jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shouyang Zhang | Bin Cao | Tianhao Su | Yue Wu | Zhenjie Feng | Jie Xiong | Tong‐Yi Zhang
Source: IUCrJ (article, 2024-06-27)
Field: Materials Science — Materials Chemistry

Crystallographic phase identifier of a convolutional self-attention neural network (CPICANN) on powder diffraction patterns Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization. Identifier ; Convolutional neural network ; Identification (biology) ; Software ; Phase (matter) ; Diffraction ; Computer science ; Powder diffraction ; Crystallography ; Materials science ; Data mining ; Artificial intelligence ; Chemistry ; Physics ; Optics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Fan Zhang | Tomoyuki Miyao | Yuuta Izumiya | Chia Hsiu Chen | Kimito Funatsu
Source: ACS Applied Polymer Materials (article, 2024-04-04)
Field: Materials Science — Materials Chemistry

Designing Heat-Resistant and Moldable Polyester Resin by the Integration of Machine Learning Models with Expert Knowledge Polyester resin has advantages in transparency and chemical resistance and is widely used in films and containers. In industrial applications, multiple conflicting properties of polyester resin must be optimized. Nevertheless, few reports have been found dealing with the design of polyester resins with machine learning (ML) models. Herein, we report a multiobjective design strategy of heat tolerant and moldable polyester resin, represented by the glass-transition temperature (Tg) and the softening point (SP). Our proposed workflow is an interplay between ML models and expert knowledge. Highly accurate interpretable linear regression models using chemical structural features were constructed for Tg and SP, which were utilized for evaluating previously uninvestigated monomers. Insights into substructures with which highly correlated properties (Tg and SP) were compromised were obtained by analyzing the regression coefficients of a linear model for SP/Tg. Based on the insight from the SP/Tg model, four dicarboxylic monomers consisting of untested molecular scaffolds were proposed and with which polyester resins were actually synthesized. The synthesized resins exhibited desired properties, consistent with prediction results by ML models. The reported workflow successfully proposed the dicarboxylic monomers with which polyester resins had desirable multiple properties. Polyester ; Computer science ; Manufacturing engineering ; Materials science ; Process engineering ; Composite material ; Engineering Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Computational Drug Discovery Methods
Authors: Lorenzo Gigli | Alexander Goscinski | Michele Ceriotti | Gareth A. Tribello
Source: Physical review. B./Physical review. B (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of density functional theory (DFT). However, running large simulation cells with DFT is computationally demanding, while simulations of small cells are often plagued with nonphysical effects that are a consequence of the system's finite size. To avoid these finite-size effects one is thus often forced to use empirical models that describe the physics of the material in terms of effective interaction terms, that are fitted using the results from DFT. In this study we use a machine-learning (ML) potential trained on DFT, in combination with accelerated sampling techniques, to converge the thermodynamic properties of barium titanate (BTO) with first-principles accuracy and a full atomistic description. Our results indicate that the predicted Curie temperature depends strongly on the choice of DFT functional and system size, because of emergent long-range directional correlations in the local dipole fluctuations. Our findings demonstrate how the combination of ML models and traditional bottom-up modeling allow one to investigate emergent phenomena with the accuracy of first-principles calculations over the large size and time scales afforded by empirical models. Ferroelectricity ; Barium titanate ; Phase transition ; Materials science ; Sampling (signal processing) ; Phase (matter) ; Condensed matter physics ; Optoelectronics ; Chemistry ; Physics ; Optics ; Dielectric ; Organic chemistry Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Haoze Qin | Shuang Kang | Wanru Tang | Zheng Liu
Source: Journal of Materials Research and Technology (article, 2024-04-06)
Field: Materials Science — Biomaterials

Study of flow stress in Mg-Gd-Y-Nd-Zr alloys based on IWOA-BPNN model This study investigates the compression deformation behavior of an Mg-Gd-Y-Nd-Zr alloy at temperatures ranging from 293 K to 573 K and strain rates ranging from 293K to 573K and strain rates ranging from 1000 s−1 to 2100 s−1 using a split Hopkinson pressure bar. A modified Johnson-Cook (JC) constitutive model and a backpropagation neural network (BPNN) model based on the improved whale optimization algorithm (IWOA) are established. Four statistical metrics, including correlation coefficient (R), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE), are employed to evaluate the predictive accuracy of the two models. The findings indicate that the flow stress of the alloy is sensitive to strain, strain rate, and temperature. Increasing strain and strain rate or decreasing deformation temperature results in higher flow stress. Error calculations revealed that the modified Johnson-Cook constitutive model has an R of 0.98731, a MAPE of 7.3653%, a MAE of 19.6305 MPa, and a RMSE of 26.5704 MPa. In contrast, the model established using the IWOA-BPNN has an R of 0.99996, a MAPE of 0.58894%, a MAE of 1.2671 MPa, and a RMSE of 1.7709 MPa. The IWOA-BPNN model demonstrates higher accuracy and accurately predicts the flow stress of the alloy. Materials science ; Flow stress ; Stress (linguistics) ; Metallurgy ; Philosophy ; Alloy Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Hydrogen Storage and Materials
Authors: Songyang He | Min Cheng | Chong Liu | Zhiwei Zhao | Shiyang Chai | Li Zhou | Xu Ji
Source: Industrial & Engineering Chemistry Research (article, 2024-01-25)
Field: Chemistry — Inorganic Chemistry

High-Throughput Virtual Screening of Biometal–Organic Frameworks for O<sub>2</sub>/N<sub>2</sub> Separation The separation/purification of oxygen (O2) from air is of great significance in the biomedical field. Biometal–organic frameworks (bio-MOFs), as a class of promising alternatives to traditional adsorbents, have attracted widespread interest. This paper proposes a strategy for screening high-performance bio-MOFs based on machine learning (ML) and molecular simulation methods. First, nontoxic and cost-effective bio-MOFs, namely, desired bio-MOFs, are selected from MOF databases using the binary decision tree method. Next, 15 descriptors, including nine structural descriptors and six chemical descriptors, are calculated to characterize the desired bio-MOFs. Next, the random forest (RF) algorithm is adopted to map the relationship between descriptors and the target property, where target properties are calculated by the grand canonical Monte Carlo (GCMC) results. High-throughput screening of the high-performance desired bio-MOFs is performed using the established RF model. Finally, high-performance desired bio-MOFs are obtained for O2/N2 adsorption separation, and their structure–property relationships are also analyzed. Separation (statistics) ; High-throughput screening ; Throughput ; Virtual screening ; Monte Carlo method ; Field (mathematics) ; Metal-organic framework ; Computer science ; Class (philosophy) ; Adsorption ; Chemistry ; Biological system ; Artificial intelligence ; Machine learning ; Molecular dynamics ; Computational chemistry ; Organic chemistry ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Industrial Gas Emission Control
Authors: Frederick Nii Ofei Bruce | Di Zhang | Xin Bai | Siwei Song | Fang Wang | Qingzhao Chu | Dongping Chen | Li Yang
Source: Fuel (article, 2024-12-09)
Field: Materials Science — Materials Chemistry

Machine learning predictions of thermochemical properties for aliphatic carbon and oxygen species Carbon fibers ; Oxygen ; Chemistry ; Thermodynamics ; Environmental chemistry ; Materials science ; Organic chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Alexander Denker | Jens Behrmann | Tobias Boskamp
Source: Analytical Chemistry (article, 2024-05-06)
Field: Chemistry — Spectroscopy

Improved Mass Calibration in MALDI MSI Using Neural Network-Based Recalibration Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is a powerful imaging method for generating molecular maps of biological samples and has numerous applications in biomedical research. A key challenge in MALDI MSI is to reliably map observed mass peaks to theoretical masses, which can be difficult due to mass shifts that occur during the measurement process. In this paper, we propose MassShiftNet, a novel self-supervised learning framework for mass recalibration. We train a neural network on a data dependent and specifically augmented training data set to directly estimate and correct the mass shift in the observed spectra. In our evaluation, we show that this method is both able to reduce the absolute mass error and to increase the relative mass alignment between peptide MSI spectra acquired from FFPE-fixated tissue using a MALDI time-of-flight (TOF) instrument. Chemistry ; Mass spectrometry ; Mass spectrometry imaging ; Calibration ; Mass spectrum ; Matrix-assisted laser desorption/ionization ; Artificial neural network ; MALDI imaging ; Biological system ; Artificial intelligence ; Data set ; Ionization ; Matrix (chemical analysis) ; Analytical Chemistry (journal) ; Pattern recognition (psychology) ; Desorption ; Chromatography ; Computer science ; Statistics ; Ion Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Yagyank Srivastava | Ankit Jain
Source: Materials Today Physics (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Accelerating thermal conductivity prediction through machine-learning: Two orders of magnitude reduction in phonon-phonon scattering rates calculation Phonon ; Materials science ; Thermal conductivity ; Magnitude (astronomy) ; Phonon scattering ; Reduction (mathematics) ; Condensed matter physics ; Scattering ; Thermal ; Thermodynamics ; Optics ; Physics ; Composite material ; Mathematics ; Geometry Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Mehdi Kiani | Mahsa Zolfaghari | Jalal Kiani
Source: Journal of Materials Science (article, 2024-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Transfer learning for inverse design of tunable graphene-based meta-surfaces Graphene ; Materials science ; Computer science ; Inverse ; Convolutional neural network ; Wireless ; Wireless power transfer ; Electronic engineering ; Nanotechnology ; Artificial intelligence ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Natsuki Watanabe | Yuta Hori | Hiroki Sugisawa | Tomonori Ida | Mitsuo Shoji | Yasuteru Shigeta
Source: Journal of Computational Chemistry (article, 2024-09-03)
Field: Materials Science — Materials Chemistry

A machine learning potential construction based on radial distribution function sampling Sampling reference data is crucial in machine learning potential (MLP) construction. Inadequate coverage of local configurations in reference data may lead to unphysical behaviors in MLP-based molecular dynamics (MLP-MD) simulations. To address this problem, this study proposes a new on-the-fly reference data sampling method called radial distribution function (RDF)-based data sampling for MLP construction. This method detects and extracts anomalous structures from the trajectories of MLP-MD simulations by focusing on the shapes of RDFs. The detected structures are added to the reference data to improve the accuracy of the MLP. This method allows us to realize a reasonable MLP construction for liquid water with minimal additional data. We prepare data from an H<sub>2</sub>O molecular cluster system and verify whether the constructed MLPs are practical for bulk water systems. MLP-MD simulations without RDF-based data sampling show unphysical behaviors, such as atomic collisions. In contrast, after applying this method, we obtain MLP-MD trajectories with features, such as RDF shapes and angle distributions, that are comparable to those of ab initio MD simulations. Our simulation results demonstrate that the RDF-based data sampling approach is useful for constructing MLPs that are robust to extrapolations from molecular cluster systems to bulk systems without any specialized know-how. Computer science ; Sampling (signal processing) ; Radial distribution function ; Molecular dynamics ; RDF ; Function (biology) ; Cluster (spacecraft) ; Artificial intelligence ; Algorithm ; Data mining ; Computational chemistry ; Chemistry ; Computer vision Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Edwin A. Bedolla-Montiel | J. Lange | Alberto Pérez de Alba Ortíz | Marjolein Dijkstra
Source: The Journal of Chemical Physics (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

Inverse design of crystals and quasicrystals in a non-additive binary mixture of hard disks The development of new materials typically involves a process of trial and error, guided by insights from past experimental and theoretical findings. The inverse design approach for soft-matter systems has the potential to optimize specific physical parameters, such as particle interactions, particle shape, or composition and packing fraction. This optimization aims to facilitate the spontaneous formation of specific target structures through self-assembly. In this study, we expand upon a recently introduced inverse design protocol for monodisperse systems to identify the required conditions and interactions for assembling crystal and quasicrystal phases within a binary mixture of two distinct species. This method utilizes an evolution algorithm to identify the optimal state point and interaction parameters, enabling the self-assembly of the desired structure. In addition, we employ a convolutional neural network (CNN) that classifies different phases based on their diffraction patterns, serving as a fitness function for the desired structure. Using our protocol, we successfully inverse design two-dimensional crystalline structures, including a hexagonal lattice and a dodecagonal quasicrystal, within a non-additive binary mixture of hard disks. Finally, we introduce a symmetry-based order parameter that leverages the encoded symmetry within the diffraction pattern. This order parameter circumvents the need for training a CNN and is used as a fitness function to inverse design an octagonal quasicrystal. Inverse ; Quasicrystal ; Binary number ; Fitness function ; Diffraction ; Computer science ; Materials science ; Statistical physics ; Algorithm ; Mathematics ; Mathematical optimization ; Genetic algorithm ; Physics ; Geometry ; Optics Material Dynamics and Properties ; Quasicrystal Structures and Properties ; Phase-change materials and chalcogenides
Authors: Justin E. Miller | Matthew P. Agdanowski | Joshua L. Dolinsky | M.R. Sawaya | Duilio Cascio | José A. Rodríguez | Todd O. Yeates
Source: Acta Crystallographica Section D Structural Biology (article, 2024-02-05)
Field: Materials Science — Materials Chemistry

<i>AlphaFold</i> -assisted structure determination of a bacterial protein of unknown function using X-ray and electron crystallography Macromolecular crystallography generally requires the recovery of missing phase information from diffraction data to reconstruct an electron-density map of the crystallized molecule. Most recent structures have been solved using molecular replacement as a phasing method, requiring an a priori structure that is closely related to the target protein to serve as a search model; when no such search model exists, molecular replacement is not possible. New advances in computational machine-learning methods, however, have resulted in major advances in protein structure predictions from sequence information. Methods that generate predicted structural models of sufficient accuracy provide a powerful approach to molecular replacement. Taking advantage of these advances, AlphaFold predictions were applied to enable structure determination of a bacterial protein of unknown function (UniProtKB Q63NT7, NCBI locus BPSS0212) based on diffraction data that had evaded phasing attempts using MIR and anomalous scattering methods. Using both X-ray and micro-electron (microED) diffraction data, it was possible to solve the structure of the main fragment of the protein using a predicted model of that domain as a starting point. The use of predicted structural models importantly expands the promise of electron diffraction, where structure determination relies critically on molecular replacement. Molecular replacement ; Phaser ; Phase problem ; Diffraction ; Protein structure ; UniProt ; Computer science ; Function (biology) ; A priori and a posteriori ; Chemistry ; Crystallography ; Algorithm ; Physics ; Crystal structure ; Biology ; Optics ; Gene ; Genetics Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Kevin D. Dorfman
Source: ACS Macro Letters (article, 2024-11-12)
Field: Materials Science — Materials Chemistry

Computational Phase Discovery in Block Polymers Self-consistent field theory (SCFT), the mean-field theory of polymer thermodynamics, is a powerful tool for understanding ordered state selection in block copolymer melts and blends. However, the nonlinear governing equations pose a significant challenge when SCFT is used for phase discovery because converging an SCFT solution typically requires an initial guess close to the self-consistent solution. This Viewpoint provides a concise overview of recent efforts where machine learning methods (particle swarm optimization, Bayesian optimization, and generative adversarial networks) have been used to make the first strides toward converting SCFT from a primarily explanatory tool into one that can be readily deployed for phase discovery. Block (permutation group theory) ; Polymer ; Materials science ; Phase (matter) ; Computer science ; Chemistry ; Composite material ; Mathematics ; Combinatorics ; Organic chemistry Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Siwoo Lee | Stefan Heinen | Danish Khan | O. Anatole von Lilienfeld
Source: Machine Learning Science and Technology (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials Abstract We present an automated data-collection pipeline involving a convolutional neural network and a large language model to extract user-specified tabular data from peer-reviewed literature. The pipeline is applied to 74 reports published between 1957 and 2014 with experimentally-measured oxidation potentials for 592 organic molecules (−0.75 to 3.58 V). After data curation (solvents, reference electrodes, and missed data points), we trained multiple supervised machine learning (ML) models reaching prediction errors similar to experimental uncertainty (∼0.2 V). For experimental measurements of identical molecules reported in multiple studies, we identified the most likely value based on out-of-sample ML predictions. Using the trained ML models, we then estimated oxidation potentials of ∼132k small organic molecules from the QM9 (quantum mechanics data for organic molecules with up to 9 atoms not counting hydrogens) data set, with predicted values spanning 0.21–3.46 V. Analysis of the QM9 predictions in terms of plausible descriptor-property trends suggests that aliphaticity increases the oxidation potential of an organic molecule on average from ∼1.5 V to ∼2 V, while an increase in number of heavy atoms lowers it systematically. The pipeline introduced offers significant reductions in human labor otherwise required for conventional manual data collection of experimental results, and exemplifies how to accelerate scientific research through automation. Computer science ; Training (meteorology) ; Data extraction ; Peer review ; Extraction (chemistry) ; Artificial intelligence ; Machine learning ; MEDLINE ; Chemistry ; Geography Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Manmeet Kaur Mahay | Akanksha Sharma | Vishal Sharma
Source: Microchemical Journal (article, 2024-11-17)
Field: Chemistry — Analytical Chemistry

Advancing tobacco Authentication through a Synergistic approach using ATR-FTIR spectroscopy and Machine learning Fourier transform infrared spectroscopy ; Spectroscopy ; Authentication (law) ; Chemistry ; Computer science ; Materials science ; Chemical engineering ; Engineering ; Physics ; Computer security Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies