jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vivin Vinod | Sayan Maity | Peter Zaspel | Ulrich Kleinekathöfer
Source: arXiv (Cornell University) (preprint, 2023-05-18)
Field: Materials Science — Materials Chemistry

Multi-Fidelity Machine Learning for Excited State Energies of Molecules 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 excited state energies. To this end, machine learning techniques can be an extremely useful tool though the cost of generating highly accurate training datasets still remains a severe challenge. To overcome this hurdle, this work proposes the use of multi-fidelity 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 the first excited state energies for three molecules of increasing size, namely, benzene, naphthalene, and anthracene. The energies are trained and tested for conformations stemming from classical molecular dynamics simulations and from real-time density functional tight-binding calculations. It can be shown that the multi-fidelity machine learning model can achieve the same accuracy as a machine learning model built only on high cost training data while having 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. Excited state ; Computer science ; Benchmark (surveying) ; Machine learning ; Fidelity ; Artificial intelligence ; High fidelity ; Work (physics) ; Algorithm ; Physics ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; CO2 Reduction Techniques and Catalysts
Authors: Bernd J Meyer
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2023-01-23)
Field: Chemistry — Analytical Chemistry

Neural network system and methods for analysis of organic materials and structures using spectral data Apparatus and processes for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation. Artificial neural network ; Spectral analysis ; Computer science ; Data mining ; Artificial intelligence ; Physics Spectroscopy and Chemometric Analyses
Authors: Shaohan Li | Zening Yang | Rasoul Khaledialidusti | Shuai Lin | Jin Yu | Mohammad Khazaei | Jing Zhang | Litao Sun | Xin Li | Weiwei Sun
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

High-Throughput Study and Machine Learning on MAX and MAB Phases: New Materials and Fingerprints of Superior Lattice Thermal Conductivity Thermal conductivity ; Lattice (music) ; Throughput ; Materials science ; Conductivity ; Computer science ; Artificial intelligence ; Condensed matter physics ; Chemistry ; Composite material ; Physics ; Physical chemistry ; Telecommunications Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Jingchao Sun | Dian Zheng | Fei Deng | Sitong Liu | Yunhao Xie | Ying Liu | Jing Xu | Wei Liu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Organic Chemistry

Machine Learning-Assisted Screening of Heterogeneous N-Heterocyclic Carbenes Supported Single-Atom Catalysts for Nitrogen Fixation Catalysis ; Nitrogen atom ; Nitrogen fixation ; Fixation (population genetics) ; Nitrogen ; Chemistry ; Combinatorial chemistry ; Atom (system on chip) ; Computer science ; Organic chemistry ; Parallel computing ; Biochemistry ; Gene Nanomaterials for catalytic reactions ; Ammonia Synthesis and Nitrogen Reduction ; Asymmetric Hydrogenation and Catalysis
Authors: Giorgio Domenichini | Christoph Dellago
Source: arXiv (Cornell University) (preprint, 2023-07-31)
Field: Chemistry — Physical and Theoretical Chemistry

Molecular Hessian matrices from a machine learning random forest regression algorithm In this article we present a machine learning model to obtain fast and accurate estimates of the molecular Hessian matrix. In this model, based on a random forest, the second derivatives of the energy with respect to redundant internal coordinates are learned individually. The internal coordinates together with their specific representation guarantee rotational and translational invariance. The model is trained on a subset of the QM7 data set, but is shown to be applicable to larger molecules picked from the QM9 data set. From the predicted Hessian it is also possible to obtain reasonable estimates of the vibrational frequencies, normal modes and zero point energies of the molecules. Hessian matrix ; Representation (politics) ; Random forest ; Algorithm ; Set (abstract data type) ; Matrix (chemical analysis) ; Random matrix ; Point (geometry) ; Hessian equation ; Energy (signal processing) ; Mathematics ; Computer science ; Artificial intelligence ; Applied mathematics ; Physics ; Mathematical analysis ; Geometry ; Chemistry ; Eigenvalues and eigenvectors ; Quantum mechanics ; Statistics Crystallography and molecular interactions ; Protein Structure and Dynamics ; Molecular spectroscopy and chirality
Authors: Vasiliki Skiada | Panagiotis Katsaris | Manousos E. Kambouris | Vassilis Gkisakis | Yiannis Manoussopoulos
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Classification of Olive Cultivars by Machine Learning Based on Olive Oil Chemical Composition Olive oil ; Cultivar ; Chemical composition ; Composition (language) ; Food science ; Chemistry ; Botany ; Horticulture ; Biology ; Organic chemistry ; Art Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis
Authors: Kendric Hood | Qiang Guan
Source:  (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

Machine Learning Applied to Single-Molecule Activity Prediction Catalytic processes are used in about 1/3 of US manufacturing, from the field of chemical engineering to renewable energy. Assessing the activity of single-molecules, or individual molecules, is necessary to the development of efficient catalysts. Their heterogeneity structure leads to particle-specific catalytic activity. Experimentation with single-molecules can be time consuming and difficult. We purpose a Machine learning (ML) model that allows chemical researchers to run shorter single-molecule experiments to obtain the same level of results. We use common and widely understood ML methods to reduce complexity and enable accessibility to the chemical engineering community. We reduce the experiment time by up to 83%. Our evaluation shows that a small data set is sufficient to train an acceptable model. 300 experiments are needed, including the validation set. We use a well understood multilayer perceptron (MLP) model. We show that more complex models are not necessary and simpler methods are not sufficient. Computer science ; Field (mathematics) ; Set (abstract data type) ; Machine learning ; Molecule ; Artificial intelligence ; Chemistry ; Mathematics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion
Authors: Mingfeng Liu | Jian-Tao Wang | Junwei Hu | Peitao Liu | Haiyang Niu | Xuexi Yan | Jiangxu Li | Haile Yan | Bo Yang | Yan Sun | Chunlin Chen | Georg Kresse | Liang Zuo | Xing‐Qiu Chen
Source: arXiv (Cornell University) (preprint, 2023-10-09)
Field: Materials Science — Materials Chemistry

Layer-by-layer phase transformation in Ti$_3$O$_5$ revealed by machine learning molecular dynamics simulations Reconstructive phase transitions involving breaking and reconstruction of primary chemical bonds are ubiquitous and important for many technological applications. In contrast to displacive phase transitions, the dynamics of reconstructive phase transitions are usually slow due to the large energy barrier. Nevertheless, the reconstructive phase transformation from $β$- to $λ$-Ti$_3$O$_5$ exhibits an ultrafast and reversible behavior. Despite extensive studies, the underlying microscopic mechanism remains unclear. Here, we discover a kinetically favorable in-plane nucleated layer-by-layer transformation mechanism through metadynamics and large-scale molecular dynamics simulations. This is enabled by developing an efficient machine learning potential with near first-principles accuracy through an on-the-fly active learning method and an advanced sampling technique. Our results reveal that the $β$-$λ$ phase transformation initiates with the formation of two-dimensional nuclei in the $ab$-plane and then proceeds layer-by-layer through a multistep barrier-lowering kinetic process via intermediate metastable phases. Our work not only provides important insight into the ultrafast and reversible nature of the $β$-$λ$ transition, but also presents useful strategies and methods for tackling other complex structural phase transitions. Metadynamics ; Metastability ; Chemical physics ; Phase (matter) ; Kinetic energy ; Phase transition ; Lambda ; Materials science ; Molecular dynamics ; Crystallography ; Chemistry ; Physics ; Condensed matter physics ; Computational chemistry ; Optics ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Hirotoshi Hirai
Source: arXiv (Cornell University) (preprint, 2023-10-27)
Field: Materials Science — Materials Chemistry

Practical application of quantum neural network to materials informatics: prediction of the melting points of metal oxides Quantum neural network (QNN) models have received increasing attention owing to their strong expressibility and resistance to overfitting. It is particularly useful when the size of the training data is small, making it a good fit for materials informatics (MI) problems. However, there are only a few examples of the application of QNN to multivariate regression models, and little is known about how these models are constructed. This study aims to construct a QNN model to predict the melting points of metal oxides as an example of a multivariate regression task for the MI problem. Different architectures (encoding methods and entangler arrangements) are explored to create an effective QNN model. Shallow-depth ansatzs could achieve sufficient expressibility using sufficiently entangled circuits. The "linear" entangler was adequate for providing the necessary entanglement. The expressibility of the QNN model could be further improved by increasing the circuit width. The generalization performance could also be improved, outperforming the classical NN model. No overfitting was observed in the QNN models with a well-designed encoder. These findings suggest that QNN can be a useful tool for MI. Overfitting ; Computer science ; Autoencoder ; Generalization ; Artificial intelligence ; Multivariate statistics ; Artificial neural network ; Machine learning ; Construct (python library) ; Materials informatics ; Health informatics ; Mathematics Machine Learning in Materials Science ; Neural Networks and Applications ; Machine Learning and ELM
Authors: Suvo Banik | Karthik Balasubramanian | Sukriti Manna | Derible Sybil | Subramanian K. R. S. Sankaranarayan
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-06-22)
Field: Materials Science — Materials Chemistry

Evaluating Generalized Feature Importance via Performance Assessment of Machine Learning Models for Predicting Elastic Properties of Materials The repository contains the trained ML models as described in the paper "Evaluating Generalized Feature Importance via Performance Assessment of Machine Learning Models for Predicting Elastic Properties of Materials" for the prediction of bulk and shear modulus data for materials. Feature (linguistics) ; Machine learning ; Computer science ; Artificial intelligence Machine Learning in Materials Science
Authors: Nikolaos Bakas | Antonis Chazirakis | Eleftherios Christofi | Vagelis Harmandaris
Source: COMPDYN Proceedings (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

DERIVING COARSE-GRAINED MODELS OF MOLECULAR SYSTEMS BY APPROXIMATING THE FREE ENERGY SURFACE WITH MACHINE LEARNING ALGORITHMS DERIVING COARSE-GRAINED MODELS OF MOLECULAR SYSTEMS BY APPROXIMATING THE FREE ENERGY SURFACE WITH MACHINE LEARNING ALGORITHMS - 9th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering - 12-14 June 2023, Athens, Greece Computer science ; Cartesian coordinate system ; Curse of dimensionality ; Representation (politics) ; Algorithm ; Boosting (machine learning) ; Range (aeronautics) ; Orientation (vector space) ; Statistical physics ; Artificial intelligence ; Physics ; Mathematics ; Materials science Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Protein Structure and Dynamics
Authors: Ziliang Lu | Jianwei Wang | Yuanfang Wu | Xiumei Guo | Tianrun Ma | Wei Xiao
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Prediction and Theoretical Investigation of Dehydrogenation Enthalpy of V-Ti-Cr-Fe Alloy Using Machine Learning and Density Function Theory Dehydrogenation ; Alloy ; Enthalpy ; Materials science ; Function (biology) ; Density functional theory ; Thermodynamics ; Metallurgy ; Physics ; Chemistry ; Computational chemistry ; Catalysis ; Organic chemistry Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Maxwell A. T. Marple | Sichi Li | Elwin Hunter‐Sellars | Simon H. Pang
Source: ChemRxiv (preprint, 2023-06-07)
Field: Chemistry — Spectroscopy

Detecting Reactive Products in Carbon Capture Polymers with Chemical Shift Anisotropy and Machine Learning Aminopolymers are attractive sorbents for CO2 direct air capture applications as their amines readily react with atmospheric levels of CO2 to form chemisorbed species. The identity of the chemisorbed species varies upon experimental conditions like amine chemistry, support material, CO2 loading, and humidity, forming a variety of carbonyl-type sites. 13C solid-state nuclear magnetic resonance (NMR) is often used to help elucidate the identity of the chemisorbed species however the chemical shift range for carbonyl sites is small and comparable to observed chemisorbed 13C peak widths. Herein, application of a 2D chemical shift anisotropy (CSA) recoupling pulse sequence (ROCSA) is used to obtain CSA tensor values at each isotropic chemical shift, overcoming the isotropic peak resolution limitation. CSA tensor values describe the local chemical environment and can readily differentiate between chemisorbed products. To aid this experimental technique, we also developed a k-nearest-neighbor (KNN) classification model to distinguish chemisorbed compounds via their CSA tensor parameters. The combination of 2D CSA measurements coupled with a KNN classification model enhances the ability to accurately identify chemisorbed products especially in the case of mixtures. This methodology is demonstrated on poly(ethylenimine) in a solid-support γ-Al2O3 exposed to CO2 followed by incomplete regeneration at 100 °C and shows a mixture of strongly bound chemisorbed products, ammonium carbamate and urea. Anisotropy ; Chemistry ; Isotropy ; Chemical species ; Chemical shift ; Chemisorption ; Chemical physics ; Organic chemistry ; Physical chemistry ; Adsorption Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Advanced MRI Techniques and Applications
Authors: ERYAN LIU | Chenxi Tan | Lili Gui | JINYANG GUO | YUTONG LI | Xianglai Liao | Wang Ning | XINYUE ZHAI | Kun Xu
Source:  (article, 2023-01-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Efficient design of structural parameters and materials of plasmonic fano-resonant metasurfaces by a tandem neural network We address inverse design of plasmonic Fano-resonant metasurfaces by using a tandem neural network (TNN) which can correctly predict both materials and structural parameters of target spectra. To train this TNN, 19530 groups of data from asymmetric double bar (ADB) nanostructures of varied dimensional parameters and different materials (Ag, Cu, and Al) respectively were collected. Our approach successfully addresses a non-uniqueness problem that commonly exists in nanophotonic inverse design. Besides, we choose target spectra generated outside the collected dataset in order to test the applicability and robustness of the TNN, which proves that the developed TNN is able to retrieve the nanoparticles of appropriate sizes and compositing material matching well Fano-profiled unknown target spectra within the spectral window of study. Plasmon ; Fano plane ; Fano resonance ; Robustness (evolution) ; Nanophotonics ; Tandem ; Artificial neural network ; Inverse ; Computer science ; Spectral line ; Materials science ; Optoelectronics ; Physics ; Chemistry ; Artificial intelligence ; Mathematics ; Geometry Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Ethan Halpren | Xue Yao | Zhiwen Chen | Chandra Veer Singh
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Design of Bcc High Entropy Alloys for Room Temperature Hydrogen Storage Hydrogen storage ; Materials science ; High entropy alloys ; Entropy (arrow of time) ; Thermodynamics ; Computer science ; Metallurgy ; Physics ; Alloy Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Hydrogen Storage and Materials
Authors: Zhiwei Sheng | Jing Xie | Shiyi Zhang | Mingyang Zhang | Chen Luo | Zuoyuan Dong | Yan Wang | Xing Wu | Chaolun Wang
Source:  (article, 2023-07-24)
Field: Materials Science — Materials Chemistry

Deep Learning for Analysis of Two-Dimensional Materials in High-Resolution Transmission Electron Microscopy Image Two-dimensional (2D) materials possess exceptional electrical, mechanical, thermal, and optical properties, making them widely applicable in fields of electronics, energy, optoelectronics, and medicine. The geometry of 2D materials at the nanoscale, such as the number of layers, interlayer spacing, and thickness of the layers are of particular interest, as they have great influence to the properties of the material. Transmission electron microscopy (TEM) with atomic resolution is an ideal research method for 2D materials. However, analyzing the properties of 2D materials by interpreting the high-resolution TEM images not only require rich expertise but also is a time-consuming and labor-intensive task. In this work, we propose a neural network based on the U-Net architecture for high efficiency TEM image analysis of the geometry of 2D materials. The effectiveness of this method is verified on MoS2 image datasets and obtained dice coefficients of 0.92. The physical parameters such as layer number, thickness, and interlayer spacing information of 2D materials can be analyzed subsequently. Our results provide an artificial intelligent approach to analyze 2D materials. Materials science ; Transmission electron microscopy ; Nanoscopic scale ; Resolution (logic) ; Electronics ; Artificial neural network ; Transmission (telecommunications) ; Computer science ; Image resolution ; High-resolution transmission electron microscopy ; Work (physics) ; Optoelectronics ; Nanotechnology ; Artificial intelligence ; Mechanical engineering ; Electrical engineering 2D Materials and Applications ; Machine Learning in Materials Science ; Ga2O3 and related materials
Authors: Tongfang Wang | Jie Cao | Tong Guo | Yongming Tu | Chao Wang | Gabriel Sas | Lennart Elfgren
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

The Role of Deep Learning in Reducing Computational Cost When Simulating Chloride Ion Attack on Hydrated Calcium Silicate with Molecular Dynamics Calcium ; Silicate ; Ion ; Chloride ; Molecular dynamics ; Computer science ; Dynamics (music) ; Chemistry ; Chemical engineering ; Materials science ; Inorganic chemistry ; Computational chemistry ; Physics ; Engineering ; Organic chemistry Machine Learning in Materials Science
Authors: Evan Askanazi | Emanuel A. Lazar | Ilya Grinberg
Source: arXiv (Cornell University) (preprint, 2023-04-05)
Field: Materials Science — Materials Chemistry

Identification of high-reliability regions of machine learning predictions in materials science using transparent conducting oxides and perovskites as examples Progress in the application of machine learning (ML) methods to materials design is hindered by the lack of understanding of the reliability of ML predictions, in particular for the application of ML to small data sets often found in materials science. Using ML prediction for transparent conductor oxide formation energy and band gap, dilute solute diffusion, and perovskite formation energy, band gap and lattice parameter as examples, we demonstrate that 1) analysis of ML results by construction of a convex hull in feature space that encloses accurately predicted systems can be used to identify regions in feature space for which ML predictions are highly reliable 2) analysis of the systems enclosed by the convex hull can be used to extract physical understanding and 3) materials that satisfy all well-known chemical and physical principles that make a material physically reasonable are likely to be similar and show strong relationships between the properties of interest and the standard features used in ML. We also show that similar to the composition-structure-property relationships, inclusion in the ML training data set of materials from classes with different chemical properties will not be beneficial and will slightly decrease the accuracy of ML prediction and that reliable results likely will be obtained by ML model for narrow classes of similar materials even in the case where the ML model will show large errors on the dataset consisting of several classes of materials. Our work suggests that analysis of the error distributions of ML predictions will be beneficial for the further development of the application of ML methods in material science. Convex hull ; Reliability (semiconductor) ; Computer science ; Band gap ; Oxide ; Lattice (music) ; Regular polygon ; Materials science ; Algorithm ; Mathematics ; Thermodynamics ; Physics ; Geometry Machine Learning in Materials Science