jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuanqing Wang | Iván Pulido | Kenichiro Takaba | Benjamin Kaminow | Jenke Scheen | Lily Wang | John D. Chodera
Source: The Journal of Physical Chemistry A (article, 2024-05-08)
Field: Materials Science — Materials Chemistry

EspalomaCharge: Machine Learning-Enabled Ultrafast Partial Charge Assignment Atomic partial charges are crucial parameters in molecular dynamics simulation, dictating the electrostatic contributions to intermolecular energies and thereby the potential energy landscape. Traditionally, the assignment of partial charges has relied on surrogates of ab initio semiempirical quantum chemical methods such as AM1-BCC and is expensive for large systems or large numbers of molecules. We propose a hybrid physical/graph neural network-based approximation to the widely popular AM1-BCC charge model that is orders of magnitude faster while maintaining accuracy comparable to differences in AM1-BCC implementations. Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserve total molecular charge. This hybrid approach scales linearly with the number of atoms, enabling for the first time the use of fully consistent charge models for small molecules and biopolymers for the construction of next-generation self-consistent biomolecular force fields. Implemented in the free and open source package EspalomaCharge, this approach provides drop-in replacements for both AmberTools antechamber and the Open Force Field Toolkit charging workflows, in addition to stand-alone charge generation interfaces. Source code is available at https://github.com/choderalab/espaloma-charge. Partial charge ; Charge (physics) ; Ab initio ; Charge conservation ; Intermolecular force ; Chemical physics ; Molecule ; Electrostatics ; Effective nuclear charge ; Artificial neural network ; Electronegativity ; Statistical physics ; Computer science ; Physics ; Computational science ; Quantum mechanics ; Ion ; Machine learning Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Mudita Nagpal | Ankit Mittal | Vinod
Source: BioNanoScience (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Application of Biosurfactants in the Green Synthesis of Inorganic Nanoparticles Nanotechnology ; Nanoparticle ; Business ; Chemistry ; Materials science Nanoparticles: synthesis and applications ; Chemistry and Chemical Engineering ; Nanotechnology research and applications
Authors: Yuxin Zhan | Xiaojie Ren | Shuai Zhao | Zhanglin Guo
Source: Solar Energy (article, 2024-08-07)
Field: Materials Science — Materials Chemistry

Improving thermodynamic stability of double perovskites with machine learning: The role of cation composition Stability (learning theory) ; Thermodynamics ; Chemical stability ; Composition (language) ; Materials science ; Computer science ; Machine learning ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Perovskite Materials and Applications
Authors: Wenjie Yang | Chenglin Zheng | Li Sun | Zhiying Bie | Yuchen Yue | Xiuhong Li | Wentao Sun | Tomiki Ikeda | Jingxia Wang | Lei Jiang
Source: Advanced Materials (article, 2024-11-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spatiotemporal Programmability of 3D Chiral Color Units Driven by Ink Spontaneous Diffusion toward Customized Printing Blue phase liquid crystals (BPLCs) have exhibited promising applications in 3D flexible displays due to their molecular-level self-assembled chiral structures, fast response, and tunable polarized colors. However, there remain challenges for spatiotemporal programming of 3D chiral color units for BPLC dynamic patterning. Herein, the programmable temporal evolution of micrometer-scale color units and spatial configuration switch of chiral modes are achieved by spontaneous ink diffusion-driven asymmetric lattice deformation in dual-chiral polymer-templated blue phases. Custom-printed colorful patterns are designed by machine learning-assisted parameter optimization, which displays programmable multidimensional encrypted information that incorporates temporal evolving colors (wavelength), spatial distribution (depth), chiral modes (L/R). The quantitative relationship between ink diffusion kinetics and blue-phase dynamic 3D structural optics is established by in situ characterization, finite element analysis, and mathematical geometry modeling. This work provides insights into the microgeometric manipulation of 3D chiral color of BPLCs in the application of information security and self-adaptive indicators. Materials science ; Structural coloration ; Inkwell ; Diffusion ; Nanotechnology ; Optoelectronics ; Photonic crystal ; Physics ; Composite material Liquid Crystal Research Advancements ; Photonic Crystals and Applications ; Advanced Materials and Mechanics
Authors: Andi Wijaya | Julian Wagner | Bernhard Sartory | Roland Brunner
Source: Communications Materials (article, 2024-04-24)
Field: Materials Science — Materials Chemistry

Analyzing microstructure relationships in porous copper using a multi-method machine learning-based approach Abstract The prediction of material properties from a given microstructure and its reverse engineering displays an essential ingredient for accelerated material design. However, a comprehensive methodology to uncover the processing-structure-property relationship is still lacking. Herein, we develop a methodology capable of understanding this relationship for differently processed porous materials. We utilize a multi-method machine learning approach incorporating tomographic image data acquisition, segmentation, microstructure feature extraction, feature importance analysis and synthetic microstructure reconstruction. Enhanced segmentation with an accuracy of about 95% based on an efficient annotation technique provides the basis for accurate microstructure quantification, prediction and understanding of the correlation of the extracted microstructure features and electrical conductivity. We show that a diffusion probabilistic model superior to a generative adversarial network model, provides synthetic microstructure images including physical information in agreement with real data, an essential step to predicting properties of unseen conditions. Microstructure ; Copper ; Porosity ; Materials science ; Computer science ; Artificial intelligence ; Metallurgy ; Composite material Machine Learning in Materials Science ; Nanoporous metals and alloys ; Advanced materials and composites
Authors: Jiahui Cong | Zhuo Liu | Song Zhou | Shoulong Gao | Xuyang Zhu
Source: Engineering Failure Analysis (article, 2024-09-10)
Field: Materials Science — Materials Chemistry

Predicting fatigue life and crack growth rate of TC4 titanium alloy based on PINN before and after ultrasonic impact treatment Titanium alloy ; Materials science ; Alloy ; Ultrasonic sensor ; Paris' law ; Metallurgy ; Structural engineering ; Composite material ; Crack closure ; Medicine ; Fracture mechanics ; Engineering High-Velocity Impact and Material Behavior ; Fatigue and fracture mechanics ; Surface Treatment and Residual Stress
Authors: Hongshan Wang | Miriding Mutailipu | Zhihua Yang | Shilie Pan | Junjie Li
Source: Angewandte Chemie International Edition (review, 2024-12-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computer‐Aided Development of New Nonlinear Optical Materials Exploring new nonlinear optical (NLO) materials is an urgent need for advanced photoelectric technologies. However, the discovery of new materials with targeted properties is time-consuming, and involves various challenges by the traditional trial-and-error experiments. Recently, the theoretical prediction-guided structural design has been demonstrated as a feasible way for efficiently developing new NLO materials, and a large number of NLO candidates with excellent optical properties have been explored. To promote the development of high-performance NLO materials, this review provides a summary on the exploration of new NLO materials aided by computer, with a particular emphasis on the state-of-the-art research advances that including crystal structure predictions, optical & thermal property calculations, high-throughput screening of NLO materials with or without machine learning; and the progress achieved in the computer-assisted design and development of new deep ultraviolet (DUV), ultraviolet (UV), infrared (IR) NLO materials in various material systems: oxide, chalcogenide, nitride, and halide. Finally, the opportunities and forthcoming challenges in the fascinating field are discussed. Computer science ; Chalcogenide ; Materials science ; Optical materials ; Ultraviolet ; Nonlinear optical ; Nanotechnology ; Engineering physics ; Optoelectronics ; Nonlinear system ; Engineering ; Physics Nonlinear Optical Materials Research ; Crystal Structures and Properties ; Organometallic Compounds Synthesis and Characterization
Authors: Zhucheng Yang | Anye Shi | Ruixuan Zhang | Zuowei Ji | Jiali Li | Jingkuan Lyu | Jing Qian | Tiankai Chen | Xiaonan Wang | Fengqi You | Jianping Xie
Source: ACS Nano (review, 2024-09-24)
Field: Materials Science — Materials Chemistry

When Metal Nanoclusters Meet Smart Synthesis Atomically precise metal nanoclusters (MNCs) represent a fascinating class of ultrasmall nanoparticles with molecule-like properties, bridging conventional metal-ligand complexes and nanocrystals. Despite their potential for various applications, synthesis challenges such as a precise understanding of varied synthetic parameters and property-driven synthesis persist, hindering their full exploitation and wider application. Incorporating smart synthesis methodologies, including a closed-loop framework of automation, data interpretation, and feedback from AI, offers promising solutions to address these challenges. In this perspective, we summarize the closed-loop smart synthesis that has been demonstrated in various nanomaterials and explore the research frontiers of smart synthesis for MNCs. Moreover, the perspectives on the inherent challenges and opportunities of smart synthesis for MNCs are discussed, aiming to provide insights and directions for future advancements in this emerging field of AI for Science, while the integration of deep learning algorithms stands to substantially enrich research in smart synthesis by offering enhanced predictive capabilities, optimization strategies, and control mechanisms, thereby extending the potential of MNC synthesis. Nanoclusters ; Materials science ; Nanotechnology ; Metal ; Metallurgy Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Rana Darweesh | Rajesh Kumar Yadav | E.L. Adler | Michal Poplinger | Adi Levi | Jea-Jung Lee | Amir Leshem | Ashwin Ramasubramaniam | Fengnian Xia | Doron Naveh
Source: Science Advances (article, 2024-05-17)
Field: Materials Science — Materials Chemistry

Nonlinear self-calibrated spectrometer with single GeSe-InSe heterojunction device Computational spectrometry is an emerging field that uses photodetection in conjunction with numerical algorithms for spectroscopic measurements. Compact single photodetectors made from layered materials are particularly attractive since they eliminate the need for bulky mechanical and optical components used in traditional spectrometers and can easily be engineered as heterostructures to optimize device performance. However, such photodetectors are typically nonlinear devices, which adds complexity to extracting optical spectra from their response. Here, we train an artificial neural network to recover the full nonlinear spectral photoresponse of a single GeSe-InSe p-n heterojunction device. The device has a spectral range of 400 to 1100 nm, a small footprint of ~25 × 25 square micrometers, and a mean reconstruction error of 2 × 10<sup>-4</sup> for the power spectrum at 0.35 nanometers. Using our device, we demonstrate a solution to metamerism, an apparent matching of colors with different power spectral distributions, which is a fundamental problem in optical imaging. Photodetection ; Photodetector ; Spectrometer ; Heterojunction ; Optoelectronics ; Optics ; Materials science ; Nonlinear system ; Optical power ; Physics ; Laser Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Nonlinear Optical Materials Research
Authors: Thanh Hai Pham | Phung K. Le | Do Ngoc Son
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A data-driven QSPR model for screening organic corrosion inhibitors for carbon steel using machine learning techniques Machine learning (ML) techniques have shown great potential for screening corrosion inhibitors. In this study, a data-driven quantitative structure-property relationship (QSPR) model using the gradient boosting decision tree (GB) algorithm combined with the permutation feature importance (PFI) technique was developed to predict the corrosion inhibition efficiency (IE) of organic compounds on carbon steel. The results showed that the PFI method effectively selected the molecular descriptors most relevant to the IE. Using these important molecular descriptors, an IE predictive model was trained on a dataset encompassing various categories of organic corrosion inhibitors for carbon steel, achieving RMSE, MAE, and <i>R</i><sup>2</sup> of 6.40%, 4.80%, and 0.72, respectively. The integration of GB with PFI within the ML workflow demonstrated significantly enhanced IE predictive capability compared to previously reported ML models. Subsequent assessments involved the application of the trained model to drug-based corrosion inhibitors. The model demonstrates robust predictive capability when validated on available and our own experimental results. Furthermore, the model has been employed to predict IE for more than 1500 drug compounds, suggesting five novel drug compounds with the highest predicted IE on carbon steel. The developed ML workflow and associated model will be useful in accelerating the development of next-generation corrosion inhibitors for carbon steel. Quantitative structure–activity relationship ; Molecular descriptor ; Corrosion ; Workflow ; Carbon steel ; Gradient boosting ; Predictive modelling ; Computer science ; Materials science ; Biological system ; Artificial intelligence ; Machine learning ; Random forest ; Metallurgy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Pablo Montero de Hijes | Christoph Dellago | Ryosuke Jinnouchi | Georg Kresse
Source: The Journal of Chemical Physics (article, 2024-10-03)
Field: Materials Science — Materials Chemistry

Density isobar of water and melting temperature of ice: Assessing common density functionals We investigate the density isobar of water and the melting temperature of ice using six different density functionals. Machine-learning potentials are employed to ensure computational affordability. Our findings reveal significant discrepancies between various base functionals. Notably, even the choice of damping can result in substantial differences. Overall, the outcomes obtained through density functional theory are not entirely satisfactory across most utilized functionals. All functionals exhibit significant deviations either in the melting temperature or equilibrium volume, with most of them even predicting an incorrect volume difference between ice and water. Our heuristic analysis indicates that a hybrid functional with 25% exact exchange and van der Waals damping averaged between zero and Becke-Johnson dampings yields the closest agreement with experimental data. This study underscores the necessity for further enhancements in the treatment of van der Waals interactions and, more broadly, density functional theory to enable accurate quantitative predictions for molecular liquids. van der Waals force ; Isobar ; Density functional theory ; Hybrid functional ; Zero temperature ; Thermodynamics ; Volume (thermodynamics) ; Statistical physics ; Melting temperature ; Materials science ; Physics ; Chemistry ; Computational chemistry ; Condensed matter physics ; Molecule ; Quantum mechanics ; Atomic physics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; nanoparticles nucleation surface interactions
Authors: Huacong Sun | Qing Yang | Jianlin Wang | Mingchao Ding | Mouyang Cheng | Lei Liao | Chen Cai | Zitao Chen | Xudan Huang | Zibing Wang | Zhi Xu | Wenlong Wang | Kaihui Liu | Lei Liu | Xuedong Bai | Ji Chen | Sheng Meng | Lifen Wang
Source: Nature Communications (article, 2024-11-02)
Field: Materials Science — Materials Chemistry

Unveiling sulfur vacancy pairs as bright and stable color centers in monolayer WS2 Color centers, arising from zero-dimensional defects, exploit quantum confinement to access internal electron quantum degrees of freedom, holding potential for quantum technologies. Despite intensive research, the structural origin of many color centers remains elusive. In this study, we employ in-situ cathodoluminescence scanning transmission electron microscopy combined with integrated differential phase contrast imaging to examine how defect configuration in tungsten sulfide determines color-center emission. Using an 80-kV accelerated electron beam, defects were deliberately produced, visualized, excited in situ and characterized in real time in monolayer WS<sub>2</sub> within hBN|WS<sub>2</sub> | hBN heterostructures at 100 K. These color centers were simultaneously measured by cathodoluminescence microscopy and differentiated by machine learning. Supported by DFT calculations, our results identified a crucial sulfur vacancy configuration organized into featured vacancy pairs, generating stable and bright luminescence at 660 nm. These findings elucidate the atomic-level structure-exciton relationship of color centers, advancing our understanding and quantum applications of defects in 2D materials. Monolayer ; Vacancy defect ; Sulfur ; Crystallography ; Materials science ; Chemical physics ; Physics ; Chemistry ; Nanotechnology 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Advanced biosensing and bioanalysis techniques
Authors: Jingtai Li | Yang Yang | YU Qingquan | Guirong Su | Wei Liu
Source: The Journal of Physical Chemistry C (article, 2024-03-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Development and Prospects in the Structure and Performance of Lanthanide Single-Molecule Magnets Single molecule magnets (SMMs) constitute a pivotal class of nanoscale molecular magnets with promising potential across various domains such as high-density information storage, quantum computing, and spin electron devices. Among these, lanthanide-based SMMs have garnered significant attention in recent research due to their distinctive magnetic and coordination properties, offering prospects for substantial advancements in information storage technologies. Over the past three decades, considerable efforts have been directed toward developing novel SMMs characterized by substantial anisotropy barrier and blocking temperature, leading to significant strides in the field. This perspective delves into the latest developments in the structure and performance of lanthanide SMMs, providing valuable insights into optimizing their functionality. The pivotal roles of ligands and central atoms in modulating the anisotropy of SMMs are emphasized alongside strategies to augment magnetic anisotropy and explore their potential surface applications. Additionally, this perspective explores the fusion of theoretical research with machine learning methodologies to identify highly anisotropic SMMs, thereby outlining a roadmap for optimizing the functionality of lanthanide-based SMMs. Lanthanide ; Magnet ; Molecule ; Single-molecule magnet ; Materials science ; Nanotechnology ; Physics ; Engineering ; Chemistry ; Mechanical engineering ; Magnetization ; Magnetic field ; Organic chemistry Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Advanced NMR Techniques and Applications
Authors: Yiming Liu | Xinyu Tan | Peng Xiang | Yibo Tu | Tianxiang Shao | Yue Zang | Xiong Li | Wensheng Yan
Source: Materials Today Physics (article, 2024-02-02)
Field: Materials Science — Materials Chemistry

Machine learning as a characterization method for analysis and design of perovskite solar cells Materials science ; Characterization (materials science) ; Perovskite (structure) ; Engineering physics ; Nanotechnology ; Chemical engineering ; Systems engineering ; Engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Machine Learning and ELM
Authors: Scott H. Snyder | Patricia A. Vignaux | Mustafa Kemal Ozalp | Jacob Gerlach | Ana C. Puhl | Thomas R. Lane | John Corbett | Fabio Urbina | Sean Ekins
Source: Communications Chemistry (article, 2024-06-12)
Field: Materials Science — Materials Chemistry

The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications Recent advances in machine learning (ML) have led to newer model architectures including transformers (large language models, LLMs) showing state of the art results in text generation and image analysis as well as few-shot learning (FSLC) models which offer predictive power with extremely small datasets. These new architectures may offer promise, yet the 'no-free lunch' theorem suggests that no single model algorithm can outperform at all possible tasks. Here, we explore the capabilities of classical (SVR), FSLC, and transformer models (MolBART) over a range of dataset tasks and show a 'goldilocks zone' for each model type, in which dataset size and feature distribution (i.e. dataset "diversity") determines the optimal algorithm strategy. When datasets are small ( < 50 molecules), FSLC tend to outperform both classical ML and transformers. When datasets are small-to-medium sized (50-240 molecules) and diverse, transformers outperform both classical models and few-shot learning. Finally, when datasets are of larger and of sufficient size, classical models then perform the best, suggesting that the optimal model to choose likely depends on the dataset available, its size and diversity. These findings may help to answer the perennial question of which ML algorithm is to be used when faced with a new dataset. Goldilocks principle ; Computer science ; Machine learning ; Artificial intelligence ; Transformer ; Language model ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics