jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthias Kellner | Michele Ceriotti
Source: Machine Learning Science and Technology (article, 2024-06-17)
Field: Materials Science — Materials Chemistry

Uncertainty quantification by direct propagation of shallow ensembles Abstract Statistical learning algorithms provide a generally-applicable framework to sidestep time-consuming experiments, or accurate physics-based modeling, but they introduce a further source of error on top of the intrinsic limitations of the experimental or theoretical setup. Uncertainty estimation is essential to quantify this error, and to make application of data-centric approaches more trustworthy. To ensure that uncertainty quantification is used widely, one should aim for algorithms that are accurate, but also easy to implement and apply. In particular, including uncertainty quantification on top of an existing architecture should be straightforward, and add minimal computational overhead. Furthermore, it should be easy to manipulate or combine multiple machine-learning predictions, propagating uncertainty over further modeling steps. We compare several well-established uncertainty quantification frameworks against these requirements, and propose a practical approach, which we dub direct propagation of shallow ensembles, that provides a good compromise between ease of use and accuracy. We present benchmarks for generic datasets, and an in-depth study of applications to the field of atomistic machine learning for chemistry and materials. These examples underscore the importance of using a formulation that allows propagating errors without making strong assumptions on the correlations between different predictions of the model. Computer science ; Propagation of uncertainty ; Uncertainty quantification ; Trustworthiness ; Overhead (engineering) ; Field (mathematics) ; Machine learning ; Artificial intelligence ; Algorithm ; Data mining ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Can Özkan | Lisa Sahlmann | Christian Feiler | Mikhail L. Zheludkevich | Sviatlana V. Lamaka | Parth Sewlikar | Agnieszka Kooijman | Peyman Taheri | J.M.C. Mol
Source: npj Materials Degradation (article, 2024-02-21)
Field: Materials Science — Materials Chemistry

Laying the experimental foundation for corrosion inhibitor discovery through machine learning Abstract Creating durable, eco-friendly coatings for long-term corrosion protection requires innovative strategies to streamline design and development processes, conserve resources, and decrease maintenance costs. In this pursuit, machine learning emerges as a promising catalyst, despite the challenges presented by the scarcity of high-quality datasets in the field of corrosion inhibition research. To address this obstacle, we have created an extensive electrochemical library of around 80 inhibitor candidates. The electrochemical behaviour of inhibitor-exposed AA2024-T3 substrates was captured using linear polarisation resistance, electrochemical impedance spectroscopy, and potentiodynamic polarisation techniques at different exposure times to obtain the most comprehensive electrochemical picture of the corrosion inhibition over a 24-h period. The experimental results yield target parameters and additional input features that can be combined with computational descriptors to develop quantitative structure–property relationship (QSPR) models augmented by mechanistic input features. Foundation (evidence) ; Corrosion ; Engineering ; Materials science ; Metallurgy ; Computer science ; History ; Archaeology Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Li Wang | Chonghui Li | Chongfeng Cao | Fengcai Lei | Xiaofei Zhao | Zhen Li | Chao Zhang | Yang Jiao | Jing Yu
Source: Advanced Functional Materials (article, 2024-11-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multifunctional Polymeric Nanoneedles with “Full‐Spectrum Intrinsic Internal Standard” for Precise SERS Biosensing Abstract Polymeric micro and nanoneedles have an exceptionally flexible bio‐SERS structure. However, during detection, Raman signals from the polymer often overlap with those from the target molecules, which is unavoidable and severely compromises the accuracy of the results. To address this issue, this study designed and fabricated a silver (poly(vinyl pyrrolidone)‐coated, PVP)/poly(vinylidene fluoride) (PVDF)/gold nanoneedles structure (Ag/PVDF/Au NNs) and proposed a full‐spectrum intrinsic internal standard (FS‐IS) analytical method. In this method, the entire spectra of PVP and PVDF within the Raman fingerprint region are used as an internal standard, and machine learning algorithms are employed to quantitatively analyze the relevant SERS detection from a statistical perspective. Both the experimental and theoretical results demonstrate that this method is more accurate than traditional analysis methods based on embedded or intrinsic internal standards. Using the FS‐IS analysis method, the Ag/PVDF/Au NNs are successfully applied to various biosensing applications, including cancer cell imaging and in situ monitoring of antibiotics in the urine of surgical patients. This study effectively addresses the issue of low detection accuracy in flexible nanoneedle SERS structures and provides valuable insights for the development of other high‐precision detection technologies based on flexible substrates. Materials science ; Biosensor ; Nanotechnology Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Xiaoxiao Liang | Shiyuan Liu | Zebin Li | Yuehua Deng | Yanbin Jiang | Huaiyu Yang
Source: European Journal of Pharmaceutics and Biopharmaceutics (article, 2024-02-02)
Field: Chemistry — Physical and Theoretical Chemistry

Efficient cocrystal coformer screening based on a Machine learning Strategy: A case study for the preparation of imatinib cocrystal with enhanced physicochemical properties Cocrystal ; Solubility ; Active ingredient ; Chemistry ; Imatinib mesylate ; Dissolution ; Combinatorial chemistry ; Imatinib ; Nanotechnology ; Materials science ; Organic chemistry ; Pharmacology ; Hydrogen bond Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Crystal structures of chemical compounds
Authors: Jinglong Lin | Huibao Zhang | Mojgan Asadi | Kai Zhao | Luming Yang | Yunlong Fan | Jintao Zhu | Qianyi Liu | Lei Sun | Wen Jun Xie | Chenru Duan | Fanyang Mo | Jin‐Hu Dou
Source: Chemistry of Materials (article, 2024-05-20)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Driven Discovery and Structure–Activity Relationship Analysis of Conductive Metal–Organic Frameworks Electrically conductive metal–organic frameworks (MOFs) are a class of materials with emergent applications in fields such as electrocatalysis, electrochemical energy storage, and chemiresistive sensors due to their unique combination of porosity and conductivity. However, due to the structural complexity and versatility, rational design of conductive MOFs is still challenging, which limits their further development and applications. To overcome this limitation, we established a database of 224 conductive MOFs, covering all of the reported conductive MOFs as far as we know, and utilized a combination of machine learning (ML) models and density functional theory (DFT) calculations to develop structure–conductivity relationship models. The interpretability of the models provided guidelines for the design of these materials and allowed us to identify new conductive MOFs through rapid screening. Subsequent experiments confirmed the model’s reliability and viability by synthesizing and validating a conductive MOF, CuTTPD, selected via the ML screening. Our results demonstrate that ML models are powerful tools for prescreening new conductive MOFs, thereby accelerating the development of this field. Metal-organic framework ; Electrical conductor ; Nanotechnology ; Key (lock) ; Materials science ; Chemistry ; Computer science ; Organic chemistry ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Electrochemical Analysis and Applications
Authors: Nan Xu | Petter Rosander | C. Schäfer | Eric Lindgren | Nicklas Österbacka | Mandi Fang | Wei Chen | Yi He | Zheyong Fan | Paul Erhart
Source: Journal of Chemical Theory and Computation (article, 2024-04-04)
Field: Materials Science — Materials Chemistry

Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra Infrared and Raman spectroscopy are widely used for the characterization of gases, liquids, and solids, as the spectra contain a wealth of information concerning, in particular, the dynamics of these systems. Atomic scale simulations can be used to predict such spectra but are often severely limited due to high computational cost or the need for strong approximations that limit the application range and reliability. Here, we introduce a machine learning (ML) accelerated approach that addresses these shortcomings and provides a significant performance boost in terms of data and computational efficiency compared with earlier ML schemes. To this end, we generalize the neuroevolution potential approach to enable the prediction of rank one and two tensors to obtain the tensorial neuroevolution potential (TNEP) scheme. We apply the resulting framework to construct models for the dipole moment, polarizability, and susceptibility of molecules, liquids, and solids and show that our approach compares favorably with several ML models from the literature with respect to accuracy and computational efficiency. Finally, we demonstrate the application of the TNEP approach to the prediction of infrared and Raman spectra of liquid water, a molecule (PTAF-), and a prototypical perovskite with strong anharmonicity (BaZrO3). The TNEP approach is implemented in the free and open source software package gpumd, which makes this methodology readily available to the scientific community. Raman spectroscopy ; Computer science ; Neuroevolution ; Infrared ; Statistical physics ; Artificial neural network ; Physics ; Artificial intelligence ; Astronomy ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: K. R. Sumesh | V. Kavimani | Ajithram Arivendan | Muhammad Imam Ammarullah
Source: Results in Engineering (article, 2024-09-23)
Field: Materials Science — Polymers and Plastics

Evaluating the effects of pineapple fiber, potato waste filler, surface treatment, and fiber length on the mechanical properties of polyethylene composites for biomedical applications This study investigates the development of a biocompatible polyethylene composite incorporating natural fiber (pineapple fiber) and potato waste filler for potential use in biomedical applications. The composite was fabricated using compression molding and optimized through Taguchi-based grey relational analysis (GRA), TOPSIS, and artificial neural network (ANN) prediction models. The hybrid combination of these materials has been minimally explored in the biomedical field. Fourier Transform Infrared (FTIR) analysis of the potato filler revealed a peak at 1336 cm⁻ 1 , indicating asymmetric deformation of C-H and C-O functional groups, contributing to enhanced compatibility with the polyethylene matrix. The natural fiber showed uniform distribution, improving stress transfer and flexural properties. The ANN model demonstrated high accuracy, with an r-value of 0.9972. Multi-response optimization identified the best configuration as 3 % NaOH treatment, 3 wt% potato filler, 30 wt% natural fiber, and a fiber length of 1.5 cm, yielding a tensile strength of 22.3 MPa, flexural strength of 23.8 MPa, and impact strength of 18.3 kJ/m 2 . This fabricated composites were lightweight and biocompatible materials, suitable for biomedical applications. • Optimization showed better results at treatment, filler (3 wt %)/30 wt % fiber and 1.5 cm fiber length. • The ANN model performs well in all stages, with remarkable overall r value of 0.9972. • The final combination results from GRA and TOPSIS gained better mechanical properties than all other Taguchi results. Composite material ; Filler (materials) ; Materials science ; Polyethylene ; Fiber Natural Fiber Reinforced Composites ; Nanocomposite Films for Food Packaging ; Electrospun Nanofibers in Biomedical Applications
Authors: Jacob Wekalao | Niranjana Siddharthan | Soman Shibu | Ganpathy Murthy | K.V. Karthikeyan | Srinivasan Mallan | Kalpanadevi Ganesan | Vijayalakshmi Sekar | Ahmed Nabih Zaki Rashed
Source: Plasmonics (article, 2024-11-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High Sensitivity Terahertz Biosensor Based on Graphene/Methylammonium Lead Halide Metasurface with Machine Learning-Enhanced Pathogen Detection Resonator ; Terahertz radiation ; Sensitivity (control systems) ; Graphene ; Multiphysics ; Biosensor ; Materials science ; Optoelectronics ; Parametric statistics ; Metamaterial ; Nanotechnology ; Computer science ; Electronic engineering ; Physics ; Finite element method ; Engineering Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications
Authors: Md Wadud Ahmed | Alin Khaliduzzaman | J.L. Emmert | Mohammed Kamruzzaman
Source: Computers and Electronics in Agriculture (article, 2024-12-18)
Field: Chemistry — Analytical Chemistry

An overview of recent advancements in hyperspectral imaging in the egg and hatchery industry • Hyperspectral imaging (HSI) for table and hatching eggs analysis are reviewed. • Fundamentals of HSI and system customization for the egg industry are presented. • Multivariate techniques, software, and GUIs were discussed. • Challenges, potentiality, and future trends were critically analyzed. Conventional egg quality analysis and compliance monitoring methods have inherent limitations, necessitating non-destructive techniques in the modern egg industry. Hyperspectral imaging (HSI) has emerged as a fast, accurate, and non-destructive tool for quality assessment, effectively determining egg’s internal and external properties. Following the fundamentals of HSI and image analysis, this review consolidates and discusses recent applications of HSI technology for table and hatching egg analysis, addressing its limitations and potential challenges. Current research demonstrates HSI’s efficacy in rapidly and accurately determining parameters such as freshness, shell integrity, defects, chemical composition, and detection of fake eggs. Despite its promising performance, the widespread industrial application of HSI would encounter multiple challenges due to the inherent properties of the samples (e.g., complex shape, light diffusivity, light and heat sensitivity) and current technological limitations. The existing research is insufficient for early predicting certain critical parameters such as fertility, egg sex, and embryonic mortality in high-throughput screening. However, current research underscores HSI’s enormous potential, highlighting that advanced machine learning combined with HSI technology can revolutionize conventional egg and hatchery operations, enhancing automation, economic sustainability, and global animal welfare. This review can guide researchers and policymakers in understanding the contemporary challenges of HSI technology, developing innovative solutions, improving regulatory frameworks, and fostering advancements to maximize the benefits of this cutting-edge green technology. Hyperspectral imaging ; Hatchery ; Engineering ; Remote sensing ; Fishery ; Artificial intelligence ; Computer science ; Geography ; Biology ; Fish <Actinopterygii> Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Tristan Maxson | Tibor Szilvási
Source: The Journal of Physical Chemistry Letters (article, 2024-03-28)
Field: Materials Science — Materials Chemistry

Transferable Water Potentials Using Equivariant Neural Networks Machine learning interatomic potentials (MLIPs) have emerged as a technique that promises quantum theory accuracy for reduced cost. It has been proposed [<i>J. Chem. Phys</i>. <b>2023</b>, <i>158</i>, 084111] that MLIPs trained on solely liquid water data cannot accurately transfer to the vapor-liquid equilibrium while recovering the many-body decomposition (MBD) analysis of gas-phase water clusters. This suggests that MLIPs do not directly learn the physically correct interactions of water molecules, limiting transferability. In this work, we show that MLIPs using equivariant architecture and trained on 3200 liquid water structures reproduces liquid-phase water properties (e.g., density within 0.003 g/cm<sup>3</sup> between 230 and 365 K), vapor-liquid equilibrium properties up to 550 K, the MBD analysis of gas-phase water cluster up to six-body interactions, and the relative energy and the vibrational density of states of ice phases. We show that potentials developed using equivariant MLIPs allow transferability for arbitrary phases of water that remain stable in nanosecond long simulations. Equivariant map ; Artificial neural network ; Computer science ; Biological system ; Environmental science ; Mathematics ; Artificial intelligence ; Biology ; Pure mathematics Machine Learning in Materials Science ; Neural Networks and Applications ; Fault Detection and Control Systems
Authors: Cameron J. Owen | Yu Xie | Anders Johansson | Lixin Sun | Boris Kozinsky
Source: Nature Communications (article, 2024-05-06)
Field: Materials Science — Materials Chemistry

Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformations remain poorly understood due to limitations of previous simulation approaches. Using active learning of Bayesian machine-learned force fields trained from ab initio calculations, we enable large-scale molecular dynamics simulations to describe the thermodynamics and time evolution of the low-index mesoscopic surface reconstructions of Au (e.g., the Au(111)-'Herringbone,' Au(110)-(1 × 2)-'Missing-Row,' and Au(100)-'Quasi-Hexagonal' reconstructions). This capability yields direct atomistic understanding of the dynamic emergence of these surface states from their initial facets, providing previously inaccessible information such as nucleation kinetics and a complete mechanistic interpretation of reconstruction under the effects of strain and local deviations from the original stoichiometry. We successfully reproduce previous experimental observations of reconstructions on pristine surfaces and provide quantitative predictions of the emergence of spinodal decomposition and localized reconstruction in response to strain at non-ideal stoichiometries. A unified mechanistic explanation is presented of the kinetic and thermodynamic factors driving surface reconstruction. Furthermore, we study surface reconstructions on Au nanoparticles, where characteristic (111) and (100) reconstructions spontaneously appear on a variety of high-symmetry particle morphologies. Mesoscopic physics ; Nucleation ; Surface reconstruction ; Surface (topology) ; Molecular dynamics ; Statistical physics ; Materials science ; Ab initio ; Chemical physics ; Spinodal decomposition ; Physics ; Chemistry ; Computational chemistry ; Thermodynamics ; Phase (matter) ; Condensed matter physics ; Mathematics ; Geometry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Theoretical and Computational Physics
Authors: Yan Zeng | Nathan J. Szymanski | Tanjin He | KyuJung Jun | Leighanne C. Gallington | Haoyan Huo | Christopher J. Bartel | Bin Ouyang | Gerbrand Ceder
Source: Science Advances (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Selective formation of metastable polymorphs in solid-state synthesis Metastable polymorphs often result from the interplay between thermodynamics and kinetics. Despite advances in predictive synthesis for solution-based techniques, there remains a lack of methods to design solid-state reactions targeting metastable materials. Here, we introduce a theoretical framework to predict and control polymorph selectivity in solid-state reactions. This framework presents reaction energy as a rarely used handle for polymorph selection, which influences the role of surface energy in promoting the nucleation of metastable phases. Through in situ characterization and density functional theory calculations on two distinct synthesis pathways targeting LiTiOPO<sub>4</sub>, we demonstrate how precursor selection and its effect on reaction energy can effectively be used to control which polymorph is obtained from solid-state synthesis. A general approach is outlined to quantify the conditions under which metastable polymorphs are experimentally accessible. With comparison to historical data, this approach suggests that using appropriate precursors could enable targeted materials synthesis across diverse chemistries through selective polymorph nucleation. Metastability ; Nucleation ; Materials science ; Solid-state ; Chemical physics ; Density functional theory ; Characterization (materials science) ; Kinetics ; Nanotechnology ; Chemistry ; Thermodynamics ; Computational chemistry ; Physical chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advancements in Battery Materials
Authors: Alaa Almansoori | Katalin Balázsi | Csaba Balázsi
Source: Nanomaterials (review, 2024-11-22)
Field: Materials Science — Ceramics and Composites

Advances, Challenges, and Applications of Graphene and Carbon Nanotube-Reinforced Engineering Ceramics Engineering ceramics and their composites are widely used owing to their excellent properties, including high wear, corrosion and heat resistance, low friction coefficient, and low thermal conductivity; thus, the current paper presents a comprehensive review of the most common types of engineering ceramics, demonstrating their key properties, advantages, potential applications, and challenges. This paper also provides prevailing methods for tackling the engineering ceramic challenges and maximizing their applicability. This review paper focuses on alumina (Al<sub>2</sub>O<sub>3</sub>), silicon carbide (SiC), zirconia (ZrO<sub>2</sub>), aluminum nitride (AlN), and silicon nitride (Si<sub>3</sub>N<sub>4</sub>), and explores their usability in automotive, aerospace, and tribological applications. Additionally, the incorporation of reinforcing nanomaterials, i.e., graphene and carbon nanotubes or their combination with second-phase reinforcing nanomaterials in these types of ceramics to improve their physico-mechanical properties is also discussed. By strategically adding these reinforcing materials, the brittleness of ceramics can be mitigated, leading to materials that are more suitable for demanding applications in various high-performance industries. Graphene ; Carbon nanotube ; Materials science ; Nanotechnology ; Ceramic ; Carbon fibers ; Composite material ; Engineering physics ; Engineering ; Composite number Advanced ceramic materials synthesis ; Graphene research and applications ; MXene and MAX Phase Materials