jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Natalia Kireeva | А. Yu. Tsivadze
Source: Journal of Solid State Chemistry (article, 2024-03-21)
Field: Materials Science — Materials Chemistry

Novelty detection in the design of synthesis of garnet-structured solid electrolytes Outlier ; Novelty ; Computer science ; Anomaly detection ; Novelty detection ; Data mining ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Ram Prakash S | Aastha Jain | Rajesh Kumar | Anirban Mitra
Source: Advanced Optical Materials (article, 2024-12-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI‐Enabled Inverse Design and Molecular Identification Using Phase Change Metamaterial Absorber Abstract Tailoring metamaterial design for desired absorption in the mid‐infrared region is pivotal for molecular sensing and identification. Surface‐enhanced infrared absorption (SEIRA) spectroscopy with metamaterials promises accurate molecular fingerprint detection. However, challenges arise due to narrow resonant wavelengths and the extensive design space of the metamaterial, thus hindering its application in accurate molecular detection. This work introduces an integrated AI workflow for designing electrically tunable metamaterial absorbers using phase‐change materials for broadband molecular fingerprint retrieval and identification. A free‐form inverse design method based on a neural‐adjoint optimization technique is utilized to predict phase‐change metamaterial absorber (PCMA) designs for desired mid‐infrared absorption spectra. Using the trained inverse neural network model coupled with a genetic algorithm, nanostructure designs spanning the spectral range from 800 cm −1 to 2000 cm −1 are predicted. The phase change is achieved by integrating the designed PCMA with an energy‐efficient microheater, allowing resonant absorption peaks to be tuned via voltage pulses. The neural network‐predicted PCMA successfully detected molecular vibration fingerprints of four molecules and their mixtures. A machine learning model based on a support vector machine developed for molecular identification shows 100% identification accuracy. This AI‐driven approach for PCMA design and molecular identification via SEIRA marks a significant advancement in accurate molecular detection. Metamaterial ; Materials science ; Absorption (acoustics) ; Identification (biology) ; Fingerprint (computing) ; Metamaterial absorber ; Microheater ; Computer science ; Optoelectronics ; Artificial intelligence ; Tunable metamaterials Metamaterials and Metasurfaces Applications ; Polydiacetylene-based materials and applications ; Phase-change materials and chalcogenides
Authors: Aikaterini-Artemis Agiomavriti | Maria P. Nikolopoulou | Thomas Bartzanas | Nikos Chorianopoulos | Konstantinos Demestichas | Athanasios Ι. Gelasakis
Source: Preprints.org (preprint, 2024-11-04)
Field: Chemistry — Analytical Chemistry

Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants Milk analysis is critical to determine its intrinsic quality, as well as its nutritional and economic value. Currently, the advancements and utilization of spectroscopy-based techniques combined with machine learning algorithms have made feasible the development of analytical tools and re-al-time monitoring and prediction systems in the dairy ruminant sector. The objectives of the cur-rent review were i) to describe the most widely applied spectroscopy-based and supervised ma-chine learning methods utilized for the evaluation of milk components, origin, technological properties, adulterants, and drugs residues, ii) to present and compare the performance and adaptability of these methods and their most efficient combinations, providing insights into the strengths, weaknesses, opportunities, and challenges of the most promising ones regarding the capacity to be applied in milk quality monitoring systems both at the point-of-care and beyond, and iii) to discuss their applicability and future perspectives for the integration of these methods in milk data analysis and decision support systems across the milk value-chain. Dairy industry ; Computer science ; Machine learning ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Meat and Animal Product Quality
Authors: Pavel Korotaev | A. V. Yanilkin
Source: Computational Materials Science (article, 2024-09-26)
Field: Materials Science — Materials Chemistry

Neural networks for prediction of swelling in austenitic steels irradiated by fast neutrons Austenite ; Swelling ; Irradiation ; Materials science ; Neutron ; Nuclear engineering ; Metallurgy ; Radiochemistry ; Nuclear physics ; Composite material ; Chemistry ; Physics ; Microstructure ; Engineering Fusion materials and technologies ; Nuclear Materials and Properties ; Nuclear Physics and Applications
Authors: Shyam Deo | Melissa E. Kreider | Gaurav A. Kamat | McKenzie A. Hubert | José A. Zamora Zeledón | Lingze Wei | Jesse Matthews | Nathaniel Keyes | Ishaan Singh | Thomas F. Jaramillo | Frank Abild‐Pedersen | Michaela Burke Stevens | Kirsten T. Winther | Johannes Voss
Source: ChemPhysChem (article, 2024-03-28)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. We finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets. Electrocatalyst ; Antimonate ; Computer science ; Artificial intelligence ; Chemistry ; Machine learning ; Nanotechnology ; Materials science ; Electrochemistry ; Electrode ; Inorganic chemistry ; Physical chemistry ; Antimony Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Calen J. Leverant | John K. Cooper | Dorina F. Sava Gallis | Jacob Harvey
Source: The Journal of Physical Chemistry C (article, 2024-09-17)
Field: Chemistry — Inorganic Chemistry

Using Active Learning to Rapidly Develop Machine Learned Diffusion Coefficients of CO<sub>2</sub> Conversion Reagents in Metal–Organic Frameworks Here, we used a combined molecular dynamics/active learning (AL) approach to create machine learning models that can predict the diffusion coefficient of epichlorohydrin and chloropropene carbonate, the reactant and product of a common CO2 cycloaddition reaction, in metal–organic frameworks (MOFs). Nanoporous MOFs are effective catalysts for the cycloaddition of CO2 to epoxides. The diffusion rates within nanoporous catalysts can control the rate of reaction as the reactants and products must diffuse to the active sites within the MOF and then out of the nanoporous material for reusability. However, the diffusion process is routinely ignored when searching for new materials in catalytic applications. We verified improvement during the AL process by consistently tracking metrics on the same groups of MOFs to ensure consistency. Metal identity was found to have little impact on diffusion rates, while structural features like pore limiting diameter act as a threshold where a minimum value is needed for high diffusion rates. We identified the MOFs with the highest epichlorohydrin and chloropropene carbonate diffusion coefficients which can be used for further studies of reaction energetics. Reagent ; Diffusion ; Active learning (machine learning) ; Metal-organic framework ; Computer science ; Metal ; Chemistry ; Combinatorial chemistry ; Materials science ; Artificial intelligence ; Organic chemistry ; Thermodynamics ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Ionic liquids properties and applications
Authors: Weiye Chen | Muyang Li | Tuo Yao | Jian Liu | Shengzhe Jia | Zhenguo Gao | Junbo Gong
Source: Industrial & Engineering Chemistry Research (article, 2024-10-15)
Field: Materials Science — Materials Chemistry

Predicting Crystalline Material Properties with AI: Bridging Molecular to Particle Scales Predicting crystalline material properties using artificial intelligence (AI) has seen significant advancement in recent years. This review aims to provide an in-depth overview of AI-based approaches for accurate and rapid prediction of chemical properties. First, various principles and models of machine learning (ML) are critically summarized, highlighting the strengths and weaknesses of different algorithms to assist researchers in selecting the most appropriate model for chemical properties prediction. Furthermore, the applications of AI in predicting the properties of metal–organic frameworks (MOFs), biomolecules, and energetic materials using molecular-scale descriptors are discussed, alongside its ability to predict the properties of lithium batteries and crystalline products at the particle scale. Compared to traditional methods (density functional theory calculations and molecular simulations) for discovering various properties, the predicted results reveal that AI-based algorithms exhibit superior capabilities, particularly in terms of accuracy, efficiency, and reduced computational costs. Lastly, challenges and opportunities in algorithm optimization, database enhancement, and feature descriptor exploration within this field are discussed. Bridging (networking) ; Particle (ecology) ; Materials science ; Mineralogy ; Chemical physics ; Chemical engineering ; Chemistry ; Geology ; Computer science ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Aditi S. Krishnapriyan | Eric Qu
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains Artificial neural network ; Interatomic potential ; Feature (linguistics) ; Noise (video) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Functional Brain Connectivity Studies
Authors: Brandon Meza-González | David Ramírez‐Palma | Pablo Carpio‐Martínez | David Vázquez-Cuevas | Karina Martínez‐Mayorga | Fernando Cortés‐Guzmán
Source: Scientific Data (article, 2024-08-29)
Field: Materials Science — Materials Chemistry

Quantum Topological Atomic Properties of 44K molecules We present a data set of quantum topological properties of atoms of 44K randomly selected molecules from the GDB-9 data set. These atomic properties were obtained as defined by the quantum theory of atoms in molecules (QTAIM) within an atomic basin, a region of real space bounded by zero-flux surfaces in the electron density gradient vector field. The wave function files were generated through DFT static calculations (B3LYP/6-31G), and the atomic properties were calculated using QTAIM. The calculated atomic properties include the energy of the atomic basin, the electronic population, the magnitude of the total dipole moment, and the magnitude of the total quadrupole moment. The atomic properties allow one to understand the chemical structure, reactivity, and molecular recognition. They can be incorporated into force fields for molecular dynamics or for predicting reactive sites. We believe that this data set could facilitate new studies in chemical informatics, machine learning applied to chemistry, and computational molecular design. Physics ; Molecule ; Chemical physics ; Topological defect ; Quantum ; Topology (electrical circuits) ; Chemistry ; Theoretical physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Udo Eckstein | Michel Kuhfuß | Tobias Fey | Kyle G. Webber
Source: Advanced Engineering Materials (article, 2024-02-15)
Field: Materials Science — Materials Chemistry

Feasibility of Powder‐Based High‐Throughput Synthesis for Ceramics Development: Case Study on the Influence of Calcination Temperature in BiFeO<sub>3</sub>‐BaTiO<sub>3</sub> Recent advances in machine learning capabilities have increased interest in materials research to improve the efficiency of materials discovery and optimization as well as to better understand the underlying phenomena responsible for the observed physical properties. While combinatorial chemistry and compositional engineering is well established in the development of pharmaceutical and chemical products, its use in the field of bulk functional polycrystalline ceramics is far from mature. In this work, a critical review of a high‐throughput powder‐based dispensing system is provided and the challenges involved with the transition from a conventional, human resources intensive workflow to a fully automated process are highlighted. Based on the lead‐free piezoelectric BiFeO 3 –BaTiO 3 binary system, the applicability and robustness of high‐throughput engineering is investigated to increase data point density in phase diagrams, including the composition variations of the resulting materials. This work presents 16 different BiFeO 3 –BaTiO 3 compositions at four different calcination temperatures, both demonstrating the potential of the system. This is coupled to automated crystal structure analysis, which will be used to investigate the role of calcination temperature on the resulting compositions at room temperature. Materials science ; Calcination ; Ceramic ; Crystallite ; Nanotechnology ; Throughput ; Process engineering ; Chemical engineering ; Computer science ; Metallurgy ; Catalysis Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Jie Chen | Pengfei Ou | Yuxin Chang | Hengrui Zhang | Xiaoyan Li | Edward H. Sargent | Wei Chen
Source: arXiv (Cornell University) (preprint, 2024-04-18)
Field: Materials Science — Materials Chemistry

Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning High-performance catalysts are crucial for sustainable energy conversion and human health. However, the discovery of catalysts faces challenges due to the absence of efficient approaches to navigating vast and high-dimensional structure and composition spaces. In this study, we propose a high-throughput computational catalyst screening approach integrating density functional theory (DFT) and Bayesian Optimization (BO). Within the BO framework, we propose an uncertainty-aware atomistic machine learning model, UPNet, which enables automated representation learning directly from high-dimensional catalyst structures and achieves principled uncertainty quantification. Utilizing a constrained expected improvement acquisition function, our BO framework simultaneously considers multiple evaluation criteria. Using the proposed methods, we explore catalyst discovery for the CO2 reduction reaction. The results demonstrate that our approach achieves high prediction accuracy, facilitates interpretable feature extraction, and enables multicriteria design optimization, leading to significant reduction of computing power and time (10x reduction of required DFT calculations) in high-performance catalyst discovery. Bayesian optimization ; Bayesian probability ; Representation (politics) ; Computer science ; Artificial intelligence ; Machine learning ; Political science Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Junling Hou | Mengfan Zhao | Yujie Chen | Qun Li | Chunguang Wang
Source: Acta Mechanica (article, 2024-10-08)
Field: Materials Science — Polymers and Plastics

Prediction of mechanical property of open-hole composite laminates using generalized regression neural network method Solid mechanics ; Composite laminates ; Property (philosophy) ; Artificial neural network ; Composite number ; Materials science ; Regression ; Composite material ; Applied mathematics ; Mathematics ; Computer science ; Artificial intelligence ; Statistics Textile materials and evaluations ; Composite Structure Analysis and Optimization ; Mechanical Behavior of Composites
Authors: LeBert Sam Billgates | Vismaya Rajeevan | R. Justin Joseyphus
Source: Physica B Condensed Matter (article, 2024-08-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Temperature-dependent magnetic properties of bcc and fcc FeNi alloys: A machine learning assisted Monte-Carlo approach Condensed matter physics ; Monte Carlo method ; Curie temperature ; Materials science ; Anisotropy ; Magnetization ; Exponent ; Scaling ; Coherent potential approximation ; Heisenberg model ; Ferromagnetism ; Physics ; Quantum mechanics ; Electronic structure ; Magnetic field ; Geometry ; Statistics Magnetic Properties and Applications ; Magnetic properties of thin films ; Theoretical and Computational Physics
Authors: Thabang R. Somo | Mykhaylo Lototskyy | Moegamat Wafeeq Davids | Serge Nyallang Nyamsi | Б. П. Тарасов | Sivakumar Pasupathi
Source: High Energy Chemistry (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Machine Learning-assisted Study of Low-, Medium-, and High-Entropy Hydrogen Storage Alloys Validated by the Experimental Data Advancing of hydrogen and metal hydride energy technologies requires purposeful development of efficient hydrogen storage materials, particularly, tuning their composition towards optimization of hydrogen sorption properties suitable for the end-use applications. This study employed linear regression modelling to analyze hydrogen storage properties of low-, medium- and high-entropy alloys with BCC, C14- and C15-AB2 and AB5 structures found in the literature (>350 entries in total) and to make predictions based on the model further validated by additional reference data and results of own experiments. It was found that the applied model gives a good qualitative correspondence with the reference data on hydrogen sorption capacity and thermodynamics of hydrogen interaction with the alloys but has a limiting predicting capacity allowing only rough quantitative estimations. It was also concluded that the unit cell volume, valence electron concentration, and, to a lesser extent, electronegativity mismatch, exhibit strong effects on the hydrogen sorption properties of the studied alloys while the influence of other factors including the mixing entropy is much less pronounced. Chemistry ; Hydrogen storage ; Hydrogen ; Organic chemistry Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Machine Learning in Materials Science
Authors: Jun Yang
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Making quantum chemistry compressive and expressive: Toward practical ab‐initio simulation Abstract Ab‐initio quantum chemistry simulations are essential for understanding electronic structure of molecules and materials in almost all areas of chemistry. A broad variety of electronic structure theories and implementations has been developed in the past decades to hopefully solve the many‐body Schrödinger equation in an approximate manner on modern computers. In this review, we present recent progress in advancing low‐rank electronic structure methodologies that rely on the wavefunction sparsity and compressibility to select the important subset of electronic configurations for both weakly and strongly correlated molecules. Representative chemistry applications that require the many‐body treatment beyond traditional density functional approximations are discussed. The low‐rank electronic structure theories have further prompted us to highlight compressive and expressive principles that are useful to catalyze idea of quantum learning models. The intersection of the low‐rank correlated feature design and the modern deep neural network learning provides new feasibilities to predict chemically accurate correlation energies of unknown molecules that are not represented in the training dataset. The results by others and us are discussed to reveal that the electronic feature sets from an extremely low‐rank correlation representation, which is very poor for explicit energy computation, are however sufficiently expressive for capturing and transferring electron correlation patterns across distinct molecular compositions, bond types and geometries. This article is categorized under: Electronic Structure Theory &gt; Ab Initio Electronic Structure Methods Software &gt; Quantum Chemistry Software &gt; Simulation Methods Electronic structure ; Quantum chemistry ; Electronic correlation ; Ab initio ; Wave function ; Rank (graph theory) ; Chemistry ; Computer science ; Quantum ; Computational chemistry ; Representation (politics) ; Statistical physics ; Theoretical computer science ; Theoretical physics ; Molecule ; Physics ; Quantum mechanics ; Mathematics ; Supramolecular chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods