jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maicon Pierre Lourenço | Breno R. L. Galvão | Lizandra Barrios Herrera | Jiří Hostaš | Alain Tchagang | Mateus X. Silva | Dennis R. Salahub
Source: Theoretical Chemistry Accounts (article, 2021-05-17)
Field: Materials Science — Materials Chemistry

A new active learning approach for global optimization of atomic clusters Chemical space ; Homonuclear molecule ; Computer science ; Global optimization ; Random search ; Cluster (spacecraft) ; Gaussian process ; Gaussian ; Artificial neural network ; Chemistry ; Machine learning ; Statistical physics ; Artificial intelligence ; Algorithm ; Molecule ; Computational chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Anton Schneider | Chu‐Chun Fu | Osamu Waseda | Cyrille Barreteau | Tilmann Hickel
Source: Physical review. B./Physical review. B (article, 2021-01-14)
Field: Materials Science — Materials Chemistry

<i>Ab initio</i> based models for temperature-dependent magnetochemical interplay in bcc Fe-Mn alloys Body-centered cubic (bcc) Fe-Mn systems are known to exhibit a complex and atypical magnetic behavior from both experiments and 0 K electronic-structure calculations, which is due to the half-filled $3d$ band of Mn. We propose effective interaction models for these alloys, which contain both atomic-spin and chemical variables. They were parameterized on a set of key density functional theory (DFT) data, with the inclusion of noncollinear magnetic configurations being indispensable. Two distinct approaches, namely a knowledge-driven and a machine-learning approach have been employed for the fitting. Employing these models in atomic Monte Carlo simulations enables the prediction of magnetic and thermodynamic properties of the Fe-Mn alloys, and their coupling, as functions of temperature. This includes the decrease of Curie temperature with increasing Mn concentration, the temperature evolution of the mixing enthalpy, and its correlation with the alloy magnetization. Also, going beyond the defect-free systems, we determined the binding free energy between a vacancy and a Mn atom, which is a key parameter controlling the atomic transport in Fe-Mn alloys. Curie temperature ; Atom (system on chip) ; Density functional theory ; Magnetization ; Thermodynamics ; Materials science ; Condensed matter physics ; Ab initio ; Magnetic moment ; Vacancy defect ; Ab initio quantum chemistry methods ; Enthalpy ; Chemistry ; Ferromagnetism ; Computational chemistry ; Physics ; Magnetic field ; Molecule Machine Learning in Materials Science ; Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels
Authors: Dominikus Brian | Xiang Sun
Source: The Journal of Physical Chemistry B (article, 2021-11-26)
Field: Materials Science — Materials Chemistry

Charge-Transfer Landscape Manifesting the Structure–Rate Relationship in the Condensed Phase <i>Via</i> Machine Learning In this work, we develop a machine learning (ML) strategy to map the molecular structure to condensed phase charge-transfer (CT) properties including CT rate constants, energy levels, electronic couplings, energy gaps, reorganization energies, and reaction free energies which are called CT fingerprints. The CT fingerprints of selected landmark structures covering the conformation space of an organic photovoltaic molecule dissolved in an explicit solvent are computed and used to train ML models using kernel ridge regression. The ML models show high predictive power with <i>R</i><sup>2</sup> > 0.97 and both mean absolute error and root-mean-square error within chemical accuracy. The CT landscape for millions of molecular dynamics sampled structures is thus constructed, which allows for instant prediction of CT rate properties, given any conformation of the molecule. We demonstrate some immediate utilities of the CT landscape such as calculating the ensemble-averaged CT rate constant and interpreting the effects of molecular structural features on the CT rate. The unprecedented CT landscape will be useful for investigating real-time CT dynamics in nanoscale- and mesoscale-condensed phase systems and for the optimal fabrication design for homogeneous and heterogeneous optoelectronic devices. Energy landscape ; Phase (matter) ; Molecular dynamics ; Mean squared error ; Chemical physics ; Charge (physics) ; Kernel (algebra) ; Statistical physics ; Materials science ; Computer science ; Physics ; Chemistry ; Mathematics ; Thermodynamics ; Computational chemistry ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: Song Wang | Tongyu Liu | De‐en Jiang
Source: ACS Applied Materials & Interfaces (article, 2021-09-30)
Field: Materials Science — Materials Chemistry

Locating Hydrides in Ligand-Protected Copper Nanoclusters by Deep Learning Hydrides play an important role in constructing atomically precise metal nanoclusters and nanoparticles. They occupy both the interstitial sites inside the metal cores and the interfacial sites between the surface of the metal core and the ligand layer. Although the heavy-atom positions can be routinely determined by single-crystal X-ray diffraction, the challenge in growing a large and high-enough-quality single crystal for neutron diffraction and the limited availability of neutron sources have prevented researchers from precisely knowing the hydride locations. A recently developed deep-learning method showed great promise in accelerating the determination of hydride sites in metal nanostructures, but it is unclear if this approach, trained on clusters up to Cu<sub>32</sub> in size, can be applied to recently discovered, much larger nanoclusters such as Cu<sub>81</sub>. Here we show that an improved deep-learning model based on convolutional neural networks is both accurate and robust. We apply it to two recently reported copper nanoclusters, [Cu<sub>32</sub>(PET)<sub>24</sub>H<sub>8</sub>Cl<sub>2</sub>]<sup>2-</sup> and [Cu<sub>81</sub>(PhS)<sub>46</sub>(<sup>t</sup>BuNH<sub>2</sub>)<sub>10</sub>H<sub>32</sub>]<sup>3+</sup>, whose hydride locations have not been determined by neutron but were proposed from density functional theory (DFT) calculations. In the former, our CNN model confirms the DFT structure; in the latter, our CNN model predicts a more stable structure with different hydride sites. Nanoclusters ; Hydride ; Materials science ; Neutron diffraction ; Density functional theory ; Ligand (biochemistry) ; Metal ; Copper ; Diffraction ; Crystallography ; Crystal structure ; Nanotechnology ; Chemical physics ; Computational chemistry ; Physics ; Chemistry ; Metallurgy ; Optics Nanocluster Synthesis and Applications ; Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Woo‐Seok Ha | Chandan Singh | François Lanusse | Srigokul Upadhyayula | Bin Yu
Source: arXiv (Cornell University) (preprint, 2021-07-19)
Field: Materials Science — Materials Chemistry

Adaptive wavelet distillation from neural networks through\n interpretations Recent deep-learning models have achieved impressive prediction performance,\nbut often sacrifice interpretability and computational efficiency.\nInterpretability is crucial in many disciplines, such as science and medicine,\nwhere models must be carefully vetted or where interpretation is the goal\nitself. Moreover, interpretable models are concise and often yield\ncomputational efficiency. Here, we propose adaptive wavelet distillation (AWD),\na method which aims to distill information from a trained neural network into a\nwavelet transform. Specifically, AWD penalizes feature attributions of a neural\nnetwork in the wavelet domain to learn an effective multi-resolution wavelet\ntransform. The resulting model is highly predictive, concise, computationally\nefficient, and has properties (such as a multi-scale structure) which make it\neasy to interpret. In close collaboration with domain experts, we showcase how\nAWD addresses challenges in two real-world settings: cosmological parameter\ninference and molecular-partner prediction. In both cases, AWD yields a\nscientifically interpretable and concise model which gives predictive\nperformance better than state-of-the-art neural networks. Moreover, AWD\nidentifies predictive features that are scientifically meaningful in the\ncontext of respective domains. All code and models are released in a\nfull-fledged package available on Github\n(https://github.com/Yu-Group/adaptive-wavelets).\n Interpretability ; Computer science ; Artificial intelligence ; Machine learning ; Artificial neural network ; Wavelet ; Inference ; Domain (mathematical analysis) ; Context (archaeology) ; Feature (linguistics) ; Deep learning ; Mathematics Machine Learning in Materials Science
Authors: Ashley Henderson | Steven K. Kauwe | Taylor D. Sparks
Source: Data in Brief (article, 2021-07-13)
Field: Materials Science — Materials Chemistry

Benchmark datasets incorporating diverse tasks, sample sizes, material systems, and data heterogeneity for materials informatics Materials discovery via machine learning has become an increasingly popular method due to its ability to rapidly predict materials properties in a time-efficient and low-cost manner. However, one limitation in this field is the lack of benchmark datasets, particularly those that encompass the size, tasks, material systems, and data modalities present in the materials informatics literature. This makes it difficult to identify optimal machine learning model choices including algorithm, model architecture, data splitting, and data featurization for a given task. Here, we attempt to address this lack of benchmark datasets by assembling a unique repository of 50 different datasets for materials properties. The data contains both experimental and computational data, data suited for regression as well as classification, sizes ranging from 12 to 6354 samples, and materials systems spanning the diversity of materials research. Data were extracted from 16 publications. In addition to cleaning the data where necessary, each dataset was split into train, validation, and test splits. For datasets with more than 100 values, train-val-test splits were created, either with a 5-fold or 10-fold cross-validation method, depending on what each respective paper did in their studies. Datasets with less than 100 values had train-test splits created using the Leave-One-Out cross-validation method. These benchmark data can serve as a basis for a more diverse benchmark dataset in the future to further improve their effectiveness in the comparison of machine learning models. Benchmark (surveying) ; Computer science ; Machine learning ; Data mining ; Materials informatics ; Benchmarking ; Task (project management) ; Field (mathematics) ; Experimental data ; Artificial intelligence ; Predictive modelling ; Modalities ; Test data ; Health informatics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Cecile Valsecchi | Viviana Consonni | Roberto Todeschini | Marco Orlandi | Fabio Gosetti | Davide Ballabio
Source: Molecules (article, 2021-11-30)
Field: Materials Science — Materials Chemistry

Parsimonious Optimization of Multitask Neural Network Hyperparameters Neural networks are rapidly gaining popularity in chemical modeling and Quantitative Structure-Activity Relationship (QSAR) thanks to their ability to handle multitask problems. However, outcomes of neural networks depend on the tuning of several hyperparameters, whose small variations can often strongly affect their performance. Hence, optimization is a fundamental step in training neural networks although, in many cases, it can be very expensive from a computational point of view. In this study, we compared four of the most widely used approaches for tuning hyperparameters, namely, grid search, random search, tree-structured Parzen estimator, and genetic algorithms on three multitask QSAR datasets. We mainly focused on parsimonious optimization and thus not only on the performance of neural networks, but also the computational time that was taken into account. Furthermore, since the optimization approaches do not directly provide information about the influence of hyperparameters, we applied experimental design strategies to determine their effects on the neural network performance. We found that genetic algorithms, tree-structured Parzen estimator, and random search require on average 0.08% of the hours required by grid search; in addition, tree-structured Parzen estimator and genetic algorithms provide better results than random search. Hyperparameter ; Hyperparameter optimization ; Computer science ; Machine learning ; Artificial neural network ; Artificial intelligence ; Tree (set theory) ; Estimator ; Random search ; Random forest ; Algorithm ; Mathematics ; Support vector machine ; Statistics Machine Learning in Materials Science ; Machine Learning and Data Classification ; Neural Networks and Applications
Authors: Heitor Luiz Ornaghi | Francisco Maciel Monticeli | Roberta Motta Neves | Lucas Dall Agnol | Otávio Bianchi
Source: Biomass Conversion and Biorefinery (article, 2021-06-24)
Field: Materials Science — Polymers and Plastics

Influence of different cellulose/hemicellulose/lignin ratios on the thermal degradation behavior: prediction and optimization Hemicellulose ; Lignin ; Cellulose ; Residue (chemistry) ; Thermogravimetric analysis ; Degradation (telecommunications) ; Chemical engineering ; Chemistry ; Materials science ; Pulp and paper industry ; Organic chemistry ; Computer science ; Engineering Natural Fiber Reinforced Composites ; Lignin and Wood Chemistry ; Polymer Foaming and Composites
Authors: Hideo Doi | Kazuaki Z. Takahashi | Takeshi Aoyagi
Source: The Journal of Chemical Physics (article, 2021-04-23)
Field: Materials Science — Materials Chemistry

Searching local order parameters to classify water structures of ice Ih, Ic, and liquid Identifying molecular structures of water and ice helps reveal the chemical nature of liquid and solid water. Real-space geometrical information on molecular systems can be precisely obtained from molecular simulations, but classifying the resulting structure is a non-trivial task. Order parameters are ordinarily introduced to effectively distinguish different structures. Many order parameters have been developed for various kinds of structures, such as body-centered cubic, face-centered cubic, hexagonal close-packed, and liquid. Order parameters for water have also been suggested but need further study. There has been no thorough investigation of the classification capability of many existing order parameters. In this work, we investigate the capability of 493 order parameters to classify the three structures of ice: Ih, Ic, and liquid. A total of 159 767 496 combinations of the order parameters are also considered. The investigation is automatically and systematically performed by machine learning. We find the best set of two bond-orientational order parameters, Q<sub>4</sub> and Q<sub>8</sub>, to distinguish the three structures with high accuracy and robustness. A set of three order parameters is also suggested for better accuracy. Liquid water ; Robustness (evolution) ; Set (abstract data type) ; Hexagonal crystal system ; Computer science ; Order (exchange) ; Water ice ; Molecular dynamics ; Space (punctuation) ; Materials science ; Biological system ; Artificial intelligence ; Chemistry ; Physics ; Crystallography ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Yunhuan Yuan | Jian Shao | Mao Zhong | Haoran Wang | Chen Zhang | Jun Wei | Kang Li | Jie Xu | Weiwei Zhao
Source: ACS Applied Materials & Interfaces (article, 2021-04-20)
Field: Materials Science — Materials Chemistry

Paper Information Recording and Security Protection Using Invisible Ink and Artificial Intelligence Conventional paper information protection mainly relies on stimuli-responsive functional materials that can display color or luminescence under external stimuli; however, this method is rather predictable and can be easily cracked. In this work, a paper information protection scheme combining fluorescent invisible ink and artificial intelligence was proposed. The ink was prepared by dissolving carbon nanoparticles in water, which has a high quantum yield and outstanding light stability and salt stability, thus ensuring the integrity of information in complex environments. A five-layer convolutional neural network (one of the two mainstream architectures in today's artificial intelligence fields) was specially trained based on ultraviolet light excited symbols printed by invisible ink. Using this scheme, the correct information could only be read with the specially trained neural network after ultraviolet (UV) irradiation. Without this trained neural network or UV irradiation, misleading messages will be presented. Moreover, it was possible to design unpredictable and highly complex password books to further increase information security. This smart strategy provides new opportunities for high-level paper information encryption and also proposes new ideas for the applications of carbon nanoparticles and artificial intelligence. Inkwell ; Computer science ; Artificial neural network ; Materials science ; Convolutional neural network ; Encryption ; Artificial intelligence ; Ultraviolet ; Nanotechnology ; Computer security ; Optoelectronics Carbon and Quantum Dots Applications
Authors: İbrahim Tanrıöver | Wisnu Hadibrata | Jacob Scheuer | Koray Aydın
Source: Optics Express (article, 2021-07-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural networks enabled forward and inverse design of reconfigurable metasurfaces Nanophotonics has joined the application areas of deep neural networks (DNNs) in recent years. Various network architectures and learning approaches have been employed to design and simulate nanophotonic structures and devices. Design and simulation of reconfigurable metasurfaces is another promising application area for neural network enabled nanophotonic design. The tunable optical response of these metasurfaces rely on the phase transitions of phase-change materials, which correspond to significant changes in their dielectric permittivity. Consequently, simulation and design of these metasurfaces requires the ability to model a diverse span of optical properties. In this work, to realize forward and inverse design of reconfigurable metasurfaces, we construct forward and inverse networks to model a wide range of optical characteristics covering from lossless dielectric to lossy plasmonic materials. As proof-of-concept demonstrations, we design a Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> (GST) tunable resonator and a VO<sub>2</sub> tunable absorber using our forward and inverse networks, respectively. Nanophotonics ; Computer science ; Plasmon ; Inverse ; Resonator ; Artificial neural network ; Optics ; Electronic engineering ; Materials science ; Optoelectronics ; Physics ; Artificial intelligence ; Engineering Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Photonic Crystals and Applications
Authors: Arka Mitra | Gourhari Jana | Ranita Pal | Pratiksha B. Gaikwad | Shamik Sural | Pratim Kumar Chattaraj
Source: Theoretical Chemistry Accounts (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Determination of stable structure of a cluster using convolutional neural network and particle swarm optimization Particle swarm optimization ; Computer science ; Global optimization ; Convolutional neural network ; Cluster (spacecraft) ; Convergence (economics) ; Quantum ; Energy minimization ; Artificial neural network ; Mathematical optimization ; Swarm behaviour ; Multi-swarm optimization ; Algorithm ; Biological system ; Artificial intelligence ; Mathematics ; Chemistry ; Physics ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Tatu Rojalin | Dexter Antonio | Ambarish Kulkarni | Randy P. Carney
Source: Applied Spectroscopy (article, 2021-08-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-Assisted Sampling of Surfance-Enhanced Raman Scattering (SERS) Substrates Improve Data Collection Efficiency Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artificial intelligence methods, these efforts have largely focused on downstream processing (e.g., classification tasks) of previously collected data. While fully automated analysis pipelines are desirable, current progress is limited by cumbersome and manually intensive sample preparation and data collection steps. Specifically, a typical lab-scale SERS experiment requires the user to evaluate the quality and reliability of the measurement (i.e., the spectra) as the data are being collected. This need for expert user-intuition is a major bottleneck that limits applicability of SERS-based diagnostics for point-of-care clinical applications, where trained spectroscopists are likely unavailable. While application-agnostic numerical approaches (e.g., signal-to-noise thresholding) are useful, there is an urgent need to develop algorithms that leverage expert user intuition and domain knowledge to simplify and accelerate data collection steps. To address this challenge, in this work, we introduce a machine learning-assisted method at the acquisition stage. We tested six common algorithms to measure best performance in the context of spectral quality judgment. For adoption into future automation platforms, we developed an open-source python package tailored for rapid expert user annotation to train machine learning algorithms. We expect that this new approach to use machine learning to assist in data acquisition can serve as a useful building block for point-of-care SERS diagnostic platforms. Computer science ; Machine learning ; Artificial intelligence ; Data collection ; Automation ; Data acquisition ; Leverage (statistics) ; Bottleneck ; Data mining ; Embedded system ; Engineering Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Anders M. N. Niklasson
Source: The European Physical Journal B (article, 2021-08-01)
Field: Materials Science — Materials Chemistry

Extended Lagrangian Born–Oppenheimer molecular dynamics: from density functional theory to charge relaxation models Formalism (music) ; Density functional theory ; Born–Oppenheimer approximation ; Statistical physics ; Molecular dynamics ; Relaxation (psychology) ; Charge (physics) ; Lagrangian ; Orbital-free density functional theory ; Physics ; Density matrix ; Charge density ; Classical mechanics ; Quantum mechanics ; Theoretical physics ; Time-dependent density functional theory ; Molecule Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies