jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abhijith S. Parackal | Rhys E. A. Goodall | Felix A. Faber | Rickard Armiento
Source: Physical Review Materials (article, 2024-10-02)
Field: Materials Science — Materials Chemistry

Identifying crystal structures beyond known prototypes from x-ray powder diffraction spectra The large amount of powder diffraction data for which the corresponding crystal structures have not yet been identified suggests the existence of numerous undiscovered, physically relevant crystal structure prototypes. In this paper, we present a scheme to resolve powder diffraction data into crystal structures with precise atomic coordinates by screening the space of all possible atomic arrangements, i.e., structural prototypes, including those not previously observed, using a pre-trained machine learning (ML) model. This involves (i) enumerating all possible symmetry-confined ways in which a given composition can be accommodated in a given space group, (ii) ranking the element-assigned prototype representations using energies predicted using the Wyckoff representation regression ML model [Goodall , ], (iii) assigning and perturbing atoms along the degree of freedom allowed by the Wyckoff positions to match the experimental diffraction data, and (iv) validating the thermodynamic stability of the material using density-functional theory. An advantage of the presented method is that it does not rely on a database of previously observed prototypes and is, therefore capable of finding crystal structures with entirely new symmetric arrangements of atoms. We demonstrate the workflow on unidentified x-ray diffraction spectra from the ICDD database and identify a number of stable structures, where a majority turns out to be derivable from known prototypes. However, at least two are found not to be part of our prior structural data sets. Published by the American Physical Society 2024 Materials science ; Diffraction ; Crystallography ; Spectral line ; Powder diffraction ; Crystal (programming language) ; X-ray ; Optics ; Physics ; Quantum mechanics ; Computer science X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advanced materials and composites
Authors: Yiru Huang | Lei Zhang
Source: Journal of Chemical Theory and Computation (article, 2024-07-22)
Field: Materials Science — Materials Chemistry

Descriptor Design for Perovskite Material with Compatible Molecules via Language Model and First-Principles Directly applying big language models for material and molecular design is not straightforward, particularly for real-scenario cases, where experimental validation accuracy is required. In this study, we propose a multimode descriptor design method for materials prediction and analysis, leveraging the advantages of the natural language processing literature model and density functional theory (DFT) calculations with the assistance of the genetic algorithm (GA). A case study on prediction of aqueous photocurrents of multisolvent engineered halide perovskite CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub> is performed, and the following-up validation experiments are carried out to demonstrate the improved accuracy of the multimode descriptors (an unprecedented experimental validation accuracy of 87.5% via the GA is achieved) for predicting aqueous photocurrents of perovskite materials (c.f. only 50% experimental accuracy for other common machine learning models). The improved experimental accuracy of the descriptors is attributed to the successful deployment of a language model incorporating concise scientific information from >1 million articles into molecular descriptors in combination with DFT calculations. The subsequent machine learning analysis suggests the importance of cation···π and crystallization in molecule-modified halide perovskite materials representing ontological and conceptual understanding. Importantly, the genetic process affords an accurate "white-box" model to describe the perovskite stability (accuracy = 90.2% for the test data set and 92.3% for the train data set) with the mathematical equation <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Stability</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>tan</mml:mi><mml:mspace></mml:mspace><mml:msub><mml:mi>F</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>×</mml:mo><mml:msub><mml:mi>F</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo>×</mml:mo><mml:msub><mml:mi>F</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>F</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:mfrac><mml:mo>+</mml:mo><mml:msub><mml:mi>F</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>F</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math>, where <i>F</i><sub>1</sub> ∼ <i>F</i><sub>5</sub> atomic-level structural and chemical details such as cation···π interactions and highest occupied molecular orbital levels. This study offers a feasible descriptor design route to accurately predict complex material properties, leveraging both language models and density functional theories. Computer science ; Perovskite (structure) ; Density functional theory ; Halide ; Material Design ; Multi-mode optical fiber ; Theoretical computer science ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Perovskite Materials and Applications
Authors: Ali Khalvandi | Saeed Saber‐Samandari | M.M. Aghdam
Source: Heliyon (article, 2024-04-01)
Field: Chemistry — Spectroscopy

A supervised learning-assisted multi-scale study for thermal and mechanical behavior of porous Silica This paper presents a comprehensive investigation of mesoporous Silica utilizing a multi-scale modeling approach under periodic boundary conditions integrated with machine learning algorithms. The study begins with Molecular Dynamics (MD) simulations to extract Silica's elastic properties and thermal conductivity at the nano-scale, employing the Tersoff potential. Subsequently, the derived material characteristics are applied to a series of generated porous Representative Volume Elements (RVEs) at the microscale. This phase involves the exploration of porosity and void shape effects on Silica's thermal and mechanical properties, considering inhomogeneities' distributions along the X-axis and random dispersion of pore cells within a three-dimensional space. Furthermore, the influence of pore shape is examined by defining open and closed-cell models, encompassing spherical and ellipsoidal voids with aspect ratios of 2 and 4. To predict the properties of porous Silica, a shallow Artificial Neural Network (ANN) is deployed, utilizing geometric parameters of the RVEs and porosity. Subsequently, it is revealed that Silica's thermal and mechanical behavior is linked to pore geometry, distribution, and porosity model. Finally, to classify the behavior of porous Silica into three categories, quasi-isotropic, orthotropic, and transversely-isotropic, three methodologies of decision tree approach, K-Nearest Neighbors (KNN) algorithm, and Support Vector Machines (SVMs) are employed. Among these, SVMs employing a quadratic kernel function demonstrate robust performance in categorizing the thermal and mechanical behavior of porous Silica. Materials science ; Porosity ; Microscale chemistry ; Isotropy ; Thermal conductivity ; Orthotropic material ; Representative elementary volume ; Support vector machine ; Transverse isotropy ; Composite material ; Geometry ; Biological system ; Artificial intelligence ; Computer science ; Mathematics ; Finite element method ; Structural engineering ; Engineering ; Optics ; Physics Aerogels and thermal insulation ; Concrete and Cement Materials Research ; Advanced ceramic materials synthesis
Authors: Thang D. Pham | Faramarz Joodaki | Filip Formalik | Randall Q. Snurr
Source: The Journal of Physical Chemistry C (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Predicting Partial Atomic Charges in Metal–Organic Frameworks: An Extension to Ionic MOFs Molecular simulation is an invaluable tool to predict and understand the usage of metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges, commonly obtained from density functional theory (DFT) calculations, are often required to model the electrostatic interactions between the MOF and adsorbates, especially when the adsorbates have dipole or quadrupole moments, such as water and CO2. Machine learning (ML) models have been previously employed to predict partial charges and avoid the computational cost associated with DFT calculations. However, previous ML models suffer from small training data sets, which limit their scope of application. In this work, we introduce two novel machine learning models, PACMOF2-neutral and PACMOF2-ionic, aimed at predicting the density-derived electrostatic and chemical (DDEC6) partial atomic charges for both neutral and ionic MOFs. These models not only yield DFT-level accuracy at a fraction of the computational cost but also demonstrate a remarkable improvement in prediction of adsorption, as validated with grand canonical Monte Carlo simulations. The robustness and fast computational time of the PACMOF2 models, along with their transferability to other porous materials such as covalent organic frameworks and zeolites, underscores their potential in high-throughput screening of MOFs for diverse applications. Metal-organic framework ; Extension (predicate logic) ; Ionic bonding ; Materials science ; Partial charge ; Metal ; Nanotechnology ; Chemical physics ; Chemistry ; Ion ; Computer science ; Adsorption ; Physical chemistry ; Metallurgy ; Molecule ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Christoph Dösinger | Thomas Hammerschmidt | Oleg E. Peil | Daniel Scheiber | Lorenz Romaner
Source: Computational Materials Science (article, 2024-11-12)
Field: Materials Science — Materials Chemistry

Descriptors based on the density of states for efficient machine learning of grain-boundary segregation energies The segregation of alloying elements to grain-boundaries (GB) has a significant impact on mechanical and functional properties of materials. The process is controlled by the segregation energies, that can accurately be computed using ab-initio methods. Over the last years, ab-initio computations have been combined with machine-learning (ML) approaches for a reduction of computational cost. Here, we show how information from the electronic structure can be incorporated in the ML. To obtain the electronic structure we use two methods, (i) density functional theory (DFT), and (ii) a recursive solution of a tight-binding (TB) Hamiltonian. With the derived descriptors we train a linear model and a Gaussian process on ab-initio segregation data from 15 coincident site lattice GBs with Σ -values up to 43, where the models are compared using cross-validation scores. Both the TB and DFT-derived descriptors are found to clearly outperform common structure-based features that have been used for ML segregation energies before. Furthermore, TB descriptors almost reach the same accuracy as DFT descriptors although their computational effort is significantly reduced. We test our approach on segregation of Ta and Re to GBs in a bcc-W matrix, which are materials of relevance for fusion-energy research. • Machine Learning of ab-initio Grain boundary segregation energies. • Machine Learning based on local Density of states. • Density of states obtained from DFT and tight binding approach. Grain boundary ; Boundary (topology) ; Materials science ; Statistical physics ; Chemical physics ; Condensed matter physics ; Physics ; Metallurgy ; Mathematics ; Microstructure ; Mathematical analysis Machine Learning in Materials Science ; Microstructure and mechanical properties ; X-ray Diffraction in Crystallography
Authors: Naotoshi Nakashima | Yoshiyuki Nonoguchi | Aleksandar Staykov
Source: Accounts of Materials Research (article, 2024-07-21)
Field: Materials Science — Materials Chemistry

Supramolecular-Based One-Pot Separation of Highly Pure Adsorbent-Free Semiconducting Single-Walled Carbon Nanotubes and Machine Learning-Based Nanotube Solubilization ConspectusCarbon nanotubes are classified into single-walled carbon nanotubes (SWNTs), double-walled carbon nanotubes and multiwalled carbon nanotubes. Among these, SWNTs have remarkable electronic, mechanical, optical, chemical and thermal properties, which are derived from their one-dimensional extended π-conjugated structures, and thus, they demonstrate a high potential toward the development of the next-generation nanoelectronics, (nano)bio, and energy and environmental materials and devices. As-produced SWNTs are a mixture of semiconducting (sem-) and metallic (met-)-SWNTs; thus, chirality sorting is highly important. So far various methods have been presented for such a separation including (i) use of chemical adsorbents such as polyfluorenes (PFOs) and their analogues and (ii) physical methods including surfactant-aided density gradient ultracentrifugation (DGU), gel chromatography techniques, and the surfactant-aided aqueous two-phase extraction method. However, such methods are not simple, and the removal of the wrapped adsorbents on the SWNTs is very difficult. Thus, the development of a method to remove the adsorbent from the sorted SWNTs is highly important to obtain adsorbent-free pure sem-SWNTs.In this Account, we provide a summary of a one-pot highly efficient sem-SWNT sorting using a solubilizer and removal of the wrapped solubilizer/adsorbent from the surfaces of the sorted tubes to provide highly pure adsorbent-free sem-SWNTs, in which the design and synthesis of adsorbents that selectively sorts sem-SWNTs from as-produced SWNTs, a mixture of sem-SWNTs and met-SWNTs, with easy removal property by a suitable method are described. In particular, we demonstrate a solubilizer-free sem-SWNT sorting based on supramolecular chemistry. The development of easy/simple and an efficient adsorbent-free sem-SWNT sorting method is highly important for proper fundamental use and application of SWNTs in industry. In addition, we describe computer simulations for selective sem-SWNT sorting based on a DFT method; in particular, we summarize our density functional theory (DFT) approach for helical wrapping of flavin molecules on the (8,6)-SWNT, leading to successful SWNT chirality separation. Finally, the introduction of a machine learning approach for SWNT solubilization is summarized. Carbon nanotube ; Solubilization ; Adsorption ; Materials science ; Nanotube ; Supramolecular chemistry ; Nanotechnology ; Chemical engineering ; Chemistry ; Organic chemistry ; Molecule Carbon Nanotubes in Composites ; Graphene research and applications ; Nanopore and Nanochannel Transport Studies
Authors: Cuauhtémoc Núñez Valencia | William Bang Lomholdt | Matthew Helmi Leth Larsen | Thomas W. Hansen | Jakob Schiøtz
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Beam induced heating in electron microscopy modeled with machine learning interatomic potentials We develop a combined theoretical and experimental method for estimating the amount of heating that occurs in metallic nanoparticles that are being imaged in an electron microscope. We model the thermal transport between the nanoparticle and the supporting material using molecular dynamics and equivariant neural network potentials. The potentials are trained to Density Functional Theory (DFT) calculations, and we show that an ensemble of potentials can be used as an estimate of the errors the neural network make in predicting energies and forces. This can be used both to improve the networks during the training phase, and to validate the performance when simulating systems too big to be described by DFT. The energy deposited into the nanoparticle by the electron beam is estimated by measuring the mean free path of the electrons and the average energy loss, both are done with Electron Energy Loss Spectroscopy (EELS) within the microscope. In combination, this allows us to predict the heating incurred by a nanoparticle as a function of its size, its shape, the support material, and the electron beam energy and intensity. Materials science ; Electron ; Nanoparticle ; Electron microscope ; Cathode ray ; Molecular physics ; Atomic physics ; Scanning electron microscope ; Density functional theory ; Computational physics ; Nanotechnology ; Optics ; Physics ; Chemistry ; Composite material ; Computational chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Chemical Physics Studies
Authors: Regine Siedentop | Maximilian Siska | Johanna Hermes | Stephan Lütz | Eric von Lieres | Katrin Rosenthal
Source: ChemCatChem (article, 2024-09-16)
Field: Materials Science — Materials Chemistry

Avoiding Replicates in Biocatalysis Experiments: Machine Learning for Enzyme Cascade Optimization Abstract The optimization of enzyme cascades is a complex and resource‐demanding task due to the multitude of parameters and synergistic effects involved. Machine learning can support the identification of optimal reaction conditions, for example, in the case of Bayesian optimization (BO), by proposing new experiments based on Gaussian process regression (GPR) and expected improvement (EI). Here, in this research BO is used to optimize the concentrations of the reaction components of an enzyme cascade. The productivity‐cost‐ratio is chosen as the optimization objective in order to achieve the highest possible productivity, which was normalized to the costs of the materials used to prevent convergence to ever‐increasing enzyme concentrations. To reduce the experimental effort, contrary to common practice in biological experiments, replicates were not used; instead, the algorithm's proposed experiments and inherent uncertainty quantification were relied upon. This approach balances parameter space exploration and exploitation, which is critical for the efficient and effective identification of optimal reaction conditions. At the optimized reaction conditions identified in this study, the productivity‐cost ratio is doubled to 38.6 mmol L −1 h −1 € −1 compared to a reference experiment. The parameter optimization required only 52 experiments while being robust to outlying experimental results. Bayesian optimization ; Cascade ; Kriging ; Computer science ; Convergence (economics) ; Identification (biology) ; Mathematical optimization ; Gaussian process ; Biochemical engineering ; Biological system ; Machine learning ; Mathematics ; Chemistry ; Gaussian ; Engineering ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Microbial Metabolic Engineering and Bioproduction
Authors: Shan Zhu | Fengxiang Sun | Kezhu Jiang | Shijian Zheng
Source: Journal of Electroanalytical Chemistry (article, 2024-04-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Decoding intrinsic features of carbons from their capacitive performance Carbon fibers ; Capacitive sensing ; Characterization (materials science) ; Capacitance ; Chemistry ; Electrochemistry ; Nanotechnology ; Electrolyte ; Process engineering ; Computer science ; Electrode ; Materials science ; Composite material Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrochemical Analysis and Applications
Authors: Jiajun Zhou | Yijie Yang | Austin M. Mroz | Kim E. Jelfs
Source: Digital Discovery (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

PolyCL: contrastive learning for polymer representation learning <i>via</i> explicit and implicit augmentations Polymers play a crucial role in a wide array of applications due to their diverse and tunable properties. Establishing the relationship between polymer representations and their properties is crucial to the computational design and screening of potential polymers <i>via</i> machine learning. The quality of the representation significantly influences the effectiveness of these computational methods. Here, we present a self-supervised contrastive learning paradigm, PolyCL, for learning robust and high-quality polymer representation without the need for labels. Our model combines explicit and implicit augmentation strategies for improved learning performance. The results demonstrate that our model achieves either better, or highly competitive, performances on transfer learning tasks as a feature extractor without an overcomplicated training strategy or hyperparameter optimisation. Further enhancing the efficacy of our model, we conducted extensive analyses on various augmentation combinations used in contrastive learning. This led to identifying the most effective combination to maximise PolyCL's performance. Representation (politics) ; Computer science ; Implicit learning ; Cognitive science ; Natural language processing ; Psychology ; Neuroscience ; Cognition Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Domain Adaptation and Few-Shot Learning
Authors: Mengze Li | Jiaqi Cai | Li Deng | Xiaopeng Li | Francesca Iacopi | Yang Yang
Source: Materials & Design (article, 2024-11-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Additively manufactured conductive and dielectric 3D metasurfaces for independent manipulation of broadband orbital angular momentum • Conductive and dielectric multimaterial printing is applied to implement dual-band broadband metasurface designs. • A new application of deep learning in the metasurface inverse design is introduced to achieve high efficiency and high accuracy. • A compact dual-band metasurface is achieved for producing two reflective orbital angular beams at 62–72 GHz and 81–92 GHz. • Material analysis and characterisation results are validated for silver nanoparticles and acrylate polymers multimaterial printing. Recent developments in conductive and dielectric multi-material additive manufacturing have generated significant interest for their potential to enhance the performance of electronic devices. Lights-out digital manufacturing, a sophisticated multi-material printing technique, facilitates the seamless sintering of silver nanoparticles and acrylate inks, enabling the creation of functional electronic devices. In this study, the LDM process is utilized to prototype intricate 3D metasurface designs. A comprehensive analysis of the properties of the printed materials confirms the practicality of this multi-material printing approach. The research examines various element distributions within multi-metal layer structures, including carbon, phosphorus, oxygen, sulfur, and fluorine, to investigate the interfaces between conductive and dielectric structures. In addition, the conductivities of silver nanoparticle inks at different curing temperatures are studied to identify optimal printing conditions. Both the conductive and dielectric inks exhibit precisely controlled particle sizes and exceptional stability, which are critical for accurate additive manufacturing. Using this sophisticated printing solution, the paper presents a broadband 3D metasurface engineered to independently manipulate two distinct orbital angular momentum states in the V-band (60–70 GHz) and W-band (79–90 GHz), suitable for high-speed wireless communications. The integration of deep neural networks helps reduce the phase coupling between the frequency bands, enabling independent control of the OAM states. The structure of the dual-band metasurface features four distinct silver layers yet is fabricated on a single ultra-thin planar substrate, demonstrating its potential for applications in future wireless communication systems. Materials science ; Broadband ; Angular momentum ; Electrical conductor ; Dielectric ; Optoelectronics ; Optics ; Composite material ; Classical mechanics ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Orbital Angular Momentum in Optics