jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Luis A. Miccio | Claudia Borredon | Gustavo A. Schwartz
Source: Computational Materials Science (article, 2024-02-17)
Field: Materials Science — Materials Chemistry

A glimpse inside materials: Polymer structure – Glass transition temperature relationship as observed by a trained artificial intelligence Glass transition ; Polymer ; Materials science ; Chemical physics ; Nanotechnology ; Polymer science ; Composite material ; Chemistry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Material Dynamics and Properties
Authors: Shashank Pathrudkar | Ponkrshnan Thiagarajan | Shivang Agarwal | Amartya Banerjee | Susanta Ghosh
Source: npj Computational Materials (article, 2024-08-12)
Field: Materials Science — Materials Chemistry

Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning Abstract The ground state electron density — obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations — contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation, making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system configurations. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data, while comprehensively sampling system configurations using thermalization. Our ML models are less reliant on heuristics, and being based on Bayesian neural networks, enable uncertainty quantification. We show that our models incur significantly lower data generation costs while allowing confident — and when verifiable, accurate — predictions for a wide variety of bulk systems well beyond training, including systems with defects, different alloy compositions, and at multi-million-atom scales. Moreover, such predictions can be carried out using only modest computational resources. Leverage (statistics) ; Computer science ; Heuristics ; Machine learning ; Density functional theory ; Transfer of learning ; Artificial intelligence ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Zhiliang Cai | Wei Li | Yongchul G. Chung | Song Li | Tiangui Liang | Teng Wu
Source: Separation and Purification Technology (article, 2024-04-30)
Field: Chemistry — Inorganic Chemistry

Transfer learning-assisted computational screening of metal-organic frameworks and covalent-organic frameworks for the separation of Xe/Kr noble gas Metal-organic framework ; Covalent bond ; Separation (statistics) ; Noble gas ; Covalent organic framework ; Noble metal ; Chemistry ; Gas separation ; Metal ; Physical chemistry ; Computer science ; Organic chemistry ; Membrane ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Weiqi Yue | Pawan K. Tripathi | Gabriel Ponon | Zhuldyz Ualikhankyzy | Donald W. Brown | B. Clausen | Maria Strantza | Darren C. Pagan | Matthew A. Willard | F. Ernst | Erman Ayday | Vipin Chaudhary | Roger H. French
Source: Integrating materials and manufacturing innovation (article, 2024-01-16)
Field: Materials Science — Materials Chemistry

Phase Identification in Synchrotron X-ray Diffraction Patterns of Ti–6Al–4V Using Computer Vision and Deep Learning Diffraction ; Beamline ; Reliability (semiconductor) ; Materials science ; Phase (matter) ; Synchrotron radiation ; Convolutional neural network ; Synchrotron ; Computer science ; Deep learning ; Population ; Artificial intelligence ; Optics ; Physics ; Thermodynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Saddam Sk | Hafijul Islam | B. Moses Abraham | Indranil Mondal | Ujjwal Pal
Source: Small Methods (review, 2024-11-30)
Field: Chemistry — Inorganic Chemistry

Defects in MOFs for Photocatalytic Water Reduction to Hydrogen Generation: From Fundamental Understanding to State‐of‐Art Materials Metal-organic frameworks (MOFs) are highly studied for solar H<sub>2</sub> production from H<sub>2</sub>O due to their abundant active sites and open pore channels. Titanium (Ti) and Zirconium (Zr) MOFs are particularly noted for their stability and optoelectronic properties, resembling conventional metal oxide semiconductors. These MOFs allow molecular-level tuning to alter optoelectronic properties, creating opportunities to enhance catalytic activity. Introducing defects in the MOF's structure is a versatile strategy for modifying molecular topology, morphology, and optical and electronic properties. This review compiles essential methods for synthesizing defect-oriented MOFs, discussing characterization techniques and their structural and electronic modifications to boost catalytic activity. It also highlights the connection between photocatalytic H<sub>2</sub> production and MOF properties, exploring strategies to address current limitations using defective Ti and Zr-based MOFs. Additionally, the role of machine learning (ML) in predicting MOF properties for faster material discovery and optimization is emphasized. This review aims to identify challenges and propose ideas for designing future defect-oriented MOF photocatalysts. Photocatalysis ; Materials science ; Metal-organic framework ; Nanotechnology ; Characterization (materials science) ; Zirconium ; Catalysis ; Hydrogen production ; Semiconductor ; Optoelectronics ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Machine Learning in Materials Science
Authors: Lowie Tomme | Yannick Ureel | Maarten R. Dobbelaere | István Lengyel | Florence H. Vermeire | Christian V. Stevens | Kevin M. Van Geem
Source: Reviews in Chemical Engineering (review, 2024-11-28)
Field: Materials Science — Materials Chemistry

Machine learning applications for thermochemical and kinetic property prediction Detailed kinetic models play a crucial role in comprehending and enhancing chemical processes. A cornerstone of these models is accurate thermodynamic and kinetic properties, ensuring fundamental insights into the processes they describe. The prediction of these thermochemical and kinetic properties presents an opportunity for machine learning, given the challenges associated with their experimental or quantum chemical determination. This study reviews recent advancements in predicting thermochemical and kinetic properties for gas-phase, liquid-phase, and catalytic processes within kinetic modeling. We assess the state-of-the-art of machine learning in property prediction, focusing on three core aspects: data, representation, and model. Moreover, emphasis is placed on machine learning techniques to efficiently utilize available data, thereby enhancing model performance. Finally, we pinpoint the lack of high-quality data as a key obstacle in applying machine learning to detailed kinetic models. Accordingly, the generation of large new datasets and further development of data-efficient machine learning techniques are identified as pivotal steps in advancing machine learning's role in kinetic modeling. Machine learning ; Computer science ; Artificial intelligence ; Property (philosophy) ; Representation (politics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Mingliang Wu | Zhilong Song | Yu Cui | Zhanzhao Fu | Kunquan Hong | Qiang Li | Zhiyang Lyu | Wei Liu | Jinlan Wang
Source: Advanced Functional Materials (article, 2024-08-19)
Field: Materials Science — Materials Chemistry

Machine Learning‐Assisted Design of Nitrogen‐Rich Covalent Triazine Frameworks Photocatalysts Abstract Covalent triazine frameworks (CTFs), noted for their rich nitrogen content, have attracted significant attention as promising photocatalysts. However, the structural complexity introduced by the diversity of nitrogen atoms in nitrogen‐rich CTFs poses a substantial challenge in discovering high‐performance CTFs. To address this challenge, a machine‐learning approach is developed to rationally design nitrogen‐rich CTFs, which is subsequently validated through experimental methods. A framework is employed based on the special orthogonal group in three dimensions (SO(3))‐invariant graph neural networks to predict photocatalytic properties of CTFs structures. This approach achieves exceptionally high accuracies with R 2 scores exceeding 0.98. From a dataset of 14920 CTFs structures, this framework identifies 45 high‐performance candidates. Guided by these predictions, a novel CTF structure, pyridine‐2,5‐dicarbaldehyde (CTF‐DCPD) is selected and successfully synthesized, which exhibits an ultrahigh hydrogen evolution rate of 17.70 mmol g −1 h −1 . This rate significantly surpasses that of the widely studied CTF‐1,4‐dicyanobenzene (CTF‐DCB, 10.41 mmol g −1 h −1 ). This work provides a new paradigm for machine learning to accelerate materials development, which can be generalized to the development of other functional materials. Triazine ; Materials science ; Nitrogen ; Covalent bond ; Photocatalysis ; Computer science ; Nanotechnology ; Chemistry ; Organic chemistry ; Polymer chemistry Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Maryam Chafiq | Abdelkarim Chaouiki | Young Gun Ko
Source: Advanced Composites and Hybrid Materials (article, 2024-11-04)
Field: Chemistry — Inorganic Chemistry

Targeted metal–organic framework discovery goes digital: machine learning’s quest from algorithms to atom arrangements Atom (system on chip) ; Computer science ; Algorithm ; Artificial intelligence ; Machine learning ; Nanotechnology ; Chemistry ; Materials science ; Parallel computing Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ruiqi Gao | Yifan Li | Roberto Car
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Enhanced deep potential model for fast and accurate molecular dynamics: application to the hydrated electron In molecular simulations, neural network force fields aim at achieving <i>ab initio</i> accuracy with reduced computational cost. This work introduces enhancements to the Deep Potential network architecture, integrating a message-passing framework and a new lightweight implementation with various improvements. Our model achieves accuracy on par with leading machine learning force fields and offers significant speed advantages, making it well-suited for large-scale, accuracy-sensitive systems. We also introduce a new iterative model for Wannier center prediction, allowing us to keep track of electron positions in simulations of general insulating systems. We apply our model to study the solvated electron in bulk water, an ostensibly simple system that is actually quite challenging to represent with neural networks. Our trained model is not only accurate, but can also transfer to larger systems. Our simulation confirms the cavity model, where the electron's localized state is observed to be stable. Through an extensive run, we accurately determine various structural and dynamical properties of the solvated electron. Molecular dynamics ; Solvated electron ; Chemical physics ; Electron ; Dynamics (music) ; Biological system ; Water model ; Computer science ; Materials science ; Nanotechnology ; Chemistry ; Statistical physics ; Physics ; Computational chemistry ; Physical chemistry ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Jinghang Dai | Zhen Zhang | Jianbin Luo | Hao Ma
Source: Materials Research Letters (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Advances in developing thermally conductive polymers Polymers, with various advantages including lightweight, low cost, flexibility and ease of processing, are popular choices for thermal management in flexible electronics, photovoltaics, fuel cells, etc.In this perspective, we review the recent progress of developing uniaxial thermally conductive polymers and three-dimensional polymers with high thermal conductivity, discuss new opportunities in applying machine learning methods to search for thermally conductive polymers, and finally point out current key challenges and future directions in simulations and experimental measurements.The useful insights in this perspective can guide the rational design of thermally conductive polymers for advanced thermal management. Materials science ; Electrical conductor ; Flexibility (engineering) ; Electronics ; Conductive polymer ; Polymer ; Photovoltaics ; Nanotechnology ; Thermal conductivity ; Engineering physics ; Perspective (graphical) ; Thermal ; Flexible electronics ; Computer science ; Photovoltaic system ; Composite material ; Electrical engineering ; Engineering Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies
Authors: Jihyeon Park | Jaeyoung Lee
Source: Trends in Chemistry (article, 2024-05-14)
Field: Materials Science — Materials Chemistry

Electrochemical energy conversion and storage processes with machine learning Battery (electricity) ; Computer science ; Artificial intelligence ; Electrochemistry ; Intersection (aeronautics) ; Electrolysis ; Computation ; Deep learning ; Field (mathematics) ; Process engineering ; Machine learning ; Engineering ; Chemistry ; Electrode ; Power (physics) ; Aerospace engineering ; Algorithm ; Mathematics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Tong Wang | Daniel P. Russo | Philip Demokritou | Xuelian Jia | Heng Huang | Xinyu Yang | Hao Zhu
Source: Nano Letters (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits Modern nanotechnology has generated numerous datasets from <i>in vitro</i> and <i>in vivo</i> studies on nanomaterials, with some available on nanoinformatics portals. However, these existing databases lack the digital data and tools suitable for machine learning studies. Here, we report a nanoinformatics platform that accurately annotates nanostructures into machine-readable data files and provides modeling toolkits. This platform, accessible to the public at https://vinas-toolbox.com/, has annotated nanostructures of 14 material types. The associated nanodescriptor data and assay test results are appropriate for modeling purposes. The modeling toolkits enable data standardization, data visualization, and machine learning model development to predict properties and bioactivities of new nanomaterials. Moreover, a library of virtual nanostructures with their predicted properties and bioactivities is available, directing the synthesis of new nanomaterials. This platform provides a data-driven computational modeling platform for the nanoscience community, significantly aiding in the development of safe and effective nanomaterials. Nanostructure ; Nanomaterials ; Nanotechnology ; Computer science ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nanotechnology research and applications
Authors: Alexandra Wahab | Renana Gershoni‐Poranne
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

COMPAS-3: a dataset of <i>peri</i> -condensed polybenzenoid hydrocarbons We introduce the third installment of the COMPAS Project - a COMputational database of Polycyclic Aromatic Systems, focused on <i>peri</i>-condensed polybenzenoid hydrocarbons. In this installment, we develop two datasets containing the optimized ground-state structures and a selection of molecular properties of ∼39k and ∼9k <i>peri</i>-condensed polybenzenoid hydrocarbons (at the GFN2-xTB and CAM-B3LYP-D3BJ/cc-pvdz//CAM-B3LYP-D3BJ/def2-SVP levels, respectively). The manuscript details the enumeration and data generation processes and describes the information available within the datasets. An in-depth comparison between the two types of computation is performed, and it is found that the geometrical disagreement is maximal for slightly-distorted molecules. In addition, a data-driven analysis of the structure-property trends of <i>peri</i>-condensed PBHs is performed, highlighting the effect of the size of <i>peri</i>-condensed islands and linearly annulated rings on the HOMO-LUMO gap. The insights described herein are important for rational design of novel functional aromatic molecules for use in, <i>e.g.</i>, organic electronics. The generated datasets provide a basis for additional data-driven machine- and deep-learning studies in chemistry. Peri ; Computer science ; Philosophy Synthesis and Properties of Aromatic Compounds ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Xianyue Wang | Bing He | Bo Liu | Maxim Avdeev | Siqi Shi
Source: Advanced Functional Materials (article, 2024-05-21)
Field: Materials Science — Materials Chemistry

A Database of Electrochemical Stability Windows Containing over 1500 Solid‐State Inorganic Compounds Abstract Electrochemical stability window (ESW) of an inorganic compound (solid‐state electrolyte (SSE) or coating) is an indispensable parameter to evaluate the interface compatibility between the electrode and electrolyte in ion batteries. The discovery of novel coatings and SSEs, driven by extensive ESW data, is expected to accelerate the design of high‐performance batteries. However, only a very small fraction of the ESW of inorganic compounds has been experimentally measured at present, which limits technological progress. Benefiting from the high‐accuracy ESW prediction with dynamically determined direct or indirect decomposition pathway proposed in the previous work, both the oxidation and reduction potentials align to the experimental data reasonably. Here, a database containing phase diagrams and electrochemical stability information is established for more than 1500 solid‐state inorganic compounds with Li + , Na + , K + , Mg 2+ , Ca 2+ , and Al 3+ as the migrating ions, and this number is still growing. The database is reproducible and provides a unified picture of the structure–activity relationships associated with electrochemical stability of inorganic compounds. This study demonstrates the validity of the improved ESW prediction method and paves the way for accelerated screening of superior SSEs or coatings based on machine learning. Materials science ; Electrochemistry ; Solid-state ; Nanotechnology ; Chemical engineering ; Database ; Engineering physics ; Physical chemistry ; Computer science ; Electrode Advancements in Solid Oxide Fuel Cells ; Thermal Expansion and Ionic Conductivity ; Advancements in Battery Materials
Authors: Tania Cecilia Hidalgo Castillo | Wentao Shan | Guorong Ma | Haoyu Zhao | Yunfei Wang | Victor Druet | Abdulelah Saleh | Xiaodan Gu | Sahika Inal
Source: Advanced Materials (article, 2024-12-12)
Field: Materials Science — Polymers and Plastics

Thermal Annealing for High Performance and Memory Behavior in n‐Type Organic Electrochemical Transistors N-type organic mixed ionic electronic conductors (n-OMIECs) struggle to match the performance of p-type counterparts, particularly in bioelectronics' flagship device, the organic electrochemical transistor. Enhancing n-type transistor performance typically necessitates the synthesis of new materials. More sustainable post-synthetic treatments, known to improve organic devices in dry and oxygen-free conditions, are not applied to n-OMIECs. This study introduces thermal annealing to enhance n-OMIECs' electron mobility without sacrificing their ability to take up ionic charges. Annealing increases the crystallinity of p(gNDI-gT2), the first designed n-OMIEC, enhancing its transistor performance to compete with new-generation NDI-based materials. Annealing reduces passive and in operando electrolyte uptake without compromising the device threshold voltage, keeping the device power demand low. The microstructure obtained by annealing, combined with the film's strong near-infrared (NIR) absorption and reduced water swelling, enables the creation of a device that retains photocurrent generated upon frequency-dependent light training. This leads to a microscale, water-compatible memory device that emulates the learning process of biological neurons triggered by light. This simple device can be implemented in artificial neural networks and face recognition platforms and achieve vector-matrix multiplication when fabricated in an array form, showcasing the potential for innovative applications in bioelectronics. Materials science ; Bioelectronics ; Transistor ; Photocurrent ; Annealing (glass) ; Optoelectronics ; Nanotechnology ; Crystallinity ; Electrolyte ; Voltage ; Biosensor ; Electrical engineering ; Electrode ; Composite material Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Sensor and Energy Harvesting Materials
Authors: Shaoqing Liu | Binbin Tao | Zuo Bin | Kaidan Zheng | Walid Abdelfattah | Jinyang Bao | Guoze Yan | Quan Wang | Ruoyu Wang | Zhengtong Li | Saad Melhi | Wafa A. Alshahrani | Xingtao Xu
Source: Chemical Engineering Journal (article, 2024-10-16)
Field: Chemistry — Inorganic Chemistry

Function-oriented design principles for adsorbent materials of uranium extraction from seawater Seawater ; Uranium ; Adsorption ; Extraction (chemistry) ; Function (biology) ; Environmental science ; Chemical engineering ; Chemistry ; Materials science ; Engineering ; Chromatography ; Geology ; Metallurgy ; Oceanography ; Organic chemistry Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Radioactive contamination and transfer
Authors: Blake Duschatko | Jonathan Vandermause | Nicola Molinari | Boris Kozinsky
Source: npj Computational Materials (article, 2024-01-08)
Field: Materials Science — Materials Chemistry

Uncertainty driven active learning of coarse grained free energy models Abstract Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales. Theoretically grounded bottom-up models are appealing due to their thermodynamic consistency with the underlying all-atom models. In this direction, machine learning approaches hold great promise to fitting complex many-body data. However, training models may require collection of large amounts of expensive data. Moreover, quantifying trained model accuracy is challenging, especially in cases of non-trivial free energy configurations, where training data may be sparse. We demonstrate a path towards uncertainty-aware models of coarse grained free energy surfaces. Specifically, we show that principled Bayesian model uncertainty allows for efficient data collection through an on-the-fly active learning framework and opens the possibility of adaptive transfer of models across different chemical systems. Uncertainties also characterize models’ accuracy of free energy predictions, even when training is performed only on forces. This work helps pave the way towards efficient autonomous training of reliable and uncertainty aware many-body machine learned coarse grain models. Computer science ; Granularity ; Consistency (knowledge bases) ; Machine learning ; Artificial intelligence ; Energy (signal processing) ; Data collection ; Bayesian probability ; Bayesian inference ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly