jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zun Wang | Chong Wang | Sibo Zhao | Yong Xu | Shaogang Hao | Chang Yu Hsieh | Bing-Lin Gu | Wenhui Duan
Source: arXiv (Cornell University) (preprint, 2021-09-02)
Field: Materials Science — Materials Chemistry

Heterogeneous relational message passing networks for molecular dynamics simulations With many frameworks based on message passing neural networks proposed to predict molecular and bulk properties, machine learning methods have tremendously shifted the paradigms of computational sciences underpinning physics, material science, chemistry, and biology. While existing machine learning models have yielded superior performances in many occasions, most of them model and process molecular systems in terms of homogeneous graph, which severely limits the expressive power for representing diverse interactions. In practice, graph data with multiple node and edge types is ubiquitous and more appropriate for molecular systems. Thus, we propose the heterogeneous relational message passing network (HermNet), an end-to-end heterogeneous graph neural networks, to efficiently express multiple interactions in a single model with {\it ab initio} accuracy. HermNet performs impressively against many top-performing models on both molecular and extended systems. Specifically, HermNet outperforms other tested models in nearly 75\%, 83\% and 94\% of tasks on MD17, QM9 and extended systems datasets, respectively. Finally, we elucidate how the design of HermNet is compatible with quantum mechanics from the perspective of the density functional theory. Besides, HermNet is a universal framework, whose sub-networks could be replaced by other advanced models. Computer science ; Message passing ; Theoretical computer science ; Graph ; Homogeneous ; Artificial neural network ; Process (computing) ; Artificial intelligence ; Distributed computing ; Statistical physics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Andrew Rosen | Justin M. Notestein | Randall Q. Snurr
Source: Current Opinion in Chemical Engineering (preprint, 2021-11-22)
Field: Materials Science — Materials Chemistry

Realizing the data-driven, computational discovery of metal-organic framework catalysts Computer science ; Field (mathematics) ; Throughput ; Catalysis ; Nanotechnology ; Work (physics) ; Metal-organic framework ; Computational model ; Biochemical engineering ; Data science ; Artificial intelligence ; Materials science ; Engineering ; Chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Emily Mazeau | Priyanka Satpute | Katrín Blöndal | Franklin Goldsmith | Richard H. West
Source: ChemRxiv (preprint, 2021-05-03)
Field: Materials Science — Materials Chemistry

Automated Mechanism Generation Using Linear Scaling Relationships and Sensitivity Analyses Applied to Catalytic Partial Oxidation of Methane Kinetic parameters for surface reactions can be predicted using a combination of DFT calculations, scaling relations, and machine learning algorithms; however, construction of microkinetic models still requires a knowledge of all the possible, or at least reasonable, reaction pathways. The recently developed Reaction Mechanism Generator (RMG) for heterogeneous catalysis, now included in RMG version 3.0, is built upon well-established, open-source software that can provide detailed reaction mechanisms from user-supplied initial conditions without making a priori assumptions. RMG is now able to estimate adsorbate thermochemistry and construct detailed microkinetic models on a range of hypothetical metal surfaces using linear scaling relationships. These relationships are a simple, computationally efficient way to estimate adsorption energies by scaling the energy of a calculated surface species on one metal to any other metal. By conducting simulations with sensitivity analyses, users can not only determine the rate limiting step on each surface by plotting a "volcano surface" for the degree of rate control of each reaction as a function of elemental binding energies, but also screen novel catalysts for desirable properties. We investigated the catalytic partial oxidation of methane to demonstrate the utility of this new tool and determined that an inlet gas C/O ratio of 0.8 on a catalyst with carbon and oxygen binding energies of -6.75 eV and -5.0 eV, respectively, yields the highest amount of synthesis gas. Sensitivity analyses show that while the dissociative adsorption of O2 has the highest degree of rate control, the interactions between individual reactions and reactor conditions are complex, which result in a dynamic rate-limiting step across differing metals. Scaling ; Catalysis ; Adsorption ; Chemistry ; Thermochemistry ; Methane ; Sensitivity (control systems) ; Linear scale ; Reaction mechanism ; Reaction rate ; Physical chemistry ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Aniket Pant | Kannatassen Appavoo
Source: Conference on Lasers and Electro-Optics (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic and Dielectric Nanostructures: Distinguishing Size, Material, and Dielectric Environment via Machine Learning We employ machine learning, coupled with linear and nonlinear dimensionality reduction strategies, to distinguish between plasmonic and dielectric optical response of nanostructures and to understand the role of structural parameters and local environment. Dielectric ; Plasmon ; Nanostructure ; Materials science ; Nonlinear system ; Curse of dimensionality ; Dimensionality reduction ; Nonlinear dimensionality reduction ; Optoelectronics ; Computer science ; Nanotechnology ; Artificial intelligence ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Surface Roughness and Optical Measurements ; Computational Drug Discovery Methods
Authors: Guang Chen | Lei Tao | Ying Li
Source: Preprints.org (preprint, 2021-05-27)
Field: Materials Science — Materials Chemistry

Predicting Polymer’s Glass Transition Temperature by A Chemical Language Processing Model We propose a chemical language processing model to predict polymers&amp;rsquo; glass transition temperature (Tg) through a polymer language (SMILES, Simplified Molecular Input Line Entry System) embedding and recurrent neural network. This model only receives the SMILES strings of polymer&amp;rsquo;s repeat units as inputs and considers the SMILES strings as sequential data at the character level. Using this method, there is no need to calculate any additional molecular descriptors or fingerprints of polymers, and thereby, being very computationally efficient and simple. More importantly, it avoids the difficulties to generate molecular descriptors for repeat units containing polymerization point `*&amp;rsquo;. Results show that the trained model demonstrates reasonable prediction accuracy on unseen polymer&amp;rsquo;s Tg. Besides, this model is further applied for high-throughput screening on an unlabeled polymer database to identify high-temperature polymers that are desired for applications in extreme environments. Our work demonstrates that the SMILES strings of polymer&amp;rsquo;s repeat units can be used as an effective feature representation to develop a chemical language processing model for predictions of Tg. The framework of this model is general and can be used to construct structure-property relationships for other polymer&amp;rsquo;s properties. Polymer ; Glass transition ; Computer science ; Polymerization ; Representation (politics) ; Artificial intelligence ; Biological system ; Natural language processing ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Sheng Gong | Shuo Wang | Tian Xie | Woo Hyun Chae | Runze Liu | Jeffrey C. Grossman
Source: arXiv (Cornell University) (preprint, 2021-10-25)
Field: Materials Science — Materials Chemistry

Calibrating DFT formation enthalpy calculations by multi-fidelity machine learning Machine learning materials properties measured by experiments is valuable yet difficult due to the limited amount of experimental data. In this work, we use a multi-fidelity random forest model to learn the experimental formation enthalpy of materials with prediction accuracy higher than the empirically corrected PBE functional (PBEfe) and meta-GGA functional (SCAN), and it outperforms the hotly studied deep neural-network based representation learning and transfer learning. We then use the model to calibrate the DFT formation enthalpy in the Materials Project database, and discover materials with underestimated stability. The multi-fidelity model is also used as a data-mining approach to find how DFT deviates from experiments by the explaining the model output. Fidelity ; Stability (learning theory) ; Enthalpy ; Representation (politics) ; Computer science ; Artificial neural network ; Experimental data ; Machine learning ; Artificial intelligence ; Density functional theory ; Work (physics) ; Algorithm ; Thermodynamics ; Chemistry ; Mathematics ; Computational chemistry ; Physics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Qingfeng Zeng | Yong Gao | Kang Guan | Jiantao Liu | Zhiqiang Feng
Source: Research Square (preprint, 2021-01-07)
Field: Materials Science — Materials Chemistry

Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide Abstract Chemical vapor deposition is an important method for the preparation of boron carbide. Knowledge of the correlation between the phase composition of the deposit and the deposition conditions (temperature, inlet gas composition, total pressure, reactor configuration, and total flow rate) has not been completely determined. In this work, a novel approach to identify the kinetic mechanisms for the deposit composition is presented. Machine leaning (ML) and computational fluid dynamic (CFD) techniques are utilized to identify core factors that influence the deposit composition. It has been shown that ML, combined with CFD, can reduce the prediction error from 25% to 7%, compared with the ML approach alone. The sensitivity coefficient study shows that BHCl 2 and BCl 3 produce the most boron atoms, while C 2 H 4 and CH 4 are the main sources of carbon atoms. The new approach can accurately predict the deposited boron-carbon ratio and provide a new design solution for other multi-element systems. Boron ; Boron carbide ; Deposition (geology) ; Chemical vapor deposition ; Carbon fibers ; Computational fluid dynamics ; Materials science ; Gas composition ; Chemical composition ; Work (physics) ; Phase (matter) ; Composition (language) ; Carbide ; Analytical Chemistry (journal) ; Volumetric flow rate ; Chemistry ; Mechanics ; Thermodynamics ; Metallurgy ; Nanotechnology ; Composite material ; Environmental chemistry ; Physics ; Composite number ; Organic chemistry ; Geology Thermal properties of materials ; Semiconductor materials and devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: Marc Stieffenhofer | Tristan Bereau | Michael Wand
Source: APL Materials (preprint, 2021-03-01)
Field: Materials Science — Materials Chemistry

Adversarial reverse mapping of condensed-phase molecular structures: Chemical transferability Switching between different levels of resolution is essential for multiscale modeling, but restoring details at higher resolution remains challenging. In our previous study, we have introduced deepBackmap, a deep neural-network-based approach to reverse-map equilibrated molecular structures for condensed-phase systems. Our method combines data-driven and physics-based aspects, leading to high-quality reconstructed structures. In this work, we expand the scope of our model and examine its chemical transferability. To this end, we train deepBackmap solely on homogeneous molecular liquids of small molecules and apply it to a more challenging polymer melt. We augment the generator’s objective with different force-field-based terms as a prior to regularize the results. The best performing physical prior depends on whether we train for a specific chemistry or transfer our model. Our local environment representation combined with the sequential reconstruction of fine-grained structures helps in reaching transferability of the learned correlations. Transferability ; Benchmark (surveying) ; Computer science ; Scope (computer science) ; Representation (politics) ; Generator (circuit theory) ; Homogeneous ; Artificial neural network ; Artificial intelligence ; Phase (matter) ; Force field (fiction) ; Deep learning ; Field (mathematics) ; Machine learning ; Biological system ; Statistical physics ; Chemistry ; Physics ; Cartography ; Mathematics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Dagmawi Delelegn Tegegn | Italo Zoppis | Sara Manzoni | Cezar Sas | Edoardo Lotti
Source:  (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Convolutional Neural Networks for Quantitative Prediction of Different Organic Materials using Near-Infrared Spectrum Convolutional neural network ; Infrared ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Physics ; Optics Spectroscopy and Chemometric Analyses
Authors: Dagmawi Delelegn Tegegn | Italo Zoppis | Sara Manzoni | Cezar Sas | Edoardo Lotti
Source:  (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Convolutional Neural Networks for Quantitative Prediction of Different Organic Materials using Near-Infrared Spectrum Advances in Near-infrared (NIR) spectroscopy technology led to an increase of interest in its applications in various industries due to its powerful non-destructive quantization tool. In this work, we used a one-dimensional CNN to determine simultaneously quantities of organic materials in a mixture using their NIR infrared spectra. The coefficient of determination (R2) and the root mean square error (RMSE) is used to test the performance of the model. We used six materials to make pairwise combinations with distinct quantities of each pair. We obtained 13 different pairwise mixtures, afterward, their near-infrared spectrum profiles is extracted. The model predicted for each mixture their percentage of composition with a result of 0.9955 R2 and RMSE 0.0199. Furthermore, we examined the performance of our model when predicting unseen composition percentages with unseen mixtures. To do so, two scenarios are carried out by filtering the training and testing set: the first one where we test on unseen composition percentage (UP) of mixtures, and the second one where we test on unseen composition percentage of unseen mixtures (UPM). The model achieved an R2 of 0.947 and 0.627 scores respectively for UP and UPM. Convolutional neural network ; Computer science ; Infrared ; Spectrum (functional analysis) ; Artificial intelligence ; Pattern recognition (psychology) ; Artificial neural network ; Physics ; Optics Spectroscopy and Chemometric Analyses
Authors: Joshua Vincent | Sreyas Mohan | Ramón Manzorro | Binh Tang | Dev Yashpal Sheth | Mitesh M. Khapra | David S. Matteson | Eero P. Simoncelli | Carlos Fernandez‐Granda | Peter A. Crozier
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Developing Deep Neural Network-based Denoising Techniques for Time-Resolved In Situ TEM of Catalyst Nanoparticles An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the 'Save PDF' action button. In situ ; Materials science ; Nanoparticle ; Artificial neural network ; Catalysis ; Chemical engineering ; Noise reduction ; Nanotechnology ; Artificial intelligence ; Computer science ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Chi Chen | Shyue Ping Ong
Source: arXiv (Cornell University) (preprint, 2021-02-04)
Field: Materials Science — Materials Chemistry

AtomSets -- A Hierarchical Transfer Learning Framework for Small and Large Materials Datasets Predicting materials properties from composition or structure is of great interest to the materials science community. Deep learning has recently garnered considerable interest in materials predictive tasks with low model errors when dealing with large materials data. However, deep learning models suffer in the small data regime that is common in materials science. Here we leverage the transfer learning concept and the graph network deep learning framework and develop the AtomSets machine learning framework for consistent high model accuracy at both small and large materials data. The AtomSets models can work with both compositional and structural materials data. By combining with transfer learned features from graph networks, they can achieve state-of-the-art accuracy from using small compositional data (&lt;400) to large structural data (&gt;130,000). The AtomSets models show much lower errors than the state-of-the-art graph network models at small data limits and the classical machine learning models at large data limits. They also transfer better in the simulated materials discovery process where the targeted materials have property values out of the training data limits. The models require minimal domain knowledge inputs and are free from feature engineering. The presented AtomSets model framework opens new routes for machine learning-assisted materials design and discovery. Leverage (statistics) ; Computer science ; Transfer of learning ; Machine learning ; Deep learning ; Feature engineering ; Artificial intelligence ; Graph ; Process (computing) ; Small data ; Data mining ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging
Authors: Martin Čalkovský | Erich Müller | Matthias Meffert | Nadejda Firman | Frederik Mayer | Martin Wegener | Dagmar Gerthsen
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Surfaces, Coatings and Films

Comparison of segmentation algorithms for FIB/SEM tomography of porous polymers: Importance of image contrast for machine learning segmentation An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button. Contrast (vision) ; Segmentation ; Materials science ; Porosity ; Artificial intelligence ; Tomography ; Image segmentation ; High contrast ; Computer vision ; Computer science ; Image (mathematics) ; Pattern recognition (psychology) ; Biomedical engineering ; Radiology ; Composite material ; Optics ; Medicine ; Physics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advanced X-ray and CT Imaging
Authors: Yinan Wang | Xiaoyang Wang | Linfeng Zhang | Ben Xu | Handong Wang
Source: arXiv (Cornell University) (preprint, 2021-08-13)
Field: Materials Science — Materials Chemistry

A Generalizable Machine-learning Potential of Ag-Au Nanoalloys and its Application on Surface Reconstruction, Segregation and Diffusion Owing to the excellent catalysis properties of Ag-Au binary nanoalloy, nanostructured Ag-Au, such as Ag-Au nanoparticles and nanopillars, have been under intense investigation. To achieve high accuracy in molecular simulations of the Ag-Au nanoalloys, the surface properties are required to be modeled with first-principles precision. In this work, we propose a generalizable machine-learning interatomic potential for the Ag-Au nanoalloys based on deep neural networks, trained from a database constructed with the first-principle calculations. This potential is highlighted by the accurate prediction of Au (111) surface reconstruction and the segregation of Au towards the Ag-Au nanoalloy surface, where the empirical force field failed in both cases. Moreover, regarding the adsorption and diffusion of adatoms on surfaces, the overall performance of our potential is better than the empirical force fields. We stress that the reported surface properties are blind to the potential modeling in the sense that none of the surface configurations is explicitly included in the training database, therefore, the reported potential is expected to have a strong ability of generalization to a wide range of properties and to play a key role in the investigation of nanostructured Ag-Au evolution, where the accurate descriptions of free surfaces are necessary. Nanopillar ; Materials science ; Force field (fiction) ; Surface (topology) ; Nanotechnology ; Diffusion ; Adsorption ; Binary number ; Field (mathematics) ; Interatomic potential ; Nanoparticle ; Computer science ; Molecular dynamics ; Nanostructure ; Artificial intelligence ; Chemistry ; Thermodynamics ; Computational chemistry ; Physical chemistry ; Physics ; Mathematics Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; nanoparticles nucleation surface interactions
Authors: Geemi P. Wellawatte | Aditi Seshadri | Andrew Dickson White
Source:  (preprint, 2021-08-16)
Field: Materials Science — Materials Chemistry

Model agnostic generation of counterfactual explanations for molecules An outstanding challenge in deep learning is its lack of interpretability. The inability of both designers and users of neural networks to explain why a prediction is made is a major drawback. This not only dissuades chemists from using deep learning predictions, but also has led to neural networks learning spurious correlations that are difficult to notice. Counterfactuals are a category of prediction explanations that provide a rationale behind the prediction value. Counterfactuals have satisfying properties like being in the same space as the features and require no deep learning expertise to understand. Counterfactuals are generated via optimization, which makes them complex to use when the features are chemical structures. Here we present an algorithm built on the "Superfast Traversal, Optimization, Novelty, Exploration and Discovery" method (Nigam et. al, 2021) that is efficient and model-agnostic for generating counterfactuals. We show this method is applicable in random forest models, sequence models, and graph neural networks in both classification and regression. Our method requires no additional training, data, or model gradients. Counterfactual conditional ; Interpretability ; Computer science ; Artificial intelligence ; Counterfactual thinking ; Spurious relationship ; Machine learning ; Deep learning ; Artificial neural network ; Tree traversal ; Novelty ; Ambiguity ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies