jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: José Carlos Santos-Ceballos | Foad Salehnia | Alfonso Romero | X Vilanova
Source: Scientific Reports (article, 2024-05-06)
Field: Materials Science — Materials Chemistry

Application of digital twins for simulation based tailoring of laser induced graphene In the era of man-machine interfaces, digital twins stand as a key technology, offering virtual representations of real-world objects, processes, and systems through computational models. They enable novel ways of interacting with, comprehending, and manipulating real-world entities within a virtual realm. The real implementation of graphene-based sensors and electronic devices remains challenging due to the integration complexities of high-quality graphene materials with existing manufacturing processes. To address this, scalable techniques for the in-situ fabrication of graphene-like materials are essential. One promising method involves using a CO<sub>2</sub> laser to convert polyimide into graphene. Optimizing this graphitization process is hindered by complex parameter interactions and nonlinear terms. This article explores how these digital replicas can enhance the fabrication of laser-induced graphene (LIG) through laser simulation and machine learning methods to enable rapid single-step LIG patterning. This approach aims to create a universal simulation for all CO<sub>2</sub> lasers, calculating optical energy flux and utilizing machine learning to control and predict LIG conductivity (ability to conduct current), morphology, and electrical resistance. The proposed procedure, integrating digital twins in the LIG production process, will avoid or reduce the preliminary tests required to determine the proper laser parameters to reach the desired LIG characteristics. Accordingly, this approach will reduce the time and costs associated with these tests and thus increase the efficiency and optimize the procedure. Graphene ; Computer science ; Scalability ; Laser ; Fabrication ; Process (computing) ; Materials science ; Nanotechnology ; Physics Graphene research and applications ; Additive Manufacturing and 3D Printing Technologies
Authors: Takashi Takeda | Yukinori Koyama | Hidekazu Ikeno | Satoru Matsuishi | Naoto Hirosaki
Source: Science and Technology of Advanced Materials (review, 2024-11-07)
Field: Materials Science — Materials Chemistry

Exploring new useful phosphors by combining experiments with machine learning New phosphors are consistently in demand for advances in solid-state lighting and displays. Conventional trial-and-error exploration experiments for new phosphors require considerable time. If a phosphor host suitable for the target luminescent property can be proposed using computational science, the speed of development of new phosphors will significantly increase, and unexpected/overlooked compositions could be proposed as candidates. As a more practical approach for developing new phosphors with target luminescent properties, we looked at combining experiments with machine learning on the topics of emission wavelength, full width at half maximum (FWHM) of the emission peak, temperature dependence of the emission spectrum (thermal quenching), new phosphors with new chemical composition or crystal structure, and high-throughput experiments. Phosphor ; Full width at half maximum ; Luminescence ; Materials science ; Throughput ; Computer science ; Quenching (fluorescence) ; Wavelength ; Optoelectronics ; Optics ; Telecommunications ; Physics ; Fluorescence Luminescence Properties of Advanced Materials ; Machine Learning in Materials Science ; Solid-state spectroscopy and crystallography
Authors: Athulya S. Palakkal | Yifei Yue | Saad Aldin Mohamed | Jianwen Jiang
Source: Environmental Science & Technology (article, 2024-09-02)
Field: Chemistry — Inorganic Chemistry

Exploiting Metal–Organic Frameworks for Vinylidene Fluoride Adsorption: From Force Field Development, Computational Screening to Machine Learning Metal-organic frameworks (MOFs) represent a distinctive class of nanoporous materials with considerable potential across a wide range of applications. Recently, a handful of MOFs has been explored for the storage of environmentally hazardous fluorinated gases (Keasler et al. <i>Science</i> 2023, 381, 1455), yet the potential of over 100,000 MOFs for this specific application has not been thoroughly investigated, particularly due to the absence of an established force field. In this study, we develop an accurate force field for nonaversive hydrofluorocarbon vinylidene fluoride (VDF) and conduct high-throughput computational screening to identify top-performing MOFs with high VDF adsorption capacities. Quantitative structure-property relationships are analyzed via machine learning models on the combinations of geometric, chemical, and topological features, followed by feature importance analysis to probe the effects of these features on VDF adsorption. Finally, from detailed structural analysis via radial distribution functions and spatial densities, we elucidate the significance of different interaction modes between VDF and metal nodes in top-performing MOFs. By synergizing force-field development, computational screening, and machine learning, our findings provide microscopic insights into VDF adsorption in MOFs that will advance the development of new nanoporous materials for high-performance VDF storage or capture. Nanoporous ; Metal-organic framework ; Hazardous waste ; Adsorption ; Fluoride ; Nanotechnology ; Materials science ; Chemistry ; Waste management ; Engineering ; Inorganic chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Mani Valleti | Rama K. Vasudevan | Maxim Ziatdinov | Sergei V. Kalinin
Source: Machine Learning Science and Technology (article, 2024-01-03)
Field: Materials Science — Materials Chemistry

Deep kernel methods learn better: from cards to process optimization Abstract The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). In the former case, the structure of the latent space is determined by the properties of the input data alone, while in the latter, the latent manifold forms as a result of an active learning process that balances the data distribution and target functionalities. We show that DKL with active learning can produce a more compact and smooth latent space which is more conducive to optimization compared to previously reported methods, such as the VAE. We demonstrate this behavior using a simple cards dataset and extend it to the optimization of domain-generated trajectories in physical systems. Our findings suggest that latent manifolds constructed through active learning have a more beneficial structure for optimization problems, especially in feature-rich target-poor scenarios that are common in domain sciences, such as materials synthesis, energy storage, and molecular discovery. The Jupyter Notebooks that encapsulate the complete analysis accompany the article. Autoencoder ; Artificial intelligence ; Computer science ; Deep learning ; Kernel (algebra) ; Representation (politics) ; Feature learning ; Domain (mathematical analysis) ; Simple (philosophy) ; Space (punctuation) ; Manifold (fluid mechanics) ; Machine learning ; Process (computing) ; Nonlinear dimensionality reduction ; Optimization problem ; Pattern recognition (psychology) ; Algorithm ; Mathematics ; Dimensionality reduction ; Engineering Machine Learning in Materials Science ; Neural Networks and Applications ; Model Reduction and Neural Networks
Authors: J. Wanni | Curt A. Bronkhorst | Dan J. Thoma
Source: npj Computational Materials (article, 2024-06-26)
Field: Materials Science — Materials Chemistry

Machine learning enhanced analysis of EBSD data for texture representation Abstract Generating reduced-order, synthetic grain structure datasets that accurately represent the measured grain structure of a material is important for reducing the cost and increasing the accuracy of computational crystal plasticity efforts. This study introduces a machine-learning-based approach, termed texture adaptive clustering and sampling (TACS), for generating representative Euler angle datasets that accurately mimic the crystallographic texture. The TACS approach employs K-means clustering and density-based sampling in a closed-loop iteration to create representative Euler angle datasets. Proof-of-principle experiments were performed on rolled and recrystallized low-carbon steel. Validation of the TACS approach was extended to twenty-two datasets, varying lattice structures, and complex crystallographic textures, thereby encompassing a broad range of materials and crystal structures. Kolmogorov-Smirnov (K-S) test comparisons underscore the performance of the TACS approach over traditional electron backscatter diffraction EBSD dataset reduction techniques, with average K-S test scores nearing 0.9, indicating a high-fidelity representation of the original datasets. In contrast, conventional methods display scores below 0.3, indicating less reliability of the structure representation. The independence of the TACS approach from material texture and its capability to autonomously generate datasets with predetermined data points demonstrates its unbiased potential in streamlining dataset preparation for crystallographic analysis. Texture (cosmology) ; Electron backscatter diffraction ; Representation (politics) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Computer vision ; Diffraction ; Optics ; Image (mathematics) ; Physics ; Political science Machine Learning in Materials Science ; Microstructure and mechanical properties ; Metallurgy and Material Forming
Authors: Yao Mu | Tianying Dai | Jiahe Fan | Yi Cheng
Source: Journal of Molecular Liquids (article, 2024-10-05)
Field: Materials Science — Materials Chemistry

Prediction of acetylene solubility by a mechanism-data hybrid-driven machine learning model constructed based on COSMO-RS theory Solubility ; Mechanism (biology) ; Acetylene ; Computer science ; Artificial intelligence ; Chemistry ; Machine learning ; Computational chemistry ; Biochemical engineering ; Biological system ; Engineering ; Organic chemistry ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Fault Detection and Control Systems
Authors: Carlos R. Salazar | Akshay Krishna Ammothum Kandy | Jean Furstoss | Quentin Gromoff | Jacek Goniakowski | Julien Lam
Source: npj Computational Materials (article, 2024-08-30)
Field: Materials Science — Materials Chemistry

Competing nucleation pathways in nanocrystal formation Despite numerous efforts from numerical approaches to complement experimental measurements, several fundamental challenges have still hindered one’s ability to truly provide an atomistic picture of the nucleation process in nanocrystals. Among them, our study resolves three obstacles: (1) Machine-learning force fields including long-range interactions able to capture the finesse of the underlying atomic interactions, (2) Data-driven characterization of the local ordering in a complex structural landscape associated with several crystal polymorphs and (3) Comparing results from a large range of temperatures using both brute-force and rare-event sampling. Altogether, our simulation strategy has allowed us to study zinc oxide crystallization from nano-droplet melt. Remarkably, our results show that different nucleation pathways compete depending on the investigated degree of supercooling. Nucleation ; Supercooling ; Nanocrystal ; Crystallization ; Chemical physics ; Materials science ; Nanotechnology ; Chemistry ; Thermodynamics ; Physics Machine Learning in Materials Science ; Theoretical and Computational Physics ; nanoparticles nucleation surface interactions
Authors: Mood Mohan | Nikhitha Gugulothu | Sreelekha Guggilam | T. Rajitha Rajeshwar | Michelle K. Kidder | Jeremy C. Smith
Source: Green Chemical Engineering (article, 2024-11-17)
Field: Chemistry — Spectroscopy

Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution The polarity of solvents plays a critical role in various research applications, particularly in their solubilities. Polarity is conveniently characterized by the Kamlet-Taft parameters that is, the hydrogen bonding acidity ( α ), the basicity ( β ), and the polarizability ( π∗ ). Obtaining Kamlet-Taft parameters is very important for designer solvents, namely ionic liquids (ILs) and deep eutectic solvents (DESs). However, given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs, experimental determination of their Kamlet-Taft parameters is impractical. To address this, the present study developed two different machine learning (ML) algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features. The ML models developed in the present study showed accurate predictions with high determination coefficient (R 2 ) and low root mean square error (RMSE) values. Further, in the context of present interest in the circular bioeconomy, the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide (CO 2 ) is discussed. Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO 2 into valuable chemicals. We report the development of machine learning models to predict the Kamlet-Taft parameters of designer solvents for CO 2 and lignin solubility using the COSMO-RS-derived molecular descriptors as input features. • Large and diverse dataset was compiled for Kamlet-Taft parameter predictions. • Machine learning predicts Kamlet-Taft parameters for diverse designer solvents. • FFNN model outperforms MLR with high R 2 and low RMSE in Kamlet-Taft parameter predictions. • High basicity solvents show increased lignin and CO 2 solubility as predicted. • SHAP analysis reveals HB acceptor moment as key for basicity prediction. Solvatochromism ; Dissolution ; Lignin ; Organic chemistry ; Chemistry ; Computational chemistry ; Materials science ; Solvent Analytical Chemistry and Chromatography ; Ionic liquids properties and applications ; Biochemical and biochemical processes
Authors: Matthew L. Brown | Bienfait Kabuyaya Isamura | Jonathan M. Skelton | Paul L. A. Popelier
Source: Journal of Chemical Theory and Computation (article, 2024-07-09)
Field: Materials Science — Materials Chemistry

Incorporating Noncovalent Interactions in Transfer Learning Gaussian Process Regression Models for Molecular Simulations FFLUX is a quantum chemical topology-based multipolar force field that uses Gaussian process regression machine learning models to predict atomic energies and multipole moments on the fly for fast and accurate molecular dynamics simulations. These models have previously been trained on monomers, meaning that many-body effects, for example, intermolecular charge transfer, are missed in simulations. Moreover, dispersion and repulsion have been modeled using Lennard-Jones potentials, necessitating careful parametrization. In this work, we take an important step toward addressing these shortcomings and show that models trained on clusters, in this case, a dimer, can be used in FFLUX simulations by preparing and benchmarking a formamide dimer model. To mitigate the computational costs associated with training higher-dimensional models, we rely on the transfer of hyperparameters from a smaller source model to a larger target model, enabling an order of magnitude faster training than with a direct learning approach. The dimer model allows for simulations that account for two-body effects, including intermolecular polarization and charge penetration, and that do not require nonbonded potentials. We show that addressing these limitations allows for simulations that are closer to quantum mechanics than previously possible with the monomeric models. Statistical physics ; Intermolecular force ; Computer science ; Molecular dynamics ; Kriging ; Computational chemistry ; Physics ; Machine learning ; Chemistry ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Carles Martí | Christian Devereux | Habib N. Najm | Judit Zádor
Source: The Journal of Physical Chemistry A (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Evaluation of Rate Coefficients in the Gas Phase Using Machine-Learned Potentials We assess the capability of machine-learned potentials to compute rate coefficients by training a neural network (NN) model and applying it to describe the chemical landscape on the C<sub>5</sub>H<sub>5</sub> potential energy surface, which is relevant to molecular weight growth in combustion and interstellar media. We coupled the resulting NN with an automated kinetics workflow code, KinBot, to perform all necessary calculations to compute the rate coefficients. The NN is benchmarked exhaustively by evaluating its performance at the various stages of the kinetics calculations: from the electronic energy through the computation of zero point energy, barrier heights, entropic contributions, the portion of the PES explored, and finally the overall rate coefficients as formulated by transition state theory. Computation ; Artificial neural network ; Computer science ; Workflow ; Potential energy surface ; Thermodynamics ; Statistical physics ; Point (geometry) ; Combustion ; Energy (signal processing) ; Kinetics ; Gas phase ; Chemistry ; Algorithm ; Mathematics ; Physics ; Artificial intelligence ; Physical chemistry ; Quantum mechanics ; Statistics ; Geometry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Huayi Lai | Qingyue Cai | Muyun Li | Sifan Kong | Yitong Wu | Huan Yang | Yong Zhang | Honglong Ning
Source: ACS Applied Materials & Interfaces (article, 2024-05-22)
Field: Materials Science — Polymers and Plastics

Machine Learning-Guided Performance Evaluation of an All-Liquid Electrochromic Device Electrochromic devices, capable of modulating light transmittance under the influence of an electric field, have garnered significant interest in the field of smart windows and car rearview mirrors. However, the development of high-performance electrochromic devices via large-scale explorations under miscellaneous experimental settings remains challenging and is still an urgent problem to be solved. In this study, we employed a two-step machine learning approach, combining machine learning algorithms such as KNN and XGBoost with the reality of electrochromic devices, to construct a comprehensive evaluation system for electrochromic materials. Utilizing our predictive evaluation system, we successfully screened the preparation conditions for the best-performing device, which was experimentally verified to have a high transmittance modulation amplitude (62.6%) and fast response time (5.7 s/7.1 s) at 70 A/m<sup>2</sup>. To test its stability, experiments over a long cycle time (1000 cycles) are performed. In this study, we develop an innovative framework for assessing the performance of electrochromic material devices. Our approach effectively filters experimental samples based on their distinct properties, substantially minimizing the expenditure of human and material resources in electrochromic research. Our approach to a mathematical machine learning evaluation framework for device performance has effectively propelled and informed research in electrochromic devices. Electrochromism ; Materials science ; Nanotechnology ; Optoelectronics ; Electrode Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; Analytical Chemistry and Sensors
Authors: Danish Khan | Anouar Benali | Scott Y. H. Kim | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2024-05-09)
Field: Chemistry — Inorganic Chemistry

Quantum mechanical dataset of 836k neutral closed shell molecules with upto 5 heavy atoms from CNOFSiPSClBr We introduce the Vector-QM24 (VQM24) dataset comprehensively covering all possible neutral closed-shell small organic and inorganic molecules with up to five heavy (\textit{p}-block) atoms: C, N, O, F, Si, P, S, Cl, Br. All valid stoichiometries, Lewis-rule-consistent graphs, and stable conformers (identified via GFN2-xTB) were enumerated combinatorially, yielding 577k conformational isomers spanning 258k constitutional isomers and 5,599 unique stoichiometries. DFT ($ω$B97X-D3/cc-pVDZ) optimizations were performed for all, and diffusion quantum Monte Carlo (DMC@PBE0(ccECP/cc-pVQZ)) energies are provided for 10,793 lowest-energy conformers with up to 4 heavy atoms. VQM24 includes structures, vibrational modes, rotational constants, thermodynamic properties (Gibbs free energies, enthalpies, ZPVEs, entropies, heat capacities), and electronic properties such as atomization, electron interaction, exchange-correlation, dispersion energies, multipole moments (dipole to hexadecapole), alchemical potentials, Mulliken charges, and wavefunctions. Machine learning models of atomization energies on this dataset reveal significantly higher complexity than QM9, with none achieving chemical accuracy. VQM24 offers a rigorous, high-fidelity benchmark for evaluating quantum machine learning models. Shell (structure) ; Space (punctuation) ; Quantum ; Quantum chemical ; Open shell ; Chemistry ; Physics ; Nanotechnology ; Theoretical physics ; Computational chemistry ; Quantum mechanics ; Materials science ; Computer science ; Composite material ; Molecule Inorganic Fluorides and Related Compounds ; Inorganic Chemistry and Materials ; Boron Compounds in Chemistry
Authors: Sung Hun Woo | Suyeon Baek | Yongseon Kim
Source: Materials Letters (article, 2024-06-17)
Field: Materials Science — Materials Chemistry

Investigation of the properties of layered perovskite materials with varying constituent elements as SOFC cathodes Materials science ; Perovskite (structure) ; Cathode ; Microstructure ; Composite material ; Chemical engineering ; Metallurgy ; Mineralogy ; Physical chemistry Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Thermal Expansion and Ionic Conductivity