jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marvin Alberts | Federico Zipoli | Alain C. Vaucher
Source: ChemRxiv (preprint, 2023-08-13)
Field: Chemistry — Spectroscopy

Learning the Language of NMR: Structure Elucidation from NMR spectra using Transformer Models The application of machine learning models in chemistry has made remarkable strides in recent years. Even though there is considerable interest in automating common proce- dure in analytical chemistry using machine learning, very few models have been adopted into everyday use. Among the analytical instruments available to chemists, Nuclear Mag- netic Resonance (NMR) spectroscopy is one of the most important, offering insights into molecular structure unobtainable with other methods. However, most processing and analysis of NMR spectra is still performed manually, making the task tedious and time consuming especially for larger quantities of spectra. We present a transformer-based machine learning model capable of predicting the molecular structure directly from the NMR spectrum. Our model is pretrained on synthetic NMR spectra, achieving a top–1 accuracy of 67.0% when predicting the structure from both the 1H and 13C spectrum. Additionally, we train a model which, given a spectrum and a set of likely compounds, selects the one corresponding to the spectrum. This model achieves a top–1 accuracy of 96.0% when trained on 1H spectra. NMR spectra database ; Spectral line ; Nuclear magnetic resonance spectroscopy ; Transformer ; Computer science ; Spectrum (functional analysis) ; Artificial intelligence ; Chemistry ; Machine learning ; Biological system ; Physics ; Stereochemistry Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Various Chemistry Research Topics
Authors: Oliver J. Burton | Zachary Winter | Kenji Watanabe | Takashi Taniguchi | Bernd Beschoten | Christoph Stampfer | Stephan Hofmann
Source: ACS Nano (article, 2023-01-03)
Field: Materials Science — Materials Chemistry

Putting High-Index Cu on the Map for High-Yield, Dry-Transferred CVD Graphene Reliable, clean transfer and interfacing of 2D material layers are technologically as important as their growth. Bringing both together remains a challenge due to the vast, interconnected parameter space. We introduce a fast-screening descriptor approach to demonstrate holistic data-driven optimization across the entirety of process steps for the graphene-Cu model system. We map the crystallographic dependences of graphene chemical vapor deposition, interfacial Cu oxidation to decouple graphene, and its dry delamination across inverse pole figures. Their overlay enables us to identify hitherto unexplored (168) higher index Cu orientations as overall optimal orientations. We show the effective preparation of such Cu orientations via epitaxial close-space sublimation and achieve mechanical transfer with a very high yield (>95%) and quality of graphene domains, with room-temperature electron mobilities in the range of 40000 cm<sup>2</sup>/(V s). Our approach is readily adaptable to other descriptors and 2D material systems, and we discuss the opportunities of such a holistic optimization. Graphene ; Sublimation (psychology) ; Materials science ; Interfacing ; Chemical vapor deposition ; Yield (engineering) ; Inverse ; Nanotechnology ; Computer science ; Composite material ; Mathematics ; Geometry Graphene research and applications ; Nanopore and Nanochannel Transport Studies ; Diamond and Carbon-based Materials Research
Authors: Tomasz Nazim | Aleksandra Lusina | Michał Cegłowski
Source: Polymers (review, 2023-09-24)
Field: Chemistry — Analytical Chemistry

Recent Developments in the Detection of Organic Contaminants Using Molecularly Imprinted Polymers Combined with Various Analytical Techniques Molecularly imprinted polymers (MIPs) encompass a diverse array of polymeric matrices that exhibit the unique capacity to selectively identify a designated template molecule through specific chemical moieties. Thanks to their pivotal attributes, including exceptional selectivity, extended shelf stability, and other distinct characteristics, this class of compounds has garnered interest in the development of highly responsive sensor systems. As a result, the incorporation of MIPs in crafting distinctive sensors and analytical procedures tailored for specific analytes across various domains has increasingly become a common practice within contemporary analytical chemistry. Furthermore, the range of polymers amenable to MIP formulation significantly influences the potential utilization of both conventional and innovative analytical methodologies. This versatility expands the array of possibilities in which MIP-based sensing can be employed in recognition systems. The following review summarizes the notable progress achieved within the preceding seven-year period in employing MIP-based sensing techniques for analyte determination. Molecularly imprinted polymer ; Analyte ; Nanotechnology ; Polymer ; Computer science ; Biochemical engineering ; Materials science ; Combinatorial chemistry ; Selectivity ; Chemistry ; Chromatography ; Organic chemistry ; Engineering ; Catalysis Analytical chemistry methods development ; Analytical Chemistry and Sensors ; Mass Spectrometry Techniques and Applications
Authors: Somayeh Tavasolikejani | Ashkan Farazin
Source: Heliyon (article, 2023-10-01)
Field: Materials Science — Ceramics and Composites

The effect of increasing temperature on simulated nanocomposites reinforced with SWBNNs and its effect on characteristics related to mechanics and the physical attributes using the MDs approach This study examines the effect of increasing temperature (300, 350, 400, 450 and 500 K) on simulated nanocomposites reinforced with exploration of the impact of single-walled boron nitride nanotubes (SWBNNTs) on both the mechanical properties (including Young's modulus, Poisson's ratio, shear modulus, and bulk modulus) and the physical property of density, achieved through molecular dynamics (MDs) simulations. MDs utilized to simulate nanocomposite models consisting of five case studies of SWBNNs with different chiralities (5, 0), (10, 0), (15, 0), (20, 0), and (25, 0) as the reinforcement and using thermoplastic polyurethane (TPU) as the common matrix. The results reveal that with increasing temperature and chiralities of SWBNNTs, the density and Poisson's ratio increase dramatically, and Young's, shear, and bulk moduli decrease continuously. At a consistent temperature, there is a noteworthy trend in the mechanical properties of SWBNNTs with various chiralities. This includes the increase in Young's modulus, Poisson's ratio, shear modulus, and bulk modulus in the simulated nanocomposite, ranging from SWBNNTs (5, 0) to (25, 0). Similarly, the physical property of density exhibits an increasing trend from SWBNNTs (5, 0) to (20, 0) and then decreases at SWBNNTs (25, 0). To validate the accuracy of these findings, a Radial Distribution Function (RDF) diagram is generated using Materials Studio software. Nanocomposite ; Materials science ; Composite material ; Mechanical engineering ; Engineering Advanced ceramic materials synthesis ; Tribology and Wear Analysis ; Boron and Carbon Nanomaterials Research
Authors: Tianhao Tan | Dong Wang
Source: The Journal of Chemical Physics (article, 2023-02-13)
Field: Materials Science — Materials Chemistry

Machine learning based charge mobility prediction for organic semiconductors Transfer integral is a crucial parameter that determines the charge mobility of organic semiconductors, and it is very sensitive to molecular packing motifs. The quantum chemical calculation of transfer integrals for all the molecular pairs in organic materials is usually an unaffordable task; fortunately, it can be accelerated by the data-driven machine learning method now. In this work, we develop machine learning models based on artificial neutral networks to predict transfer integrals accurately and efficiently for four typical organic semiconductor molecules: quadruple thiophene (QT), pentacene, rubrene, and dinaphtho[2,3-b:2',3'-f]thieno[3,2-b]thiophene (DNTT). We test various forms of features and labels and evaluate the accuracy of different models. With the implementation of a data augmentation scheme, we have achieved a very high accuracy with the determination coefficient of 0.97 and mean absolute error of 4.5 meV for QT, and similar accuracy for the other three molecules. We apply these models to studying charge transport in organic crystals with dynamic disorders at 300 K and obtain the charge mobility and anisotropy in perfect agreement with the brutal force quantum chemical calculation. If more molecular packings representing the amorphous phase of organic solids are supplemented to the dataset, the current models can be refined to study charge transport in organic thin films with polymorphs and static disorders. Organic semiconductor ; Rubrene ; Charge (physics) ; Pentacene ; Anisotropy ; Materials science ; Thiophene ; Chemical physics ; Chemistry ; Statistical physics ; Physics ; Optoelectronics ; Nanotechnology ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Electronics and Photovoltaics
Authors: Maicon Pierre Lourenço | Lizandra Barrios Herrera | Jiří Hostaš | Patrizia Calaminici | Andreas M. Köster | Alain Tchagang | Dennis R. Salahub
Source: Journal of Chemical Theory and Computation (article, 2023-08-15)
Field: Materials Science — Materials Chemistry

QMLMaterial─A Quantum Machine Learning Software for Material Design and Discovery Structural elucidation of chemical compounds is challenging experimentally, and theoretical chemistry methods have added important insight into molecules, nanoparticles, alloys, and materials geometries and properties. However, finding the optimum structures is a bottleneck due to the huge search space, and global search algorithms have been used successfully for this purpose. In this work, we present the quantum machine learning software/agent for materials design and discovery (QMLMaterial), intended for automatic structural determination <i>in silico</i> for several chemical systems: atomic clusters, atomic clusters and the spin multiplicity together, doping in clusters or solids, vacancies in clusters or solids, adsorption of molecules or adsorbents on surfaces, and finally atomic clusters on solid surfaces/materials or encapsulated in porous materials. QMLMaterial is an artificial intelligence (AI) software based on the active learning method, which uses machine learning regression algorithms and their uncertainties for decision making on the next unexplored structures to be computed, increasing the probability of finding the global minimum with few calculations as more data is obtained. The software has different acquisition functions for decision making (e.g., expected improvement and lower confidence bound). Also, the Gaussian process is available in the AI framework for regression, where the uncertainty is obtained analytically from Bayesian statistics. For the artificial neural network and support vector regressor algorithms, the uncertainty can be obtained by K-fold cross-validation or nonparametric bootstrap resampling methods. The software is interfaced with several quantum chemistry codes and atomic descriptors, such as the many-body tensor representation. QMLMaterial's capabilities are highlighted in the current work by its applications in the following systems: Na<sub>20</sub>, Mo<sub>6</sub>C<sub>3</sub> (where the spin multiplicity was considered), H<sub>2</sub>O@CeNi<sub>3</sub>O<sub>5</sub>, Mg<sub>8</sub>@graphene, Na<sub>3</sub>Mg<sub>3</sub>@CNT (carbon nanotube). Computer science ; Artificial neural network ; Machine learning ; Software ; Artificial intelligence ; Curse of dimensionality ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electron and X-Ray Spectroscopy Techniques
Authors: Santiago Pereznieto | Russlan Jaafreh | Jung-gu Kim | Kotiba Hamad
Source: Materials Letters (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

Discovery of solid-state electrolytes for Na-ion batteries using machine learning Electrolyte ; Ionic conductivity ; Materials science ; Fast ion conductor ; Ionic bonding ; Ion ; Conductivity ; Identification (biology) ; Solid-state ; Random forest ; Machine learning ; Computer science ; Engineering physics ; Physical chemistry ; Electrode ; Physics ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advancements in Battery Materials
Authors: Ziwen Zhang | Jianchun Chu | Hengfei Zhang | Xiangyang Liu | Maogang He
Source: Journal of Energy Storage (article, 2023-11-17)
Field: Materials Science — Materials Chemistry

Mining ionic conductivity descriptors of antiperovskite electrolytes for all-solid-state batteries via machine learning Antiperovskite ; Ionic conductivity ; Lithium (medication) ; Conductivity ; Ab initio ; Electrolyte ; Ionic bonding ; Materials science ; Fast ion conductor ; Machine learning ; Computer science ; Ion ; Chemistry ; Nanotechnology ; Physical chemistry Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials
Authors: Hanqing Yu | Kaiyi Yang | Lisheng Zhang | Wentao Wang | Mengzheng Ouyang | Bin Ma | Shichun Yang | Junfu Li | Xinhua Liu
Source: Chemical Engineering Journal (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

Multi-output ensemble deep learning: A framework for simultaneous prediction of multiple electrode material properties Computer science ; Robustness (evolution) ; Artificial intelligence ; Artificial neural network ; Machine learning ; Deep learning ; Ensemble learning ; Mean squared error ; Mathematics Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Bo Guan | Chao Chen | Yunchang Xin | Jing Xu | Bo Feng | Xiaoxu Huang | Qing Liu
Source: Journal of Magnesium and Alloys (article, 2023-08-03)
Field: Materials Science — Biomaterials

Predicting the Hall-Petch slope of magnesium alloys by machine learning Hall-Petch slope (k) is an important material parameter, while there is a great challenge to accurately predict the k value of magnesium alloys due to a high dependence of k on the material parameters, deformation history and testing conditions. The present study demonstrates that machine learning could provide opportunities to overcome this challenge. Two machine learning models, artificial neural network (ANN) and random forest (RF), were built and validated using 138 data. The results showed that increasing the training data set would enhance the prediction efficiency of both models. Comparing to the RF model, the ANN model showed higher accuracy. The correlations between individual attribute and k values were also discussed. Materials science ; Artificial neural network ; Random forest ; Machine learning ; Deformation (meteorology) ; Artificial intelligence ; Magnesium ; Metallurgy ; Computer science ; Composite material Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Aluminum Alloys Composites Properties
Authors: Nihang Fu | Jeffrey Hu | Ying Feng | Gregory Morrison | Hans‐Conrad zur Loye | Jianjun Hu
Source: Advanced Science (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models Oxidation states (OS) are the charges on atoms due to electrons gained or lost upon applying an ionic approximation to their bonds. As a fundamental property, OS has been widely used in charge-neutrality verification, crystal structure determination, and reaction estimation. Currently, only heuristic rules exist for guessing the oxidation states of a given compound with many exceptions. Recent work has developed machine learning models based on heuristic structural features for predicting the oxidation states of metal ions. However, composition-based oxidation state prediction still remains elusive so far, which has significant implications for the discovery of new materials for which the structures have not been determined. This work proposes a novel deep learning-based BERT transformer language model BERTOS for predicting the oxidation states for all elements of inorganic compounds given only their chemical composition. This model achieves 96.82% accuracy for all-element oxidation states prediction benchmarked on the cleaned ICSD dataset and achieves 97.61% accuracy for oxide materials. It is also demonstrated how it can be used to conduct large-scale screening of hypothetical material compositions for materials discovery. Oxidation state ; Computer science ; Heuristic ; Ionic bonding ; Oxide ; Artificial intelligence ; Machine learning ; Ion ; Materials science ; Algorithm ; Metal ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Xin Tian | Frederic Béen | Yiqun Sun | P. van Thienen | Patrick S. Bäuerlein
Source: Environmental Science & Technology Letters (article, 2023-01-11)
Field: Chemistry — Analytical Chemistry

Identification of Polymers with a Small Data Set of Mid-infrared Spectra: A Comparison between Machine Learning and Deep Learning Models Identifying environmental polymers and microplastics is crucial for the scientific world, environmental agencies, and water authorities to estimate their environmental impact and increase efforts to decrease emissions. On the basis of different spectroscopy techniques, e.g., laser-directed infrared imaging and Raman spectroscopy, polymers can be observed and represented as spectroscopic signals. The latter can be further analyzed and classified by data science, in particular, machine learning (ML). Past studies applied a variety of ML models to identify polymers from small or large data sets. However, a comprehensive comparison of multiple models across different data set sizes is still needed, which is presented in this study. Furthermore, we also provide a practical data augmentation technique to generate synthetic samples when only a limited number of samples are available. Our results show that the ensemble ML model, compared to neural network models, takes the least training time to achieve the best performance, i.e., a classification accuracy of 99.5%. This study provides a generic framework for selecting ML models and boosting model performance to accurately identify polymers. Microplastics ; Boosting (machine learning) ; Data set ; Computer science ; Artificial intelligence ; Machine learning ; Artificial neural network ; Identification (biology) ; Set (abstract data type) ; Pattern recognition (psychology) ; Biological system ; Data mining ; Chemistry ; Environmental chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Yifeng Zhang | Hui Huang | Jie Tian | Chengwei Li | Yuchen Jiang | Zeng Fan | Lujun Pan
Source: Energy storage materials (article, 2023-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modelling electrified microporous carbon/electrolyte electrochemical interface and unraveling charge storage mechanism by machine learning accelerated molecular dynamics Microporous material ; Electrolyte ; Materials science ; Molecular dynamics ; Chemical physics ; Capacitance ; Electrochemistry ; Carbon fibers ; Electrode ; Ion ; Ab initio ; Nanotechnology ; Chemical engineering ; Computational chemistry ; Physical chemistry ; Chemistry ; Composite material ; Organic chemistry ; Composite number Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Battery Materials and Technologies
Authors: Qiyuan Zhao | Dylan M. Anstine | Olexandr Isayev | Brett M. Savoie
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Δ <sup>2</sup> machine learning for reaction property prediction The emergence of Δ-learning models, whereby machine learning (ML) is used to predict a correction to a low-level energy calculation, provides a versatile route to accelerate high-level energy evaluations at a given geometry. However, Δ-learning models are inapplicable to reaction properties like heats of reaction and activation energies that require both a high-level geometry and energy evaluation. Here, a Δ<sup>2</sup>-learning model is introduced that can predict high-level activation energies based on low-level critical-point geometries. The Δ<sup>2</sup> model uses an atom-wise featurization typical of contemporary ML interatomic potentials (MLIPs) and is trained on a dataset of ∼167 000 reactions, using the GFN2-xTB energy and critical-point geometry as a low-level input and the B3LYP-D3/TZVP energy calculated at the B3LYP-D3/TZVP critical point as a high-level target. The excellent performance of the Δ<sup>2</sup> model on unseen reactions demonstrates the surprising ease with which the model implicitly learns the geometric deviations between the low-level and high-level geometries that condition the activation energy prediction. The transferability of the Δ<sup>2</sup> model is validated on several external testing sets where it shows near chemical accuracy, illustrating the benefits of combining ML models with readily available physical-based information from semi-empirical quantum chemistry calculations. Fine-tuning of the Δ<sup>2</sup> model on a small number of Gaussian-4 calculations produced a 35% accuracy improvement over DFT activation energy predictions while retaining xTB-level cost. The Δ<sup>2</sup> model approach proves to be an efficient strategy for accelerating chemical reaction characterization with minimal sacrifice in prediction accuracy. Property (philosophy) ; Computer science ; Machine learning ; Artificial intelligence ; Chemical reaction ; State (computer science) ; Chemistry ; Algorithm ; Organic chemistry ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Vivin Vinod | Sayan Maity | Peter Zaspel | Ulrich Kleinekathöfer
Source: Journal of Chemical Theory and Computation (article, 2023-10-20)
Field: Materials Science — Materials Chemistry

Multifidelity Machine Learning for Molecular Excitation Energies The accurate but fast calculation of molecular excited states is still a very challenging topic. For many applications, detailed knowledge of the energy funnel in larger molecular aggregates is of key importance, requiring highly accurate excitation energies. To this end, machine learning techniques can be a very useful tool, though the cost of generating highly accurate training data sets still remains a severe challenge. To overcome this hurdle, this work proposes the use of multifidelity machine learning where very little training data from high accuracies is combined with cheaper and less accurate data to achieve the accuracy of the costlier level. In the present study, the approach is employed to predict vertical excitation energies to the first excited state for three molecules of increasing size, namely, benzene, naphthalene, and anthracene. The energies are trained and tested for conformations stemming from classical molecular dynamics and density functional based tight-binding simulations. It can be shown that the multifidelity machine learning model can achieve the same accuracy as a machine learning model built only on high-cost training data while expending a much lower computational effort to generate the data. The numerical gain observed in these benchmark test calculations was over a factor of 30 but certainly can be much higher for high-accuracy data. Computer science ; Excitation ; Excited state ; Machine learning ; Benchmark (surveying) ; Artificial intelligence ; Physics ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies