jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ronak Kumar Chaudhari | Priyanka A. Shah | Pranav S. Shrivastav
Source: Discover Nano (article, 2023-10-30)
Field: Materials Science — Materials Chemistry

Green synthesis of silver nanoparticles using Adhatoda vasica leaf extract and its application in photocatalytic degradation of dyes The paper describes biogenic synthesis of silver nanoparticles (AgNPs) using Adhatoda vasica leaf extracts at room temperature. The prepared AgNPs were characterized by UV-visible spectroscopy, Fourier-transform infrared spectroscopy, powder X-ray diffraction, Energy dispersive X-ray (EDX), High Resolution Transmission Electron Microscope, Scanning Electron Microscopy and Thermogravimetric analyser. The bio reduction method is devoid of any toxic chemicals, organic solvents, and external reducing, capping and stabilizing agent. The synthesized AgNPs had spherical shape with particle size ranging between 3.88 and 23.97 nm and had face centered cubic structure. UV-visible spectral analysis confirmed the formation of AgNPs with a characteristic surface plasmon resonance band at 419 nm. The EDX pattern revealed the presence of elemental Ag in AgNPs. The prepared AgNPs were used for degradation of Amaranth, Allura red and Fast green in aqueous medium, with ≥ 92.6% efficiency within 15 min using 5 mg of AgNPs. The optical bandgap, Eg value of 2.26 eV for AgNPs was found to be effective for rapid photocatalytic degradation of all the three dyes. The degradation process was observed to follow pseudo first order kinetics. Silver nanoparticle ; Scanning electron microscope ; Thermogravimetric analysis ; Photocatalysis ; Nuclear chemistry ; High-resolution transmission electron microscopy ; Surface plasmon resonance ; Fourier transform infrared spectroscopy ; Materials science ; Transmission electron microscopy ; Diffuse reflectance infrared fourier transform ; Analytical Chemistry (journal) ; Chemistry ; Brilliant green ; Nanoparticle ; Spectroscopy ; Chemical engineering ; Nanotechnology ; Organic chemistry ; Catalysis Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Synthesis and bioactivity of alkaloids
Authors: Maxwell C. Venetos | Mingjian Wen | Kristin A. Persson
Source: The Journal of Physical Chemistry A (article, 2023-03-02)
Field: Chemistry — Spectroscopy

Machine Learning Full NMR Chemical Shift Tensors of Silicon Oxides with Equivariant Graph Neural Networks The nuclear magnetic resonance (NMR) chemical shift tensor is a highly sensitive probe of the electronic structure of an atom and furthermore its local structure. Recently, machine learning has been applied to NMR in the prediction of isotropic chemical shifts from a structure. Current machine learning models, however, often ignore the full chemical shift tensor for the easier-to-predict isotropic chemical shift, effectively ignoring a multitude of structural information available in the NMR chemical shift tensor. Here we use an equivariant graph neural network (GNN) to predict full <sup>29</sup>Si chemical shift tensors in silicate materials. The equivariant GNN model predicts full tensors to a mean absolute error of 1.05 ppm and is able to accurately determine the magnitude, anisotropy, and tensor orientation in a diverse set of silicon oxide local structures. When compared with other models, the equivariant GNN model outperforms the state-of-the-art machine learning models by 53%. The equivariant GNN model also outperforms historic analytical models by 57% for isotropic chemical shift and 91% for anisotropy. The software is available as a simple-to-use open-source repository, allowing similar models to be created and trained with ease. Isotropy ; Anisotropy ; Chemical shift ; Equivariant map ; Tensor (intrinsic definition) ; Artificial neural network ; Computer science ; Artificial intelligence ; Materials science ; Chemistry ; Nuclear magnetic resonance ; Molecular physics ; Mathematics ; Physics ; Geometry ; Quantum mechanics Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Santiago Pereznieto | Russlan Jaafreh | Jung-gu Kim | Kotiba Hamad
Source: Materials Letters (article, 2023-01-20)
Field: Materials Science — Materials Chemistry

Solid electrolytes for Li-ion batteries via machine learning Materials science ; Electrolyte ; Ionic conductivity ; Lithium (medication) ; Ion ; Fast ion conductor ; Ionic bonding ; Work (physics) ; Conductivity ; Thermodynamics ; Physical chemistry ; Physics ; Electrode ; Chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Tung Doan Nguyen | Mona Bavarian
Source: Polymer (article, 2023-03-28)
Field: Materials Science — Materials Chemistry

Machine learning approach to polymer reaction engineering: Determining monomers reactivity ratios Reactivity (psychology) ; Copolymer ; Monomer ; Computer science ; Polymer ; Graph ; Chemical reaction engineering ; Materials science ; Artificial intelligence ; Biological system ; Chemistry ; Organic chemistry ; Theoretical computer science ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Ruiming Zhu | Siyu Tian | Zekun Ren | Jiali Li | Tonio Buonassisi | Kedar Hippalgaonkar
Source: ACS Omega (article, 2023-02-22)
Field: Materials Science — Materials Chemistry

Predicting Synthesizability using Machine Learning on Databases of Existing Inorganic Materials Defining the metric for synthesizability and predicting new compounds that can be experimentally realized in the realm of data-driven research is a pressing problem in contemporary materials science. The increasing computational power and advancements in machine learning (ML) algorithms provide a new avenue to solve the synthesizability challenge. In this work, using the Inorganic Crystal Structure Database (ICSD) and the Materials Project (MP) database, we represent crystal structures in Fourier-transformed crystal properties (FTCP) representation and use a deep learning model to predict synthesizability in the form of a synthesizability score (SC). Such an SC model, as a synthesizability filter for new materials, enables an efficient and accurate classification to identify promising material candidates. The SC prediction model achieved 82.6/80.6% (precision/recall) overall accuracy in predicting ternary crystal materials. We also trained the SC model by only considering compounds uploaded on the MP before 2015 as the training set and testing on multiple sets of materials uploaded after 2015. In the post-2019 test set, we obtain a high 88.60% true positive rate accuracy, coupled with 9.81% precision, indicating that newly added materials remain unexplored and have high synthesis potential. Further, we provide a list of 100 materials predicted to be synthesizable from this post-2019 dataset (highest SC) for future studies, and our SC model, as a validation filter, is beneficial for future material screening and discovery. Computer science ; Machine learning ; Metric (unit) ; Database ; Test set ; Artificial intelligence ; Filter (signal processing) ; Set (abstract data type) ; Upload ; Representation (politics) ; Algorithm ; Data mining ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Jeļena Fiošina | Philipp Sievers | Marco Drache | Sabine Beuermann
Source: Computers & Chemical Engineering (article, 2023-07-17)
Field: Materials Science — Materials Chemistry

Polymer reaction engineering meets explainable machine learning Molar mass ; Polymerization ; Polymer ; Monomer ; Computer science ; Molar mass distribution ; Process (computing) ; Monte Carlo method ; Chemical reaction engineering ; Algorithm ; Materials science ; Biological system ; Chemistry ; Mathematics ; Statistics ; Organic chemistry ; Catalysis ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Zhihao Dong | Jie Feng | Yujin Ji | Youyong Li
Source: The Journal of Physical Chemistry A (article, 2023-07-07)
Field: Materials Science — Materials Chemistry

SLI-GNN: A Self-Learning-Input Graph Neural Network for Predicting Crystal and Molecular Properties Since the structures of crystals/molecules are often non-Euclidean data in real space, graph neural networks (GNNs) are regarded as the most prospective approach for their capacity to represent materials by graph-based inputs and have emerged as an efficient and powerful tool in accelerating the discovery of new materials. Here, we propose a self-learning-input GNN framework, named self-learning-input GNN (SLI-GNN), to uniformly predict the properties for both crystals and molecules, in which we design a dynamic embedding layer to self-update the input features along with the iteration of the neural network and introduce the Infomax mechanism to maximize the average mutual information between the local features and the global features. Our SLI-GNN can reach ideal prediction accuracy with fewer inputs and more message passing neural network (MPNN) layers. The model evaluations on the Materials Project dataset and QM9 dataset verify that the overall performance of our SLI-GNN is comparable to that of other previously reported GNNs. Thus, our SLI-GNN framework presents excellent performance in material property prediction, which is thereby promising for accelerating the discovery of new materials. Computer science ; Embedding ; Graph ; Artificial neural network ; Infomax ; Artificial intelligence ; Euclidean space ; Theoretical computer science ; Machine learning ; Data mining ; Algorithm ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: James Dean | Matthias Scheffler | Thomas A. R. Purcell | S. V. Barabash | Rahul Bhowmik | Timur Bazhirov
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2023-10-12)
Field: Materials Science — Materials Chemistry

Interpretable machine learning for materials design Interpretability ; Machine learning ; Artificial intelligence ; Computer science ; Key (lock) ; Throughput ; Focus (optics) ; Data science Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Anupam Bhattacharya | Ivan Timokhin | Ratnamala Chatterjee | Qian Yang | Artem Mishchenko
Source: npj Computational Materials (article, 2023-06-08)
Field: Materials Science — Materials Chemistry

Deep learning approach to genome of two-dimensional materials with flat electronic bands Abstract Electron-electron correlations play central role in condensed matter physics, governing phenomena from superconductivity to magnetism and numerous technological applications. Two-dimensional (2D) materials with flat electronic bands provide natural playground to explore interaction-driven physics, thanks to their highly localized electrons. The search for 2D flat band materials has attracted intensive efforts, especially now with open science databases encompassing thousands of materials with computed electronic bands. Here we automate the otherwise daunting task of materials search and classification by combining supervised and unsupervised machine learning algorithms. To this end, convolutional neural network was employed to identify 2D flat band materials, which were then subjected to symmetry-based analysis using a bilayer unsupervised learning algorithm. Such hybrid approach of exploring materials databases allowed us to construct a genome of 2D materials hosting flat bands and to reveal material classes outside the known flat band paradigms. Artificial neural network ; Artificial intelligence ; Computer science ; Deep learning ; Magnetism ; Electron ; Unsupervised learning ; Physics ; Nanotechnology ; Materials science ; Condensed matter physics Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Akhil Thomas | Ali Riza Durmaz | Mehwish Alam | Peter Gumbsch | Harald Sack | Christoph Eberl
Source: Scientific Reports (article, 2023-08-02)
Field: Materials Science — Materials Chemistry

Materials fatigue prediction using graph neural networks on microstructure representations The local prediction of fatigue damage within polycrystals in a high-cycle fatigue setting is a long-lasting and challenging task. It requires identifying grains tending to accumulate plastic deformation under cyclic loading. We address this task by transcribing ferritic steel microtexture and damage maps from experiments into a microstructure graph. Here, grains constitute graph nodes connected by edges whenever grains share a common boundary. Fatigue loading causes some grains to develop slip markings, which can evolve into microcracks and lead to failure. This data set enables applying graph neural network variants on the task of binary grain-wise damage classification. The objective is to identify suitable data representations and models with an appropriate inductive bias to learn the underlying damage formation causes. Here, graph convolutional networks yielded the best performance with a balanced accuracy of 0.72 and a F<sub>1</sub>-score of 0.34, outperforming phenomenological crystal plasticity (+ 68%) and conventional machine learning (+ 17%) models by large margins. Further, we present an interpretability analysis that highlights the grains along with features that are considered important by the graph model for the prediction of fatigue damage initiation, thus demonstrating the potential of such techniques to reveal underlying mechanisms and microstructural driving forces in critical grain ensembles. Computer science ; Artificial neural network ; Graph ; Microstructure ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Materials science ; Composite material Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Chuan-Nan Li | Han-Pu Liang | Xie Zhang | Zijing Lin | Su‐Huai Wei
Source: npj Computational Materials (article, 2023-09-30)
Field: Materials Science — Materials Chemistry

Graph deep learning accelerated efficient crystal structure search and feature extraction Abstract Structural search and feature extraction are a central subject in modern materials design, the efficiency of which is currently limited, but can be potentially boosted by machine learning (ML). Here, we develop an ML-based prediction-analysis framework, which includes a symmetry-based combinatorial crystal optimization program (SCCOP) and a feature additive attribution model, to significantly reduce computational costs and to extract property-related structural features. Our method is highly accurate and predictive, and extracts structural features from desired structures to guide materials design. We first test SCCOP on 35 typical compounds to demonstrate its generality. As a case study, we apply our approach to a two-dimensional B-C-N system, which identifies 28 previously undiscovered stable structures out of 82 compositions; our analysis further establishes the structural features that contribute most to energy and bandgap. Compared to conventional approaches, SCCOP is about 10 times faster while maintaining a comparable accuracy. Our framework is generally applicable to all types of systems for precise and efficient structural search, providing insights into the relationship between ML-extracted structural features and physical properties. Generality ; Computer science ; Feature (linguistics) ; Feature extraction ; Graph ; Artificial intelligence ; Property (philosophy) ; Machine learning ; Algorithm ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Boron and Carbon Nanomaterials Research
Authors: Alejandro Rodriguez | Changpeng Lin | Chen Shen | Kunpeng Yuan | Mohammed Al‐Fahdi | Xiaoliang Zhang | Hongbin Zhang | Ming Hu
Source: Communications Materials (article, 2023-08-15)
Field: Materials Science — Materials Chemistry

Unlocking phonon properties of a large and diverse set of cubic crystals by indirect bottom-up machine learning approach Abstract Although first principles based anharmonic lattice dynamics is one of the most common methods to obtain phonon properties, such method is impractical for high-throughput search of target thermal materials. We develop an elemental spatial density neural network force field as a bottom-up approach to accurately predict atomic forces of ~80,000 cubic crystals spanning 63 elements. The primary advantage of our indirect machine learning model is the accessibility of phonon transport physics at the same level as first principles, allowing simultaneous prediction of comprehensive phonon properties from a single model. Training on 3182 first principles data and screening 77,091 unexplored structures, we identify 13,461 dynamically stable cubic structures with ultralow lattice thermal conductivity below 1 Wm −1 K −1 , among which 36 structures are validated by first principles calculations. We propose mean square displacement and bonding-antibonding as two low-cost descriptors to ease the demand of expensive first principles calculations for fast screening ultralow thermal conductivity. Our model also quantitatively reveals the correlation between off-diagonal coherence and diagonal populations and identifies the distinct crossover from particle-like to wave-like heat conduction. Our algorithm is promising for accelerating discovery of novel phononic crystals for emerging applications, such as thermoelectrics, superconductivity, and topological phonons for quantum information technology. Phonon ; Anharmonicity ; Condensed matter physics ; Thermal conductivity ; Materials science ; Superconductivity ; Statistical physics ; Physics ; Machine learning ; Computer science ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Mitsuru Ohno | Yoshihiro Hayashi | Qi Zhang | Yu Kaneko | Ryo Yoshida
Source: Journal of Chemical Information and Modeling (article, 2023-08-21)
Field: Materials Science — Materials Chemistry

SMiPoly: Generation of a Synthesizable Polymer Virtual Library Using Rule-Based Polymerization Reactions Recent advances in machine learning have led to the rapid adoption of various computational methods for de novo molecular design in polymer research, including high-throughput virtual screening and inverse molecular design. In such workflows, molecular generators play an essential role in creation or sequential modification of candidate polymer structures. Machine learning-assisted molecular design has made great technical progress over the past few years. However, the difficulty of identifying synthetic routes to such designed polymers remains unresolved. To address this technical limitation, we present Small Molecules into Polymers (SMiPoly), a Python library for virtual polymer generation that implements 22 chemical rules for commonly applied polymerization reactions. For given small organic molecules to form a candidate monomer set, the SMiPoly generator conducts possible polymerization reactions to generate an exhaustive list of potentially synthesizable polymers. In this study, using 1083 readily available monomers, we generated 169,347 unique polymers forming seven different molecular types: polyolefin, polyester, polyether, polyamide, polyimide, polyurethane, and polyoxazolidone. By comparing the distribution of the virtually created polymers with approximately 16,000 real polymers synthesized so far, it was found that the coverage and novelty of the SMiPoly-generated polymers can reach 48 and 53%, respectively. Incorporating the SMiPoly library into a molecular design workflow will accelerate the process of de novo polymer synthesis by shortening the step to select synthesizable candidate polymers. Polymer ; Polymerization ; Monomer ; Computer science ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Fernando Jaume-Santero | Alban Bornet | Alain Valery | Nona Naderi | David Vicente Alvarez | Dimitrios Proios | Anthony Yazdani | Colin Bournez | Thomas C. Fessard | Douglas Teodoro
Source: Journal of Chemical Information and Modeling (article, 2023-03-23)
Field: Materials Science — Materials Chemistry

Transformer Performance for Chemical Reactions: Analysis of Different Predictive and Evaluation Scenarios The prediction of chemical reaction pathways has been accelerated by the development of novel machine learning architectures based on the deep learning paradigm. In this context, deep neural networks initially designed for language translation have been used to accurately predict a wide range of chemical reactions. Among models suited for the task of language translation, the recently introduced molecular transformer reached impressive performance in terms of forward-synthesis and retrosynthesis predictions. In this study, we first present an analysis of the performance of transformer models for product, reactant, and reagent prediction tasks under different scenarios of data availability and data augmentation. We find that the impact of data augmentation depends on the prediction task and on the metric used to evaluate the model performance. Second, we probe the contribution of different combinations of input formats, tokenization schemes, and embedding strategies to model performance. We find that less stable input settings generally lead to better performance. Lastly, we validate the superiority of round-trip accuracy over simpler evaluation metrics, such as top-<i>k</i> accuracy, using a committee of human experts and show a strong agreement for predictions that pass the round-trip test. This demonstrates the usefulness of more elaborate metrics in complex predictive scenarios and highlights the limitations of direct comparisons to a predefined database, which may include a limited number of chemical reaction pathways. Computer science ; Transformer ; Machine learning ; Artificial intelligence ; Metric (unit) ; Deep learning ; Machine translation ; Artificial neural network ; Embedding ; Data mining ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research