermometric precision: modulating the temperature of maximum sensitivity via erbium dopant addition in Ba 2 GdV 3 O 11 nano phosphors: Tm 3+ /Yb 3+ . Dopant ; Erbium ; Analytical Chemistry (journal) ; Materials science ; Doping ; Sensitivity (control systems) ; Chemistry ; Optoelectronics ; Chromatography Luminescence Properties of Advanced Materials ; Gas Sensing Nanomaterials and Sensors ; Atomic and Subatomic Physics Research
Authors: Cong Su | Eli Janzen | Mingze He | Chi Li | Alex Zettl | Joshua D. Caldwell | James H. Edgar | Igor Aharonovich
Source: Advances in Optics and Photonics (article, 2024-03-19)
Field: Materials Science — Materials Chemistry

Fundamentals and emerging optical applications of hexagonal boron nitride: a tutorial Hexagonal boron nitride (hBN), also known as white graphite, is a transparent layered crystal with a wide bandgap. Its crystal structure resembles graphite, featuring layers composed of honeycomb lattices held together through van der Waals forces. The layered crystal structure of hBN facilitates exfoliation into thinner flakes and makes it highly anisotropic in in-plane and out-of-plane directions. Unlike graphite, hBN is both insulating and transparent, making it an ideal material for isolating devices from the environment and acting as a waveguide. As a result, hBN has found extensive applications in optical devices, electronic devices, and quantum photonic devices. This comprehensive tutorial aims to provide readers with a thorough understanding of hBN, covering its synthesis, lattice and spectroscopic characterization, and various applications in optoelectronic and quantum photonic devices. This tutorial is designed for both readers without prior experience in hBN and those with expertise in specific fields seeking to understand its relevance and connections to others. Materials science ; Optoelectronics ; Hexagonal boron nitride ; Boron nitride ; Graphite ; Photonic crystal ; van der Waals force ; Nanotechnology ; Anisotropy ; Graphene ; Optics ; Physics Graphene research and applications ; 2D Materials and Applications ; Ga2O3 and related materials
Authors: Sergei V. Kalinin | Maxim Ziatdinov | Mahshid Ahmadi | Ayana Ghosh | Kevin M. Roccapriore | Yongtao Liu | Rama K. Vasudevan
Source: Applied Physics Reviews (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Designing workflows for materials characterization Experimental science is enabled by the combination of synthesis, imaging, and functional characterization organized into evolving discovery loop. Synthesis of new material is typically followed by a set of characterization steps aiming to provide feedback for optimization or discover fundamental mechanisms. However, the sequence of synthesis and characterization methods and their interpretation, or research workflow, has traditionally been driven by human intuition and is highly domain specific. Here, we explore concepts of scientific workflows that emerge at the interface between theory, characterization, and imaging. We discuss the criteria by which these workflows can be constructed for special cases of multiresolution structural imaging and functional characterization, as a part of more general material synthesis workflows. Some considerations for theory–experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupts the classical progression from ideation, orchestration, and execution stages of workflow development. To accelerate this transition, we propose the framework for workflow design, including universal hyperlanguages describing laboratory operation, ontological domain matching, reward functions and their integration between domains, and policy development for workflow optimization. These tools will enable knowledge-based workflow optimization; enable lateral instrumental networks, sequential and parallel orchestration of characterization between dissimilar facilities; and empower distributed research. Workflow ; Computer science ; Orchestration ; Characterization (materials science) ; Domain (mathematical analysis) ; Cloud computing ; Workflow management system ; Software engineering ; Artificial intelligence ; Data science ; Database Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced X-ray and CT Imaging
Authors: Santiago Miret | N. M. Anoop Krishnan
Source: arXiv (Cornell University) (preprint, 2024-02-07)
Field: Materials Science — Materials Chemistry

Are LLMs Ready for Real-World Materials Discovery? Large Language Models (LLMs) create exciting possibilities for powerful language processing tools to accelerate research in materials science. While LLMs have great potential to accelerate materials understanding and discovery, they currently fall short in being practical materials science tools. In this position paper, we show relevant failure cases of LLMs in materials science that reveal current limitations of LLMs related to comprehending and reasoning over complex, interconnected materials science knowledge. Given those shortcomings, we outline a framework for developing Materials Science LLMs (MatSci-LLMs) that are grounded in materials science knowledge and hypothesis generation followed by hypothesis testing. The path to attaining performant MatSci-LLMs rests in large part on building high-quality, multi-modal datasets sourced from scientific literature where various information extraction challenges persist. As such, we describe key materials science information extraction challenges which need to be overcome in order to build large-scale, multi-modal datasets that capture valuable materials science knowledge. Finally, we outline a roadmap for applying future MatSci-LLMs for real-world materials discovery via: 1. Automated Knowledge Base Generation; 2. Automated In-Silico Material Design; and 3. MatSci-LLM Integrated Self-Driving Materials Laboratories. Business ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Mineral Processing and Grinding
Authors: Sai C. Yelishala | Connor Murphy | Longji Cui
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Molecular perspective and engineering of thermal transport and thermoelectricity in polymers This review highlights molecular and nanoscale engineering of electrically insulating and semiconducting polymers for improved heat transport and thermoelectricity. Thermoelectric effect ; Nanoscopic scale ; Polymer ; Materials science ; Perspective (graphical) ; Nanotechnology ; Thermoelectric materials ; Thermal ; Engineering physics ; Thermal conductivity ; Engineering ; Physics ; Computer science ; Composite material ; Thermodynamics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Organic Electronics and Photovoltaics
Authors: Mingyang Wang | Shuai Li | Jike Wang | Odin Zhang | Hongyan Du | Dejun Jiang | Zhenxing Wu | Yafeng Deng | Yu Kang | Peichen Pan | Dan Li | Xiaorui Wang | Xiaojun Yao | Tingjun Hou | Chang‐Yu Hsieh
Source: Nature Communications (article, 2024-11-22)
Field: Chemistry — Organic Chemistry

ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning Despite the significant potential of generative models, low synthesizability of many generated molecules limits their real-world applications. In response to this issue, we develop ClickGen, a deep learning model that utilizes modular reactions like click chemistry to assemble molecules and incorporates reinforcement learning along with inpainting technique to ensure that the proposed molecules display high diversity, novelty and strong binding tendency. ClickGen demonstrates superior performance over the other reaction-based generative models in terms of novelty, synthesizability, and docking conformation similarity for existing binders targeting the three proteins. We then proceeded to conduct wet-lab validation on the ClickGen's proposed molecules for poly adenosine diphosphate-ribose polymerase 1. Due to the guaranteed high synthesizability and model-generated synthetic routes for reference, we successfully produced and tested the bioactivity of these novel compounds in just 20 days, much faster than typically expected time frame when handling sufficiently novel molecules. In bioactivity assays, two lead compounds demonstrated superior anti-proliferative efficacy against cancer cell lines, low toxicity, and nanomolar-level inhibitory activity to PARP1. We demonstrate that ClickGen and related models may represent a new paradigm in molecular generation, bringing AI-driven, automated experimentation and closed-loop molecular design closer to realization. Reinforcement learning ; Chemical space ; Modular design ; Computer science ; Generative model ; Novelty ; Template ; Generative grammar ; Artificial intelligence ; Computational biology ; Chemistry ; Combinatorial chemistry ; Drug discovery ; Biology ; Biochemistry Click Chemistry and Applications ; Protein Degradation and Inhibitors ; CRISPR and Genetic Engineering
Authors: Yongbo Xia | Lei Wang | Xiaoru Li | Tingting Liao | Jichao Zhai | Xiaohui Wang | Kaifu Huo
Source: Battery energy (article, 2024-07-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Biomass‐based functional separators for rechargeable batteries Abstract The global transition toward sustainable energy sources has prompted a paradigm shift in the field of energy storage. The separator is an important component in rechargeable batteries, which facilitates the rapid passage of ions and ensures the safety and efficiency of the electrochemical process by preventing direct contact between the anode and cathode. Traditional polyolefin‐based separators induce environmental concerns due to their nonbiodegradable nature. Biomass‐based separators derived from renewable sources such as plant fibers, agricultural waste, and biopolymers have emerged as promising alternatives to traditional polymer separators. In this review, we summarize the current state and development of biomass‐based separators for high‐performance batteries, including innovative manufacturing techniques, novel biomass materials, functionalization strategies, performance evaluation methods, and potential applications. The review also delves into the environmental impact and sustainability analysis of biomass‐based separators, offering insights into the potential of biomass as the most sustainable resource for future energy storage solutions. This review could provide a holistic understanding of the advancements and potential of biomass‐based separators, shedding light on the path toward sustainable and efficient energy storage based on biomass‐derived separators. Renewable energy ; Biomass (ecology) ; Polyolefin ; Separator (oil production) ; Energy storage ; Environmental science ; Sustainability ; Process engineering ; Waste management ; Biochemical engineering ; Nanotechnology ; Materials science ; Engineering ; Power (physics) ; Ecology Supercapacitor Materials and Fabrication ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Kyle Noordhoek | Christopher J. Bartel
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials The surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important. The relevant surface(s) and their properties are determined, in large part, by the material's synthesis or operating conditions. These conditions dictate thermodynamic driving forces and kinetic rates responsible for yielding the observed surface structure and morphology. Computational surface science methods have long been applied to connect thermochemical conditions to surface phase stability, particularly in the heterogeneous catalysis and thin film growth communities. This review provides a brief introduction to first-principles approaches to compute surface phase diagrams before introducing emerging data-driven approaches. The remainder of the review focuses on the application of machine learning, predominantly in the form of learned interatomic potentials, to study complex surfaces. As machine learning algorithms and large datasets on which to train them become more commonplace in materials science, computational methods are poised to become even more predictive and powerful for modeling the complexities of inorganic surfaces at the nanoscale. Interatomic potential ; Materials science ; Computer science ; Chemical physics ; Computational chemistry ; Chemistry ; Molecular dynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Mingan Chen | Xinyu Jiang | Lehan Zhang | Xiaoxu Chen | Yiming Wen | Zhiyong Gu | Xutong Li | Mingyue Zheng
Source: Medicinal Research Reviews (review, 2024-01-03)
Field: Materials Science — Materials Chemistry

The emergence of machine learning force fields in drug design In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design. Scalability ; Computer science ; Implementation ; Field (mathematics) ; Quality (philosophy) ; Machine learning ; Artificial intelligence ; Force field (fiction) ; Data science ; Software engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Hamdy M. Naguib | Eman O. Taha | Asmaa S. El-Deeb | M. M. Abdel Kader | Mona A. Ahmed
Source: Polymer Bulletin (article, 2024-02-20)
Field: Materials Science — Polymers and Plastics

Influence of chromium oxide nanoparticles and fiber fillers on silicone rubber nanocomposite Abstract The development of polymer composites, considering the environmental issues, is aimed for minimizing the industrial content. New environmental-friendly nanocomposites, based on silicone rubber matrix and different concentrations of natural fiber/chromium oxide fillers, are prepared for the first time. The microstructural analysis confirms the successful obtaining of microfibers after treatment and chromium oxide nanoparticles and the formation of the final composite. The nanocomposites achieved improved physical and mechanical properties due to the dual effect of both fillers and attachment to the matrix. The 1% optimized nanocomposite achieved the highest modulus, dielectric properties, hydrophobicity, and surface stability. The effect of exposure to weathering conditions was studied. The characterization sentence asserts the preparation of a novel silicone rubber nanocomposite with improved properties, along with the environmental impact, regarding the dual effect of natural fiber waste and chromium oxide nanoparticles. Nanocomposite ; Materials science ; Silicone rubber ; Composite material ; Nanoparticle ; Oxide ; Microfiber ; Polymer ; Natural rubber ; Composite number ; Fiber ; Electrospinning ; Silicone ; Chromium ; Nanotechnology ; Metallurgy Polymer Nanocomposites and Properties ; Polymer Nanocomposite Synthesis and Irradiation ; Dielectric materials and actuators
Authors: Farid Taheri‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬ | Jesse R. J. G. Llanos
Source: Polymers (article, 2024-05-24)
Field: Materials Science — Polymers and Plastics

Comparative Performance of Kevlar, Glass and Basalt Epoxy- and Elium-Based Composites under Static-, Low- and High-Velocity Loading Scenarios—Introduction to an Effective Recyclable and Eco-Friendly Composite In general, the majority of fiber-reinforced polymer composites (FRPs) used in structural applications comprise carbon, glass, and aramid fibers reinforced with epoxy resin, with the occasional utilization of polyester and vinyl ester resins. This study aims to assess the feasibility of utilizing recyclable and sustainable materials to create a resilient composite suitable for structural applications, particularly in scenarios involving low-velocity and high-velocity impact (LVI, HVI) loading. The paper presents a comparative analysis of the performance of E-glass, aramid, and eco-friendly basalt-reinforcing fabrics as reinforcement fibers in both thermosetting (epoxy) and recyclable thermoplastic (Elium<sup>©</sup>) resins. Given the limited research on Elium composites, especially those incorporating basalt-reinforcing fiber, there is an urgent need to expand the databases of fundamental mechanical properties for these diverse composites. This necessity is exacerbated by the scarcity of the literature regarding their performance under low- and high-velocity impact loadings. The results of this study will demonstrate the potential of basalt-reinforced Elium composite as an effective recyclable and environmentally friendly structural material system for both static and dynamic loading conditions. Epoxy ; Materials science ; Composite material ; Basalt fiber ; Environmentally friendly ; Aramid ; Vinyl ester ; Thermosetting polymer ; Composite number ; Kevlar ; Fibre-reinforced plastic ; Glass fiber ; Composite epoxy material ; Polymer ; Fiber ; Copolymer Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Structural Response to Dynamic Loads
Authors: Kaiyuan Zheng | Zhongping He | Lun Che | Hong Cheng | Meiling Ge | Tianyu Si | Xiaotian Xu
Source: Materials Science in Semiconductor Processing (article, 2024-05-18)
Field: Materials Science — Materials Chemistry

Deep alloys: Metal materials empowered by deep learning Materials science ; Metallurgy ; Deep learning ; Metal ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Welding Techniques and Residual Stresses
Authors: Lina Tang | Puiki Leung | Qian Xu | Cristina Flox
Source: ChemElectroChem (article, 2024-06-10)
Field: Materials Science — Materials Chemistry

Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery Abstract This review exploits the crucial role of computational methods in discovering and optimizing materials for redox flow batteries (RFBs). Integration of high‐throughput computational screening (HTCS) and machine learning (ML) accelerates materials discovery, guided by algorithms categorizing RFBs. A collaborative exploration, spanning macroscopic to mesoscopic scales, combines quantum machine learning with reinforcement learning, transfer learning, time series analysis, Bayesian optimization, active learning and various generative models. The collaborative integration of ML with computational techniques and experimental methods, anchored in experimentally validated Density Functional Theory (DFT) calculations and molecular dynamics (MD) simulations, proves indispensable for cost‐effective RFBs. Data collection and feature engineering are explored, emphasizing the integration of optimization goals and precise data collection within the ML framework. Feature analysis importance is highlighted, utilizing methods such as the filter, embedded, wrapper and deep learning methods for efficient energy materials exploration. Computational perspectives on materials features and operating conditions encompass membrane characteristics, fluid dynamics, temperature dependence and pressure sensitivity. Time‐dependent features and ML‐generated insights are crucial for understanding cycling performance intricacies, providing a comprehensive understanding of RFB materials. Computer science ; Bayesian optimization ; Artificial intelligence ; Machine learning ; Reinforcement learning ; Feature (linguistics) Machine Learning in Materials Science ; Advanced battery technologies research ; Electrochemical Analysis and Applications
Authors: Yanbin Ning | Feng Yang | Yan Zhang | Zhuomin Qiang | Geping Yin | Jiajun Wang | Shuaifeng Lou
Source: Matter (article, 2024-05-20)
Field: Materials Science — Materials Chemistry

Bridging multimodal data and battery science with machine learning Bridging (networking) ; Computer science ; Battery (electricity) ; Machine learning ; Artificial intelligence ; Field (mathematics) ; Big data ; Artificial neural network ; Domain (mathematical analysis) ; Deep learning ; Data science ; Data mining Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Jongmin Han | Youngchun Kwon | Youn-Suk Choi | Seokho Kang
Source: Journal of Cheminformatics (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Improving chemical reaction yield prediction using pre-trained graph neural networks Graph neural networks (GNNs) have proven to be effective in the prediction of chemical reaction yields. However, their performance tends to deteriorate when they are trained using an insufficient training dataset in terms of quantity or diversity. A promising solution to alleviate this issue is to pre-train a GNN on a large-scale molecular database. In this study, we investigate the effectiveness of GNN pre-training in chemical reaction yield prediction. We present a novel GNN pre-training method for performance improvement.Given a molecular database consisting of a large number of molecules, we calculate molecular descriptors for each molecule and reduce the dimensionality of these descriptors by applying principal component analysis. We define a pre-text task by assigning a vector of principal component scores as the pseudo-label to each molecule in the database. A GNN is then pre-trained to perform the pre-text task of predicting the pseudo-label for the input molecule. For chemical reaction yield prediction, a prediction model is initialized using the pre-trained GNN and then fine-tuned with the training dataset containing chemical reactions and their yields. We demonstrate the effectiveness of the proposed method through experimental evaluation on benchmark datasets. Computer science ; Artificial neural network ; Yield (engineering) ; Graph ; Machine learning ; Artificial intelligence ; Data mining ; Theoretical computer science ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation