jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alex K. Chew | Mohammad Atif Faiz Afzal | Anand Chandrasekaran | Jan Henk Kamps | V. Ramakrishnan
Source: Machine Learning Science and Technology (article, 2024-10-18)
Field: Materials Science — Materials Chemistry

Designing the next generation of polymers with machine learning and physics-based models Abstract The development of next-generation polymers necessitates optimizing several key properties simultaneously, a task that is expensive and infeasible using traditional trial-and-error experimental approaches. A promising alternative is employing a combination of machine learning and physics-based tools to rapidly screen the polymer design space and provide suggestions of new polymers that meet the critical properties required for industrial applications. In this study, we introduce a comprehensive workflow that utilizes machine learning and molecular modeling approaches to design new polymers with the focus on improving five polymer properties: (1) glass transition temperature, (2) dielectric constant, (3) refractive index, (4) stress optic coefficient, and (5) linear coefficient of thermal expansion. Using a small dataset ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>&lt;</mml:mo> </mml:mrow> </mml:math> 200 unique polymers), we developed quantitative structure-property relationships (QSPRs) models to accurately predict the experimental polymer properties for both homo- and co-polymer systems. We tested several ML algorithms and identified the best models for predicting these polymer properties, achieving test set R 2 greater than 0.77 across all properties. We then explored new polymers by creating a library of over ∼10 000 homopolymers using R-group enumeration tools and applied the trained QSPR models to rapidly predict the five polymer properties. The predictions of QSPR models were used to create a multi-parameter optimization score, which helped downselect the large polymer space to ∼10 promising candidates. The properties of these selected polymer candidates were subsequently validated with classical molecular dynamics simulations and density functional theory, revealing a strong correlation with the QSPR model predictions. Finally, one of the top candidates was validated by experiments, which showed good agreement against QSPR and physics-based models. Our workflow underscores the power of combining data-driven and theoretical methods in the polymer design process given a small dataset size, offering a valuable resource for experimentalists looking to leverage computer-aided strategies in materials innovation. Polymer physics ; Polymer ; Computer science ; Physics Machine Learning in Materials Science
Authors: Kwangnam Kim | Nicole Adelstein | Aniruddha Dive | Andrew Grieder | Sungsu Kang | Brandon C. Wood | Lingyun Wan
Source: Energy storage materials (article, 2024-10-16)
Field: Materials Science — Materials Chemistry

Probing degradation at solid-state battery interfaces using machine-learning interatomic potential Materials science ; Interatomic potential ; Battery (electricity) ; Degradation (telecommunications) ; Solid-state ; Nanotechnology ; Chemical physics ; Engineering physics ; Molecular dynamics ; Thermodynamics ; Computer science ; Computational chemistry ; Power (physics) Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Nian Ran | Liang Yin | Wujie Qiu | Jianjun Liu
Source: Science China Materials (article, 2024-03-12)
Field: Materials Science — Materials Chemistry

Recent advances in machine learning interatomic potentials for cross-scale computational simulation of materials Computer science ; Scale (ratio) ; Interatomic potential ; Field (mathematics) ; Data science ; Sampling (signal processing) ; Molecular dynamics ; Nanotechnology ; Computational science ; Materials science ; Physics ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Hele Bi | Jiale Jiang | Junzhao Chen | Xiaojun Kuang | Jinxiao Zhang
Source: Materials (article, 2024-04-04)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules The aggregation-induced emission (AIE) effect exhibits a significant influence on the development of luminescent materials and has made remarkable progress over the past decades. The advancement of high-performance AIE materials requires fast and accurate predictions of their photophysical properties, which is impeded by the inherent limitations of quantum chemical calculations. In this work, we present an accurate machine learning approach for the fast predictions of quantum yields and wavelengths to screen out AIE molecules. A database of about 563 organic luminescent molecules with quantum yields and wavelengths in the monomeric/aggregated states was established. Individual/combined molecular fingerprints were selected and compared elaborately to attain appropriate molecular descriptors. Different machine learning algorithms combined with favorable molecular fingerprints were further screened to achieve more accurate prediction models. The simulation results indicate that combined molecular fingerprints yield more accurate predictions in the aggregated states, and random forest and gradient boosting regression algorithms show the best predictions in quantum yields and wavelengths, respectively. Given the successful applications of machine learning in quantum yields and wavelengths, it is reasonable to anticipate that machine learning can serve as a complementary strategy to traditional experimental/theoretical methods in the investigation of aggregation-induced luminescent molecules to facilitate the discovery of luminescent materials. Quantum ; Luminescence ; Molecule ; Wavelength ; Quantum yield ; Computer science ; Boosting (machine learning) ; Quantum chemistry ; Materials science ; Biological system ; Chemical physics ; Chemistry ; Machine learning ; Physics ; Quantum mechanics ; Optoelectronics ; Fluorescence ; Organic chemistry Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Machine Learning in Materials Science
Authors: Satoki Ishiai | Ikki Yasuda | Katsuhiro Endo | Kenji Yasuoka
Source: Journal of Chemical Theory and Computation (article, 2024-01-08)
Field: Materials Science — Materials Chemistry

Graph-Neural-Network-Based Unsupervised Learning of the Temporal Similarity of Structural Features Observed in Molecular Dynamics Simulations Classification of molecular structures is a crucial step in molecular dynamics (MD) simulations to detect various structures and phases within systems. Molecular structures, which are commonly identified using order parameters, were recently identified using machine learning (ML), that is, the ML models acquire structural features using labeled crystals or phases via supervised learning. However, these approaches may not identify unlabeled or unknown structures, such as the imperfect crystal structures observed in nonequilibrium systems and interfaces. In this study, we proposed the use of a novel unsupervised learning framework, denoted temporal self-supervised learning (TSSL), to learn structural features and design their parameters. In TSSL, the ML models learn that the structural similarity is learned via contrastive learning based on minor short-term variations caused by perturbations in MD simulations. This learning framework is applied to a sophisticated architecture of graph neural network models that use bond angle and length data of the neighboring atoms. TSSL successfully classifies water and ice crystals based on high local ordering, and furthermore, it detects imperfect structures typical of interfaces such as the water-ice and ice-vapor interfaces. Computer science ; Artificial intelligence ; Similarity (geometry) ; Unsupervised learning ; Artificial neural network ; Molecular dynamics ; Graph ; Machine learning ; Structural similarity ; Imperfect ; Pattern recognition (psychology) ; Biological system ; Theoretical computer science ; Chemistry ; Image (mathematics) ; Computational chemistry Machine Learning in Materials Science ; Topic Modeling ; Quantum, superfluid, helium dynamics
Authors: Jian 健 Zhang 张 | Hao-Chun 昊春 Zhang 张 | Weifeng 伟峰 Li 李 | Gang 刚 Zhang 张
Source: Chinese Physics B (article, 2024-01-05)
Field: Materials Science — Materials Chemistry

Thermal conductivity of GeTe crystals based on machine learning potentials GeTe has attracted extensive research interest for thermoelectric applications. In this paper, we first train a neuro-evolution potential (NEP) based on a dataset constructed by ab initio molecular dynamics, with the Gaussian approximation potential (GAP) as a reference. The phonon density of states is then calculated by two machine learning potentials and compared with density functional theory results, with the GAP potential having higher accuracy. Next, the thermal conductivity of a GeTe crystal at 300 K is calculated by the equilibrium molecular dynamics method using both machine learning potentials, and both of them are in good agreement with the experimental results; however, the calculation speed when using the NEP potential is about 500 times faster than when using the GAP potential. Finally, the lattice thermal conductivity in the range of 300 K–600 K is calculated using the NEP potential. The lattice thermal conductivity decreases as the temperature increases due to the phonon anharmonic effect. This study provides a theoretical tool for the study of the thermal conductivity of GeTe. Thermal conductivity ; Materials science ; Thermal ; Thermodynamics ; Composite material ; Physics Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Mengwei Wu | Wei Yong | Cunqin Fu | Chun-Mei Ma | Ruiping Liu
Source: International Journal of Minerals Metallurgy and Materials (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature Shape-memory alloy ; Materials science ; Diffusionless transformation ; Work (physics) ; Component (thermodynamics) ; Phase transition ; Phase (matter) ; Transformation (genetics) ; Support vector machine ; Computer science ; Basis (linear algebra) ; Artificial intelligence ; Martensite ; Thermodynamics ; Metallurgy ; Mathematics ; Physics ; Chemistry Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; High Entropy Alloys Studies
Authors: Artur R. Davletshin | Elena A. Korznikova | Andrey A. Kistanov
Source: The Journal of Physical Chemistry Letters (article, 2024-12-20)
Field: Materials Science — Biomaterials

Machine Learning Prediction of the Corrosion Rate of Zinc-Based Alloys Containing Copper, Lithium, Magnesium, and Silver Implementation of machine learning (ML) techniques in materials science often requires large data sets. However, a proper choice of features and regression methods allows the construction of accurate ML models able to work with a relatively small data set. In this work, an extensive, although still limited, experimental data set of corrosion-related properties of Zn-based alloys used in biomedicine was created. On the basis of this data set, a robust and accurate model was built to predict the corrosion behavior of Zn-based alloys. This work highlights the effectiveness of ML methods for assessing the corrosion behavior of Zn-based alloys, which can facilitate their application in bioimplants. Corrosion ; Magnesium ; Copper ; Materials science ; Lithium (medication) ; Zinc ; Set (abstract data type) ; Experimental data ; Work (physics) ; Computer science ; Metallurgy ; Machine learning ; Mechanical engineering ; Engineering ; Mathematics ; Statistics Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Titanium Alloys Microstructure and Properties
Authors: Chenyang Cao | Shuo Cao | Yuan-Xu Zhu | Haikuan Dong | Yanzhou Wang | Ping Qian
Source: International Journal of Heat and Mass Transfer (article, 2024-02-26)
Field: Materials Science — Materials Chemistry

Thermal transports of 2D phosphorous carbides by machine learning molecular dynamics simulations Molecular dynamics ; Thermal ; Carbide ; Materials science ; Dynamics (music) ; Chemical physics ; Thermodynamics ; Composite material ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Thermal properties of materials ; Electronic and Structural Properties of Oxides
Authors: Susumu Fujii | Junji Hyodo | Kazuki Shitara | Akihide Kuwabara | Shusuke Kasamatsu | Yoshihiro Yamazaki
Source: Science and Technology of Advanced Materials (review, 2024-10-29)
Field: Materials Science — Materials Chemistry

Emerging computational and machine learning methodologies for proton-conducting oxides: materials discovery and fundamental understanding This review presents computational and machine learning methodologies developed during a 5-year research project on proton-conducting oxides. The main goal was to develop methodologies that could assist in materials discovery or provide new insights into complex proton-conducting oxides. Through these methodologies, three new proton-conducting oxides, including both perovskite and non-perovskites, have been discovered. In terms of gaining insights, octahedral tilt/distortions and oxygen affinity are found to play a critical role in determining proton diffusivities and conductivities in doped barium zirconates. Replica exchange Monte Carlo approach has enabled to reveal realistic defect configurations, hydration behavior, and their temperature dependence in oxides. Our approach 'Materials discovery through interpretation', which integrates new insights or tendencies obtained from computations and experiments to sequential explorations of materials, has also identified perovskites that exhibit proton conductivity exceeding 0.01 S/cm and high chemical stability at 300 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi> </mml:mi> <mml:mo>∘</mml:mo></mml:math> C. Nanotechnology ; Materials science ; Computer science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advancements in Solid Oxide Fuel Cells
Authors: Yaoyao Tang | Quan Xu | Xinyao Zhang | Rongye Zhu | Nuo Zhao | Juncheng Wang
Source: Nano Research (article, 2024-09-07)
Field: Materials Science — Materials Chemistry

Expediting carbon dots synthesis by the active adaptive method with machine learning and applications in dental diagnosis and treatment Synthesis of functional nanostructures with the least number of tests is paramount towards the propelling materials development. However, the synthesis method containing multivariable leads to high uncertainty, exhaustive attempts, and exorbitant manpower costs. Machine learning (ML) burgeons and provokes an interest in rationally designing and synthesizing materials. Here, we collect the dataset of nano-functional materials carbon dots (CDs) on synthetic parameters and optical properties. ML is applied to assist the synthesis process to enhance photoluminescence quantum yield (QY) by building the methodology named active adaptive method (AAM), including the model selection, max points screen, and experimental verification. An interactive iteration strategy is the first time considered in AAM with the constant acquisition of the furnished data by itself to perfect the model. CDs exhibit a strong red emission with QY up to 23.3% and enhancement of around 200% compared with the pristine value obtained through the AAM guidance. Furthermore, the guided CDs are applied as metal ions probes for Co2+ and Fe3+, with a concentration range of 0–120 and 0–150 µM, and their detection limits are 1.17 and 0.06 µM. Moreover, we also apply CDs for dental diagnosis and treatment using excellent optical ability. It can effectively detect early caries and treat mineralization combined with gel. The study shows that the error of experiment verification gradually decreases and QY improves double with the effective feedback loops by AAM, suggesting the great potential of utilizing ML to guide the synthesis of novel materials. Finally, the code is open-source and provided to be referenced for further investigation on the novel inorganic material prediction. Expediting ; Nanotechnology ; Carbon fibers ; Materials science ; Computer science ; Engineering ; Systems engineering ; Composite material Carbon and Quantum Dots Applications ; Scientific and Engineering Research Topics ; Dental Radiography and Imaging
Authors: Ji Wei Yoon | Adithya Kumar | Pawan Kumar | Kedar Hippalgaonkar | J. Senthilnath | Vijila Chellappan
Source: Knowledge-Based Systems (article, 2024-04-22)
Field: Materials Science — Materials Chemistry

Explainable machine learning to enable high-throughput electrical conductivity optimization and discovery of doped conjugated polymers Conjugated system ; Throughput ; Polymer ; Doping ; Materials science ; Conductivity ; Conductive polymer ; Nanotechnology ; Computer science ; Artificial intelligence ; Optoelectronics ; Chemistry ; Composite material ; Telecommunications Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Conducting polymers and applications
Authors: Mingwei Hu | Qiyang Tan | Ruth Knibbe | Miao Xu | Guofang Liang | Jianxin Zhou | Jun Xu | Bin Jiang | Xue Li | Mahendra Ramajayam | Thomas Dorin | Mingxing Zhang
Source: Computational Materials Science (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Designing unique and high-performance Al alloys via machine learning: Mitigating data bias through active learning Data-driven modelling, such as machine learning (ML), has great potential to streamline the complexity involved in designing new alloys. However, such powerful predictive models require a high-quality dataset, which is limited in the alloy field due to selection and reporting biases. These biases lead to out-of-distribution (OOD) regions where ML models suffer from predictive performance degradation, limiting the design of innovative alloys. To overcome this problem, we propose a ML-based design strategy for unique and high-performance aluminium (Al) alloys, incorporating multi-objective genetic algorithm (MOGA) and active learning. To guide active learning, the cosine similarity metric embedded MOGA pinpoints unique and high-strength Al alloys in OOD regions for experiments. Our study demonstrates the deficiency of initial ML models when trained on the biased dataset and subsequent improvement in retrained models after applying active learning with alloys suggested by MOGA. On this basis, a new Al alloy that is distinct from the existing dataset is developed with a yield tensile strength of 688 MPa, ultimate tensile strength of 738 MPa, and elongation of 7.5 %. This finding highlights the importance of the inclusion of OOD results and the efficiency of ML in alloy design. Metric (unit) ; Ultimate tensile strength ; Computer science ; Machine learning ; Artificial intelligence ; Limiting ; Alloy ; Performance metric ; Selection (genetic algorithm) ; Field (mathematics) ; Materials science ; Genetic algorithm ; Engineering ; Metallurgy ; Mathematics ; Mechanical engineering Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Hongwei Liu | Zhenming Cui | Zhennan Qiao | X.L An | Yongzhen Wang
Source: Journal of Materials Informatics (article, 2024-10-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-assisted prediction, screen, and interpretation of porous carbon materials for high-performance supercapacitors Porous carbon materials (PCMs) are preferred as electrode materials for supercapacitor energy storage applications due to their superior characteristics. However, the optimal performance of these electrodes requires trial and error experimental exploration due to the complexity of influencing factors. To address this limitation, we develop a machine learning (ML) combined experimental validation approach to predict, screen and interpret ideal PCMs for supercapacitors. Four ML models are used for predicting the specific capacitance (SC) properties of PCMs and the light gradient boosting machine (LGBM) model exhibits the best prediction performance with an R2 value of 0.92. Through comprehensive interpretability analysis of ML, important variables influencing SC properties are identified and their impact range is determined. By analyzing the deviation of key values during experimental verification, accurate predictions of SC properties of PCMs are made, facilitating precise material screening. Additionally, the accuracy and applicability of the ML model predictions are evaluated. This research pioneered a key eigenvalue fall-point screening approach based on a combination of ML experiments for accurately predicting SC performance and screening of superior energy storage materials, providing a compelling strategy for advancing energy storage materials technology. Supercapacitor ; Interpretation (philosophy) ; Materials science ; Porosity ; Computer science ; Artificial intelligence ; Nanotechnology ; Process engineering ; Engineering ; Composite material ; Capacitance ; Chemistry Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Advancements in Battery Materials
Authors: Kieran Nehil-Puleo | Co D. Quach | Nicholas C. Craven | Clare MCabe | Peter T. Cummings
Source: The Journal of Physical Chemistry B (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

E(<i>n</i>) Equivariant Graph Neural Network for Learning Interactional Properties of Molecules We have developed a multi-input E(<i>n</i>) equivariant graph convolution-based model designed for the prediction of chemical properties that result from the interaction of heterogeneous molecular structures. By incorporating spatial features and constraining the functions learned from these features to be equivariant to E(<i>n</i>) symmetries, the interactional-equivariant graph neural network (IEGNN) can efficiently learn from the 3D structure of multiple molecules. To verify the IEGNN's capability to learn interactional properties, we tested the model's performance on three molecular data sets, two of which are curated in this study and made publicly available for future interactional model benchmarking. To enable the loading of these data sets, an open-source data structure based on the PyTorch Geometric library for batch loading multigraph data points is also created. Finally, the IEGNN's performance on a data set consisting of an unknown interactional relationship (the frictional properties resulting between monolayers with variable composition) is examined. The IEGNN model developed was found to have the lowest mean absolute percent error for the predicted tribological properties of four of the six data sets when compared to previous methods. Equivariant map ; Graph ; Computer science ; Artificial neural network ; Benchmarking ; Homogeneous space ; Set (abstract data type) ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Pure mathematics ; Programming language ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics