jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fazel Shojaei | Qinghua Zhang | Xiaoying Zhuang | Bohayra Mortazavi
Source: Discover Nano (article, 2024-06-11)
Field: Materials Science — Materials Chemistry

Remarkably high tensile strength and lattice thermal conductivity in wide band gap oxidized holey graphene C2O nanosheet Recently, the synthesis of oxidized holey graphene with the chemical formula C<sub>2</sub>O has been reported (J. Am. Chem. Soc. 2024, 146, 4532). We herein employed a combination of density functional theory (DFT) and machine learning interatomic potential (MLIP) calculations to investigate the electronic, optical, mechanical and thermal properties of the C<sub>2</sub>O monolayer, and compared our findings with those of its C<sub>2</sub>N counterpart. Our analysis shows that while the C<sub>2</sub>N monolayer exhibits delocalized π-conjugation and shows a 2.47 eV direct-gap semiconducting behavior, the C<sub>2</sub>O counterpart exhibits an indirect gap of 3.47 eV. We found that while the C<sub>2</sub>N monolayer exhibits strong absorption in the visible spectrum, the initial absorption peaks in the C<sub>2</sub>O lattice occur at around 5 eV, falling within the UV spectrum. Notably, we found that the C<sub>2</sub>O nanosheet presents significantly higher tensile strength compared to its C<sub>2</sub>N counterpart. MLIP-based calculations show that at room temperature, the C<sub>2</sub>O nanosheet can exhibit remarkably high tensile strength and lattice thermal conductivity of 42 GPa and 129 W/mK, respectively. The combined insights from DFT and MLIP-based results provide a comprehensive understanding of the electronic and optical properties of C<sub>2</sub>O nanosheets, suggesting them as mechanically robust and highly thermally conductive wide bandgap semiconductors. Nanosheet ; Materials science ; Band gap ; Monolayer ; Delocalized electron ; Density functional theory ; Graphene ; Ultimate tensile strength ; Semiconductor ; Direct and indirect band gaps ; Condensed matter physics ; Nanotechnology ; Computational chemistry ; Composite material ; Optoelectronics ; Chemistry ; Organic chemistry Graphene research and applications ; Thermal properties of materials ; 2D Materials and Applications
Authors: Wanqiu Zhang | Nathan Heath Patterson | Nico Verbeeck | Jessica L. Moore | Alice Ly | Richard M. Caprioli | Bart De Moor | Jeremy L. Norris | Marc Claesen
Source: PLoS ONE (article, 2024-05-31)
Field: Chemistry — Spectroscopy

Multimodal MALDI imaging mass spectrometry for improved diagnosis of melanoma Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis. Microscopy, nevertheless, forms the basis for traditional histopathology and thus contains invaluable morphological information. In this work, we developed a multimodal classification pipeline that uses deep learning, in the form of a pre-trained artificial neural network, to extract the meaningful morphological features from histopathological images, and combine it with the IMS data. To test whether this deep learning-based classification strategy can improve on our previous results in classification of melanocytic neoplasia, we utilized MALDI IMS data with collected serial H&E stained sections for 331 patients, and compared this multimodal classification pipeline to classifiers using either exclusively microscopy or IMS data. The multimodal pipeline achieved the best performance, with ROC-AUCs of 0.968 vs. 0.938 vs. 0.931 for the multimodal, unimodal microscopy and unimodal IMS pipelines respectively. Due to the use of a pre-trained network to perform the morphological feature extraction, this pipeline does not require any training on large amounts of microscopy data. As such, this framework can be readily applied to improve classification performance in other experimental settings where microscopy data is acquired in tandem with IMS experiments. Computer science ; Artificial intelligence ; Pipeline (software) ; Microscopy ; Pattern recognition (psychology) ; Deep learning ; Mass spectrometry imaging ; MALDI imaging ; Virtual microscopy ; Pathology ; Mass spectrometry ; Medicine ; Matrix-assisted laser desorption/ionization ; Chemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Cell Image Analysis Techniques
Authors: Chao He | Xingxing Dong | Junqiu Zhang | Shao-Gang Xu | Xiang Huang | Hui Wang | Changchun He | Xiaowei Liang | Maohai Xie | Hu Xu
Source: Advanced Functional Materials (article, 2024-12-17)
Field: Chemistry — Inorganic Chemistry

Theoretical Design and Synthesis of Metal–Inorganic Frameworks Using Host Atom‐Centered Building Blocks for Efficient Catalysis with Diverse Reactive Sites Abstract The modular assembly of organic molecules commonly guides the design of metal–organic frameworks (MOFs), yet the systematic exploration of inorganic building blocks remains limited. Given the promising applications in catalytic reactions and topological materials, the study of metal–inorganic frameworks (MIFs)—which fundamentally differ from the well‐established MOFs—has become urgent. This study introduces a strategy for designing MIFs using host atom‐centered building blocks, applied to the platinum–Phosphorus (Pt–P) system. By combining experimental observations with theoretical calculations, a Pt 18 P 18 framework utilizing PtP 3 building blocks, demonstrating significant energetic benefits is proposed. PtP 3 units are employed to characterize Pt 3 P 6 clusters within the pores, leading to a “compass” model that aligns with experimental findings. To address computational challenges associated with the large periodicity of the superstructure, a robust machine learning force field is developed. The analysis, combining surface nucleation studies, first‐principles calculations, and machine learning techniques, provides a comprehensive understanding of MIF structure. This validated Pt–P MIF exhibits exceptional catalytic properties with diverse reaction sites, significantly outperforming Pt(111) in the hydrogen evolution reaction. The findings not only present additional candidates for practical applications of metal phosphides but also highlight the vast potential of MIFs, paving the way for the discovery of numerous promising materials. Nanotechnology ; Metal-organic framework ; Materials science ; Catalysis ; Modular design ; Computer science ; Chemistry ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Xiangchao Xie | Yunsong Pang | Shipeng Rao | Chen Zeng | Leicong Zhang | Chenxu Zhang | Shifeng Guo | Jianbin Xu | Xiaoliang Zeng | Rong Sun
Source: ACS Applied Polymer Materials (article, 2024-10-16)
Field: Materials Science — Materials Chemistry

Thermal Interface Materials with High Adhesion and Enhanced Thermal Conductivity via Optimization of Polydimethylsiloxane Networks and Aluminum Fillers With the rapid growth and widespread adoption of cloud services, machine learning, and artificial intelligence, thermal interface materials have become increasingly vital for efficient thermal management in electronic devices. However, traditional thermal interface materials often struggle to strike a delicate balance between adhesion performance and thermal conductivity. Here, we report a design of polymer and filler networks to fabricate a thermal interface material consisting of polydimethylsiloxane/aluminum fillers, with high adhesion performance (adhesion strength of 3.30 ± 0.26 MPa, adhesion energy of 913.2 ± 152.71 J m–2), and enhanced thermal conductivity (5.24 ± 0.26 W m–1 K–1). These merit properties stem from the optimization of both the polydimethylsiloxane and aluminum filler networks through adjustments in branching point, cross-linking fraction, and filler mass fraction. We envision that the design strategy of optimizing polymer and filler networks can be adopted in the development of thermal interface materials with desirable multifunctionality. Polydimethylsiloxane ; Materials science ; Thermal conductivity ; Aluminium ; Adhesion ; Thermal ; Composite material ; Thermal grease ; Interface (matter) Thermal properties of materials ; Advanced Sensor and Energy Harvesting Materials ; Additive Manufacturing and 3D Printing Technologies
Authors: Ting Yuan | Xianzhi Song | Yuxin Shi | Shuyan Wei | Yuyi Han | Linjuan Yang | Yang Zhang | Xiaohong Li | Yunchao Li | L. Shen | Louzhen Fan
Source: Chemistry - An Asian Journal (article, 2024-02-07)
Field: Materials Science — Materials Chemistry

Perspectives on Development of Optoelectronic Materials in Artificial Intelligence Age Optoelectronic devices, such as light-emitting diodes, have been demonstrated as one of the most demanded forthcoming display and lighting technologies because of their low cost, low power consumption, high brightness, and high contrast. The improvement of device performance relies on advances in precisely designing novelty functional materials, including light-emitting materials, hosts, hole/electron transport materials, and yet which is a time-consuming, laborious and resource-intensive task. Recently, machine learning (ML) has shown great prospects to accelerate material discovery and property enhancement. This review will summarize the workflow of ML in optoelectronic materials discovery, including data collection, feature engineering, model selection, model evaluation and model application. We highlight multiple recent applications of machine-learned potentials in various optoelectronic functional materials, ranging from semiconductor quantum dots (QDs) or perovskite QDs, organic molecules to carbon-based nanomaterials. We furthermore discuss the current challenges to fully realize the potential of ML-assisted materials design for optoelectronics applications. It is anticipated that this review will provide critical insights to inspire new exciting discoveries on ML-guided of high-performance optoelectronic devices with a combined effort from different disciplines. Workflow ; Computer science ; Materials science ; Nanotechnology ; Novelty ; Optoelectronics Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Advanced Photocatalysis Techniques
Authors: Chi-Huan Tung | Ju Li
Source: Extreme Mechanics Letters (article, 2024-05-06)
Field: Materials Science — Materials Chemistry

The anti-dogbone: Evaluating and designing optimal tensile specimens for deep learning of constitutive relations Ultimate tensile strength ; Tensile testing ; Constitutive equation ; Curvature ; Test data ; Stress (linguistics) ; Hardening (computing) ; Structural engineering ; Computer science ; Artificial intelligence ; Materials science ; Machine learning ; Mathematics ; Composite material ; Geometry ; Engineering ; Finite element method High-Velocity Impact and Material Behavior ; Non-Destructive Testing Techniques ; Rock Mechanics and Modeling
Authors: Kai-Yuan Kuan | Chao‐Ping Hsu
Source: The Journal of Physical Chemistry A (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Predicting Selectivity with a Bifurcating Surface: Inaccurate Model or Inaccurate Statistics of Dynamics? Reactions on post-transition-state bifurcation (PTSB) energy surfaces are an important class of reaction in which classical rate theories, such as the transition state theory, fail to account for the selectivity. Quasiclassical trajectory molecular dynamic (QCT-MD) simulation is an important computational approach to understanding reactions mechanisms, especially for reactions that cannot be predicted from conventional rate theories. However, the applicability of direct dynamic simulations is hampered by huge computational costs for collecting a statistically meaningful set of trajectories, making it difficult to compare simulation results with theoretical or physical insights-based predictions (non-MD predictions). In this work, we examine the PTSB of Schmidt-Aubé reactions studied by Tantillo and co-workers. With machine-learning using kernel-ridge regression (KRR) to predict atomic forces, statistical reliability was enhanced by significantly increasing the number of trajectories. With KRR, the bottleneck of simulating dynamics (atomic forces in QCT-MD with density functional theory) was accelerated more than 100-fold. We found that this KRR-aided QCT-MD approach is successful in predicting branching ratios with a much larger number of trajectories. With our approach, statistical errors are greatly reduced, and hypothetical non-MD models for predicting selectivity are tested with much higher confidence. By comparison with non-MD models, dynamical properties that affect branching ratios become more clearly described. Statistics ; Dynamics (music) ; Surface (topology) ; Mathematics ; Statistical physics ; Econometrics ; Psychology ; Physics ; Geometry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Computational Drug Discovery Methods
Authors: Indrasis Dasgupta | Biplab Das | Sk. Abdul Amin | Shovanlal Gayen
Source: Materials Today Communications (article, 2024-11-23)
Field: Materials Science — Surfaces, Coatings and Films

ML-based Read-Across Structure-Property Relationship (RASPR) strategy for predicting protein resistance of self-assembled monolayers (SAMs) as anti-biofouling materials Biofouling ; Materials science ; Monolayer ; Property (philosophy) ; Nanotechnology ; Membrane Polymer Surface Interaction Studies ; Marine Biology and Environmental Chemistry ; Advanced biosensing and bioanalysis techniques
Authors: Nuttawat Parse | Jose Recatala‐Gomez | Ruiming Zhu | Andre K. Y. Low | Kedar Hippalgaonkar | Tomoya Mato | Yukari Katsura | Supree Pinitsoontorn
Source: Advanced Theory and Simulations (article, 2024-08-17)
Field: Materials Science — Materials Chemistry

Predicting High‐Performance Thermoelectric Materials With StarryData2 Abstract In recent years, machine learning (ML) has emerged as a potential tool in the exploration of thermoelectric (TE) materials. This study exploits the StarryData2 public database to construct an ML model for predicting the figure‐of‐merit ZT of TE materials. The original dataset from StarryData2 (372,480 datapoints) underwent systematic cleaning, resulting in a refined dataset of 18,126 instances with 2,761 unique compounds. The cleaned data is employed to train an XGBoost regressor model, utilizing chemical formulas of TE compounds as features to predict ZT at given temperatures. The XGBoost regressor exhibited high prediction accuracy, achieving the coefficient of determination ( R 2 ) scores of 0.815 and mean absolute error (MAE) of 0.103 for the test set, further evaluated through cross‐validation across 5 folds. The learning curve analysis demonstrated improved model performance with increased training data. Furthermore, the contributions of different chemical descriptors to ZT are analyzed based on feature importance analysis. Beyond conventional TE families in the training set, the trained model is applied to predict ZT for promising unexplored TE materials and estimate optimal doping concentrations. This comprehensive study shows the impact of ML on TE material research, offering valuable insights and accelerating the discovery of materials with enhanced TE properties. Figure of merit ; Test set ; Training set ; Machine learning ; Computer science ; Artificial intelligence ; Set (abstract data type) ; Construct (python library) ; Materials science ; Data mining Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Xuan Yu | Yuhua Wang | Guiyong Wang | Qianqiao Shen | Boshun Zeng | Shuchao He
Source: Processes (article, 2024-01-20)
Field: Materials Science — Materials Chemistry

Nonlinear Predictive Control of Diesel Engine DOC Outlet Temperature In the regeneration mode, precise control of the Diesel Oxidation Catalyst (DOC) outlet temperature is crucial for the complete combustion of carbon Particulate Matter (PM) in the subsequent Diesel Particulate Filter (DPF) and the effective conversion of Nitrogen Oxides (NOx) in the Selective Catalytic Reduction (SCR). The temperature elevation process of the DOC involves a series of intricate physicochemical reactions characterized by high nonlinearity, substantial time delays, and uncertainties. These factors render effective and stable control of the DOC outlet temperature challenging. To address these issues, this study proposes an approach based on Long Short-Term Memory (LSTM) neural networks for Model Predictive Control (MPC), emphasizing precise control of the Diesel Oxidation Catalyst’s outlet temperature during the regeneration mode. To tackle the system’s nonlinear characteristics, LSTM is employed to construct a predictive model for the outlet temperature of the Diesel Oxidation Catalyst, thereby enhancing prediction accuracy. Simultaneously, model predictive control is applied to mitigate the significant time delays inherent in the system. The gradient descent algorithm is utilized within a rolling optimization cycle to optimize the objective function, enabling the rapid determination of the control law. To validate the performance of the proposed control strategy, tracking performance and disturbance rejection tests are conducted. Simulation results demonstrate that, compared to the traditional Proportional Integral Derivative (PID) controller, this control strategy exhibits superior tracking performance and disturbance rejection capabilities. In the regeneration mode, the adoption of this control strategy enables more effective and precise control of the Diesel Oxidation Catalyst’s outlet temperature. Model predictive control ; PID controller ; Control theory (sociology) ; Diesel fuel ; Diesel particulate filter ; Diesel engine ; Selective catalytic reduction ; Controller (irrigation) ; Artificial neural network ; Exhaust gas recirculation ; NOx ; Gradient descent ; Computer science ; Combustion ; Exhaust gas ; Temperature control ; Engineering ; Automotive engineering ; Control engineering ; Control (management) ; Catalysis ; Chemistry ; Waste management ; Artificial intelligence Catalytic Processes in Materials Science ; Advanced Combustion Engine Technologies ; Advanced Control Systems Optimization
Authors: Junyi Gong | Ziwei Deng | Huilin Xie | Zijie Qiu | Zheng Zhao | Ben Zhong Tang
Source: Advanced Science (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Deciphering Design of Aggregation‐Induced Emission Materials by Data Interpretation This work presents a novel methodology for elucidating the characteristics of aggregation-induced emission (AIE) systems through the application of data science techniques. A new set of chemical fingerprints specifically tailored to the photophysics of AIE systems is developed. The fingerprints are readily interpretable and have demonstrated promising efficacy in addressing influences related to the photophysics of organic light-emitting materials, achieving high accuracy and precision in the regression of emission transition energy (mean absolute error (MAE) ∼ 0.13eV) and the classification of optical features and excited state dynamics mechanisms (F1score ∼ 0.94). Furthermore, a conditional variational autoencoder and integrated gradient analysis are employed to examine the trained neural network model, thereby gaining insights into the relationship between the structural features encapsulated in the fingerprints and the macroscopic photophysical properties. This methodology promotes a more profound and thorough comprehension of the characteristics of AIE and guides the development strategies for AIE systems. It offers a solid and overarching framework for the theoretical analysis involved in the design of AIE-generating compounds and elucidates the optical phenomena associated with these compounds. Aggregation-induced emission ; Computer science ; Complex system ; Biological system ; Excited state ; Artificial neural network ; Autoencoder ; Set (abstract data type) ; Artificial intelligence ; Data mining ; Physics Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Molecular Sensors and Ion Detection
Authors: Suvo Banik | P.S. Dutta | Sukriti Manna | Subramanian K. R. S. Sankaranarayanan
Source: The Journal of Physical Chemistry A (article, 2024-11-13)
Field: Materials Science — Materials Chemistry

Development of a Machine Learning Potential to Study the Structure and Thermodynamics of Nickel Nanoclusters Machine learning (ML) potentials such as the Gaussian approximation potential (GAP) have demonstrated impressive capabilities in mapping structure to properties across diverse systems. Here, we introduce a GAP model for low-dimensional Ni nanoclusters and demonstrate its flexibility and effectiveness in capturing the energetics, structural diversity, and thermodynamic properties of Ni nanoclusters across a broad size range. Through a systematic approach encompassing model development, validation, and application, we evaluate the model's efficacy in representing energetics and configurational features in low-dimensional regimes while also examining its extrapolative nature to vastly different spatiotemporal regimes. Our analysis and discussion shed light on the data quality required to effectively train such models. Trajectories from large-scale MD simulations using the GAP model analyzed with data-driven models like graph neural networks reveal intriguing insights into the size-dependent phase behavior and thermomechanical stability characteristics of porous Ni nanoparticles. Overall, our work underscores the potential of ML models, which coupled with data-driven approaches serve as versatile tools for studying low-dimensional systems and complex material dynamics. Nanoclusters ; Nickel ; Thermodynamics ; Materials science ; Statistical physics ; Metallurgy ; Physics ; Nanotechnology Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Computational Drug Discovery Methods
Authors: Chenglong Qin | Jinde Liu | Shiyin Ma | Jiguang Du | Gang Jiang | Liang Zhao
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Inverse design of experimentally synthesizable crystal structures by leveraging computational and experimental data A novel collaborative approach was proposed for crystal structure prediction that utilizes advanced deep learning models and optimization algorithms combined with experimental data. Inverse ; Computer science ; Parallel computing ; Inverse problem ; Data structure ; Computational science ; Algorithm ; Computer architecture ; Theoretical computer science ; Programming language ; Mathematics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies