jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yi Shi | Tianren Zhang | Rui Guo | Zihan Zhang | Amanda L. McCahill | Yao Tang | Sabrina E. Liskey | Dai-Bei Yang | Christopher J. Kloxin | Jeffery G. Saven | Darrin J. Pochan
Source: Nature Communications (article, 2024-11-20)
Field: Materials Science — Materials Chemistry

Ordered assemblies of peptide nanoparticles with only positive charge Surface charge patchiness of different charge types can influence the solution behaviours of colloidal particles and globular proteins. Herein, coiled-coil 'bundlemer' nanoparticles that display only a single type of surface charge (SC) are computationally designed to compare their solution behaviours to mixed charge-type (MC) counterparts with both positively and negatively charged side chains. Nematic and columnar liquid crystal phases are discovered in low concentrations of SC particles, indicative of particle end-to-end stacking into columns combined with lateral electrostatic repulsion between columns, while MC particles with the same net charge and particle shape produced only amorphous, soluble aggregates. Similarly, porous lattices are formed in mixtures of SC/MC particles of opposite charges while MC/MC mixtures of opposite charges produce only amorphous aggregates. The lattice structure is inferred with a machine learning optimization approach. The differences between SC and MC particle behaviours directly show the importance of surface electrostatic patchiness. Surface charge ; Chemical physics ; Nanoparticle ; Charge (physics) ; Materials science ; Amorphous solid ; Particle (ecology) ; Colloid ; Electrostatics ; Stacking ; Charge density ; Crystallography ; Static electricity ; Nanotechnology ; Chemistry ; Physics ; Physical chemistry ; Organic chemistry Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems ; Proteins in Food Systems
Authors: Zihao Wang | Shuai Chang | Xiaoqian Bao | Haijun Yu | Shengen Guan | Kunyuan Zhu | Yang Zheng | Jiheng Li | Xuexu Gao
Source: Journal of Alloys and Compounds (article, 2024-09-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interpretable prediction of remanence in sintered NdFeB through machine learning strategy Remanence ; Neodymium magnet ; Materials science ; Geology ; Artificial intelligence ; Mineralogy ; Metallurgy ; Geochemistry ; Computer science ; Magnet ; Physics ; Engineering ; Mechanical engineering ; Magnetization Magnetic Properties of Alloys ; Rare-earth and actinide compounds ; Magnetic properties of thin films
Authors: Davide Avagliano | Marta Skreta | Sebastian Arellano-Rubach | Alán Aspuru‐Guzik
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

DELFI: a computer oracle for recommending density functionals for excited states calculations Density functional theory (DFT) is the workhorse of computational quantum chemistry. One of its main limitations is that choosing the right functional is a non-trivial task left for human experts. The choice is particularly hard for excited state calculations when using its time-dependent formulation (TD-DFT). This is due to the approximations of the method, but also because the photophysical properties of a molecule are defined by a manifold of states that all need to be properly described. This includes not only the relative energy of the states, but also capturing the correct character, order, and intensity of the transitions. In this work, we developed a neural network to recommend functionals to be used on molecules for TD-DFT calculations, by simultaneously considering all these properties for a manifold of states. This was possible by developing a scoring system to define the accuracy of an excited state's calculation against a higher-accuracy reference. The scoring system is generalizable to any level of theory; we here applied it to evaluate the performance of common functionals of different rungs against a higher accuracy method on a large set of organic molecules. The results are collected in a database that we released and made open, providing four million data points to the community for future applications. The scoring system assigns a value between zero and one hundred to each functional for each molecule, transforming the complicated task of learning photophysical properties into a simpler regression task. We used the dataset to train a graph attention neural network to predict the scores for unseen molecules. We call this oracle DELFI (Data-driven EvaLuation of Functionals by Inference), which can be used to quickly screen and predict the ranking of functionals to calculate the optical properties of organic molecules. We validated DELFI in two <i>in silico</i> experiments: choosing a common functional for a series of spiropyran-merocyanine isomers and a unique functional to screen a large dataset of over 50 000 organic photovoltaic molecules, for which an extensive benchmark would be unfeasible. A corresponding web application allows DELFI to be easily run and the results to be analyzed, alleviating the hurdle of choosing the right functional for TD-DFT calculations. Excited state ; Density functional theory ; Computer science ; Oracle ; Set (abstract data type) ; Task (project management) ; Artificial neural network ; Manifold (fluid mechanics) ; Algorithm ; Artificial intelligence ; Computational chemistry ; Chemistry ; Quantum mechanics ; Physics Photochromic and Fluorescence Chemistry ; Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Ha Anh Nguyen | Mai Quan Doan | Dao Thi Nguyet Nga | Minh Khanh Pham | Quoc Khanh Nguyen | Trong Hiep | Van Thien Luong | Vũ Đình Lãm | Anh-Tuan Le
Source: Nanoscale Advances (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Paper/GO/e-Au flexible SERS sensors for <i>in situ</i> detection of tricyclazole in orange juice and on cucumber skin at the sub-ppb level: machine learning-assisted data analysis Despite being an excellent surface enhanced Raman scattering (SERS) active material, gold nanoparticles were difficult to be loaded onto the surface of filter paper to fabricate flexible SERS substrates. In this study, electrochemically synthesized gold nanoparticles (e-AuNPs) were deposited on graphene oxide (GO) nanosheets in solution by ultrasonication, resulting in the formation of a GO/Au hybrid material. Thanks to the support of GO, the hybrid material could adhere onto the surface of filter paper, which was immersed into a GO/Au solution for 24 h and dried naturally at room temperature. The paper-based materials were then employed as substrates for a surface enhanced Raman scattering (SERS) sensing platform to detect tricyclazole (TCZ), a widely used pesticide, resulting in better sensitivity compared to the use of paper/Au SERS sensors. With the most optimal GO content of 4%, paper/GO/Au SERS sensors could achieve a limit of detection of 1.32 × 10<sup>-10</sup> M in standard solutions. Furthermore, the filter paper-based SERS sensors also exhibited significant advantages in sample collection in real samples. On one hand, the sensors were dipped into orange juice, allowing TCZ molecules in this real sample to be adsorbed onto their SERS active surface. On the other hand, they were pasted onto cucumber skin to collect the analytes. As a result, the paper/GO/Au SERS sensors could sense TCZ in orange juice and on cucumber skin at concentrations as low as 10<sup>-9</sup> M (∼2 ppb). In addition, a machine learning model was designed and developed, allowing the sensing system to discriminate TCZ from nine other organic compounds and predict the presence of TCZ on cucumber skin at concentrations down to 10<sup>-9</sup> M. Orange (colour) ; In situ ; Orange juice ; Computer science ; Artificial intelligence ; Chemistry ; Food science ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Yi Cao | Mohamed Amine Khadimallah | Mohd Ahmed | Hamid Assilzadeh
Source: Synthetic Metals (article, 2024-05-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancing structural analysis and electromagnetic shielding in carbon foam composites with applications in concrete integrating XGBoost machine learning, carbon nanotubes, and montmorillonite Montmorillonite ; Electromagnetic shielding ; Composite material ; Carbon nanotube ; Materials science ; Carbon fibers ; Composite number Electromagnetic wave absorption materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Smart Materials for Construction
Authors: Xin He | Tian Lu | Chunying Rong | Wenjian Liu | Paul W. Ayers | Shubin Liu
Source: Journal of Chemical Theory and Computation (article, 2024-07-12)
Field: Chemistry — Organic Chemistry

Energetic Information from Information-Theoretic Approach in Density Functional Theory as Quantitative Measures of Physicochemical Properties The Hohenberg-Kohn theorem of density functional theory (DFT) stipulates that energy is a universal functional of electron density in the ground state, so energy can be thought of having encoded essential information for the density. Based on this, we recently proposed to quantify energetic information within the framework of information-theoretic approach (ITA) of DFT (<i>J. Chem. Phys</i>. <b>2022</b>, 157, 101103). In this study, we systematically apply energetic information to a variety of chemical phenomena to validate the use of energetic information as quantitative measures of physicochemical properties. To that end, we employed six ITA quantities such as Shannon entropy and Fisher information for five energetic densities, yielding twenty-six viable energetic information quantities. Then, they are applied to correlate with physicochemical properties of molecular systems, including chemical bonding, conformational stability, intermolecular interactions, acidity, aromaticity, cooperativity, electrophilicity, nucleophilicity, and reactivity. Our results show that different quantities of energetic information often behave differently for different properties but a few of them, such as Shannon entropy of the total kinetic energy density and information gain of the Pauli energy density, stand out and strongly correlate with several properties across different categories of molecular systems. These results suggest that they can be employed as quantitative measures of physicochemical properties. This work not only enriches the body of our knowledge about the relationship between energy and information, but also provides scores of newly introduced explicit density functionals to quantify physicochemical properties, which can serve as robust features for building machine learning models in future studies. Density functional theory ; Computer science ; Information theory ; Data science ; Statistical physics ; Chemistry ; Physics ; Mathematics ; Computational chemistry ; Statistics Chemical Thermodynamics and Molecular Structure ; thermodynamics and calorimetric analyses ; Computational Drug Discovery Methods
Authors: Y.K. Wang | Bianca Baldassarri | Jiahong Shen | Jiangang He | Chris Wolverton
Source: Chemistry of Materials (article, 2024-06-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Landscape of Thermodynamic Stabilities of <i>A</i><sub>2</sub><i>BB</i>′O<sub>6</sub> Compounds Perovskite oxides have been extensively studied for their wide range of compositions and structures, as well as their valuable properties for various applications. Expanding from single-perovskite ABO3 to double-perovskite A2BB′O6 significantly enhances the ability to tailor specific physical and chemical properties. However, the vast number of potential compositions of A2BB′O6 makes it impractical to explore all of them experimentally. In this study, we conducted high-throughput calculations to systematically investigate the structures and stabilities of 4900 A2BB′O6 compositions (with A = Ca, Sr, Ba, and La; B and B′ representing metal elements) through over 42 000 density functional theory (DFT) calculations. Our analysis lead to the discovery of more than 1500 new thermodynamically stable A2BB′O6 compounds, with over 1100 of them exhibiting double perovskite structures, predominantly in the P21/c space group. By leveraging the high-throughput dataset, we developed machine learning models that achieved mean absolute errors of 0.0422 and 0.0329 eV/atom for formation energy and decomposition energy, respectively. Using these models, we identified 803 stable or metastable compositions beyond the chemical space covered in our initial calculations, with 612 of them having DFT-validated decomposition energies below 0.1 eV/atom, resulting in a success rate of 76.2%. This study delineates the stability landscape of A2BB′O6 compounds and offers new insights for exploration of these materials. Chemistry ; Materials science ; Thermodynamics ; Physical chemistry ; Physics Crystal Structures and Properties ; Solid-state spectroscopy and crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Aojun Gong | Weihua Huang | Yongkai Xiao | Yu Yuan | Lianbo Guo
Source: Knowledge-Based Systems (article, 2024-08-31)
Field: Chemistry — Analytical Chemistry

CFNet: Cross-modal data augmentation empowered fuzzy neural network for spectral fluctuation Modal ; Artificial neural network ; Fuzzy logic ; Computer science ; Artificial intelligence ; Materials science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Neural Networks and Applications
Authors: Tao Hu | Yanxiao Hu | Wenqiu Shang | Li Li | Chunbao Feng | Ping Zhou | Dengfeng Li
Source: AIP Advances (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Thermoelectric properties of lead-free anti-perovskites X3BN (B = Bi, Sb, X = Mg, Ca, Sr): A theoretical study based on first-principles calculations and machine learning interatomic potential The search for non-toxic and low-cost perovskites with high thermoelectric performance is still a challenge despite low thermal conductivity. The thermoelectric properties of nitride anti-perovskites X3BN (B = Bi, Sb, X = Mg, Ca, Sr) with a cubic structure were investigated using density functional theory and machine learning interatomic potential. The low Debye temperature and thermal conductivity were obtained due to strong lattice anharmonicity, and the phonon vibration modes were also analyzed. The high band degeneracy and suitable bandgap lead to a large power factor. The maximum power factor is 7.54 mW/mk2 for Mg3BiN at 900 K. We obtained a maximum ZT of 1.49 for p-type Sr3BiN, and it is 1.22 for n-type doping at 900 K. The ZT for Mg3BiN is 1.18 and 1.19 for p-type and n-type doping, respectively. Our calculations reveal that these anti-perovskites are excellent materials for non-toxic and low-cost thermoelectric applications. Thermoelectric effect ; Materials science ; Lead (geology) ; Interatomic potential ; Thermoelectric materials ; Condensed matter physics ; Thermodynamics ; Chemistry ; Physics ; Computational chemistry ; Molecular dynamics ; Geology Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity ; Perovskite Materials and Applications
Authors: Matteo Cioni | Massimo Delle Piane | Daniela Polino | Daniele Rapetti | Martina Crippa | Ece Arslan Irmak | Sandra Van Aert | Sara Bals | Giovanni M. Pavan
Source: Advanced Science (article, 2024-04-24)
Field: Materials Science — Materials Chemistry

Sampling Real‐Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic-resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state-of-the-art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark-field scanning transmission electron microscopy enables the acquisition of ten high-resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allow resolving the real-time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions. Molecular dynamics ; Computer science ; Biological system ; Nanotechnology ; Dynamics (music) ; Sampling (signal processing) ; Statistical physics ; Materials science ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Paul L. Houston | Chen Qu | Qi Yu | Priyanka Pandey | Riccardo Conte | Apurba Nandi | Joel M. Bowman
Source: Journal of Chemical Theory and Computation (article, 2024-04-09)
Field: Materials Science — Materials Chemistry

No Headache for PIPs: A PIP Potential for Aspirin Runs Much Faster and with Similar Precision Than Other Machine-Learned Potentials Assessments of machine-learning (ML) potentials are an important aspect of the rapid development of this field. We recently reported an assessment of the linear-regression permutationally invariant polynomial (PIP) method for ethanol, using the widely used (revised) rMD17 data set. We demonstrated that the PIP approach outperformed numerous other methods, e.g., ANI, PhysNet, sGDML, and p-KRR, with respect to precision and notably with respect to speed [Houston et al., <i>J. Chem. Phys.</i> <b>2022</b>, 156, 044120]. Here, we extend this assessment to the 21-atom aspirin molecule, using the rMD17 data set, with a focus on the speed of evaluation. Both energies and forces are used for training, and the precision of several PIPs is examined for both. Normal mode frequencies, the methyl torsional potential, and 1d vibrational energies for an OH stretch are presented. We show that the PIP approach achieves the level of precision obtained from other ML methods, e.g., atom-centered neural network methods, linear regression ACE, and kernel methods, as reported by Kovács et al. in <i>J. Chem. Theory Comput.</i> <b>2021</b>, 17, 7696-7711. More significantly, we show that the PIP PESs run much faster than all other ML methods, whose timings were evaluated in that paper. We also show that the PIP PES extrapolates well enough to describe several internal motions of aspirin, including an OH stretch. Computer science ; Linear regression ; Set (abstract data type) ; Algorithm ; Polynomial ; Invariant (physics) ; Machine learning ; Physics ; Mathematics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jieyu Chen | Lulu Lei | Chao Ji | Sixian Yang | Zefei Cheng | Jinhong Fan | Tao Yu
Source: Composites Communications (article, 2024-11-19)
Field: Materials Science — Polymers and Plastics

Cluster analysis of acoustic emission signals for bending damage mechanism of flax fiber reinforced composites under hydrothermal aging Acoustic emission ; Composite material ; Hydrothermal circulation ; Materials science ; Bending ; Cluster (spacecraft) ; Structural engineering ; Computer science ; Engineering Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Innovative concrete reinforcement materials
Authors: Chris J. Pickard
Source: Faraday Discussions (article, 2024-08-06)
Field: Materials Science — Materials Chemistry

Beyond theory-driven discovery: introducing hot random search and datum-derived structures Data-driven methods have transformed the prospects of the computational chemical sciences, with machine-learned interatomic potentials (MLIPs) speeding up calculations by several orders of magnitude. I reflect on theory-driven, as opposed to data-driven, discovery based on <i>ab initio</i> random structure searching (AIRSS), and then introduce two new methods that exploit machine-learning acceleration. I show how long high-throughput anneals, between direct structural relaxation, enabled by ephemeral data-derived potentials (EDDPs), can be incorporated into AIRSS to bias the sampling of challenging systems towards low-energy configurations. Hot AIRSS (hot-AIRSS) preserves the parallel advantage of random search, while allowing much more complex systems to be tackled. This is demonstrated through searches for complex boron structures in large unit cells. I then show how low-energy carbon structures can be directly generated from a single, experimentally determined, diamond structure. An extension to the generation of random sensible structures, candidates are stochastically generated and then optimised to minimise the difference between the EDDP environment vector and that of the reference diamond structure. The distance-based cost function is captured in an actively learned EDDP. Graphite, small nanotubes and caged, fullerene-like, structures emerge from searches using this potential, along with a rich variety of tetrahedral framework structures. Using the same approach, the pyrope, Mg<sub>3</sub>Al<sub>2</sub>(SiO<sub>4</sub>)<sub>3</sub>, garnet structure is recovered from a low-energy AIRSS structure generated in a smaller unit cell with a different chemical composition. The relationship of this approach to modern diffusion-model-based generative methods is discussed. Geodetic datum ; Computer science ; Statistical physics ; Theoretical computer science ; Physics ; Geology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography