jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiashun Mao | Jianmin Wang | Kwang‐Hwi Cho | Kyoung Tai No
Source: ChemRxiv (preprint, 2023-05-15)
Field: Materials Science — Materials Chemistry

iupacGPT: IUPAC-based large-scale molecular pre-trained model for property prediction and molecule generation The IUPAC (International Union of Pure and Applied Chemistry) nomenclature is a globally recognized unique naming system which assigns names to chemical compounds. As a form of molecular representation closest to natural language, it allows to estimate molecular data in a large-scale pre-trained paradigm by employing machine learning approaches for natural language processing (NLP). Although, SMILES is currently popular molecular representation used by most generative models, different molecular representation is suitable for different scenarios, and considering the advantages of IUPAC in terms of readability, it becomes meaningful to explore the difference of these two different molecular representations for molecular generation and regression/classification tasks. In this paper, we attempt to adapt the capabilities of transformer to a large IUPAC corpus by constructing a GPT-2-like language model named iupacGPT. For each task in addition to the molecular generation, we freeze model parameters and attach trainable lightweight networks to fine tune. The results show that pre-trained iupacGPT can capture general knowledge that can be successfully transferred to the downstream tasks such as molecule generation and binary classification and property regression prediction. What’s more, with a same setup, iupacGPT outperforms the model smilesGPT in term of the downstream tasks. Overall, transformer-like language models pretrained on IUPAC corpora are promising alternatives that obtain more intuitive in terms of interpretability and semantic level than on SMILES corpora, and scale well with the pretraining data size. https://github.com/AspirinCode/iupacGPT Chemical nomenclature ; Computer science ; Interpretability ; Natural language processing ; Artificial intelligence ; Representation (politics) ; Transformer ; Readability ; Property (philosophy) ; Chemistry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Jan Schuetzke | Simon Schweidler | Friedrich R. Muenke | André Orth | Anurag Khandelwal | Ben Breitung | Jasmin Aghassi‐Hagmann | Markus Reischl
Source: Advanced Intelligent Systems (article, 2023-12-24)
Field: Materials Science — Materials Chemistry

Accelerating Materials Discovery: Automated Identification of Prospects from X‐Ray Diffraction Data in Fast Screening Experiments New materials are frequently synthesized and optimized with the explicit intention to improve their properties to meet the ever‐increasing societal requirements for high‐performance and energy‐efficient electronics, new battery concepts, better recyclability, and low‐energy manufacturing processes. This often involves exploring vast combinations of stoichiometries and compositions, a process made more efficient by high‐throughput robotic platforms. Nonetheless, subsequent analytical methods are essential to screen the numerous samples and identify promising material candidates. X‐ray diffraction is a commonly used analysis method available in most laboratories which gives insight into the crystalline structure and reveals the presence of phases in a powder sample. Herein, a method for automating the analysis of XRD patterns, which uses a neural network model to classify samples into nondiffracting, single‐phase, and multi‐phase structures, is presented. To train neural networks for identifying materials with compositions not matching known crystallographic structures, a synthetic data generation approach is developed. The application of the neural networks on high‐entropy oxides experimental data is demonstrated, where materials frequently deviate from anticipated structures. Our approach, not limited to these materials, seamlessly integrates into high‐throughput data analysis pipelines, either filtering acquired patterns or serving as a standalone method for automated material exploration workflows. Workflow ; Computer science ; Artificial neural network ; Throughput ; Process (computing) ; Identification (biology) ; Characterization (materials science) ; Materials science ; Process engineering ; Nanotechnology ; Artificial intelligence ; Database ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Yougen Xu | Ya-Dong Gao | Lebin Su | Haiting Wu | Hao Tian | Majian Zeng | Chunqiu Xu | Xinwei Zhu | Kuangbiao Liao
Source: Angewandte Chemie (article, 2023-10-10)
Field: Chemistry — Organic Chemistry

High‐Throughput Experimentation and Machine Learning‐Assisted Optimization of Iridium‐Catalyzed Cross‐Dimerization of Sulfoxonium Ylides Abstract A novel and convenient approach that combines high‐throughput experimentation (HTE) with machine learning (ML) technologies to achieve the first selective cross‐dimerization of sulfoxonium ylides via iridium catalysis is presented. A variety of valuable amide‐, ketone‐, ester‐, and N‐heterocycle‐substituted unsymmetrical E ‐alkenes are synthesized in good yields with high stereoselectivities. This mild method avoids the use of diazo compounds and is characterized by simple operation, high step‐economy, and excellent chemoselectivity and functional group compatibility. The combined experimental and computational studies identify an amide‐sulfoxonium ylide as a carbene precursor. Furthermore, a comprehensive exploration of the reaction space is also performed (600 reactions) and a machine learning model for reaction yield prediction has been constructed. Carbene ; Amide ; Iridium ; Catalysis ; Chemoselectivity ; Diazo ; Combinatorial chemistry ; Chemistry ; Yield (engineering) ; Ketone ; Computer science ; Organic chemistry ; Materials science Cyclopropane Reaction Mechanisms ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Beom Chan Ryu | Seon Yoo Hwang | Sung Sin Kang | Jeong Won Kang | Dongsoo Yang
Source: International Journal of Thermophysics (article, 2023-02-08)
Field: Materials Science — Materials Chemistry

Group Contribution Based Graph Convolution Network: Predicting Vapor–Liquid Equilibrium with COSMO-SAC-ML UNIFAC ; COSMO-RS ; UNIQUAC ; Computer science ; Quantum chemical ; Thermodynamics ; Graph ; Activity coefficient ; Algorithm ; Applied mathematics ; Mathematics ; Chemistry ; Physical chemistry ; Theoretical computer science ; Physics ; Ionic liquid ; Non-random two-liquid model ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Guo-Yong Shi | Huaijun Sun | Songyou Wang | Hong Jiang | Chao Zhang | Feng Zhang | Kai‐Ming Ho | Cai‐Zhuang Wang
Source: Computational Materials Science (article, 2023-07-22)
Field: Materials Science — Materials Chemistry

Simulation studies of the stability and growth kinetics of Pt-Sn phases using a machine learning interatomic potential Interatomic potential ; Molecular dynamics ; Thermodynamics ; Gibbs free energy ; Kinetics ; Density functional theory ; Chemistry ; Materials science ; Chemical physics ; Computational chemistry ; Physics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Jian Wu | Jia Zhang | Zihan Wang | Guangren Qian | Tong‐Yi Zhang
Source: Journal of environmental chemical engineering (article, 2023-06-16)
Field: Materials Science — Materials Chemistry

Water-tolerant and anti-dust CeCo-MnO2 membrane catalysts for low temperature selective catalytic reduction of nitrogen oxides NOx ; Catalysis ; Selective catalytic reduction ; Chemistry ; Moisture ; Brønsted–Lowry acid–base theory ; Chemical engineering ; Water vapor ; Sulfide ; Nitrogen ; Membrane ; Waste management ; Inorganic chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Dawn Raja Somu | Timothy Cracchiolo | Elena Longo | Imke Greving | Vivian Merk
Source: Acta Biomaterialia (article, 2023-01-25)
Field: Materials Science — Biomaterials

On stars and spikes: Resolving the skeletal morphology of planktonic Acantharia using synchrotron X-ray nanotomography and deep learning image segmentation Sponge spicule ; Spicule ; Artificial intelligence ; Segmentation ; Synchrotron ; Materials science ; Paleontology ; Geometry ; Geology ; Computer science ; Physics ; Optics ; Mathematics Calcium Carbonate Crystallization and Inhibition ; Paleontology and Stratigraphy of Fossils ; Geomagnetism and Paleomagnetism Studies
Authors: Haiwei Ji | Huan-Huan Chen | Tianzhi Mao | Lingfeng Zhao | Ke Zhang | Majun Yang | Xiaobo Zhou | Qi Wang | Li Wu | Yuling Qin
Source: Advanced Optical Materials (article, 2023-06-18)
Field: Materials Science — Materials Chemistry

Encryption‐Decryption Strategy of High Security Constructed via Encoded Fluorescence Sensing and Linear Discriminant Analysis Technique Abstract Innovative fluorescence materials combining the special reagent stimuli response have considerable potential to provide a convenient and highly secure communication strategy for encoding messages. With the unique decryption method, encrypted information that is previously unidentifiable can now be accurately parsed without the risk of interception or decipherment. Herein, white‐emitting light semiconducting polymer dots for advanced secret communication are developed, along with a machine learning‐based encryption‐decryption strategy. The encrypted data are readily hidden by the fluorescent polymer‐stimulus system, which can be identified by fluorescence imaging equipment and only be decrypted via the special linear discriminant analysis route pre‐transferred to the recipient. The results presented here suggest that chemical‐stimulated fluorescence information storage and the unique decoding strategy can be developed as prospective methods for advanced data communication and security protection. Encryption ; Decoding methods ; Linear discriminant analysis ; Computer science ; Fluorescence ; Interception ; Artificial intelligence ; Materials science ; Computer hardware ; Computer security ; Algorithm ; Optics ; Biology Luminescence and Fluorescent Materials ; Advanced biosensing and bioanalysis techniques ; Molecular Sensors and Ion Detection
Authors: Doha M. Sayed | Mahmoud G. Metwally | Ahmed I. Nour El-Dein | Essam A. Mourad | Hassan R. Idress | Khalid A. Khodira | Youssef A. Mohammed | Rasha M. El Nashar
Source: Journal of The Electrochemical Society (article, 2023-12-01)
Field: Chemistry — Analytical Chemistry

Review—Exploitation of Modified Electrochemical Sensors for Fast and Reliable Detection of Plastics Endocrine-Disrupting Contaminants Endocrine-disrupting chemicals (EDCs) are synthetic compounds widely used in various industrial and consumer products, raising concerns about their potential adverse effects on human health and the environment. Among the numerous EDCs, Bisphenols, phthalates, and per- and poly-fluoroalkyl substances (PFAS) have attracted significant attention due to their prevalence and adverse effects. Accurately identifying and quantifying these EDCs is important for assessing exposure levels and evaluating the associated risks. This review article shed the light on recent advancements in modified electrochemical sensors and biosensors using nanomaterials, molecularly imprinted polymers (MIPs), Metal organic frameworks and nanocomposite-based electrodes, and their applicability for detecting EDCs during the past five years. The development of innovative electrode materials, optimization strategies used to increase the sensitivity and selectivity of electrochemical sensors. The challenges of real-world sample analysis are investigated as well along with possible solutions, such as matrix interference and detection limitations. Future perspectives and emerging trends in the electrochemical detection of EDCs, are also elaborated with a particular emphasis on developments in portable and miniaturized devices, multiplexed sensing platforms, and the incorporation of machine learning and artificial intelligence for better data analysis, and on-site detection. Nanotechnology ; Complex matrix ; Biochemical engineering ; Human health ; Biosensor ; Environmental science ; Computer science ; Materials science ; Chemistry ; Engineering ; Chromatography ; Medicine Analytical chemistry methods development ; Electrochemical sensors and biosensors ; Effects and risks of endocrine disrupting chemicals
Authors: Xiaoyang Zhu | Yu Mao | Jizi Liu | Yi Chen | Chuan Chen | Yan Li | Xiao Huang | Ning Gu
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Deep learning-assisted analysis of HRTEM images of crystalline nanoparticles Accurate analysis of high-resolution transmission electron microscopy (HRTEM) images is important for the characterization and design of materials. However, conventional analyses rely mostly on manual procedures, which are time-consuming and lack accuracy, especially when the image contrast is low. Here, we propose an advanced analysis method for extracting crystal features from HRTEM images based on a 2D fast Fourier transform and U-Net based deep learning model. By using HRTEM images of iron oxide nanoparticles as examples, we show that our method is capable of providing information on the crystallinity profile, distribution of crystal planes, phases and defects automatically with high accuracy. In an era of data-driven technological development, we believe that deep learning based analysis tools will facilitate great progress in fundamental research on crystalline materials. High-resolution transmission electron microscopy ; Nanoparticle ; Materials science ; Nanotechnology ; Transmission electron microscopy X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Pigment Synthesis and Properties
Authors: Mao Su | Ji‐Hui Yang | Hongjun Xiang | Xin-Gao Gong
Source: Machine Learning Science and Technology (article, 2023-04-06)
Field: Materials Science — Materials Chemistry

Efficient determination of the Hamiltonian and electronic properties using graph neural network with complete local coordinates Abstract Despite the successes of machine learning methods in physical sciences, the prediction of the Hamiltonian, and thus the electronic properties, is still unsatisfactory. Based on graph neural network (NN) architecture, we present an extendable NN model to determine the Hamiltonian from ab initio data, with only local atomic structures as inputs. The rotational equivariance of the Hamiltonian is achieved by our complete local coordinates (LCs). The LC information, encoded using a convolutional NN and designed to preserve Hermitian symmetry, is used to map hopping parameters onto local structures. We demonstrate the performance of our model using graphene and SiGe random alloys as examples. We show that our NN model, although trained using small-size systems, can predict the Hamiltonian, as well as electronic properties such as band structures and densities of states for large-size systems within the ab initio accuracy, justifying its extensibility. In combination with the high efficiency of our model, which takes only seconds to get the Hamiltonian of a 1728-atom system, the present work provides a general framework to predict electronic properties efficiently and accurately, which provides new insights into computational physics and will accelerate the research for large-scale materials. Hamiltonian (control theory) ; Ab initio ; Artificial neural network ; Electronic structure ; Convolutional neural network ; Computer science ; Topology (electrical circuits) ; Statistical physics ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Mathematical optimization ; Combinatorics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Boron and Carbon Nanomaterials Research
Authors: Bin Yang | Yizhen Lv | Zhaocai Zhang | Xinying He | Yu Zhu
Source: Journal of Building Engineering (article, 2023-09-20)
Field: Materials Science — Materials Chemistry

Anticorrosion mechanism of natural acidic amino acids on steel in chloride solution: Experimental, theoretical and machine learning approaches Dielectric spectroscopy ; Aspartic acid ; Linear sweep voltammetry ; Glutamic acid ; Corrosion ; Density functional theory ; Corrosion inhibitor ; Chemistry ; Molecular dynamics ; Adsorption ; Electrochemistry ; Amino acid ; Materials science ; Cyclic voltammetry ; Inorganic chemistry ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Biochemistry ; Electrode Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Cultural Heritage Materials Analysis
Authors: Jan Trieschmann | Luca Vialetto | Tobias Gergs
Source: arXiv (Cornell University) (preprint, 2023-06-30)
Field: Chemistry — Spectroscopy

Machine learning for advancing low-temperature plasma modeling and simulation Machine learning has had an enormous impact in many scientific disciplines. Also in the field of low-temperature plasma modeling and simulation it has attracted significant interest within the past years. Whereas its application should be carefully assessed in general, many aspects of plasma modeling and simulation have benefited substantially from recent developments within the field of machine learning and data-driven modeling. In this survey, we approach two main objectives: (a) We review the state-of-the-art focusing on approaches to low-temperature plasma modeling and simulation. By dividing our survey into plasma physics, plasma chemistry, plasma-surface interactions, and plasma process control, we aim to extensively discuss relevant examples from literature. (b) We provide a perspective of potential advances to plasma science and technology. We specifically elaborate on advances possibly enabled by adaptation from other scientific disciplines. We argue that not only the known unknowns, but also unknown unknowns may be discovered due to the inherent propensity of data-driven methods to spotlight hidden patterns in data. Field (mathematics) ; Plasma ; Computer science ; Perspective (graphical) ; Process (computing) ; Data science ; Artificial intelligence ; Management science ; Machine learning ; Engineering ; Physics ; Mathematics Mass Spectrometry Techniques and Applications ; Advanced Data Storage Technologies ; Machine Learning in Materials Science
Authors: Inhyo Lee | Joonchul Kim | Taehyun Park | Kyoungmin Min
Source: Materials Today Advances (article, 2023-05-04)
Field: Materials Science — Materials Chemistry

Predicting mechanical properties of newly generated two-dimensional materials with minimum uncertainty Two-dimensional (2D) materials exhibit exceptional properties. Thus, many studies have been conducted to discover novel 2D materials with unique characteristics or to find new ways of utilizing existing 2D materials. However, the existing open databases of 2D materials are often inefficient for this purpose. In this study, a material discovery framework is developed to identify new 2D materials using a deep learning-based generative model. First, a previous 2D database is adopted as a training set to develop a machine learning-based surrogate model for predicting the mechanical properties. Next, 2D candidates are generated, and their structural validity is confirmed by employing a classification model and checking their similarities to existing 2D materials. The uncertainty in the predicted mechanical properties of the generated materials is measured and the actual values are verified using density functional theory calculations. A total of 360 structures are newly identified according to the exploration method and the mean absolute error is significantly reduced from 206.025 to 10.185 N/m. We believe that the developed framework is general and can be further modified to search for novel 2D materials satisfying target physicochemical properties. Computer science ; Set (abstract data type) ; Generative grammar ; Artificial intelligence ; Machine learning ; Data mining Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: Tao Jin | Dean Wei | Junshi Yu | Qianhua Kan | Guozheng Kang | Xu Zhang
Source: Theoretical and Applied Mechanics Letters (article, 2023-11-22)
Field: Materials Science — Materials Chemistry

Micropillar compression using discrete dislocation dynamics and machine learning Discrete dislocation dynamics (DDD) simulations reveal the evolution of dislocation structures and the interaction of dislocations. This study investigated the compression behavior of single-crystal copper micropillars using few-shot machine learning with data provided by DDD simulations. Two types of features are considered: external features comprising specimen size and loading orientation and internal features involving dislocation source length, Schmid factor, the orientation of the most easily activated dislocations and their distance from the free boundary. The yielding stress and stress-strain curves of single-crystal copper micropillar are predicted well by incorporating both external and internal features of the sample as separate or combined inputs. It is found that the Machine learning accuracy predictions for single-crystal micropillar compression can be improved by incorporating easily activated dislocation features with external features. However, the effect of easily activated dislocation on yielding is less important compared to the effects of specimen size and Schmid factor which includes information of orientation but becomes more evident in small-sized micropillars. Overall, incorporating internal features, especially the information of most easily activated dislocations, improves predictive capabilities across diverse sample sizes and orientations. Dislocation ; Materials science ; Compression (physics) ; Orientation (vector space) ; Crystallography ; Peierls stress ; Dislocation creep ; Mechanics ; Geometry ; Composite material ; Physics ; Mathematics ; Chemistry Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Fusion materials and technologies
Authors: Kazuumi Fujioka | Eric Lam | Brandon Loi | Rui Sun
Source: Carbon Trends (article, 2023-03-11)
Field: Materials Science — Materials Chemistry

Ab initio molecular dynamics benchmarking study of machine-learned potential energy surfaces for the HBr+ + HCl reaction Machine learning has grown in use for constructing potential energy surfaces for their ability to theoretically recreate any function given enough training as well as their fast predictive powers after being trained. When trained on ab initio data, this enables simulation of a large number of ab-initio-quality trajectories. Here, rigorous benchmarking of these machine-learned potential energy surfaces—both in terms of their static errors and dynamics errors—is carried out for the HBr+ + HCl system. In a novel comparison, both neural networks and a kernel regression method are compared for a global potential energy surface, covering multiple dissociation channels. Further, comparison with ab initio molecular dynamics simulations enables one of the first direct comparisons of dynamic, ensemble-average properties of the system. Finally, comparison with experimental results reveals remarkable agreement for the sGDML method for training sets of thousands to tens of thousands of molecular configurations. Benchmarking ; Ab initio ; Computational chemistry ; Potential energy surface ; Dynamics (music) ; Molecular dynamics ; Energy (signal processing) ; Chemistry ; Computer science ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Nuclear Physics and Applications