jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haoke Qiu | Jingying Wang | Xuepeng Qiu | Xuemin Dai | Zhao‐Yan Sun
Source: Macromolecules (article, 2024-04-12)
Field: Materials Science — Materials Chemistry

Heat-Resistant Polymer Discovery by Utilizing Interpretable Graph Neural Network with Small Data Polymers with exceptional heat resistance are critically valuable in numerous domains, particularly as essential components of flexible organic light-emitting diodes. Among these, polyimides (PIs) demonstrate significant potential as substrate candidates for these next-generation flexible displays due to their robustness. However, traditional Edisonian approaches struggle to navigate the vast chemical space of PIs and also pose challenges of small data, which constrains the learnable chemical space for machine learning (ML). In this study, we propose a chemical-knowledge-based strategy to facilitate the design of PIs with high glass transition temperature (Tg) utilizing an atom-wise graph neural network and small data. Inspired by chemical intuition, our strategy leverages the available data on the same property (i.e., Tg) from other polymers, which is beneficial for expanding the chemical space used for ML. The trained ML model achieves an impressive performance in predicting Tg of polymers. We have also investigated the impact of the chemical space encompassed by the data sets on the performance of ML models. Through interpretability analysis, it has been demonstrated that our ML model has learned more accurate chemical knowledge. Utilizing the ML model, 89 PIs were rapidly discovered from over 106 candidates, with experimental validation confirmed their exceptional heat resistance of the most promising PIs, which have been found to possess a Tg exceeding 405 °C and even 450 °C. These results, along with the trained ML model, have the potential to accelerate the discovery of polymer substrate materials for next-generation flexible display devices. Chemical space ; Interpretability ; Robustness (evolution) ; Polymer ; Computer science ; Artificial intelligence ; Artificial neural network ; Biological system ; Intuition ; Machine learning ; Glass transition ; Materials science ; Nanotechnology ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Conducting polymers and applications ; Synthesis and properties of polymers
Authors: Mohd Nur Ikhmal Salehmin | Tiong Sieh Kiong | Hassan Mohamed | Dallatu Abbas Umar | Kai Yu | Hwai Chyuan Ong | Saifuddin Nomanbhay | Swee Su Lim
Source: Journal of Energy Chemistry (article, 2024-07-31)
Field: Materials Science — Materials Chemistry

Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development Production (economics) ; Computer science ; Hydrogen production ; Development (topology) ; Artificial intelligence ; Hydrogen ; Machine learning ; Biochemical engineering ; Algorithm ; Engineering ; Chemistry ; Mathematics ; Economics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Dongyu Liu | Bipeng Wang | Yifan Wu | Andrey S. Vasenko | Oleg V. Prezhdo
Source: Proceedings of the National Academy of Sciences (article, 2024-08-30)
Field: Materials Science — Materials Chemistry

Breaking the size limitation of nonadiabatic molecular dynamics in condensed matter systems with local descriptor machine learning Nonadiabatic molecular dynamics (NA-MD) is a powerful tool to model far-from-equilibrium processes, such as photochemical reactions and charge transport. NA-MD application to condensed phase has drawn tremendous attention recently for development of next-generation energy and optoelectronic materials. Studies of condensed matter allow one to employ efficient computational tools, such as density functional theory (DFT) and classical path approximation (CPA). Still, system size and simulation timescale are strongly limited by costly ab initio calculations of electronic energies, forces, and NA couplings. We resolve the limitations by developing a fully machine learning (ML) approach in which all the above properties are obtained using neural networks based on local descriptors. The ML models correlate the target properties for NA-MD, implemented with DFT and CPA, directly to the system structure. Trained on small systems, the neural networks are applied to large systems and long timescales, extending NA-MD capabilities by orders of magnitude. We demonstrate the approach with dependence of charge trapping and recombination on defect concentration in MoS<sub>2</sub>. Defects provide the main mechanism of charge losses, resulting in performance degradation. Charge trapping slows with decreasing defect concentration; however, recombination exhibits complex dependence, conditional on whether it occurs between free or trapped charges, and relative concentrations of carriers and defects. Delocalized shallow traps can become localized with increasing temperature, changing trapping and recombination behavior. Completely based on ML, the approach bridges the gap between theoretical models and realistic experimental conditions and enables NA-MD on thousand-atom systems and many nanoseconds. Dynamics (music) ; Statistical physics ; Chemical physics ; Molecular dynamics ; Active matter ; Physics ; Computer science ; Classical mechanics ; Quantum mechanics ; Biology Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Gaurav Kumar Yogesh | Debabrata Nandi | Rungsima Yeetsorn | Waritnan Wanchan | Chandni Devi | Ravi Pratap Singh | Aditya Vasistha | Mukesh Kumar | Pankaj Koinkar | Kamlesh Yadav
Source: Energy Advances (article, 2024-12-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A machine learning approach for estimating supercapacitor performance of graphene oxide nano-ring based electrode materials A novel machine learning approach for supercapacitor performance prediction of graphene oxide nano-ring-based electrode materials. Graphene ; Supercapacitor ; Nano- ; Electrode ; Oxide ; Materials science ; Nanotechnology ; Computer science ; Composite material ; Electrochemistry ; Chemistry ; Metallurgy Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Siyu Liu | Tongqi Wen | Arvind Pattamatta | David J. Srolovitz
Source: Materials Today (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

A prompt-engineered large language model, deep learning workflow for materials classification Workflow ; Computer science ; Artificial intelligence ; Deep learning ; Natural language processing ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Mineral Processing and Grinding
Authors: Robin Ruff | Patrick Reiser | Jan Stühmer | Pascal Friederich
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Connectivity optimized nested line graph networks for crystal structures Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry. We report a nested line-graph neural network achieving state-of-the-art performance in multiple benchmarks. Computer science ; Nested set model ; Graph ; Theoretical computer science ; Data mining Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Xinyu Chen | Shuaihua Lu | Qian Chen | Qionghua Zhou | Jinlan Wang
Source: Nature Communications (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning Data scarcity is one of the critical bottlenecks to utilizing machine learning in material discovery. Transfer learning can use existing big data to assist property prediction on small data sets, but the premise is that there must be a strong correlation between large and small data sets. To extend its applicability in scenarios with different properties and materials, here we develop a hybrid framework combining adversarial transfer learning and expert knowledge, which enables the direct prediction of carrier mobility of two-dimensional (2D) materials using the knowledge learned from bulk effective mass. Specifically, adversarial training ensures that only common knowledge between bulk and 2D materials is extracted while expert knowledge is incorporated to further improve the prediction accuracy and generalizability. Successfully, 2D carrier mobilities are predicted with the accuracy over 90% from only crystal structure, and 21 2D semiconductors with carrier mobilities far exceeding silicon and suitable bandgap are successfully screened out. This work enables transfer learning in simultaneous cross-property and cross-material scenarios, providing an effective tool to predict intricate material properties with limited data. Generalizability theory ; Computer science ; Knowledge transfer ; Transfer of learning ; Machine learning ; Materials informatics ; Premise ; Artificial intelligence ; Data mining ; Knowledge management Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Yuntian Xiao | Chuanhua Wu | Ling Zhou | Patrick Commins | Liang Li | Pancě Naumov | Qiuxiang Yin
Source: Coordination Chemistry Reviews (article, 2024-06-25)
Field: Chemistry — Physical and Theoretical Chemistry

Current trends and advancements in crystallization and single-crystal structural analysis of small molecules Chemistry ; Crystallization ; Current (fluid) ; Single crystal ; Molecule ; Crystallography ; Nanotechnology ; Organic chemistry ; Thermodynamics Crystallography and molecular interactions ; Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography
Authors: Zilong Wang | Yunfeng Li | Jinglei Zhai | Siwei Yang | Biao Sun | Pei Liang
Source: Talanta (article, 2024-04-25)
Field: Chemistry — Analytical Chemistry

Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach Raman spectroscopy ; Interpretability ; Artificial intelligence ; Convolutional neural network ; Robustness (evolution) ; Artificial neural network ; Chemistry ; Deep learning ; Pattern recognition (psychology) ; Biological system ; Analytical Chemistry (journal) ; Computer science ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Andreas Erlebach | Martin Šípka | Indranil Saha | Petr Nachtigall | Christopher J. Heard | Lukáš Grajciar
Source: Nature Communications (article, 2024-05-17)
Field: Materials Science — Materials Chemistry

A reactive neural network framework for water-loaded acidic zeolites Under operating conditions, the dynamics of water and ions confined within protonic aluminosilicate zeolite micropores are responsible for many of their properties, including hydrothermal stability, acidity and catalytic activity. However, due to high computational cost, operando studies of acidic zeolites are currently rare and limited to specific cases and simplified models. In this work, we have developed a reactive neural network potential (NNP) attempting to cover the entire class of acidic zeolites, including the full range of experimentally relevant water concentrations and Si/Al ratios. This NNP has the potential to dramatically improve sampling, retaining the (meta)GGA DFT level accuracy, with the capacity for discovery of new chemistry, such as collective defect formation mechanisms at the zeolite surface. Furthermore, we exemplify how the NNP can be used as a basis for further extensions/improvements which include data-efficient adoption of higher-level (hybrid) references via Δ-learning and the acceleration of rare event sampling via automatic construction of collective variables. These developments represent a significant step towards accurate simulations of realistic catalysts under operando conditions. Zeolite ; Aluminosilicate ; Catalysis ; Hydrothermal circulation ; Computer science ; Sampling (signal processing) ; Chemistry ; Chemical engineering ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Electrocatalysts for Energy Conversion
Authors: Valentine P. Ananikov
Source: Artificial Intelligence Chemistry (article, 2024-07-27)
Field: Materials Science — Materials Chemistry

Top 20 influential AI-based technologies in chemistry The beginning and ripening of digital chemistry is analyzed focusing on the role of artificial intelligence (AI) in an expected leap in chemical sciences to bring this area to the next evolutionary level. The analytic description selects and highlights the top 20 AI-based technologies and 7 broader themes that are reshaping the field. It underscores the integration of digital tools such as machine learning, big data, digital twins, the Internet of Things (IoT), robotic platforms, smart control of chemical processes, virtual reality and blockchain, among many others, in enhancing research methods, educational approaches, and industrial practices in chemistry. The significance of this study lies in its focused overview of how these digital innovations foster a more efficient, sustainable, and innovative future in chemical sciences. This article not only illustrates the transformative impact of these technologies but also draws new pathways in chemistry, offering a broad appeal to researchers, educators, and industry professionals to embrace these advancements for addressing contemporary challenges in the field. Data science ; Chemistry ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Hong Zhou | Dongxiao Li | Zhihao Ren | Cheng Xu | Lin‐Fa Wang | Chengkuo Lee
Source: Science Advances (article, 2024-05-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface plasmons-phonons for mid-infrared hyperspectral imaging Surface plasmons have proven their ability to boost the sensitivity of mid-infrared hyperspectral imaging by enhancing light-matter interactions. Surface phonons, a counterpart technology to plasmons, present unclear contributions to hyperspectral imaging. Here, we investigate this by developing a plasmon-phonon hyperspectral imaging system that uses asymmetric cross-shaped nanoantennas composed of stacked plasmon-phonon materials. The phonon modes within this system, controlled by light polarization, capture molecular refractive index intensity and lineshape features, distinct from those observed with plasmons, enabling more precise and sensitive molecule identification. In a deep learning-assisted imaging demonstration of severe acute respiratory syndrome coronavirus (SARS-CoV), phonons exhibit enhanced identification capabilities (230,400 spectra/s), facilitating the de-overlapping and observation of the spatial distribution of two mixed SARS-CoV spike proteins. In addition, the plasmon-phonon system demonstrates increased identification accuracy (93%), heightened sensitivity, and enhanced detection limits (down to molecule monolayers). These findings extend phonon polaritonics to hyperspectral imaging, promising applications in imaging-guided molecule screening and pharmaceutical analysis. Phonon ; Plasmon ; Hyperspectral imaging ; Surface plasmon ; Materials science ; Infrared ; Optics ; Optoelectronics ; Physics ; Condensed matter physics ; Remote sensing ; Geology Gold and Silver Nanoparticles Synthesis and Applications ; Photoacoustic and Ultrasonic Imaging ; Plasmonic and Surface Plasmon Research
Authors: Lei Jin | Gurpreet Singh Selopal | Xiao Wei Sun | Federico Rosei
Source: Advanced Energy Materials (article, 2024-10-28)
Field: Materials Science — Materials Chemistry

Core‐Shell Colloidal Quantum Dots for Energy Conversion Abstract Colloidal quantum dots (QDs) are promising building blocks in optoelectronic devices, mainly due to their size/shape/composition‐tunable properties. Core–shell QDs, in particular, offer enhanced stability, mitigated photoluminescence blinking, and suppressed non‐radiative recombination compared to plain QDs, making them highly promising for energy conversion applications such as photovoltaic devices, luminescent solar concentrators, solar‐driven hydrogen production, and light‐emitting diodes. Here, a comprehensive analysis of core–shell QDs in energy conversion technologies is provided. Emerging design strategies are explored and various synthetic methods focusing on optimizing band structure, band alignment, and optical properties are critically explored. Insights into the structure‐property relationship are discussed, highlighting recent advancements and the most effective strategies to enhance energy conversion performance. The review is concluded by addressing key challenges and proposing future research directions, emphasizing the need for rational design, precise synthesis, effective surface engineering, and the integration of machine learning to achieve optimized properties for technological applications. Materials science ; Quantum dot ; Colloid ; Core (optical fiber) ; Shell (structure) ; Nanotechnology ; Chemical engineering ; Composite material Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Shaohan Tian | Xue Jiang | Weiren Wang | Zhihua Jing | Chi Zhang | Cheng Zhang | Turab Lookman | Yanjing Su
Source: Acta Materialia (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

Steel design based on a large language model Materials science ; Metallurgy ; Forensic engineering ; Engineering Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Dai‐Ming Tang | Ovidiu Cretu | Shinsuke Ishihara | Yongjia Zheng | Keigo Otsuka | Rong Xiang | Shigeo Maruyama | Hui–Ming Cheng | Chang Liu | Dmitri Golberg
Source: Nature Reviews Electrical Engineering (article, 2024-02-14)
Field: Materials Science — Materials Chemistry

Chirality engineering for carbon nanotube electronics Chirality (physics) ; Carbon nanotube ; Electronics ; Nanotechnology ; Materials science ; Nanotube ; Engineering physics ; Physics ; Engineering ; Electrical engineering ; Quantum mechanics ; Chiral symmetry Carbon Nanotubes in Composites ; Graphene research and applications ; Mechanical and Optical Resonators
Authors: J. Tong | Yang Ming Fu | Daniil Domaretskiy | Flaviano Della Pia | Parveen Dagar | L. Powell | Daniel Bahamón | Shiqi Huang | B. Xin | R. N. Costa Filho | Lourdes F. Vega | I. V. Grigorieva | F. M. Peeters | Angelos Michaelides | M. Lozada-Hidalgo
Source: Nature (preprint, 2024-06-19)
Field: Materials Science — Materials Chemistry

Control of proton transport and hydrogenation in double-gated graphene The basal plane of graphene can function as a selective barrier that is permeable to protons<sup>1,2</sup> but impermeable to all ions<sup>3,4</sup> and gases<sup>5,6</sup>, stimulating its use in applications such as membranes<sup>1,2,7,8</sup>, catalysis<sup>9,10</sup> and isotope separation<sup>11,12</sup>. Protons can chemically adsorb on graphene and hydrogenate it<sup>13,14</sup>, inducing a conductor-insulator transition that has been explored intensively in graphene electronic devices<sup>13-17</sup>. However, both processes face energy barriers<sup>1,12,18</sup> and various strategies have been proposed to accelerate proton transport, for example by introducing vacancies<sup>4,7,8</sup>, incorporating catalytic metals<sup>1,19</sup> or chemically functionalizing the lattice<sup>18,20</sup>. But these techniques can compromise other properties, such as ion selectivity<sup>21,22</sup> or mechanical stability<sup>23</sup>. Here we show that independent control of the electric field, E, at around 1 V nm<sup>-1</sup>, and charge-carrier density, n, at around 1 × 10<sup>14</sup> cm<sup>-2</sup>, in double-gated graphene allows the decoupling of proton transport from lattice hydrogenation and can thereby accelerate proton transport such that it approaches the limiting electrolyte current for our devices. Proton transport and hydrogenation can be driven selectively with precision and robustness, enabling proton-based logic and memory graphene devices that have on-off ratios spanning orders of magnitude. Our results show that field effects can accelerate and decouple electrochemical processes in double-gated 2D crystals and demonstrate the possibility of mapping such processes as a function of E and n, which is a new technique for the study of 2D electrode-electrolyte interfaces. Graphene ; Proton ; Control (management) ; Double layered ; Chemistry ; Materials science ; Nanotechnology ; Physics ; Computer science ; Nuclear physics ; Composite material ; Artificial intelligence Graphene research and applications ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Jessica Lalonde | Ghanshyam Pilania | Babetta L. Marrone
Source: Polymer Chemistry (review, 2024-10-15)
Field: Materials Science — Biomaterials

Materials designed to degrade: structure, properties, processing, and performance relationships in polyhydroxyalkanoate biopolymers Conventional plastics pose significant environmental and health risks across their life cycle, driving intense interest in sustainable alternatives. Among these, polyhydroxyalkanoates (PHAs) stand out for their biocompatibility, degradation characteristics, and diverse applications. Yet, challenges like production cost, scalability, and limited chemical variety hinder their widespread adoption, impacting material selection and design. This review examines PHA research through the lens of the classical materials tetrahedron, exploring property-structure-processing-performance (PSPP) relationships. By analyzing recent literature and addressing current limitations, we gain valuable insights into PHA development. Despite challenges, we remain optimistic about the role of PHAs in transitioning towards a circular plastic economy, emphasizing the need for further research to unlock their full potential. Polyhydroxyalkanoates ; Biopolymer ; Materials science ; Biochemical engineering ; Chemical engineering ; Chemistry ; Polymer ; Composite material ; Engineering ; Geology ; Bacteria biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Bone Tissue Engineering Materials