jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qiyuan Fan | Yun‐Pei Liu | H. Zhu | Fu‐Qiang Gong | Ye Wang | E Weinan | Xinhe Bao | Zhong‐Qun Tian | Jun Cheng
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Entropy in catalyst dynamics under confinement Entropy during the dynamic structural evolution of catalysts has a non-trivial influence on chemical reactions. Confinement significantly affects the catalyst dynamics and thus impacts the reactivity. However, a full understanding has not been clearly established. To investigate catalyst dynamics under confinement, we utilize the active learning scheme to effectively train machine learning potentials for computing free energies of catalytic reactions. The scheme enables us to compute the reaction free energies and entropies of O<sub>2</sub> dissociation on Pt clusters with different sizes confined inside a carbon nanotube (CNT) at the timescale of tens of nanoseconds while keeping <i>ab initio</i> accuracy. We observe an entropic effect owing to liquid-to-solid phase transitions of clusters at finite temperatures. More importantly, the confinement effect enhances the structural dynamics of the cluster and leads to a lower melting temperature than those of the bare cluster and cluster outside the CNT, consequently facilitating the reaction to occur at lower temperatures and preventing the catalyst from forming unfavorable oxides. Our work reveals the important influence of confinement on structural dynamics, providing useful insight into entropy in dynamic catalysis. Catalysis ; Entropy (arrow of time) ; Dynamics (music) ; Statistical physics ; Chemical physics ; Nanotechnology ; Chemistry ; Physics ; Materials science ; Thermodynamics ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Zihan Yan | Yizhou Zhu
Source: Chemistry of Materials (article, 2024-11-19)
Field: Materials Science — Materials Chemistry

Impact of Lithium Nonstoichiometry on Ionic Diffusion in Tetragonal Garnet-Type Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> Understanding ion transport mechanisms on the atomistic scale in solid-state electrolytes is crucial for the development of all-solid-state batteries. Li7La3Zr2O12 (LLZO) is a promising oxide solid electrolyte material, whose phase transition behavior and ion transport mechanisms have attracted significant research attention. Previous studies have primarily focused on ion transport in the cubic phase (intrinsic high-temperature phase or doped variants). In contrast, the tetragonal phase of LLZO, despite its close relationship with the cubic phase, has received less attention due to its relatively low ionic conductivity and high computational cost. A few recent computational studies have shown significant discrepancies in conductivity and activation energy between calculated and experimental values. Therefore, the unclear ion transport mechanisms in the tetragonal phase of LLZO are critical to understanding and designing oxide solid electrolytes. In this study, we employ state-of-the-art machine-learning-based neuroevolution potential molecular dynamics simulations to investigate the effects of lithium nonstoichiometry on the ionic conductivity and phase stability of LLZO. We demonstrate that small deviations from stoichiometry, particularly lithium deficiency, dramatically reduce the activation energy for Li+ diffusion in tetragonal LLZO from 1.227 to 0.425 eV, increasing room-temperature ionic conductivity by 10 orders of magnitude. The slight lithium nonstoichiometry, which commonly occurs during high-temperature synthesis, has a significant effect on ion transport in the tetragonal phase. Our findings highlight the crucial role of lithium nonstoichiometry and defect chemistry in enhancing LLZO performance and provide insights for the rational design of high-performance solid electrolytes through defect engineering. Tetragonal crystal system ; Lithium (medication) ; Materials science ; Diffusion ; Ionic bonding ; Inorganic chemistry ; Crystallography ; Ionic radius ; Mineralogy ; Ion ; Chemistry ; Crystal structure ; Thermodynamics ; Physics Solid-state spectroscopy and crystallography ; Microwave Dielectric Ceramics Synthesis ; Luminescence Properties of Advanced Materials
Authors: Yusei Hisata | Takashi Washio | Shinobu Takizawa | Sensuke Ogoshi | Yoichi Hoshimoto
Source: Nature Communications (article, 2024-05-07)
Field: Chemistry — Organic Chemistry

In-silico-assisted derivatization of triarylboranes for the catalytic reductive functionalization of aniline-derived amino acids and peptides with H2 Cheminformatics-based machine learning (ML) has been employed to determine optimal reaction conditions, including catalyst structures, in the field of synthetic chemistry. However, such ML-focused strategies have remained largely unexplored in the context of catalytic molecular transformations using Lewis-acidic main-group elements, probably due to the absence of a candidate library and effective guidelines (parameters) for the prediction of the activity of main-group elements. Here, the construction of a triarylborane library and its application to an ML-assisted approach for the catalytic reductive alkylation of aniline-derived amino acids and C-terminal-protected peptides with aldehydes and H<sub>2</sub> is reported. A combined theoretical and experimental approach identified the optimal borane, i.e., B(2,3,5,6-Cl<sub>4</sub>-C<sub>6</sub>H)(2,6-F<sub>2</sub>-3,5-(CF<sub>3</sub>)<sub>2</sub>-C<sub>6</sub>H)<sub>2</sub>, which exhibits remarkable functional-group compatibility toward aniline derivatives in the presence of 4-methyltetrahydropyran. The present catalytic system generates H<sub>2</sub>O as the sole byproduct. Aniline ; Chemistry ; Catalysis ; Derivatization ; Reductive amination ; Combinatorial chemistry ; Alkylation ; In silico ; Amino acid ; Context (archaeology) ; Borane ; Surface modification ; Organic chemistry ; Biochemistry Organoboron and organosilicon chemistry ; Chemical Synthesis and Analysis ; Asymmetric Hydrogenation and Catalysis
Authors: Jiaying Chen | Linxi Hou | Jing Nan | Bangqing Ni | Wei Dai | Xin Ge
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2024-09-06)
Field: Chemistry — Organic Chemistry

Prediction of critical micelle concentration (CMC) of surfactants based on structural differentiation using machine learning Critical micelle concentration ; Micelle ; Chemistry ; Computer science ; Chemical engineering ; Chromatography ; Organic chemistry ; Engineering Surfactants and Colloidal Systems ; Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography
Authors: Sangsuk Lee | Michael R. Shirts | Anthony P. Straub
Source: Journal of Membrane Science (article, 2024-05-24)
Field: Materials Science — Materials Chemistry

Molecular fingerprint-aided prediction of organic solute rejection in reverse osmosis and nanofiltration Nanofiltration ; Interpretability ; Reverse osmosis ; Membrane ; Biological system ; Chemistry ; Fingerprint (computing) ; Molecular descriptor ; Ultrafiltration (renal) ; Osmosis ; Work (physics) ; Quantitative structure–activity relationship ; Process engineering ; Computer science ; Chromatography ; Artificial intelligence ; Thermodynamics ; Engineering Machine Learning in Materials Science ; SARS-CoV-2 detection and testing ; Advanced biosensing and bioanalysis techniques
Authors: Gang Seob Jung | Jong Youl Choi | Sangkeun Lee
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Active learning of neural network potentials for rare events Developing an automated active learning framework for Neural Network Potentials, focusing on accurately simulating bond-breaking in hexane chains through steered molecular dynamics sampling and assessing model transferability. Transferability ; Artificial neural network ; Computer science ; Artificial intelligence ; Machine learning ; Rare events ; Sampling (signal processing) ; Computer vision ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yeong-Seo Park | Byeong Uk Park | Hee‐Jae Jeon
Source: Frontiers in Chemistry (review, 2024-10-17)
Field: Materials Science — Materials Chemistry

Advances in machine learning-enhanced nanozymes Nanozymes, synthetic nanomaterials that mimic the catalytic functions of natural enzymes, have emerged as transformative technologies for biosensing, diagnostics, and environmental monitoring. Since their introduction, nanozymes have rapidly evolved with significant advancements in their design and applications, particularly through the integration of machine learning (ML). Machine learning (ML) has optimized nanozyme efficiency by predicting ideal size, shape, and surface chemistry, reducing experimental time and resources. This review explores the rapid advancements in nanozyme technology, highlighting the role of ML in improving performance across various bioapplications, including real-time monitoring and the development of chemiluminescent, electrochemical and colorimetric sensors. We discuss the evolution of different types of nanozymes, their catalytic mechanisms, and the impact of ML on their property optimization. Furthermore, this review addresses challenges related to data quality, scalability, and standardization, while highlighting future directions for ML-driven nanozyme development. By examining recent innovations, this review highlights the potential of combining nanozymes with ML to drive the development of next-generation diagnostic and detection technologies. Nanotechnology ; Computer science ; Biochemical engineering ; Transformative learning ; Scalability ; Materials science ; Engineering Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Francesco Ricci | Sebastian E. Reyes‐Lillo | Stephanie A. Mack | Jeffrey B. Neaton
Source: npj Computational Materials (article, 2024-01-20)
Field: Materials Science — Materials Chemistry

Candidate ferroelectrics via ab initio high-throughput screening of polar materials Abstract Ferroelectrics are a class of polar and switchable functional materials with diverse applications, from microelectronics to energy conversion. Computational searches for new ferroelectric materials have been constrained by accurate prediction of the polarization and switchability with electric field, properties that, in principle, require a comparison with a nonpolar phase whose atomic-scale unit cell is continuously deformable from the polar ground state. For most polar materials, such a higher-symmetry nonpolar phase does not exist or is unknown. Here, we introduce a general high-throughput workflow that screens polar materials as potential ferroelectrics. We demonstrate our workflow on 1978 polar structures in the Materials Project database, for which we automatically generate a nonpolar reference structure using pseudosymmetries, and then compute the polarization difference and energy barrier between polar and nonpolar phases, comparing the predicted values to known ferroelectrics. Focusing on a subset of 182 potential ferroelectrics, we implement a systematic ranking strategy that prioritizes candidates with large polarization and small polar-nonpolar energy differences. To assess stability and synthesizability, we combine information including the computed formation energy above the convex hull, the Inorganic Crystal Structure Database id number, a previously reported machine learning-based synthesizability score, and ab initio phonon band structures. To distinguish between previously reported ferroelectrics, materials known for alternative applications, and lesser-known materials, we combine this ranking with a survey of the existing literature on these candidates through Google Scholar and Scopus databases, revealing ~130 promising materials uninvestigated as ferroelectric. Our workflow and large-scale high-throughput screening lays the groundwork for the discovery of novel ferroelectrics, revealing numerous candidates materials for future experimental and theoretical endeavors. Ferroelectricity ; Polar ; Ab initio ; Materials science ; Polarization (electrochemistry) ; Computer science ; Chemical physics ; Chemistry ; Dielectric ; Physics ; Optoelectronics ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Machine Learning in Materials Science
Authors: Xi Yang | Yuting Li | Yitao Liu | Qian Li | Tingna Yang | Hongxing Jia
Source: Energies (article, 2024-07-22)
Field: Materials Science — Materials Chemistry

Crystal Structure Prediction and Performance Assessment of Hydrogen Storage Materials: Insights from Computational Materials Science Hydrogen storage materials play a pivotal role in the development of a sustainable hydrogen economy. However, the discovery and optimization of high-performance storage materials remain a significant challenge due to the complex interplay of structural, thermodynamic and kinetic factors. Computational materials science has emerged as a powerful tool to accelerate the design and development of novel hydrogen storage materials by providing atomic-level insights into the storage mechanisms and guiding experimental efforts. In this comprehensive review, we discuss the recent advances in crystal structure prediction and performance assessment of hydrogen storage materials from a computational perspective. We highlight the applications of state-of-the-art computational methods, including density functional theory (DFT), molecular dynamics (MD) simulations, and machine learning (ML) techniques, in screening, evaluating, and optimizing storage materials. Special emphasis is placed on the prediction of stable crystal structures, assessment of thermodynamic and kinetic properties, and high-throughput screening of material space. Furthermore, we discuss the importance of multiscale modeling approaches that bridge different length and time scales, providing a holistic understanding of the storage processes. The synergistic integration of computational and experimental studies is also highlighted, with a focus on experimental validation and collaborative material discovery. Finally, we present an outlook on the future directions of computationally driven materials design for hydrogen storage applications, discussing the challenges, opportunities, and strategies for accelerating the development of high-performance storage materials. This review aims to provide a comprehensive and up-to-date account of the field, stimulating further research efforts to leverage computational methods to unlock the full potential of hydrogen storage materials. Hydrogen storage ; Leverage (statistics) ; Computer science ; Nanotechnology ; Biochemical engineering ; Materials science ; Hydrogen ; Machine learning ; Chemistry ; Engineering Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Hybrid Renewable Energy Systems
Authors: Nafees Ahmad | Bandar R. Alsehli | Asif Mahmood | Yingping Zou
Source: Journal of Photochemistry and Photobiology A Chemistry (article, 2024-09-27)
Field: Materials Science — Polymers and Plastics

Generation of library of dyes for photovoltaic applications: A machine learning assisted framework Chemistry ; Photovoltaic system ; Nanotechnology ; Biochemical engineering Conducting polymers and applications ; Color Science and Applications ; Water Quality Monitoring and Analysis
Authors: Miroslava Šťastná
Source: FEBS Journal (review, 2024-03-05)
Field: Chemistry — Spectroscopy

Post‐translational modifications of proteins in cardiovascular diseases examined by proteomic approaches Over 400 different types of post-translational modifications (PTMs) have been reported and over 200 various types of PTMs have been discovered using mass spectrometry (MS)-based proteomics. MS-based proteomics has proven to be a powerful method capable of global PTM mapping with the identification of modified proteins/peptides, the localization of PTM sites and PTM quantitation. PTMs play regulatory roles in protein functions, activities and interactions in various heart related diseases, such as ischemia/reperfusion injury, cardiomyopathy and heart failure. The recognition of PTMs that are specific to cardiovascular pathology and the clarification of the mechanisms underlying these PTMs at molecular levels are crucial for discovery of novel biomarkers and application in a clinical setting. With sensitive MS instrumentation and novel biostatistical methods for precise processing of the data, low-abundance PTMs can be successfully detected and the beneficial or unfavorable effects of specific PTMs on cardiac function can be determined. Moreover, computational proteomic strategies that can predict PTM sites based on MS data have gained an increasing interest and can contribute to characterization of PTM profiles in cardiovascular disorders. More recently, machine learning- and deep learning-based methods have been employed to predict the locations of PTMs and explore PTM crosstalk. In this review article, the types of PTMs are briefly overviewed, approaches for PTM identification/quantitation in MS-based proteomics are discussed and recently published proteomic studies on PTMs associated with cardiovascular diseases are included. Computational biology ; Posttranslational modification ; Proteomics ; Biology ; Bioinformatics ; Chemistry ; Biochemistry ; Enzyme Advanced Proteomics Techniques and Applications ; Ubiquitin and proteasome pathways ; Sirtuins and Resveratrol in Medicine
Authors: Apurba Nandi | Priyanka Pandey | Paul L. Houston | Chen Qu | Qi Yu | Riccardo Conte | Alexandre Tkatchenko | Joel M. Bowman
Source: Journal of Chemical Theory and Computation (article, 2024-10-03)
Field: Materials Science — Materials Chemistry

Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(<i>T</i>) Level Illustrated for Ethanol Progress in machine learning has facilitated the development of potentials that offer both the accuracy of first-principles techniques and vast increases in the speed of evaluation. Recently, Δ-machine learning has been used to elevate the quality of a potential energy surface (PES) based on low-level, e.g., density functional theory (DFT) energies and gradients to close to the gold-standard coupled cluster level of accuracy. We have demonstrated the success of this approach for molecules, ranging in size from H<sub>3</sub>O<sup>+</sup> to 15-atom acetyl-acetone and tropolone. These were all done using the B3LYP functional. Here, we investigate the generality of this approach for the PBE, M06, M06-2X, and PBE0 + MBD functionals, using ethanol as the example molecule. Linear regression with permutationally invariant polynomials is used to fit both low-level and correction PESs. These PESs are employed for standard RMSE analysis for training and test data sets, and then general fidelity tests such as energetics of stationary points, normal-mode frequencies, and torsional potentials are examined. We achieve similar improvements in all cases. Interestingly, we obtained significant improvement over DFT gradients where coupled cluster gradients were not used to correct the low-level PES. Finally, we present some results for correcting a recent molecular mechanics force field for ethanol and comment on the possible generality of this approach. Force field (fiction) ; Field (mathematics) ; Potential field ; Computational chemistry ; Computer science ; Physics ; Chemistry ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Computational Drug Discovery Methods
Authors: Kai Wang | Huitao Yu | Jing-Li Gao | Yiyu Feng | Wei Feng
Source: Journal of Materials Chemistry C (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Optimizing the performance of phase-change azobenzene: from trial and error to machine learning Machine learning can predict the properties of phase change azobenzene derivatives and guide molecular design to further improve their photothermal conversion performance. Azobenzene ; Materials science ; Photothermal therapy ; Phase (matter) ; Phase change ; Nanotechnology ; Engineering physics ; Polymer ; Composite material ; Organic chemistry ; Engineering Photochromic and Fluorescence Chemistry ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Nicolas Castel | Dune André | Connor Edwards | Jack D. Evans | François‐Xavier Coudert
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks Accurate microscopic models of amorphous metal–organic frameworks (MOFs) are difficult to create. Machine learning potentials based on data from ab initio molecular dynamics offer a novel way to achieve this goal. Zeolitic imidazolate framework ; Amorphous solid ; Imidazolate ; Materials science ; Nanotechnology ; Chemical physics ; Computer science ; Chemical engineering ; Chemistry ; Crystallography ; Physical chemistry ; Engineering ; Metal-organic framework ; Adsorption Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Memory and Neural Computing
Authors: Pavan Ravindra | Xavier R. Advincula | Christoph Schran | Angelos Michaelides | Venkat Kapil
Source: Nature Communications (article, 2024-08-24)
Field: Materials Science — Materials Chemistry

Quasi-one-dimensional hydrogen bonding in nanoconfined ice The Bernal-Fowler ice rules stipulate that each water molecule in an ice crystal should form four hydrogen bonds. However, in extreme or constrained conditions, the arrangement of water molecules deviates from conventional ice rules, resulting in properties significantly different from bulk water. In this study, we employ machine learning-driven first-principles simulations to identify a new stabilization mechanism in nanoconfined ice phases. Instead of forming four hydrogen bonds, nanoconfined crystalline ice can form a quasi-one-dimensional hydrogen-bonded structure that exhibits only two hydrogen bonds per water molecule. These structures consist of strongly hydrogen-bonded linear chains of water molecules that zig-zag along one dimension, stabilized by van der Waals interactions that stack these chains along the other dimension. The unusual interplay of hydrogen bonding and van der Waals interactions in nanoconfined ice results in atypical proton behavior such as potential ferroelectric behavior, low dielectric response, and long-range proton dynamics. van der Waals force ; Hydrogen bond ; Chemical physics ; Molecule ; Proton ; Materials science ; Hydrogen ; Ice crystals ; Stack (abstract data type) ; Molecular dynamics ; Ice Ih ; Crystallography ; Chemistry ; Computational chemistry ; Physics Material Dynamics and Properties ; Electrostatics and Colloid Interactions ; Methane Hydrates and Related Phenomena