jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Georg Kastlunger | Sudarshan Vijay | Xi Chen | Shubham Sharma | Andrew A. Peterson
Source: ChemPhysChem (article, 2024-03-21)
Field: Chemistry — Electrochemistry

On the Thermodynamic Equivalence of Grand Canonical, Infinite‐Size, and Capacitor‐Based Models in First‐Principle Electrochemistry First principles-based computational and theoretical methods are constantly evolving trying to overcome the many obstacles towards a comprehensive understanding of electrochemical processes on an atomistic level. One of the major challenges has been the determination of reaction energetics under a constant potential. Here, a theoretical framework was proposed applying standard electronic structure methods and extrapolating to the infinite-cell size limit where reactions do not alter the potential. Today, electronically grand canonical modifications to electronic structure methods, holding the potential constant by varying the number of electrons in a finite simulation cell, become increasingly popular. In this perspective, we show that these two schemes are thermodynamically equivalent. Further, we link these methods to capacitive models of the interface, in the limit that the capacitance of the charging components (whether continuum or atomistic) are equal and invariant along the reaction pathway. We benchmark the three approaches with an example of alkali cation adsorption on Pt(111) showing that all three approaches converge in the cases of Li, Na and K. For Cs, however, strong deviation from the ideal conditions leads to a spread in the respective results. We discuss the latter by highlighting the cases of broken equivalence and assumptions among the approaches. Equivalence (formal languages) ; Electrochemistry ; Grand canonical ensemble ; Capacitor ; Statistical physics ; Thermodynamics ; Materials science ; Chemistry ; Physics ; Theoretical physics ; Mathematics ; Physical chemistry ; Quantum mechanics ; Pure mathematics ; Electrode ; Voltage ; Statistics Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Spectroscopy and Quantum Chemical Studies
Authors: Rao Shahid Aziz | Sławomir Kozieł | Anna Pietrenko‐Dabrowska
Source: Scientific Reports (article, 2024-07-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Millimeter wave negative refractive index metamaterial antenna array In this paper, a novel negative refractive index metamaterial (NIM) is developed and characterized. The proposed metamaterial exhibits negative effective permittivity (ε<sub>effe</sub>) and negative effective permeability (µ<sub>effe</sub>) at millimeter wave frequency of 28 GHz. This attractive feature is utilized to enhance the gain of a microstrip patch antenna (MPA). Two thin layers of 5 × 5 subwavelength unit cell array of NIM are placed above a single MPA to enhance the gain of the antenna. Each unit cell has an area of 3.4 × 3.4 mm<sup>2</sup>. A gain increase of 7.9 dBi has been observed when using the proposed NIM as a superstrate. Furthermore, the NIM array is placed over a 2 × 2 array of MPAs with four ports to demonstrate versatility of the metamaterial. The total size of the 2 × 2 antenna array system with N-MTM is about 61.1 × 34 × 16mm<sup>3</sup> (5.71λ × 3.18λ × 1.5λ, where λ is the free-space wavelength at 28 GHz). The measurement result indicate that the maximum gain of the antenna array is 13.5dBi. A gain enhancement of 7.55 dB in E-Plane and 7.25 dB in H-Plane at the resonant frequency of 28 GHz is obtained. The proposed antenna structure is suitable for 5G millimeter wave communications, in particular, for possible implementation in future millimeter wave access points and cellular base stations. Metamaterial ; Metamaterial antenna ; Extremely high frequency ; Optics ; Antenna (radio) ; Millimeter ; Optoelectronics ; Antenna gain ; Antenna array ; Physics ; Dipole antenna ; Materials science ; Antenna aperture ; Telecommunications ; Antenna factor ; Computer science Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Dexin Zhu | Hong‐Hui Wu | Faguo Hou | Jinyong Zhang | Zilin Gao | Chunlei Shang | Shuize Wang | Guilin Wu | Junheng Gao | Kunming Pan | Liudong Hou | Jing Ma | Turab Lookman | Xinping Mao
Source: Scripta Materialia (article, 2024-06-03)
Field: Materials Science — Metals and Alloys

A transfer learning strategy for tensile strength prediction in austenitic stainless steel across temperatures Materials science ; Ultimate tensile strength ; Austenite ; Metallurgy ; Austenitic stainless steel ; Composite material ; Microstructure ; Corrosion Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Sebastian Bauer | Peter Benner | Tristan Bereau | Volker Blüm | Mario Boley | Christian Carbogno | C. Richard A. Catlow | Gerhard Dehm | Sebastian Eibl | Ralph Ernstorfer | Ádám Fekete | Lucas Foppa | Peter Fratzl | Christoph Freysoldt | Baptiste Gault | Luca M. Ghiringhelli | Sajal Kumar Giri | Anton Gladyshev | Pawan Goyal | Jason Hattrick‐Simpers | Lara Kabalan | Petr Karpov | Mohammad S. Khorrami | Christoph T. Koch | Sebastian Kokott | Thomas Kosch | Igor Kowalec | Kurt Kremer | Andreas Leitherer | Yue Li | Christian H. Liebscher | Andrew J. Logsdail | Zhongwei Lu | Phuc Luong | Andreas Marek | F. Merz | Jaber Rezaei Mianroodi | Jörg Neugebauer | Zongrui Pei | Thomas A. R. Purcell | Dierk Raabe | Markus Rampp | Mariana Rossi | Jan M. Rost | James E. Saal | Ulf Saalmann | K.N. Sasidhar | Alaukik Saxena | Luigi Sbailò | Markus Scheidgen | Marcel Schloz | Daniel F. Schmidt | Simon Teshuva | Annette Trunschke | Ye Wei | Gerhard Weikum | R. Patrick Xian | Yiyu Yao | Junqi Yin | Meng Zhao | Matthias Scheffler
Source: Modelling and Simulation in Materials Science and Engineering (article, 2024-05-17)
Field: Materials Science — Materials Chemistry

Roadmap on data-centric materials science Abstract Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research. Materials science ; Engineering physics ; Engineering ethics ; Nanotechnology ; Systems engineering ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Adrian Mirza | Nawaf Alampara | Sreekanth Kunchapu | Ríos-García, Martiño | Benedict Emoekabu | Aswanth Krishnan | Tanya Gupta | Schilling-Wilhelmi, Mara | Macjonathan Okereke | Aneesh, Anagha | Amir Mohammad Elahi | Mehrdad Asgari | J. Eberhardt | Elbeheiry, Hani M. | Gil, María Victoria | Maximilian Greiner | Caroline T. Holick | Christina Glaubitz | Tim Hoffmann | Ibrahim, Abdelrahman | Lea C. Klepsch | Yannik Köster | Fabian Alexander Kreth | Jakob Meyer | Santiago Miret | Peschel, Jan Matthias | Michael Ringleb | Nicole C. Roesner | Schreiber, Johanna | Ulrich S. Schubert | Leanne M. Stafast | Dinga Wonanke | Michael Pieler | Philippe Schwaller | Kevin Maik Jablonka
Source: arXiv (Cornell University) (preprint, 2024-04-01)
Field: Chemistry — Physical and Theoretical Chemistry

Are large language models superhuman chemists? Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained. However, we possess only a limited systematic understanding of the chemical capabilities of LLMs, which would be required to improve models and mitigate potential harm. Here, we introduce "ChemBench," an automated framework for evaluating the chemical knowledge and reasoning abilities of state-of-the-art LLMs against the expertise of chemists. We curated more than 2,700 question-answer pairs, evaluated leading open- and closed-source LLMs, and found that the best models outperformed the best human chemists in our study on average. However, the models struggle with some basic tasks and provide overconfident predictions. These findings reveal LLMs' impressive chemical capabilities while emphasizing the need for further research to improve their safety and usefulness. They also suggest adapting chemistry education and show the value of benchmarking frameworks for evaluating LLMs in specific domains. Polymer science ; Chemistry History and advancements in chemistry ; Various Chemistry Research Topics
Authors: Xiaoyu Yang | Kun Zhou | Xin He | Lijun Zhang
Source: Science China Materials (article, 2024-03-19)
Field: Materials Science — Materials Chemistry

Methods and applications of machine learning in computational design of optoelectronic semiconductors Computer science ; Workflow ; Machine learning ; Artificial intelligence ; Expediting ; Representation (politics) ; Systems engineering ; Engineering ; Database Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Quantum Dots Synthesis And Properties
Authors: Josiah Roberts | Biswas Rijal | Simon Divilov | Jon‐Paul Maria | William G. Fahrenholtz | Douglas A. Wolfe | Donald W. Brenner | Stefano Curtarolo | Eva Zurek
Source: npj Computational Materials (article, 2024-07-02)
Field: Materials Science — Materials Chemistry

Machine learned interatomic potentials for ternary carbides trained on the AFLOW database Abstract Large-density functional theory (DFT) databases are a treasure trove of energies, forces, and stresses that can be used to train machine-learned interatomic potentials for atomistic modeling. Herein, we employ structural relaxations from the AFLOW database to train moment tensor potentials (MTPs) for four carbide systems: CHfTa, CHfZr, CMoW, and CTaTi. The resulting MTPs are used to relax ~6300 random symmetric structures, and are subsequently improved via active learning to generate robust potentials (RP) that can relax a wide variety of structures, and accurate potentials (AP) designed for the relaxation of low-energy systems. This protocol is shown to yield convex hulls that are indistinguishable from those predicted by AFLOW for the CHfTa, CHfZr, and CTaTi systems, and in the case of the CMoW system to predict thermodynamically stable structures that are not found within AFLOW, highlighting the potential of the employed protocol within crystal structure prediction. Relaxation of over three hundred (Mo 1− x W x )C stoichiometry crystals first with the RP then with the AP yields formation enthalpies that are in excellent agreement with those obtained via DFT. Ternary operation ; Carbide ; Interatomic potential ; Database ; Materials science ; Computer science ; Chemistry ; Metallurgy ; Computational chemistry ; Molecular dynamics ; Programming language Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Advanced materials and composites
Authors: Filippo Bigi | Sergey N. Pozdnyakov | Michele Ceriotti
Source: The Journal of Chemical Physics (article, 2024-07-26)
Field: Materials Science — Materials Chemistry

Wigner kernels: Body-ordered equivariant machine learning without a basis Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials. Among the many different approaches that have been pursued, the description of local atomic environments in terms of their discretized neighbor densities has been used widely and very successfully. We propose a novel density-based method, which involves computing "Wigner kernels." These are fully equivariant and body-ordered kernels that can be computed iteratively at a cost that is independent of the basis used to discretize the density and grows only linearly with the maximum body-order considered. Wigner kernels represent the infinite-width limit of feature-space models, whose dimensionality and computational cost instead scale exponentially with the increasing order of correlations. We present several examples of the accuracy of models based on Wigner kernels in chemical applications, for both scalar and tensorial targets, reaching an accuracy that is competitive with state-of-the-art deep-learning architectures. We discuss the broader relevance of these findings to equivariant geometric machine-learning. Discretization ; Curse of dimensionality ; Equivariant map ; Computer science ; Basis (linear algebra) ; Artificial intelligence ; Representation (politics) ; Point cloud ; Scalar (mathematics) ; Mathematics ; Algorithm ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Macaulay M. Owen | Leong Sing Wong | Emmanuel O. Achukwu | Mohd Shahneel Saharudin | Hazizan Md Akil
Source: Construction and Building Materials (article, 2024-07-31)
Field: Materials Science — Polymers and Plastics

Enhanced mechanical, thermal, and morphological properties of waste PET plastics reinforced with coated biodegradable kenaf fibers for infrastructure applications The surge in polyethylene terephthalate (PET) plastic bottle production has led to a rise in waste PET plastics. Recycling these waste PET plastics for infrastructural purposes offers a feasible solution to curb environmental pollution and the climatic impact caused by dumping vast quantities into landfills. Hence, efforts have been directed towards creating waste PET plastic composites reinforced with coated biodegradable natural kenaf fibers for infrastructural use. In the current research, waste PET plastic bottles (WPET) were processed into value-added composites compounded with coated kenaf natural fibers (CK) using the twin-screw extruder (TSE) and compression molding machines at an optimized temperature of 250 °C with a constant fiber weight percentage and epoxy coating concentration of 10 wt% and 25 % conc., respectively. The composites underwent analysis for mechanical, thermal, and microstructural properties based on relevant ASTM standards. Results indicate significant enhancement in surface interactions/interface and improved mechanical and thermal properties of kenaf fibers due to epoxy coating. Thermal stability improved to 409.4 °C from 397.0 °C due to coating, with a higher melting peak of 252.8 °C. These materials exhibit suitability for energy and building infrastructural applications, presenting an alternative, economical, and sustainable approach to managing the abundance of waste PET plastic bottles. • Waste PET bottles reinforced with coated kenaf fibers for infrastructure application. • The reinforcing coated fibers are necessary for improved interface and properties. • The developed waste plastic composites are of immersed benefits and viabilities. Kenaf ; Materials science ; Composite material ; Thermal ; Biodegradation ; Waste management ; Fiber Natural Fiber Reinforced Composites ; Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties
Authors: Lung-Yi Chen | Yi‐Pei Li
Source: Journal of Cheminformatics (article, 2024-06-27)
Field: Materials Science — Materials Chemistry

AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry This paper presents AutoTemplate, an innovative data preprocessing protocol, addressing the crucial need for high-quality chemical reaction datasets in the realm of machine learning applications in organic chemistry. Recent advances in artificial intelligence have expanded the application of machine learning in chemistry, particularly in yield prediction, retrosynthesis, and reaction condition prediction. However, the effectiveness of these models hinges on the integrity of chemical reaction datasets, which are often plagued by inconsistencies like missing reactants, incorrect atom mappings, and outright erroneous reactions. AutoTemplate introduces a two-stage approach to refine these datasets. The first stage involves extracting meaningful reaction transformation rules and formulating generic reaction templates using a simplified SMARTS representation. This simplification broadens the applicability of templates across various chemical reactions. The second stage is template-guided reaction curation, where these templates are systematically applied to validate and correct the reaction data. This process effectively amends missing reactant information, rectifies atom-mapping errors, and eliminates incorrect data entries. A standout feature of AutoTemplate is its capability to concurrently identify and correct false chemical reactions. It operates on the premise that most reactions in datasets are accurate, using these as templates to guide the correction of flawed entries. The protocol demonstrates its efficacy across a range of chemical reactions, significantly enhancing dataset quality. This advancement provides a more robust foundation for developing reliable machine learning models in chemistry, thereby improving the accuracy of forward and retrosynthetic predictions. AutoTemplate marks a significant progression in the preprocessing of chemical reaction datasets, bridging a vital gap and facilitating more precise and efficient machine learning applications in organic synthesis. SCIENTIFIC CONTRIBUTION: The proposed automated preprocessing tool for chemical reaction data aims to identify errors within chemical databases. Specifically, if the errors involve atom mapping or the absence of reactant types, corrections can be systematically applied using reaction templates, ultimately elevating the overall quality of the database. Computer science ; Retrosynthetic analysis ; Template ; Artificial intelligence ; Machine learning ; Preprocessor ; Cheminformatics ; Data mining ; Missing data ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Zihao Jiao | Ya Liu | Ziyun Wang
Source: The Journal of Chemical Physics (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Application of graph neural network in computational heterogeneous catalysis Heterogeneous catalysis, as a key technology in modern chemical industries, plays a vital role in social progress and economic development. However, its complex reaction process poses challenges to theoretical research. Graph neural networks (GNNs) are gradually becoming a key tool in this field as they can intrinsically learn atomic representation and consider connection relationship, making them naturally applicable to atomic and molecular systems. This article introduces the basic principles, current network architectures, and datasets of GNNs and reviews the application of GNN in heterogeneous catalysis from accelerating the materials screening and exploring the potential energy surface. In the end, we summarize the main challenges and potential application prospects of GNNs in future research endeavors. Computer science ; Key (lock) ; Graph ; Field (mathematics) ; Process (computing) ; Biochemical engineering ; Data science ; Management science ; Artificial intelligence ; Nanotechnology ; Theoretical computer science ; Engineering ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Nil Sanosa | David Dalmau | Diego Sampedro | Juan V. Alegre‐Requena | Ignacio Funes‐Ardoiz
Source: Artificial Intelligence Chemistry (article, 2024-04-27)
Field: Materials Science — Materials Chemistry

Recent advances of machine learning applications in the development of experimental homogeneous catalysis Machine Learning (ML) stands as a disruptive technology, finding application across a diverse array of scientific disciplines. When applied to homogeneous catalysis, this technology accelerates catalyst discovery through virtual screening, which not only reduces experimental iterations but also yields significant savings in time, resources, and waste generation. ML algorithms, often integrated with cheminformatic tools and quantum mechanics featurization, excel in predicting reaction outcomes that guide the engineering of catalysts for desired reactivity and selectivity. This minireview presents recent studies regarding databases as well as supervised and unsupervised problems, offering a general yet insightful perspective on the current ML-driven progress in homogeneous catalysis. Homogeneous ; Development (topology) ; Computer science ; Artificial intelligence ; Biochemical engineering ; Engineering ; Physics ; Mathematics ; Statistical physics Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling
Authors: Gang Xu | You Xue | Xiaoxiao Geng | Xinmei Hou | Jinwu Xu
Source: Materials Genome Engineering Advances (article, 2024-06-04)
Field: Materials Science — Materials Chemistry

A machine learning‐based crystal graph network and its application in development of functional materials Abstract An active area of MGI (Materials Genome Initiative)/MGE (Materials Genome Engineering) is to accelerate the development of new materials by means of active learning and “digital trial‐error” using a prediction model of material property. Machine learning methods have widely been employed for predicting crystalline materials properties with crystal graph neural networks (CGNN). The prediction accuracy of the state‐of‐the‐art (SOTA) CGNN models based on big models and big data is generally higher. However, for the development of some classes of materials, the datasets obtained by experiments are usually lacking due to costly experiments and measurement costs. The lack of datasets will impact the accuracy of CGNN models and may result in overfitting during training models. This paper proposes a simplified crystal graph convolutional neural network (S‐CGCNN) which possesses higher prediction accuracy while reducing the vast amount of train datasets and computation costs. The S‐CGCNN model has successfully predicted properties of crystalline materials, such as piezoelectric materials and dielectric materials, and increased the prediction accuracy up to 12%–20% than existing SOTA CGNN models. Furthermore, the distribution map between properties and compositions of materials has been built to screen the latent space of candidate materials efficiently by principal component analysis. Computer science ; Graph ; Artificial intelligence ; Development (topology) ; Theoretical computer science ; Machine learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography