jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniel Struble | Bradley G. Lamb | Boran Ma
Source: MRS Communications (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

A prospective on machine learning challenges, progress, and potential in polymer science Abstract Artificial intelligence and machine learning (ML) continue to see increasing interest in science and engineering every year. Polymer science is no different, though implementation of data-driven algorithms in this subfield has unique challenges barring widespread application of these techniques to the study of polymer systems. In this Prospective, we discuss several critical challenges to implementation of ML in polymer science, including polymer structure and representation, high-throughput techniques and limitations, and limited data availability. Promising studies targeting resolution of these issues are explored, and contemporary research demonstrating the potential of ML in polymer science despite existing obstacles are discussed. Finally, we present an outlook for ML in polymer science moving forward. Graphical Abstract Polymer ; Science and engineering ; Computer science ; Representation (politics) ; Materials science ; Nanotechnology ; Resolution (logic) ; Throughput ; Artificial intelligence ; Data science ; Machine learning ; Systems engineering ; Management science ; Engineering ethics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Praveen Kumar Basivi | Tayssir Hamieh | Vijay Kakani | Visweswara Rao Pasupuleti | Sasikala Ganapathy | Sung Min Heo | Kedhareswara Sairam Pasupuleti | Moon‐Deock Kim | Venkata Subbaiah Munagapati | Nadavala Siva Kumar | Jet‐Chau Wen | Chang Woo Kim
Source: TrAC Trends in Analytical Chemistry (article, 2024-03-16)
Field: Chemistry — Spectroscopy

Exploring advanced materials: Harnessing the synergy of inverse gas chromatography and artificial vision intelligence Inverse gas chromatography ; Gas chromatography ; Artificial intelligence ; Environmental science ; Computer science ; Chromatography ; Chemistry Adsorption, diffusion, and thermodynamic properties of materials ; Phase Equilibria and Thermodynamics ; Chemical Thermodynamics and Molecular Structure
Authors: Yiwen Zheng | Prakash Thakolkaran | Agni Kumar Biswal | Jake A. Smith | Ziheng Lu | Shuxin Zheng | Bichlien H. Nguyen | Siddhant Kumar | Aniruddh Vashisth
Source: Advanced Science (article, 2024-12-16)
Field: Materials Science — Polymers and Plastics

AI‐Guided Inverse Design and Discovery of Recyclable Vitrimeric Polymers Vitrimer is a new, exciting class of sustainable polymers with healing abilities due to their dynamic covalent adaptive networks. However, a limited choice of constituent molecules restricts their property space and potential applications. To overcome this challenge, an innovative approach coupling molecular dynamics (MD) simulations and a novel graph variational autoencoder (VAE) model for inverse design of vitrimer chemistries with desired glass transition temperature (T<sub>g</sub>) is presented. The first diverse vitrimer dataset of one million chemistries is curated and T<sub>g</sub> for 8,424 of them is calculated by high-throughput MD simulations calibrated by a Gaussian process model. The proposed VAE employs dual graph encoders and a latent dimension overlapping scheme which allows for individual representation of multi-component vitrimers. High accuracy and efficiency of the framework are demonstrated by discovering novel vitrimers with desirable T<sub>g</sub> beyond the training regime. To validate the effectiveness of the framework in experiments, vitrimer chemistries are generated with a target T<sub>g</sub> = 323 K. By incorporating chemical intuition, a novel vitrimer with T<sub>g</sub> of 311-317 K is synthesized, experimentally demonstrating healability and flowability. The proposed framework offers an exciting tool for polymer chemists to design and synthesize novel, sustainable polymers for various applications. Computer science ; Inverse ; Polymer ; Multiscale modeling ; Biochemical engineering ; Artificial intelligence ; Theoretical computer science ; Materials science ; Mathematics ; Chemistry ; Computational chemistry ; Engineering Polymer composites and self-healing ; Advanced Polymer Synthesis and Characterization ; Conducting polymers and applications
Authors: Inam Ur Rehman | Inam Ullah | Habib Ullah Khan | Mohammed Seghir Guellil | Jakeoung Koo | J.M. Min | Shabana Habib | Muhammad Islam | Mi Young Lee
Source: Nanotechnology Reviews (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A comprehensive systematic literature review of ML in nanotechnology for sustainable development Abstract The rapid expansion of nanotechnology has transformed numerous sectors, with nanoproducts now ubiquitous in everyday life, electronics, healthcare, and pharmaceuticals. Despite their widespread adoption, concerns persist regarding potential adverse effects, necessitating vigilant risk management. This systematic literature review advocates for leveraging artificial intelligence (AI) and machine learning (ML) methodologies to enhance simulations and refine safety assessments for nanomaterials (NMs). Through a comprehensive examination of the existing literature, this study seeks to explain the pivotal role of AI in boosting NMs sustainability efforts across six key research themes. It explores their significance in advancing sustainability, hazard identification, and their diverse applications in this field. In addition, it evaluates the past sustainability strategies for NMs while proposing innovative avenues for future exploration. By conducting this comprehensive analysis, the research aims to illuminate the current landscape, identify challenges, and outline potential pathways for integrating AI and ML to promote sustainable practices within nanotechnology. Furthermore, it advocates for extending these technologies to monitor the real-world behaviour of NMs delivery. Through its thorough investigation, this systematic literature review endeavours to address current obstacles and pave the way for the safe and sustainable utilization of nanotechnology, thereby minimizing associated risks. Nanotechnology ; Materials science Nanoparticles: synthesis and applications ; Recycling and Waste Management Techniques
Authors: A. Kiruthika
Source: Journal of Engineering and Applied Science (review, 2024-01-25)
Field: Materials Science — Polymers and Plastics

A review of leaf fiber reinforced polymer composites Abstract The utilization of natural fiber-reinforced polymer composite has received greater attention in various fields due to its recyclability; inexpensive, nonabrasive, specific properties; light-weight, naturally decomposed, abundant availability; etc. Natural fibers are generally lignocellulosic and multicellular, a better alternate to the synthetic materials. Among the natural fibers, leaf fibers are hard fibers, used in the making of filaments, threads, ropes, mats, fabrics, etc. PALF, sisal, henequen, cantala, fique, alfa, and sansevieria family are the examples of the leaf fibers. The present comprehensive review aims to provide different types of leaf fibers, their properties, and their reinforced composites. The effect of various factors like fiber volume fraction, fiber aspect ratio (length/diameter), fiber orientation, packing arrangement, matrix content and coupling agents, and processing techniques towards the mechanical properties of leaf fiber-reinforced polymer composites, is discussed. The surface modification of fiber such as alkaline, silane, KMnO 4 , and their effects on the mechanical properties is given. Scanning electron microscopy (SEM) and water absorption (WA) characteristics are also discussed. Composite material ; Materials science ; Fiber Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Nanocomposite Films for Food Packaging
Authors: Qingchun Yang | Zhou Jianlong | Runjie Bao | Dongwen Rong | Lei Zhao | Dawei Zhang
Source: Chemical Engineering Science (article, 2024-05-25)
Field: Materials Science — Materials Chemistry

Optimization and prediction of catalysts for precise synthesis of methyl glycolate from dimethyl oxalate using machine learning coupled with particle swarm optimization algorithm Particle swarm optimization ; Oxalate ; Catalysis ; Computer science ; Chemistry ; Algorithm ; Organic chemistry Machine Learning in Materials Science ; Fault Detection and Control Systems ; Process Optimization and Integration
Authors: Chao Han | Dongdong Zhang | Xia Song | Yingkai Zhang
Source: Journal of Chemical Theory and Computation (article, 2024-06-06)
Field: Chemistry — Spectroscopy

Accurate Prediction of NMR Chemical Shifts: Integrating DFT Calculations with Three-Dimensional Graph Neural Networks Computer prediction of NMR chemical shifts plays an increasingly important role in molecular structure assignment and elucidation for organic molecule studies. Density functional theory (DFT) and gauge-including atomic orbital (GIAO) have established a framework to predict NMR chemical shifts but often at a significant computational expense with a limited prediction accuracy. Recent advancements in deep learning methods, especially graph neural networks (GNNs), have shown promise in improving the accuracy of predicting experimental chemical shifts, either by using 2D molecular topological features or 3D conformational representation. This study presents a new 3D GNN model to predict <sup>1</sup>H and <sup>13</sup>C chemical shifts, CSTShift, that combines atomic features with DFT-calculated shielding tensor descriptors, capturing both isotropic and anisotropic shielding effects. Utilizing the NMRShiftDB2 data set and conducting DFT optimization and GIAO calculations at the B3LYP/6-31G(d) level, we prepared the NMRShiftDB2-DFT data set of high-quality 3D structures and shielding tensors with corresponding experimentally measured <sup>1</sup>H and <sup>13</sup>C chemical shifts. The developed CSTShift models achieve the state-of-the-art prediction performance on both the NMRShiftDB2-DFT test data set and external CHESHIRE data set. Further case studies on identifying correct structures from two groups of constitutional isomers show its capability for structure assignment and elucidation. The source code and data are accessible at https://yzhang.hpc.nyu.edu/IMA. Chemical shift ; Density functional theory ; Computer science ; Isotropy ; Electromagnetic shielding ; Test set ; Artificial neural network ; Tensor (intrinsic definition) ; Computational chemistry ; Chemistry ; Artificial intelligence ; Machine learning ; Physics ; Mathematics ; Physical chemistry ; Quantum mechanics Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Matthew S. Johnson | William H. Green
Source: Reaction Chemistry & Engineering (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A machine learning based approach to reaction rate estimation Development of a machine learning algorithm for automatically training subgraph isomorphic decision trees (SIDT) to predict reaction rate coefficients. Machine learning ; Decision tree ; Computer science ; Estimation ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Iman Peivaste | Saba Ramezani | Ghasem Alahyarizadeh | Reza Ghaderi | Ahmed Makradi | Salim Belouettar
Source: Scientific Reports (article, 2024-01-02)
Field: Materials Science — Materials Chemistry

Rapid and accurate predictions of perfect and defective material properties in atomistic simulation using the power of 3D CNN-based trained artificial neural networks This article introduces an innovative approach that utilizes machine learning (ML) to address the computational challenges of accurate atomistic simulations in materials science. Focusing on the field of molecular dynamics (MD), which offers insight into material behavior at the atomic level, the study demonstrates the potential of trained artificial neural networks (tANNs) as surrogate models. These tANNs capture complex patterns from built datasets, enabling fast and accurate predictions of material properties. The article highlights the application of 3D convolutional neural networks (CNNs) to incorporate atomistic details and defects in predictions, a significant advancement compared to current 2D image-based, or descriptor-based methods. Through a dataset of atomistic structures and MD simulations, the trained 3D CNN achieves impressive accuracy, predicting material properties with a root-mean-square error below 0.65 GPa for the prediction of elastic constants and a speed-up of approximately 185 to 2100 times compared to traditional MD simulations. This breakthrough promises to expedite materials design processes and facilitate scale-bridging in materials science, offering a new perspective on addressing computational demands in atomistic simulations. Computer science ; Bridging (networking) ; Convolutional neural network ; Molecular dynamics ; Artificial neural network ; Artificial intelligence ; Field (mathematics) ; Machine learning ; Physics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Leonardo Barneschi | Leonardo Rotondi | Daniele Padula
Source: The Journal of Physical Chemistry A (article, 2024-03-14)
Field: Materials Science — Materials Chemistry

Molecular Geometry Impact on Deep Learning Predictions of Inverted Singlet–Triplet Gaps We present a deep learning model able to predict excited singlet–triplet gaps with a mean absolute error (MAE) of ≈20 meV to obtain potential inverted singlet–triplet (IST) candidates. We exploit cutting-edge spherical message passing graph neural networks designed specifically for generating 3D graph representations in molecular learning. In a nutshell, the model takes as input a list of unsaturated heavy atom Cartesian coordinates and atomic numbers, producing singlet–triplet gaps as output. We exploited available large data collections to train the model on ≈40,000 heterogeneous density functional theory (DFT) geometries with available ADC(2)/cc-pVDZ singlet–triplet gaps. We ascertain the predictive power of the model from a quantitative perspective obtaining predictions on a test set of ≈14,000 molecules, whose geometries have been generated at DFT level (the same employed for the geometries in the training set), at GFN2-xTB level, and through Molecular Mechanics. We notice performance degradation upon switching to lower-quality geometries, with GFN2-xTB ones maintaining satisfactory results (MAE ≈ 50 meV on GFN2-xTB geometries, MAE ≈ 180 meV on generalized AMBER force field geometries), hinting at caution when dealing with specific chemical classes. Finally, we verify the performance of the model from the qualitative point of view, obtaining predictions on a different data set of ≈15,000 molecules already used to identify new IST molecules. We obtained predictions using both DFT and experimental X-ray geometries, with results on IST candidates similar to those provided by quantum chemical methods, with clear hints for the path toward improved performance. Singlet state ; Density functional theory ; Cartesian coordinate system ; Test set ; Excited state ; Computer science ; Physics ; Algorithm ; Statistical physics ; Mathematics ; Geometry ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Luminescence and Fluorescent Materials ; CO2 Reduction Techniques and Catalysts
Authors: Zhenchang Xu | Baoyu Cai | Luchun Yan | Xiaolu Pang | Kewei Gao
Source: Corrosion Science (article, 2024-04-24)
Field: Materials Science — Metals and Alloys

Statistical analysis of metastable pitting behavior of 2024 aluminum alloy based on deep learning Pitting corrosion ; Alloy ; Materials science ; Metastability ; Metallurgy ; Aluminium ; Corrosion ; 6111 aluminium alloy ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Fatigue and fracture mechanics
Authors: Ehsan Alibagheri | Ahmad Ranjbar | Mohammad Khazaei | Thomas D. Kühne | S. Mehdi Vaez Allaei
Source: Advanced Functional Materials (article, 2024-05-15)
Field: Materials Science — Materials Chemistry

Remarkable Optoelectronic Characteristics of Synthesizable Square‐Octagon Haeckelite Structures: Machine Learning Materials Discovery Abstract The research explores new compounds with structures similar to square‐octagonal beryllium oxide, commonly known as Haeckelites. The goal is to identify semiconducting variations compounds that can be synthesized and show promising potential in optoelectronic devices. Start with 1083 binary Haeckelite structures, and to test the synthesis potential of these materials quickly, develop new descriptors and use machine learning techniques. As a result, it identifies a subset of 350 materials with suitable properties in terms of phase stability and electronic perspective. These materials are further investigated to analyze their electronic structure and phase stability, which are determined through density functional theory calculations. The phase stabilities of the predicted semiconducting Haeckelites are also compared to other binary compounds using an evolutionary structure search algorithm. The comprehensive methodology also includes examining the dynamic stabilities through phonon calculations and mechanical properties through elastic constant calculations. Eventually, 13 new Haeckelite compounds are discovered, demonstrating exceptional stability and performance in electronic tests. Among these compounds, eight have shown remarkable absorption coefficients and are considered promising candidates with high reflectivity. Additionally, they have exhibited high electron mobility. These findings strongly suggest the potential of these compounds for synthesis and their application in optoelectronic devices. Materials science ; Square (algebra) ; Optoelectronics ; Nanotechnology ; Geometry Machine Learning in Materials Science ; 2D Materials and Applications ; Boron and Carbon Nanomaterials Research
Authors: Yihuan Zhao | Kuan Chen | Binhai Yu | Qianwen Wan | You Wang | Fushan Tang | Xinmin Li
Source: Journal of Molecular Structure (article, 2024-06-24)
Field: Materials Science — Materials Chemistry

Development of organic aggregation-induced emission fluorescent materials based on machine learning models and experimental validation Chemistry ; Aggregation-induced emission ; Fluorescence ; Nanotechnology ; Biological system Luminescence and Fluorescent Materials ; Conducting polymers and applications ; Organic Light-Emitting Diodes Research
Authors: Suvo Banik | Karthik Balasubramanian | Sukriti Manna | Sybil Derrible | Subramanian Sankaranarayananan
Source: Computational Materials Science (article, 2024-02-12)
Field: Materials Science — Materials Chemistry

Evaluating generalized feature importance via performance assessment of machine learning models for predicting elastic properties of materials Interpretability ; Predictability ; Machine learning ; Computer science ; Artificial intelligence ; Feature (linguistics) ; Predictive modelling ; Ranking (information retrieval) ; Feature engineering ; Property (philosophy) ; Set (abstract data type) ; Data mining ; Deep learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yukun Teng | Guangcun Shan
Source: APL Materials (article, 2024-08-01)
Field: Chemistry — Inorganic Chemistry

Interpretable machine learning for materials discovery: Predicting CO2 adsorption properties of metal–organic frameworks Metal–organic frameworks (MOFs), as novel porous crystalline materials with high porosity and a large specific surface area, have been increasingly utilized for CO2 adsorption. Machine learning (ML) combined with molecular simulations is used to identify MOFs with high CO2 adsorption capacity from millions of MOF structures. In this study, 23 structural and molecular features and 765 calculated features were proposed for the ML model and trained on a hypothetical MOF dataset for CO2 adsorption at different pressures. The calculated features improved the prediction accuracy of the ML model by 15%–20% and revealed its interpretability, consistent with the analysis of the interaction potential. Subsequently, the importance of the relevant features was ranked at different pressures. Regardless of the pressure, the molecular structure and pore size were the most critical factors. van der Waals force-related descriptors gained more competitive advantages at low pressures, whereas electrical-field-related descriptors gradually dominated at high pressures. Overall, this study provides a novel perspective to guide the initial high-throughput screening of MOFs as high-performance CO2 adsorption materials. Metal-organic framework ; Materials science ; Adsorption ; Nanotechnology ; Organic chemistry ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Mohammed Elkabous | Anass Karzazi | Yasser Karzazi
Source: Computational Materials Science (article, 2024-06-02)
Field: Materials Science — Materials Chemistry

Deep learning-driven QSPR models for accurate properties estimation in organic solar cells using extended connectivity fingerprints Quantitative structure–activity relationship ; Biological system ; Organic solar cell ; Artificial intelligence ; Deep learning ; Estimation ; Computer science ; Chemistry ; Machine learning ; Organic chemistry ; Engineering ; Polymer ; Biology Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Yuhang Wang | Yaqin Zhang | Ninggui Ma | Jun Zhao | Yu Xiong | Shuang Luo | Jun Fan
Source: Journal of Material Science and Technology (article, 2024-06-27)
Field: Materials Science — Materials Chemistry

Machine learning accelerated catalysts design for CO reduction: An interpretability and transferability analysis Interpretability ; Transferability ; Reduction (mathematics) ; Computer science ; Catalysis ; Learning design ; Artificial intelligence ; Machine learning ; Chemistry ; Psychology ; Mathematics ; Mathematics education Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts