jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ashwin Rajeev | Dhiraj Bhatia
Source: Nanoscale (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

DNA-templated fluorescent metal nanoclusters and their illuminating applications After the discovery of DNA during the mid-20<sup>th</sup> century, a multitude of novel methodologies have surfaced which exploit DNA for its various properties. One such recently developed application of DNA is as a template in metal nanocluster formation. In the early years of the new millennium, a group of researchers found that DNA can be adopted as a template for the binding of metal nanoparticles that ultimately form nanoclusters. Three metal nanoclusters have been studied so far, including silver, gold, and copper, which have a plethora of biological applications. This review focuses on the synthesis, mechanisms, and novel applications of DNA-templated metal nanoclusters, including the therapies that have employed them for their wide range of fluorescent properties, and the future perspectives related to their development by exploiting machine learning algorithms and molecular dynamics simulation studies. Nanoclusters ; Fluorescence ; Materials science ; Metal ; Nanotechnology ; DNA ; Chemistry ; Metallurgy ; Optics ; Biochemistry ; Physics Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Wenhui Zhong | Jun Jiang
Source: The Journal of Physical Chemistry Letters (article, 2024-05-15)
Field: Materials Science — Materials Chemistry

The Rational Design of Atomically Dispersed Catalysts via Spin Manipulation The catalytic activity of transition-metal-based atomically dispersed catalysts is closely related to the spin states. Manipulating the spin state of metal active centers could directly adjust the d orbital occupancy and optimize the adsorption behavior and electron transfer of the intermediates and transition metals, which would enhance the catalytic activity. We summarize the means of manipulating spin states and the spin-related catalytic descriptors. In future work, we will build a quantifiable and accurate prediction intelligent model through artificial intelligence (AI) and machine learning tools. Furthermore, we will develop new spin regulation methods to carry out the directional regulation of atomically dispersed catalysts through this model, providing new insight into the rational design of transition-metal-based atomically dispersed catalysts through spin manipulation. Catalysis ; Rational design ; Spin (aerodynamics) ; Transition metal ; Spin states ; Materials science ; Nanotechnology ; Design elements and principles ; Chemical physics ; Chemistry ; Computer science ; Physics ; Inorganic chemistry ; Organic chemistry ; Thermodynamics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Isaiah A. Moses | Wesley F. Reinhart
Source: Materials Characterization (article, 2024-01-29)
Field: Materials Science — Materials Chemistry

Quantitative analysis of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mi>MoS</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> thin film micrographs with machine learning Artificial intelligence ; Materials science ; Characterization (materials science) ; Algorithm ; Computer science ; Machine learning ; Nanotechnology Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Force Microscopy Techniques and Applications
Authors: Alexey S. Kotykhov | Konstantin Gubaev | Vadim Sotskov | Christian Tantardini | Max Hodapp | Alexander V. Shapeev | Ivan S. Novikov
Source: Computational Materials Science (preprint, 2024-08-29)
Field: Materials Science — Materials Chemistry

Fitting to magnetic forces improves the reliability of magnetic Moment Tensor Potentials We developed a method for fitting machine-learning interatomic potentials with magnetic degrees of freedom, namely, magnetic Moment Tensor Potentials (mMTP). The main feature of our method consists in fitting mMTP to magnetic forces (negative derivatives of energies with respect to magnetic moments) as obtained spin-polarized density functional theory calculations. We test our method on the bcc Fe–Al system with different compositions. Specifically, we calculate formation energies, equilibrium lattice parameter, and total cell magnetization. Our findings demonstrate an accurate correspondence between the values calculated with mMTP and those obtained by DFT at zero temperature. Additionally, using molecular dynamics, we estimate the finite-temperature lattice parameter and capture the cell expansion as was previously revealed in experiment. Furthermore, we demonstrate that fitting to magnetic forces increases the reliability of structure relaxation (or, equilibration), in the sense of ensuring that every relaxation run ends up with a successfully relaxed structure (the failure may otherwise be caused by falsely driving a configuration away from the region covered in the training set). • Developed a method for fitting machine-learning potentials with magnetic degrees of freedom. • mMTP accurately matches magnetic forces from spin-polarized DFT calculations. • Tested mMTP on bcc Fe–Al system, achieving close alignment with DFT at zero temperature. • Molecular dynamics with mMTP predicts finite-temperature lattice parameters and expansion. • Fitting to magnetic forces improves reliability in structure relaxation and equilibration. Magnetic moment ; Tensor (intrinsic definition) ; Magnetization ; Relaxation (psychology) ; Condensed matter physics ; Statistical physics ; Lattice (music) ; Moment (physics) ; Density functional theory ; Physics ; Spin (aerodynamics) ; Mathematics ; Magnetic field ; Classical mechanics ; Quantum mechanics ; Geometry ; Thermodynamics Machine Learning in Materials Science ; Magnetic properties of thin films ; Magnetic Properties and Applications
Authors: Daisuke Takaya | Shinichi Ohno | T Miyagishi | Shinya Tanaka | Koji Okuwaki | Chiduru Watanabe | Koichiro Kato | Yu‐Shi Tian | Kaori Fukuzawa
Source: Scientific Data (article, 2024-10-23)
Field: Materials Science — Materials Chemistry

Quantum chemical calculation dataset for representative protein folds by the fragment molecular orbital method The function of a biomacromolecule is not only determined by its three-dimensional structure but also by its electronic state. Quantum chemical calculations are promising non-empirical methods available for determining the electronic state of a given structure. In this study, we used the fragment molecular orbital (FMO) method, which applies to biopolymers such as proteins, to provide physicochemical property values on representative structures in the SCOP2 database of protein families, a subset of the Protein Data Bank. Our dataset was constructed by over 5,000 protein structures, including over 200 million inter-fragment interaction energies (IFIEs) and their energy components obtained by pair interaction energy decomposition analysis (PIEDA) using FMO-MP2/6-31 G*. Moreover, three basis sets, 6-31 G*, 6-31 G**, and cc-pVDZ, were used for the FMO calculations of each structure, making it possible to compare the energies obtained with different basis functions for the same fragment pair. The total data size is approximately 6.7 GB. Our dataset will be useful for functional analyses and machine learning based on the physicochemical property values of proteins. Fragment molecular orbital ; Fragment (logic) ; Protein Data Bank ; Electronic structure ; Quantum chemical ; Basis (linear algebra) ; Function (biology) ; Property (philosophy) ; Chemistry ; Protein structure ; Computational chemistry ; Biological system ; Statistical physics ; Computer science ; Physics ; Molecular orbital ; Molecule ; Algorithm ; Mathematics ; Quantum mechanics ; Biology ; Geometry ; Genetics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Li Tang | Li Wang | Bin Wang | Yong Pei | Shuxin Wang
Source: Chemistry - A European Journal (article, 2024-05-23)
Field: Materials Science — Materials Chemistry

Discovering of Atomically Precise Metal Nanoclusters by High‐Throughput Syntheses Platform Understanding the complete structure of noble metal nanoclusters is both academically and practically significant. However, progress has been hindered by the low synthetic efficiency of many nanocluster syntheses. In this study, we present the first high-throughput syntheses of homo-gold, homo-copper, and gold-copper alloy nanoclusters in dichloromethane at room temperature. Through high-throughput screening, we successfully obtained three nanoclusters in a single reaction: Au<sub>18</sub>(SC<sub>6</sub>H<sub>11</sub>)<sub>14</sub>, [Au<sub>41</sub>Cu<sub>66</sub>(SC<sub>6</sub>H<sub>11</sub>)<sub>44</sub>](SbF<sub>6</sub>)<sub>3</sub>, and an unidentified copper cluster (referred to as Au<sub>18</sub>, Au<sub>41</sub>Cu<sub>66</sub> <sub>,</sub> and Cu-NC). The optimized synthesis route was achieved with the assistance of machine learning for experimental data analysis, which also guided the synthesis of other metal nanoclusters such as Au<sub>40</sub>Cu<sub>34</sub>(4-S-PhF)<sub>40</sub> (Au<sub>40</sub>Cu<sub>34</sub>), [Au<sub>6</sub>Cu<sub>6</sub>(SPh)<sub>12</sub>]<sub>n</sub> ([Au<sub>6</sub>Cu<sub>6</sub>]<sub>n</sub>), and Au<sub>18</sub>Cu<sub>32</sub>(3,5-C<sub>8</sub>H<sub>9</sub>S)<sub>36</sub> (Au<sub>18</sub>Cu<sub>32</sub>)). This research demonstrates that high-throughput screening can be a valuable tool in accelerating the development of nanocluster syntheses. Nanoclusters ; Throughput ; Copper ; Nanotechnology ; Materials science ; Cluster (spacecraft) ; Alloy ; Dichloromethane ; Noble metal ; Metal ; Computer science ; Chemistry ; Metallurgy ; Organic chemistry Nanocluster Synthesis and Applications ; Pharmacological Effects and Toxicity Studies ; Machine Learning in Materials Science
Authors: Jianbo Hu | Xian Suo | Lifeng Yang | Jiadong Zhu | Jianjun Zhang | Huabin Xing | Xili Cui
Source: The Journal of Physical Chemistry C (article, 2024-01-04)
Field: Chemistry — Inorganic Chemistry

High-Throughput Computation Evaluation of Metal–Organic Frameworks for Efficient Perfluorocarbons Recovery The recovery of perfluorocarbons (PFCs), such as CF4 and C2F6, from exhaust gas can not only reduce the emissions of greenhouse gas but also improve the utilization of PFCs in the semiconductor industry. In this work, a high-throughput computational evaluation for nearly 10 000 MOFs in the CoRE MOF database was performed to evaluate the potential of metal–organic frameworks (MOFs) for the recovery of trace CF4 and C2F6 from N2-containing gas. Various adsorbent performance metrics, including adsorption selectivity, working capacity, recovery rate, and adsorbent performance score, were calculated to evaluate the top-performing MOFs, and 10 top-performing MOFs for efficient capture of CF4 and C2F6 over N2 were identified from a computation-ready experimental (CoRE) MOF database. The machine learning model analysis reveals that the LCD as well as the adsorption heat difference between PFCs with N2 play dominant roles in PFCs recovery. Furthermore, five design and optimization strategies, including adjustment or functionalization of the organic linker, substitution of metal node, regulation of topology net, and optimization of synthesis condition, were provided to guide the development of high-performing MOFs for PFCs recovery. Metal-organic framework ; Adsorption ; Computation ; Process engineering ; Materials science ; Computer science ; Chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Inorganic Fluorides and Related Compounds
Authors: Kévin Alhada–Lahbabi | Damien Deleruyelle | Brice Gautier
Source: Computer Methods in Applied Mechanics and Engineering (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Transfer learning for accelerating phase-field modeling of ferroelectric domain formation in large-scale 3D systems High-throughput phase-field simulations emerge as a compelling technique to predict the evolution of domain structures in ferroelectric materials. Despite their potential, their widespread use is impeded by significant computational costs, particularly for large 3D systems, limiting applicability in real-size scenarios and extensive parameter exploration. Here, we present a machine-learning approach tailored for accelerating 3D ferroelectric phase-field modeling. Our model exhibits remarkable accuracy in forecasting domain formation trajectories, providing stable predictions throughout entire simulation paths. Furthermore, we introduce a transfer learning strategy that facilitates the model extension to larger systems in a computationally tractable way, broadening its applicability to realistic scenarios. With a relative error below 10% when compared to high-fidelity methods, our surrogate serves as an efficient replacement for ferroelectric phase-field. By achieving speed-ups of up to 3300-fold compared to direct numerical solvers, the presented approach contributes to significantly accelerate the discovery and design of ferroelectric materials. Ferroelectricity ; Scale (ratio) ; Domain (mathematical analysis) ; Phase (matter) ; Field (mathematics) ; Computer science ; Materials science ; Physics ; Mathematics ; Mathematical analysis ; Dielectric ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Piezoelectric Actuators and Control
Authors: Christopher Ikechukwu Ekeocha | Ikechukwu Nelson Uzochukwu | Ini‐Ibehe Nabuk Etim | Ikenna B. Onyeachu | Emeka E. Oguzie
Source: Materials Today Communications (article, 2024-08-25)
Field: Materials Science — Materials Chemistry

Machine learning models and computational simulation techniques for prediction of anti-corrosion properties of novel benzimidazole derivatives Materials science ; Benzimidazole ; Corrosion ; Machine learning ; Predictive modelling ; Artificial intelligence ; Computer science ; Composite material ; Organic chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yubai Shi | Yifan Shan | Hongyu Wu | Zhicheng Zhong | Run‐Wei Li | Ri He
Source: Physical review. B./Physical review. B (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Compositional ordering driven morphotropic phase boundary in ferroelectric solid solutions Ferroelectric solid solutions usually exhibit giant dielectric response and high piezoelectricity in the vicinity of the morphotropic phase boundary (MPB), where the structural phase transitions between the rhombohedral and the tetragonal phases as a result of the composition or strain variation. Here, we propose a compositional ordering driven MPB in the specified compositional solid solutions. By preforming machine-learning potential-based molecular dynamics simulations on lead zirconate titanate, we find a phase transition from the rhombohedral to tetragonal phase with the decrease of compositional ordering, leading to the MPB on temperature-ordering phase diagram. The compositional ordering driven MPB can enhance the piezoelectricity with a magnitude comparable to that at the composition driven MPB. Finally, we demonstrate that the mechanism of high piezoelectricity is polarization rotation driven by external field. This work provides an additional degree of freedom, compositional ordering, to design the high-performance piezoelectric materials. Phase boundary ; Ferroelectricity ; Materials science ; Solid solution ; Phase (matter) ; Condensed matter physics ; Boundary (topology) ; Mathematics ; Mathematical analysis ; Physics ; Dielectric ; Metallurgy Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Ultrasonics and Acoustic Wave Propagation
Authors: Kabiru Haruna | Sani I. Abba | Jamil Usman | A. G. Usman | Abdulrahman Musa | Tawfik A. Saleh | Isam H. Aljundi
Source: Carbon Trends (article, 2024-06-13)
Field: Materials Science — Materials Chemistry

Machine learning insight into inhibition efficiency modelling based on modified graphene oxide of diaminohexane (DAH-GO) and diaminooctane (DAO-GO) The effective prediction of corrosion inhibition efficiency (%IE) of modified graphene oxides (GOs); diaminohexane-modified graphene oxide (DAH-GO) and diaminooctane-modified graphene oxide (DAO-GO) is vital for advanced material applications. This study employs a dual-modelling scheme to predict the %IE, for this purpose, four stand-alone machine learning (ML) models (Multivariate Regression (MVR), Gaussian Process Regression (GPR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Neural Network (NN)), and five simple averaging (SA) ensemble paradigms (MVR-SA, GPR-SA, ANFIS-SA, NN-SA, and Decision Tree-SA (DT-SA)). Feature selection processes were carried out to develop three distinct models, leading to a comprehensive comparative analysis. The results demonstrated that the non-linear stand-alone models (GPR, ANFIS, NN) significantly outperform the linear MVR model, with the M2 model configuration yielding the highest performance across all models. Remarkably, GPR-M2 achieved perfect model tuning with zero error rates, indicating its superior predictive capabilities. Ensemble techniques further improved performance, reflecting the experimental data's complexities in %IE modelling. The hierarchical order of performance in the training phase in the testing phase is DT-SA < MVR-SA < ANFIS-SA < NN-SA < GPR-SA. The GPR-SA ensemble emerged as the most accurate technique, substantially enhancing the predictive accuracy of the ensemble models by up to 67.73% in the training phase and 50.71% in the testing phase. These findings suggest the potential of GPR-SA in boosting the performance of ensemble approaches in material science applications. The study recommended a promising future for ML in the development and application of corrosion-inhibitors. Adaptive neuro fuzzy inference system ; Artificial intelligence ; Machine learning ; Kriging ; Graphene ; Artificial neural network ; Decision tree ; Support vector machine ; Computer science ; Linear regression ; Gradient boosting ; Predictive modelling ; Fuzzy logic ; Materials science ; Random forest ; Nanotechnology ; Fuzzy control system Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Non-Destructive Testing Techniques
Authors: Shengcai Zhu | Ziyan Chen | Zhi‐Pan Liu | Yanglong Hou
Source: ACS Materials Letters (article, 2024-07-16)
Field: Materials Science — Materials Chemistry

Thermodynamics and Catalytic Activity of the Reduced Cu on a Cu<sub>2</sub>O Surface from Machine Learning Atomic Simulation Electrochemical carbon dioxide reduction, converting CO2 into high-value-added chemical products, is a key technology to realize a natural carbon cycle. As one of the most effective catalysts, oxide-derived copper (OD-Cu) is widely used in CO2 reduction because of its ability to produce hydrocarbons. However, the atomic structure of Cu on the Cu2O surface and why such a structure shows more catalytic activity than directly synthesized Cu are still unknown. Here, by using stochastic surface walking global optimization combined with a global neural network potential (SSW-NN) method, we explore the phase diagram of CuxO, the possible atomic structure of the Cu2O/Cu interfaces, the Cu2O surface reduction, and the Cu catalytic performance. By continuously deleting a certain amount of O atoms to mimic the reduction process, we find that a metastable phase, namely hcp-Cu, is formed on the Cu2O (001) surface because of the good atomic position and lattice match between Cu2O (001) and hcp-Cu (110), rather than fcc-Cu (111). The total energy barrier for the reduction of CO2 to methanol is 1.40 eV on the hcp-Cu (110) surface, much lower than the 1.97 eV on the fcc-Cu (111) surface, indicating the excellent catalytic performance of hcp-Cu. This proposed in situ-formed hcp-Cu not only reconciles the longstanding debate regarding the high catalytic activity of OD-Cu but also guides the rational design of electrochemical CO2 reduction catalysts via phase engineering. Catalysis ; Copper ; Thermodynamics ; Surface (topology) ; Physical chemistry ; Chemistry ; Materials science ; Metallurgy ; Physics ; Mathematics ; Organic chemistry Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts ; Machine Learning in Materials Science
Authors: Maximilian Schebek | Michele Invernizzi | Frank Noé | Jutta Rogal
Source: Machine Learning Science and Technology (article, 2024-10-08)
Field: Materials Science — Materials Chemistry

Efficient mapping of phase diagrams with conditional Boltzmann Generators Abstract The accurate prediction of phase diagrams is of central importance for both the fundamental understanding of materials as well as for technological applications in material sciences. However, the computational prediction of the relative stability between phases based on their free energy is a daunting task, as traditional free energy estimators require a large amount of simulation data to obtain uncorrelated equilibrium samples over a grid of thermodynamic states. In this work, we develop deep generative machine learning models based on the Boltzmann Generator approach for entire phase diagrams, employing normalizing flows conditioned on the thermodynamic states, e.g. temperature and pressure, that they map to. By training a single normalizing flow to transform the equilibrium distribution sampled at only one reference thermodynamic state to a wide range of target temperatures and pressures, we can efficiently generate equilibrium samples across the entire phase diagram. Using a permutation-equivariant architecture allows us, thereby, to treat solid and liquid phases on the same footing. We demonstrate our approach by predicting the solid–liquid coexistence line for a Lennard-Jones system in excellent agreement with state-of-the-art free energy methods while significantly reducing the number of energy evaluations needed. Phase (matter) ; Computer science ; Statistical physics ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Sanggyu Chong | Filippo Bigi | Federico Grasselli | Philip Loche | Matthias Kellner | Michele Ceriotti
Source: Faraday Discussions (article, 2024-08-23)
Field: Materials Science — Materials Chemistry

Prediction rigidities for data-driven chemistry The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency whilst guaranteeing interpretability and transferability. In this work, we demonstrate the wide utility of prediction rigidities, a family of metrics derived from the loss function, in understanding the robustness of ML model predictions. We show that the prediction rigidities allow the assessment of the model not only at the global level, but also on the local or the component-wise level at which the intermediate (<i>e.g.</i> atomic, body-ordered, or range-separated) predictions are made. We leverage these metrics to understand the learning behavior of different ML models, and to guide efficient dataset construction for model training. We finally implement the formalism for a ML model targeting a coarse-grained system to demonstrate the applicability of the prediction rigidities to an even broader class of atomistic modeling problems. Interpretability ; Transferability ; Leverage (statistics) ; Robustness (evolution) ; Computer science ; Machine learning ; Training set ; Artificial intelligence ; Formalism (music) ; Experimental data ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics