jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wei Gou | Zhang‐Zhi Shi | Yuman Zhu | Xinfu Gu | Fu‐Zhi Dai | Xingyu Gao | Lu‐Ning Wang
Source: Materials Genome Engineering Advances (article, 2024-06-27)
Field: Materials Science — Biomaterials

Multi‐objective optimization of three mechanical properties of Mg alloys through machine learning Abstract Conventional trial‐and‐error method is usually time‐consuming and expensive for multi‐objective optimization of Mg alloys. Although machine learning exhibits great potential to accelerate related research studies, machine learning prediction of properties of Mg alloys is often a prediction of a single target at a time. To address this, this paper integrates non‐dominated sorting genetic algorithm III multi‐objective optimization algorithm with light gradient boosting machine algorithm to simultaneously optimize yield strength, ultimate tensile strength, and elongation of Mg alloys. This is the first time that simultaneous machine learning optimization of these three objectives has been achieved for Mg alloys. Sorting ; Boosting (machine learning) ; Computer science ; Gradient boosting ; Ultimate tensile strength ; Optimization algorithm ; Machine learning ; Elongation ; Artificial intelligence ; Genetic algorithm ; Algorithm ; Materials science ; Mathematical optimization ; Mathematics ; Composite material Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Litao Chen | Bingxu Wang | Wentao Zhang | Shisheng Zheng | Zhefeng Chen | Mingzheng Zhang | Cheng Dong | Feng Pan | Shunning Li
Source: Journal of the American Chemical Society (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning Determining the structures of previously unseen compounds from experimental characterizations is a crucial part of materials science. It requires a step of searching for the structure type that conforms to the lattice of the unknown compound, which enables the pattern matching process for characterization data, such as X-ray diffraction (XRD) patterns. However, this procedure typically places a high demand on domain expertise, thus creating an obstacle for computer-driven automation. Here, we address this challenge by leveraging a deep-learning model composed of a union of convolutional residual neural networks. The accuracy of the model is demonstrated on a dataset of over 60,000 different compounds for 100 structure types, and additional categories can be integrated without the need to retrain the existing networks. We also unravel the operation of the deep-learning black box and highlight the way in which the resemblance between the unknown compound and a structure type is quantified based on both local and global characteristics in XRD patterns. This computational tool opens new avenues for automating structure analysis on materials unearthed in high-throughput experimentation. Convolutional neural network ; Deep learning ; Artificial intelligence ; Diffraction ; Process (computing) ; Chemistry ; Automation ; Crystal structure ; Computer science ; Matching (statistics) ; Residual ; Artificial neural network ; Pattern recognition (psychology) ; Algorithm ; Crystallography ; Engineering ; Optics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling
Authors: Mohammed Al‐Fahdi | Kunpeng Yuan | Yagang Yao | Riccardo Rurali | Ming Hu
Source: Applied Physics Reviews (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

High-throughput thermoelectric materials screening by deep convolutional neural network with fused orbital field matrix and composition descriptors Thermoelectric materials harvest waste heat and convert it into reusable electricity. Thermoelectrics are also widely used in inverse ways such as refrigerators and cooling electronics. However, most popular and known thermoelectric materials to date were proposed and found by intuition, mostly through experiments. Unfortunately, it is extremely time and resource consuming to synthesize and measure the thermoelectric properties through trial-and-error experiments. Here, we develop a convolutional neural network (CNN) classification model that utilizes the fused orbital field matrix and composition descriptors to screen a large pool of materials to discover new thermoelectric candidates with power factor higher than 10 μW/cm K2. The model used our own data generated by high-throughput density functional theory calculations coupled with ab initio scattering and transport package to obtain electronic transport properties without assuming constant relaxation time of electrons, which ensures more reliable electronic transport properties calculations than previous studies. The classification model was also compared to some traditional machine learning algorithms such as gradient boosting and random forest. We deployed the classification model on 3465 cubic dynamically stable structures with non-zero bandgap screened from Open Quantum Materials Database. We identified many high-performance thermoelectric materials with ZT &amp;gt; 1 or close to 1 across a wide temperature range from 300 to 700 K and for both n- and p-type doping with different doping concentrations. Moreover, our feature importance and maximal information coefficient analysis demonstrates two previously unreported material descriptors, namely, mean melting temperature and low average deviation of electronegativity, that are strongly correlated with power factor and thus provide a new route for quickly screening potential thermoelectrics with high success rate. Our deep CNN model with fused orbital field matrix and composition descriptors is very promising for screening high power factor thermoelectrics from large-scale hypothetical structures. Convolutional neural network ; Composition (language) ; Computer science ; Field (mathematics) ; Throughput ; Matrix (chemical analysis) ; Artificial intelligence ; Materials science ; Mathematics ; Telecommunications ; Composite material Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques
Authors: R. Zhou | Kun Luo | Steve W. Martin | Qi An
Source: ACS Applied Materials & Interfaces (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

Insights into Lithium Sulfide Glass Electrolyte Structures and Ionic Conductivity via Machine Learning Force Field Simulations Sulfide-based solid electrolytes (SEs) are important for advancing all-solid-state batteries (ASSBs), primarily due to their high ionic conductivities and robust mechanical stability. Glassy SEs (GSEs) comprising mixed Si and P glass formers are particularly promising for their synthesis process and their ability to prevent lithium dendrite growth. However, to date, the complexity of their glassy structures hinders a complete understanding of the relationships between their structures and properties. This study introduces a new machine learning force field (ML-FF) tailored for lithium sulfide-based GSEs, enabling the exploration of their structural characteristics, mechanical properties, and lithium ionic conductivities. Using molecular dynamic (MD) simulations with this ML-FF, we explore the glass structures in varying compositions, including binary Li<sub>2</sub>S-SiS<sub>2</sub> and Li<sub>2</sub>S-P<sub>2</sub>S<sub>5</sub> as well as ternary Li<sub>2</sub>S-SiS<sub>2</sub>-P<sub>2</sub>S<sub>5</sub>. Our simulations yielded consistent results in terms of density, elastic modulus, radial distribution functions, and neutron structure factors compared to DFT and experimental work. Our findings reveal distinct local environments for Si and P within these glasses, with most Si atoms in edge-sharing configurations in Li<sub>2</sub>S-SiS<sub>2</sub> and a mix of corner- and edge-sharing tetrahedra in the ternary Li<sub>2</sub>S-SiS<sub>2</sub>-P<sub>2</sub>S<sub>5</sub> composition. For lithium ionic conductivity at 300 K, the 50Li<sub>2</sub>S-50SiS<sub>2</sub> glass displayed the lowest conductivity at 2.1 mS/cm, while the 75Li<sub>2</sub>S-25P<sub>2</sub>S<sub>5</sub> composition exhibited the highest conductivity at 3.6 mS/cm. The ternary glass showed a conductivity of 2.6 mS/cm, sitting between the two. Moreover, an in-depth analysis of lithium ion diffusion over the MD trajectory in the ternary glass demonstrated a significant correlation between diffusion pathways and the rotational dynamics of nearby SiS<sub>4</sub> or PS<sub>4</sub> tetrahedra. The ML-FF developed in this study provides an important tool for exploring a broad spectrum of solid-state and mixed former sulfide-based electrolytes. Materials science ; Ionic conductivity ; Ternary operation ; Lithium (medication) ; Fast ion conductor ; Conductivity ; Electrolyte ; Ionic bonding ; Sulfide ; Ion ; Chemical engineering ; Physical chemistry ; Chemistry ; Metallurgy Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Solid-state spectroscopy and crystallography
Authors: Xiao Lu | Gang Wang | Weimin Long | Peter K. Liaw | Jingli Ren
Source: Engineering Fracture Mechanics (article, 2024-01-11)
Field: Materials Science — Metals and Alloys

Fatigue life prediction of the FCC-based multi-principal element alloys via domain knowledge-based machine learning Extrapolation ; Ductility (Earth science) ; Principal component analysis ; Yield (engineering) ; Principal (computer security) ; Finite element method ; Computer science ; Structural engineering ; Artificial intelligence ; Feature (linguistics) ; Machine learning ; Materials science ; Mathematics ; Engineering ; Statistics ; Metallurgy Hydrogen embrittlement and corrosion behaviors in metals ; High Entropy Alloys Studies ; High Temperature Alloys and Creep
Authors: Mingxiang Wang | Qingbin Zeng | Dachang Chen | Yiyi Zhang | Jiefeng Liu | Changyou Ma | Pengfei Jia
Source: Journal of Hazardous Materials (article, 2024-11-19)
Field: Materials Science — Materials Chemistry

A machine learning feature descriptor approach: Revealing potential adsorption mechanisms for SF6 decomposition product gas-sensitive materials Adsorption ; Feature (linguistics) ; Decomposition ; Product (mathematics) ; Chemistry ; Computer science ; Artificial intelligence ; Process engineering ; Biochemical engineering ; Engineering ; Physical chemistry ; Organic chemistry ; Mathematics High voltage insulation and dielectric phenomena ; Aerosol Filtration and Electrostatic Precipitation ; Recycling and Waste Management Techniques
Authors: Alessandro Campari | Federico Ustolin | Antonio Alvaro | Nicola Paltrinieri
Source: Reliability Engineering & System Safety (article, 2024-07-09)
Field: Materials Science — Metals and Alloys

Inspection of hydrogen transport equipment: A data-driven approach to predict fatigue degradation Hydrogen is an environmentally friendly fuel that can facilitate the upcoming energy transition. The development of an extensive infrastructure for hydrogen transport and storage is crucial. However, the mechanical properties of structural materials are significantly degraded in H2 environments, leading to early component failures. Pipelines are designed following defect-tolerant principles and are subjected to periodic pressure fluctuations. Hence, these systems are potentially prone to fatigue degradation, often accelerated in pressurized hydrogen gas. Inspection and maintenance activities are crucial to guarantee the integrity and fitness for service of this infrastructure. This study predicts the severity of hydrogen-enhanced fatigue in low-alloy steels commonly employed for H2 transport and storage equipment. Three machine-learning algorithms, i.e., Linear Model, Deep Neural Network, and Random Forest, are used to categorize the severity of the fatigue degradation. The models are critically compared, and the best-performing algorithm is trained to predict the Fatigue Acceleration Factor. This approach shows good prediction capability and can estimate the fatigue crack propagation in low-alloy steels. These results allow for estimating the probability of failure of hydrogen pipelines, thus facilitating the inspection and maintenance planning. Degradation (telecommunications) ; Hydrogen ; Computer science ; Hydrogen storage ; Reliability engineering ; Pipeline transport ; Structural engineering ; Materials science ; Engineering ; Alloy ; Mechanical engineering ; Metallurgy Hydrogen embrittlement and corrosion behaviors in metals ; Fatigue and fracture mechanics ; Non-Destructive Testing Techniques
Authors: Jintu Zhang | Leo H. Bonati | Enrico Trizio | Odin Zhang | Yu Kang | Tingjun Hou | Michele Parrinello
Source: Journal of Chemical Theory and Computation (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Descriptor-Free Collective Variables from Geometric Graph Neural Networks Enhanced sampling simulations make the computational study of rare events feasible. A large family of such methods crucially depends on the definition of some collective variables (CVs) that could provide a low-dimensional representation of the relevant physics of the process. Recently, many methods have been proposed to semiautomatize the CV design by using machine learning tools to learn the variables directly from the simulation data. However, most methods are based on feedforward neural networks and require some user-defined physical descriptors. Here, we propose bypassing this step using a graph neural network to directly use the atomic coordinates as input for the CV model. This way, we achieve a fully automatic approach to CV determination that provides variables invariant under the relevant symmetries, especially the permutational one. Furthermore, we provide different analysis tools to favor the physical interpretation of the final CV. We prove the robustness of our approach using different methods from the literature for the optimization of the CV, and we prove its efficacy on several systems, including a small peptide, an ion dissociation in explicit solvent, and a simple chemical reaction. Computer science ; Graph ; Artificial neural network ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Data mining Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: M. Arshad Zahangir Chowdhury | Matthew A. Oehlschlaeger
Source: Sensors (article, 2024-03-14)
Field: Chemistry — Spectroscopy

Deep Learning for Gas Sensing via Infrared Spectroscopy Deep learning methods, a powerful form of artificial intelligence, have been applied in a number of spectroscopy and gas sensing applications. However, the speciation of multi-component gas mixtures from infrared (IR) absorption spectra using deep learning remains to be explored. Here, we propose a one-dimensional deep convolutional neural network gas classification model for the identification of small molecules of interest based on IR absorption spectra in flexible user-defined frequency ranges. The molecules considered include ten that are of interest in the atmosphere or in industrial and environmental processes: water vapor, carbon dioxide, ozone, nitrous oxide, carbon monoxide, methane, nitric oxide, sulfur dioxide, nitrogen dioxide, and ammonia. A simulated dataset of IR absorption spectra for mixtures of these molecules diluted in air was generated and used to train a deep learning model. The model was tested against simulated spectra containing noise and was found to provide speciation predictions with accuracy from 82 to 97%. The internal operation of the model was investigated using class activation maps that illustrate how the model prioritizes spectral information for classification. Finally, the model was demonstrated for the prediction of speciation for two synthetic experimental mixture spectra. The proposed model and the dataset generation strategies are generalized and can be implemented for other gases, different frequency ranges, and spectroscopy types. The multi-component speciation method developed herein is the first application of a convolutional neural network model, trained on HITRAN-based simulations, for spectral identification. HITRAN ; Infrared spectroscopy ; Spectroscopy ; Water vapor ; Absorption spectroscopy ; Chemistry ; Absorption (acoustics) ; Trace gas ; Artificial intelligence ; Computer science ; Materials science ; Physics ; Optics ; Organic chemistry Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Gas Sensing Nanomaterials and Sensors
Authors: Xiang Huang | Chenxiao Zhao | Hong Wang | Shenghong Ju
Source: Materials Today Physics (preprint, 2024-04-17)
Field: Materials Science — Materials Chemistry

AI-assisted inverse design of sequence-ordered high intrinsic thermal conductivity polymers Inverse ; Sequence (biology) ; Thermal conductivity ; Polymer ; Materials science ; Inverse temperature ; Thermal ; Polymer science ; Composite material ; Physics ; Mathematics ; Thermodynamics ; Chemistry ; Geometry Thermal properties of materials
Authors: Yi Lu | Linjie Nie | Xinyu Guo | Tiantian Pan | Rongqin Chen | Xunyue Liu | Xiaolong Li | Tingqiang Li | Fei Liu
Source: Ecotoxicology and Environmental Safety (article, 2024-07-11)
Field: Chemistry — Analytical Chemistry

Rapid assessment of heavy metal accumulation capability of Sedum alfredii using hyperspectral imaging and deep learning Hyperaccumulators are the material basis and key to the phytoremediation of heavy metal contaminated soils. Conventional methods for screening hyperaccumulators are highly dependent on the time- and labor-consuming sampling and chemical analysis. In this study, a novel spectral approach assisted with multi-task deep learning was proposed to streamline accumulating ecotype screening, heavy metal stress discrimination, and heavy metals quantification in plants. The significant Cd/Zn co-hyperaccumulator Sedum alfredii and its non-accumulating ecotype were stressed by Cd, Zn, and Pb. Spectral images of leaves were rapidly acquired by hyperspectral imaging. The self-designed deep learning architecture was composed of a shallow network (ENet) for accumulating ecotype identification, and a multi-task network (HMNet) for heavy metal stress type and accumulation prediction simultaneously. To further assess the robustness of the networks, they were compared with conventional machine learning models (i.e., partial least squares (PLS) and support vector machine (SVM)) on a series of evaluation metrics of classification, multi-label classification, and regression. S. alfredii with heavy metals accumulation capability was identified by ENet with 100 % accuracy. HMNet reduced overfitting and outperformed machine learning models with the average exact match ratio (EMR) of heavy metal stress discrimination increased by 7.46 %, and residual prediction deviations (RPD) of heavy metal concentrations prediction increased by 53.59 %. The method succeeded in rapidly and accurately discriminating heavy metal stress with EMRs over 91 % and accuracies over 96 %, and in predicting heavy metals accumulation with an average RPD of 3.29 for Zn, 2.57 for Cd, and 2.53 for Pb, indicating the satisfactory practicability and potential for sensing heavy metals accumulation. This study provides a relatively novel spectral method to facilitate hyperaccumulator screening and heavy metals accumulation prediction in the phytoremediation process. Hyperspectral imaging ; Environmental chemistry ; Metal ; Environmental science ; Remote sensing ; Chemistry ; Geology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Remote-Sensing Image Classification
Authors: Abhishek Sharma | Stefano Sanvito
Source: npj Computational Materials (article, 2024-10-08)
Field: Materials Science — Materials Chemistry

Quantum-accurate machine learning potentials for metal-organic frameworks using temperature driven active learning Understanding structural flexibility of metal-organic frameworks (MOFs) via molecular dynamics simulations is crucial to design better MOFs. Density functional theory (DFT) and quantum-chemistry methods provide highly accurate molecular dynamics, but the computational overheads limit their use in long time-dependent simulations. In contrast, classical force fields struggle with the description of coordination bonds. Here we develop a DFT-accurate machine-learning spectral neighbor analysis potentials for two representative MOFs. Their structural and vibrational properties are then studied and tightly compared with available experimental data. Most importantly, we demonstrate an active-learning algorithm, based on mapping the relevant internal coordinates, which drastically reduces the number of training data to be computed at the DFT level. Thus, the workflow presented here appears as an efficient strategy for the study of flexible MOFs with DFT accuracy, but at a fraction of the DFT computational cost. Density functional theory ; Flexibility (engineering) ; Molecular dynamics ; Workflow ; Computer science ; Quantum ; Limit (mathematics) ; Metal-organic framework ; Quantum chemical ; Statistical physics ; Biological system ; Materials science ; Molecule ; Artificial intelligence ; Computational chemistry ; Chemistry ; Physics ; Mathematics ; Physical chemistry ; Quantum mechanics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions
Authors: Eric Chung‐Yueh Yuan | Anup Kumar | Xingyi Guan | Eric Hermes | Andrew Rosen | Judit Zádor | Teresa Head‐Gordon | Samuel M. Blau
Source: Nature Communications (article, 2024-10-14)
Field: Materials Science — Materials Chemistry

Analytical ab initio hessian from a deep learning potential for transition state optimization Identifying transition states-saddle points on the potential energy surface connecting reactant and product minima-is central to predicting kinetic barriers and understanding chemical reaction mechanisms. In this work, we train a fully differentiable equivariant neural network potential, NewtonNet, on thousands of organic reactions and derive the analytical Hessians. By reducing the computational cost by several orders of magnitude relative to the density functional theory (DFT) ab initio source, we can afford to use the learned Hessians at every step for the saddle point optimizations. We show that the full machine learned (ML) Hessian robustly finds the transition states of 240 unseen organic reactions, even when the quality of the initial guess structures are degraded, while reducing the number of optimization steps to convergence by 2-3× compared to the quasi-Newton DFT and ML methods. All data generation, NewtonNet model, and ML transition state finding methods are available in an automated workflow. Hessian matrix ; Saddle point ; Maxima and minima ; Ab initio ; Transition state ; Computer science ; Potential energy surface ; Density functional theory ; Differentiable function ; Computational chemistry ; Statistical physics ; Mathematical optimization ; Chemistry ; Physics ; Applied mathematics ; Mathematics ; Quantum mechanics ; Geometry ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics