jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bowen Hou | Jinyuan Wu | Diana Y. Qiu
Source: Nature Communications (article, 2024-11-02)
Field: Materials Science — Materials Chemistry

Unsupervised representation learning of Kohn–Sham states and consequences for downstream predictions of many-body effects Representation learning for the electronic structure problem is a major challenge of machine learning in computational condensed matter and materials physics. Within quantum mechanical first principles approaches, density functional theory (DFT) is the preeminent tool for understanding electronic structure, and the high-dimensional DFT wavefunctions serve as building blocks for downstream calculations of correlated many-body excitations and related physical observables. Here, we use variational autoencoders (VAE) for the unsupervised learning of DFT wavefunctions and show that these wavefunctions lie in a low-dimensional manifold within latent space. Our model autonomously determines the optimal representation of the electronic structure, avoiding limitations due to manual feature engineering. To demonstrate the utility of the latent space representation of the DFT wavefunction, we use it for the supervised training of neural networks (NN) for downstream prediction of quasiparticle bandstructures within the GW formalism. The GW prediction achieves a low error of 0.11 eV for a combined test set of two-dimensional metals and semiconductors, suggesting that the latent space representation captures key physical information from the original data. Finally, we explore the generative ability and interpretability of the VAE representation. Wave function ; Feature learning ; Interpretability ; Representation (politics) ; Artificial intelligence ; Computer science ; Unsupervised learning ; Electronic structure ; Quasiparticle ; Kohn–Sham equations ; Physics ; Machine learning ; Density functional theory ; Statistical physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electronic and Structural Properties of Oxides
Authors: H.-Q. Jin | Kenneth M. Merz
Source: Journal of Chemical Information and Modeling (article, 2024-04-08)
Field: Materials Science — Materials Chemistry

Modeling Zinc Complexes Using Neural Networks Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build potential energy surfaces and explore chemical space. However, most of this work has focused on organic molecules due to the simplicity of their electronic structures as well as the availability of data sets. In this work, we build a deep learning architecture to model the energetics of zinc organometallic complexes. To achieve this, we have compiled a configurationally and conformationally diverse data set of zinc complexes using metadynamics to overcome the limitations of traditional sampling methods. In terms of the neural network potentials, our results indicate that for zinc complexes, partial charges play an important role in modeling the long-range interactions with a neural network. Our developed model outperforms semiempirical methods in predicting the relative energy of zinc conformers, yielding a mean absolute error (MAE) of 1.32 kcal/mol with reference to the double-hybrid PWPB95 method. Metadynamics ; Chemical space ; Zinc ; Artificial neural network ; Biological system ; Computer science ; Molecule ; Range (aeronautics) ; Conformational isomerism ; Set (abstract data type) ; Computational chemistry ; Chemistry ; Artificial intelligence ; Molecular dynamics ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Y AlDouri Abdulwahab S Z Lahewil N M Ahmed | Nestor Ankah | Nasirudeen Ogunlakin | Ihsan‐ul‐Haq Toor | Wasif Farooq
Source: Corrosion Reviews (review, 2024-12-24)
Field: Materials Science — Biomaterials

Exploring the future of metallic implants: a review of biodegradable and non-biodegradable solutions Abstract As advancements in medical technology continue to evolve, the demand for innovative implant materials has become increasingly vital for enhancing overall experience of patients. Traditional non-biodegradable implants, while effective, often necessitate removal through invasive and costly surgical interventions, leading to significant clinical challenges. To address these issues, the development of biodegradable materials has gained prominence due to their ability to gradually degrade and be absorbed by the body, presenting a compelling alternative to permanent implants. This review examines both biodegradable and non-biodegradable metallic implants, focusing on key aspects such as biocompatibility, mechanical properties, and degradation kinetics. Furthermore, it explores the applications of these materials across various medical fields, emphasizing their potential to improve patient care. This review aims to bridge the gap between laboratory innovations, clinical practices, and industrial applications by summarizing current research. It offers valuable insights for researchers, clinicians, and industry professionals, contributing to the ongoing dialogue regarding the future of implant technology and advancing the understanding of material selection for diverse medical applications. Materials science ; Biodegradable polymer ; Materials processing ; Metal ; Nanotechnology ; Metallurgy ; Composite material ; Process engineering ; Engineering ; Polymer Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty
Authors: Amir Rafati | Hamid Mirshekali | Hamid Reza Shaker | Navid Bayati
Source: Smart Cities (review, 2024-12-03)
Field: Materials Science — Materials Chemistry

Power Grid Renovation: A Comprehensive Review of Technical Challenges and Innovations for Medium Voltage Cable Replacement The rapid growth of electrical energy demands raises the need for the modernization of distribution grids. Medium-voltage (MV) aged cables are infrastructures facing significant challenges that can compromise the security of supply and reduce the reliability of power grids. To address the challenges, there is a growing interest in optimizing cable replacement and management strategies. This comprehensive review focuses on the technical challenges and innovations associated with MV cable replacement, highlighting defect detection, lifetime estimation, reliability assessment, and management strategies. Various methods for detecting and monitoring cable defects and discussing their advantages and limitations are surveyed. Moreover, different models and techniques for estimating the remaining useful life of MV cables are explored, emphasizing the importance of accurate predictions for assessing cable reliability and optimizing replacement schedules. Furthermore, emerging technologies that enhance cable management strategies are also highlighted. This review provides insights and recommendations for future research and development, paving the way for the sustainable evolution of power grids. Reliability (semiconductor) ; Reliability engineering ; Grid ; Computer science ; Risk analysis (engineering) ; Smart grid ; Power (physics) ; Engineering ; Electrical engineering ; Business High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Thermal Analysis in Power Transmission
Authors: Haiming Liang | Yuxi Wu | Bin Han | Nan Lin | Junqiang Wang | Zheng Zhang | Yanbao Guo
Source: Applied Sciences (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

Corrosion of Buried Pipelines by Stray Current in Electrified Railways: Mechanism, Influencing Factors, and Protection Metal pipes are often interfered with by currents of irregular frequency and wavelength. This is called stray current interference. The corrosion of metal pipes caused by stray current interference is one of the many factors contributing to their deterioration. Pipeline corrosion and leakage caused by stray currents can significantly impact the safety, environment, and economy of the surrounding areas. Over the past few years, stray current interference has become one of the research hotspots in the petrochemical and natural gas transportation industries. This review article investigates accident cases caused by stray currents and compares the corrosion mechanisms of DC stray currents and AC stray currents, analyzes the influence of pipeline material, environment, voltage and current, stress, and other factors on corrosion, and puts forward corrosion protection measures, such as cathodic protection, coating technology, internal corrosion control, and means of detecting stray current corrosion. Finally, it provides an outlook on future research directions on corrosion protection against stray currents. Stray voltage ; Corrosion ; Cathodic protection ; Current (fluid) ; Pipeline transport ; Engineering ; Forensic engineering ; Materials science ; Environmental science ; Metallurgy ; Electrical engineering ; Anode ; Mechanical engineering ; Electrode ; Chemistry Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Concrete Corrosion and Durability
Authors: Hanfeng Wang | Kunal L. Tiwari | Kurt Jacobs | Michael W. Judy | Xin Zhang | Dirk Englund | Matthew E. Trusheim
Source: Nature Communications (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

A spin-refrigerated cavity quantum electrodynamic sensor Quantum sensors based on solid-state defects, in particular nitrogen-vacancy (NV) centers in diamond, enable precise measurement of magnetic fields, temperature, rotation, and electric fields. Cavity quantum electrodynamic (cQED) readout, in which an NV ensemble is hybridized with a microwave mode, can overcome limitations in optical spin detection and has resulted in leading magnetic sensitivities at the pT-level. This approach, however, remains far from the intrinsic spin-projection noise limit due to thermal Johnson-Nyquist noise and spin saturation effects. Here we tackle these challenges by combining recently demonstrated spin refrigeration techniques with comprehensive nonlinear modeling of the cQED sensor operation. We demonstrate that the optically-polarized NV ensemble simultaneously provides magnetic sensitivity and acts as a heat sink for the deleterious thermal microwave noise background, even when actively probed by a microwave field. Optimizing the NV-cQED system, we demonstrate a broadband sensitivity of 576 ± 6 fT/ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt><mml:mrow><mml:mi>Hz</mml:mi></mml:mrow> </mml:msqrt> </mml:math> around 15 kHz in ambient conditions. We then discuss the implications of this approach for the design of future magnetometers, including near-projection-limited devices approaching 3 fT/ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt><mml:mrow><mml:mi>Hz</mml:mi></mml:mrow> </mml:msqrt> </mml:math> sensitivity enabled by spin refrigeration. Sensitivity (control systems) ; Physics ; Quantum sensor ; Spin (aerodynamics) ; Noise (video) ; Condensed matter physics ; Quantum ; Nuclear magnetic resonance ; Quantum computer ; Quantum mechanics ; Computer science ; Quantum simulator ; Artificial intelligence ; Thermodynamics ; Electronic engineering Diamond and Carbon-based Materials Research ; Atomic and Subatomic Physics Research ; Mechanical and Optical Resonators
Authors: Andrea Loddo | Cecilia Di Ruberto | Giuliano Armano | Andrea Manconi
Source: Computers in Industry (article, 2024-09-09)
Field: Chemistry — Analytical Chemistry

Detecting coagulation time in cheese making by means of computer vision and machine learning techniques Cheese production, a globally cherished culinary tradition, faces challenges in ensuring consistent product quality and production efficiency. The critical phase of determining cutting time during curd formation significantly influences cheese quality and yield. Traditional methods often struggle to address variability in coagulation conditions, particularly in small-scale factories. In this paper, we present several key practical contributions to the field, including the introduction of CM-IDB, the first publicly available image dataset related to the cheese-making process. Also, we propose an innovative artificial intelligence-based approach to automate the detection of curd-firming time during cheese production using a combination of computer vision and machine learning techniques. The proposed method offers real-time insights into curd firmness, aiding in predicting optimal cutting times. Experimental results show the effectiveness of integrating sequence information with single image features, leading to improved classification performance. In particular, deep learning-based features demonstrate excellent classification capability when integrated with sequence information. The study suggests the suitability of the proposed approach for integration into real-time systems, especially within dairy production, to enhance product quality and production efficiency. • The first publicly available image dataset related to the cheese-making process is proposed. • An Artificial Intelligence-based method to detect curd-firming time is proposed. • The approach leverages both image features and temporal characteristics. • The approach is non-invasive and non-destructive. • An evaluation with machine learning and computer vision techniques is conducted. Computer science ; Coagulation ; Artificial intelligence ; Machine vision ; Engineering drawing ; Computer vision ; Machine learning ; Computer graphics (images) ; Engineering ; Psychology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Shiwei Su | Tsukuru Masuda | Madoka Takai
Source: The Journal of Physical Chemistry B (article, 2024-07-01)
Field: Materials Science — Surfaces, Coatings and Films

Explainable Prediction of Hydrophilic/Hydrophobic Property of Polymer Brush Surfaces by Chemical Modeling and Machine Learning Polymer informatics has attracted increasing attention as a specialized branch of material informatics. Hydrophilicity/hydrophobicity is one of the most important properties of interfaces involved in antifouling, self-cleaning, antifogging, oil/water separation, protein adsorption, and bioseparation. Establishing a quantitative structure-property relationship for the hydrophilicity/hydrophobicity of polymeric interfaces could significantly benefit from machine learning modeling. In this study, we aimed to construct machine learning models that could predict the static water contact angle (CA) as an indicator of hydrophilicity/hydrophobicity based on a data set of polymer brushes. The features of the polymer brush surfaces were numerically described using their grafted structures (thickness) and molecular descriptors derived from their chemical structures. We achieved accurate prediction and understanding of important parameters by employing appropriate molecular descriptors considering the Pearson correlation and machine learning models trained with nested cross-validation. The model interpretation by Shapley additive extension analysis indicated that the amount of partial polar/nonpolar structure in the molecule as well as the averaged hydrophobicity represented by MolLogP plays an important role in determining the CA. Moreover, the model can predict the CAs of polymer brushes composed of chemical structures that are not present in existing databases. The CA values of the hypothetical polymer brushes are predicted. Brush ; Polymer ; Chemical engineering ; Property (philosophy) ; Polymer science ; Materials science ; Chemistry ; Computer science ; Composite material ; Engineering ; Philosophy Polymer Surface Interaction Studies ; Lubricants and Their Additives ; Surface Modification and Superhydrophobicity
Authors: Shuo Wang | Sheng Gong | Thorben Böger | Jon A. Newnham | Daniele Vivona | Muy Sokseiha | Kiarash Gordiz | Abhishek Aggarwal | Taishan Zhu | Wolfgang G. Zeier | Jeffrey C. Grossman | Yang Shao‐Horn
Source: Chemistry of Materials (article, 2024-11-29)
Field: Materials Science — Materials Chemistry

Multimodal Machine Learning for Materials Science: Discovery of Novel Li-Ion Solid Electrolytes The widespread adoption of multimodal machine learning (ML) models such as GPT-4 and Gemini has revolutionized various research domains, including computer vision and natural language processing. However, their implementation in materials informatics remains underexplored, despite the availability of diverse modalities in materials data. This study introduces an approach to multimodal machine learning in materials science via composition-structure bimodal learning and proposes the COmposition-Structure Bimodal Network (COSNet). The COSNet demonstrates significantly improved performance in predicting a variety of material properties, such as lithium-ion conductivity in solid electrolytes, band gap, refractive index, and formation enthalpy. This research highlights the critical importance of representation alignment in multimodal learning for materials science, enabling knowledge transfer between modalities and avoiding biased or divergent learning. Furthermore, we present an integrated paradigm that combines multimodal learning, transfer learning, ensemble methods, and atomic simulation to facilitate the discovery of novel superionic conductors. Electrolyte ; Materials science ; Ion ; Nanotechnology ; Fast ion conductor ; Engineering physics ; Computer science ; Chemistry ; Engineering ; Physical chemistry ; Organic chemistry ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; X-ray Diffraction in Crystallography
Authors: Niklas Adebar | Julian Keupp | Victor N. Emenike | Jonas Kühlborn | Lisa vom Dahl | Robert Möckel | Jens Smiatek
Source: The Journal of Physical Chemistry A (article, 2024-01-25)
Field: Materials Science — Materials Chemistry

Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions Emerging concepts from scientific deep machine learning such as physics-informed neural networks (PINNs) enable a data-driven approach for the study of complex kinetic problems. We present an extended framework that combines the advantages of PINNs with the detailed consideration of experimental parameter variations for the simulation and prediction of chemical reaction kinetics. The approach is based on truncated Taylor series expansions for the underlying fundamental equations, whereby the external variations can be interpreted as perturbations of the kinetic parameters. Accordingly, our method allows for an efficient consideration of experimental parameter settings and their influence on the concentration profiles and reaction kinetics. A particular advantage of our approach, in addition to the consideration of univariate and multivariate parameter variations, is the robust model-based exploration of the parameter space to determine optimal reaction conditions in combination with advanced reaction insights. The benefits of this concept are demonstrated for higher-order chemical reactions including catalytic and oscillatory systems in combination with small amounts of training data. All predicted values show a high level of accuracy, demonstrating the broad applicability and flexibility of our approach. Univariate ; Flexibility (engineering) ; Chemical kinetics ; Computer science ; Artificial neural network ; Multivariate statistics ; Parameter space ; Chemical reaction ; Biological system ; Machine learning ; Kinetics ; Mathematics ; Chemistry ; Physics ; Statistics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Fuel Cells and Related Materials
Authors: Jaehyun Park | Farshud Sorourifar | Madhav R. Muthyala | Abigail M. Houser | Madison R. Tuttle | Joel A. Paulson | Shiyu Zhang
Source: Journal of the American Chemical Society (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning Organic electrode materials (OEMs), composed of abundant elements such as carbon, nitrogen, and oxygen, offer sustainable alternatives to conventional electrode materials that depend on finite metal resources. The vast structural diversity of organic compounds provides a virtually unlimited design space; however, exploring this space through Edisonian trial-and-error approaches is costly and time-consuming. In this work, we develop a new framework, SPARKLE, that combines computational chemistry, molecular generation, and machine learning to achieve zero-shot predictions of OEMs that simultaneously balance reward (specific energy), risk (solubility), and cost (synthesizability). We demonstrate that SPARKLE significantly outperforms alternative black-box machine learning algorithms on interpolation and extrapolation tasks. By deploying SPARKLE over a design space of more than 670,000 organic compounds, we identified ≈5000 novel OEM candidates. Twenty-seven of them were synthesized and fabricated into coin-cell batteries for experimental testing. Among SPARKLE-discovered OEMs, 62.9% exceeded benchmark performance metrics, representing a 3-fold improvement over OEMs selected by human intuition alone (20.8% based on six years of prior lab experience). The top-performing OEMs among the 27 candidates exhibit specific energy and cycling stability that surpass the state-of-the-art while being synthesizable at a fraction of the cost. Chemistry ; Zero (linguistics) ; Battery (electricity) ; Shot (pellet) ; Organic chemistry ; Thermodynamics Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Mohammed Alghadeer | Nyimas Aisyah | Mahmoud Hezam | Saad M. Alqahtani | Ahmer A.B. Baloch | Fahhad H. Alharbi
Source: Chemical Physics Reviews (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Machine learning prediction of materials properties from chemical composition: Status and prospects In materials science, machine learning (ML) has become an essential and indispensable tool. ML has emerged as a powerful tool in materials science, particularly for predicting material properties based on chemical composition. This review provides a comprehensive overview of the current status and future prospects of using ML in this domain, with a special focus on physics-guided machine learning (PGML). By integrating physical principles into ML models, PGML ensures that predictions are not only accurate but also interpretable, addressing a critical need in the physical sciences. We discuss the foundational concepts of statistical learning and PGML, outline a general framework for materials informatics, and explore key aspects such as data analysis, feature reduction, and chemical composition representation. Additionally, we survey the latest advancements in the prediction of geometric structures, electronic properties, and other material characteristics from chemical formulas. The review also provides resource tables listing essential databases, tools, and predictors, offering a valuable reference for researchers. As the field rapidly expands, this review aims to guide future efforts in harnessing ML for materials discovery and development. Biochemical engineering ; Materials science ; Machine learning ; Computer science ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods