jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrew L. Ferguson | John D. Tovar
Source: Langmuir (review, 2022-12-07)
Field: Materials Science — Biomaterials

Evolution of π-Peptide Self-Assembly: From Understanding to Prediction and Control Supramolecular materials derived from the self-assembly of engineered molecules continue to garner tremendous scientific and technological interest. Recent innovations include the realization of nano- and mesoscale particles (0D), rods and fibrils (1D), sheets (2D), and even extended lattices (3D). Our research groups have focused attention over the past 15 years on one particular class of supramolecular materials derived from oligopeptides with embedded π-electron units, where the oligopeptides can be viewed as substituents or side chains to direct the assembly of the central π-electron cores. Upon assembly, the π-systems are driven into close cofacial architectures that facilitate a variety of energy migration processes within the nanomaterial volume, including exciton transport, voltage transmission, and photoinduced electron transfer. Like many practitioners of supramolecular materials science, many of our initial molecular designs were designed with substantial inspiration from biologically occurring self-assembly coupled with input from chemical intuition and molecular modeling and simulation. In this feature article, we summarize our current understanding of the π-peptide self-assembly process as documented through our body of publications in this area. We address fundamental spectroscopic and computational tools used to extract information regarding the internal structures and energetics of the π-peptide assemblies, and we address the current state of the art in terms of recent applications of data science tools in conjunction with high-throughput computational screening and experimental assays to guide the efficient traversal of the π-peptide molecular design space. The abstract image details our integrated program of chemical synthesis, spectroscopic and functional characterization, multiscale simulation, and machine learning which has advanced the understanding and control of the assembly of synthetic π-conjugated peptides into supramolecular nanostructures with energy and biomedical applications. Supramolecular chemistry ; Nanotechnology ; Self-assembly ; Computer science ; Materials science ; Chemistry ; Molecule Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Chemical Synthesis and Analysis
Authors: Lipeng Jiang | Xue Jiang | Guocai Lv | Yanjing Su
Source: Journal of Materials Informatics (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

A mini review of machine learning in inorganic phosphors Machine learning has promoted the rapid development of materials science. In this review, we provide an overview of recent advances in machine learning for inorganic phosphors. We take two aspects of material properties prediction and optimization based on iterative experiments as entry points to outline the applications of machine learning for inorganic phosphors in terms of Debye temperature prediction and luminescence intensity and thermal stability optimization. By analyzing the machine learning methods and their application objectives, current problems are summarized and suggestions for subsequent development are proposed. Phosphor ; Computer science ; Stability (learning theory) ; Machine learning ; Thermal stability ; Artificial intelligence ; Materials science ; Engineering ; Chemical engineering ; Optoelectronics Luminescence Properties of Advanced Materials ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science
Authors: Jake Burner | Jun Luo | Andrew J. P. White | Adam Mirmiran | Ohmin Kwon | Peter G. Boyd | Steven M. Maley | Marco Gibaldi | Scott Simrod | Victoria Ogden | Tom K. Woo
Source: ChemRxiv (preprint, 2022-07-31)
Field: Chemistry — Inorganic Chemistry

ARC-MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning Metal-organic frameworks (MOFs) are a class of crystalline materials composed of metal nodes or clusters connected via semi-rigid organic linkers. Owing to their high surface area, porosity, and tunability, MOFs have received significant attention for numerous applications such as gas separation and storage. Atomistic simulations and data-driven methods (e.g., machine learning) have been successfully employed to screen large databases and successfully develop new experimentally synthesized and validated MOFs for CO2 capture. To enable data-driven materials discovery for any application, the first (and arguably most crucial) step is database curation. This work introduces the ab initio REPEAT charge MOF (ARC-MOF) database. This is a database of ~280,000 MOFs which have been either experimentally characterized or computationally generated, spanning all publicly available MOF databases. A key feature of ARC-MOF is that it contains DFT-derived electrostatic potential fitted partial atomic charges for each MOF. Additionally, ARC-MOF contains pre-computed descriptors for out-of-the-box machine learning applications. An in-depth analysis of the diversity of ARC-MOF with respect to the currently mapped design space of MOFs was performed – a critical, yet commonly overlooked aspect of previously reported MOF databases. Using this analysis, balanced subsets from ARC-MOF for various machine learning purposes have been identified. Other chemical and geometric diversity analyses are presented, with an analysis on the effect of charge assignment method on atomistic simulation of gas uptake in MOFs. Metal-organic framework ; Arc (geometry) ; Key (lock) ; Database ; Porosity ; Materials science ; Computer science ; Ab initio ; Nanotechnology ; Chemistry ; Mechanical engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Murat Cihan Sorkun | Dajt Mullaj | J. M. V. A. Koelman | Süleyman Er
Source: Chemistry - Methods (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

ChemPlot, a Python Library for Chemical Space Visualization Abstract Invited for this month's cover is the Autonomous Energy Materials Discovery [AMD] Group of Dr. Süleyman Er at DIFFER, and colleagues at CCER and Eindhoven University of Technology (Netherlands). The cover picture shows the ChemPlot‐visualized reduced chemical space of molecules enhanced with two‐dimensional illustrations of molecules. In addition to being easy‐to‐use, free and open source, a noteworthy feature of ChemPlot is the application of tailored similarity for the property‐sensitive visualization of chemical spaces. ChemPlot streamlines the analysis of molecular datasets by reducing the information to human perception level, tackling the activity/property cliff problem, and facilitating the assessment of the applicability domain of machine learning models in molecular studies. Read the full text of their Research Article at 10.1002/cmtd.202200005 . Python (programming language) ; Visualization ; Chemical space ; Computer science ; Property (philosophy) ; Space (punctuation) ; Cover (algebra) ; Feature (linguistics) ; Human–computer interaction ; Data science ; Information retrieval ; Artificial intelligence ; Bioinformatics ; Engineering ; Programming language ; Biology ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Yinong Liu | Weina Ren | Meng An | Lan Dong | Lei Gao | Xuxia Shai | Tingting Wei | Linru Nie | Shiqian Hu | Chunhua Zeng
Source: Frontiers in Materials (article, 2022-05-04)
Field: Materials Science — Materials Chemistry

A Qualitative Study of the Disorder Effect on the Phonon Transport in a Two-Dimensional Graphene/h-BN Heterostructure Recently, massive efforts have been made to control phonon transport via introducing disorder. Meanwhile, materials informatics, an advanced material-discovery technology that combines data-driven search algorithms and material property simulations, has made significant progress and shown accurate prediction ability in studying the target properties of new materials. However, with the introduction of disorder, the design space of random structures is greatly expanded. Global optimization for the entire domain is nearly impossible with the current computer resource even when materials informatics reduces the design space to a few percent. Toward the goal of reducing design space, we investigate the effect of different types of disorders on phonon transport in two-dimensional graphene/hexagonal boron nitride heterostructure using non-equilibrium molecular dynamics simulation. The simulation results show that when the hexagonal boron nitride is distributed disorderly in the coherent phonon-dominated structure, that is, the structure with a period length of 1.23 nm, the thermal conductivity is significantly reduced due to the appearance of coherent phonon localization. By qualitatively analyzing different types of disorder, we found that the introduction of disordered structure in the cross direction with a larger shift distance can further reduce the thermal conductivity. Further physical mechanism analysis revealed that the structures with lower thermal conductivity were caused by weak propagation and strong localization of phonon. Our findings have implications for accelerating machine learning in the search for structures with the lowest thermal conductivity, and provide some guidance for the future synthesis of 2D heterostructures with unique thermal properties. Graphene ; Phonon ; Thermal conductivity ; Heterojunction ; Materials science ; Condensed matter physics ; Chemical physics ; Nanotechnology ; Computer science ; Optoelectronics ; Physics ; Composite material Thermal properties of materials ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Simone Perego | Daniele Dragoni | Silvia Gabardi | Davide Campi | Marco Bernasconi
Source: physica status solidi (RRL) - Rapid Research Letters (article, 2022-12-28)
Field: Materials Science — Materials Chemistry

Structure and Crystallization Kinetics of As‐Deposited Films of the GeTe Phase Change Compound from Atomistic Simulations Models of the amorphous phase of the GeTe compound have been generated by depositing individual atoms on a substrate by molecular dynamics simulations. This material is of interest for applications in phase change memories. The kinetic energy of the atoms impinging on the surface is in the range 1.5–10 eV which is typical of magnetron sputtering growth. The simulations with up to 8000 atoms exploit a machine learning potential devised previously by the group. The structural properties of the films depend very little on the energy of the impinging atoms in the range 1.5–10 eV and they are also very similar to those of amorphous models generated by quenching from the melt, but for a larger fraction of GeGe bonds and Ge atoms in tetrahedral geometries in the films. The crystal nucleation and growth are then simulated at 600 K which is close to the temperature of maximal crystal growth velocity exploited in the memory devices. Despite the structural differences mentioned above, the kinetics of crystallization at 600 K is very similar for the as‐deposited and melt‐quenched amorphous models. Nucleation ; Amorphous solid ; Crystallization ; Materials science ; Quenching (fluorescence) ; Crystal (programming language) ; Chemical physics ; Phase (matter) ; Kinetic energy ; Crystallography ; Molecular dynamics ; Sputter deposition ; Sputtering ; Crystal growth ; Thin film ; Thermodynamics ; Nanotechnology ; Chemistry ; Computational chemistry ; Optics Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Transition Metal Oxide Nanomaterials
Authors: Markus J. Buehler
Source: Materials Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Prediction of atomic stress fields using cycle-consistent adversarial neural networks based on unpaired and unmatched sparse datasets Deep learning holds great promise for applications in materials science, including the discovery of physical laws and materials design. However, the availability of proper data remains a challenge - often, data lacks labels, or does not contain direct pairing between input and output property of interest. Here we report an approach based on an adversarial neural network model - composed of four individual deep neural nets - to yield atomistic-level prediction of stress fields directly from an input atomic microstructure, illustrated here for defected graphene sheets under tension. The primary question we address is whether it is possible to predict stress fields without any microstructure-to-stress fields pairings, nor the existence of any input-output pairs whatsoever, in the dataset. Using a cycle-consistent adversarial neural net with either U-Net, ResNet and a hybrid U-Net-ResNet architecture, applied to a system of graphene lattices with defects we devise an algorithmic framework that enables us to successfully train and validate a model that reliably predicts atomistic-level field data of unknown microstructures, generalizing to reproduce well-known nano- and micromechanical features such as stress concentrations, size effects, and crack shielding. In a series of validation analyses, we show that the model closely reproduces reactive molecular dynamics simulations but at significant computational efficiency, and without <i>a priori</i> knowledge of any physical laws that govern this complex fracture problem. The model opens an avenue for upscaling where the mechanistic insights, and predictions from the model, can be used to construct analyses of very large systems, based off relatively small and sparse datasets. Since the model is trained to achieve cycle consistency, a trained model features both forward (microstructure to stress) and inverse (stress to microstructure) generators; offering potential applications in materials design to achieve a certain stress field. Another application is the prediction of stress fields based off experimentally acquired structural data, where the knowledge of solely positions of atoms is sufficient to predict physical quantities for augmentation or analysis processes. Adversarial system ; Computer science ; Artificial intelligence ; Artificial neural network ; Deep learning ; Field (mathematics) ; Stress (linguistics) ; A priori and a posteriori ; Algorithm ; Machine learning ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Thermal properties of materials
Authors: Yunsie Chung | Florence H. Vermeire | Haoyang Wu | Pierre J. Walker | Michael H. Abraham | William H. Green
Source:  (preprint, 2022-01-04)
Field: Materials Science — Materials Chemistry

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed group contribution method uses atom-centered functional groups with corrections for ring and polycyclic strain whilst the machine learning models adopt a directed message passing neural network. The solute parameters predicted from SoluteGC and SoluteML are used to calculate solvation energy and enthalpy via linear free energy relationships. Extensive data sets containing 8366 solute parameters, 20253 solvation free energies, and 6322 solvation enthalpies are compiled in this work to train the models. The three models are each evaluated on the same test sets using both random and substructure-based solute splits for solvation energy and enthalpy predictions. The results show that the DirectML model is superior to the SoluteML and SoluteGC models for both predictions and can provide accuracy comparable to that of advanced quantum chemistry methods. Yet, even though the DirectML model performs better in general, all three models are useful for various purposes. Uncertain predicted values can be identified by comparing the 3 models, and when the 3 models are combined together, they can provide even more accurate predictions than any one of them individually. Finally, we present our compiled solute parameter, solvation energy, and solvation enthalpy databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide public access to our final prediction models through a simple web-based tool, software package, and source code. Solvation ; Enthalpy ; Implicit solvation ; Chemistry ; Thermodynamics ; Computer science ; Statistical physics ; Machine learning ; Physics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Shulin Luo | Bangyu Xing | Muhammad Faizan | Jiahao Xie | Kun Zhou | Ruoting Zhao | Tianshu Li | Xinjiang Wang | Yuhao Fu | Xin He | Jian Lv | Lijun Zhang
Source: The Journal of Physical Chemistry A (article, 2022-06-22)
Field: Materials Science — Materials Chemistry

Inorganic Crystal Structure Prototype Database Based on Unsupervised Learning of Local Atomic Environments Recognition of structure prototypes from tremendous known inorganic crystal structures has been an important subject beneficial for materials science research and new materials design. The existing databases of inorganic crystal structure prototypes were mostly constructed by classifying materials in terms of the crystallographic space group information. Herein, we employed a distinct strategy to construct the inorganic crystal structure prototype database, relying on the classification of materials in terms of local atomic environments (LAEs) accompanied by unsupervised machine learning method. Specifically, we adopted a hierarchical clustering approach onto all experimentally known inorganic crystal structure data to identify structure prototypes. The criterion for hierarchical clustering is the LAE represented by the state-of-the-art structure fingerprints of the improved bond-orientational order parameters and the smooth overlap of atomic positions. This allows us to build up a LAE-based Inorganic Crystal Structure Prototype Database (LAE-ICSPD) containing 15,613 structure prototypes with defined stoichiometries. In addition, we have developed a Structure Prototype Generator Infrastructure (SPGI) package, which is a useful toolkit for structure prototype generation. Our developed SPGI toolkit and LAE-ICSPD are beneficial for investigating inorganic materials in a global way as well as accelerating the materials discovery process in the data-driven mode. Cluster analysis ; Crystal structure ; Computer science ; Crystal (programming language) ; Crystal structure prediction ; Hierarchical clustering ; Construct (python library) ; Database ; Generator (circuit theory) ; Unsupervised learning ; Artificial intelligence ; Data mining ; Materials science ; Crystallography ; Chemistry ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Jingrui Wei | Ben Blaiszik | Aristana Scourtas | Dane Morgan | Paul M. Voyles
Source: Microscopy and Microanalysis (article, 2022-12-23)
Field: Materials Science — Materials Chemistry

Benchmark Tests of Atom Segmentation Deep Learning Models with a Consistent Dataset The information content of atomic-resolution scanning transmission electron microscopy (STEM) images can often be reduced to a handful of parameters describing each atomic column, chief among which is the column position. Neural networks (NNs) are high performance, computationally efficient methods to automatically locate atomic columns in images, which has led to a profusion of NN models and associated training datasets. We have developed a benchmark dataset of simulated and experimental STEM images and used it to evaluate the performance of two sets of recent NN models for atom location in STEM images. Both models exhibit high performance for images of varying quality from several different crystal lattices. However, there are important differences in performance as a function of image quality, and both models perform poorly for images outside the training data, such as interfaces with large difference in background intensity. Both the benchmark dataset and the models are available using the Foundry service for dissemination, discovery, and reuse of machine learning models. Benchmark (surveying) ; Segmentation ; Deep learning ; Artificial intelligence ; Machine learning ; Computer science ; Pattern recognition (psychology) ; Geography ; Cartography Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Xin Huang | Dong Lv | Chaoyang Zhang | Xi Yao
Source: Chemical Engineering Journal (article, 2022-12-21)
Field: Materials Science — Materials Chemistry

Machine-learning reveals the virtual screening strategies of solid hydrogen-bonded oligomeric assemblies for thermo-responsive applications van der Waals force ; Hydrogen bond ; Materials science ; Nanotechnology ; Computer science ; Molecule ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis ; Polymer composites and self-healing
Authors: Russlan Jaafreh | Jung‐Gu Kim | Kotiba Hamad
Source: Crystals (article, 2022-09-02)
Field: Materials Science — Biomaterials

Interpretable Machine Learning Analysis of Stress Concentration in Magnesium: An Insight beyond the Black Box of Predictive Modeling In the present work, machine learning (ML) was employed to build a model, and through it, the microstructural features (parameters) affecting the stress concentration (SC) during plastic deformation of magnesium (Mg)-based materials are determined. As a descriptor for the SC, the kernel average misorientation (KAM) was used, and starting from the microstructural features of pure Mg and AZ31 Mg alloy, as recorded using electron backscattered diffraction (EBSD), the ML model was trained and constructed using various types of ML algorithms, including Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Naive Bayes Classifier (NBC), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), and Extremely Randomized Trees (ERT). The results show that the accuracy of the ERT-based model was higher compared to other models, and accordingly, the nine most-important features in the ERT-based model, those with a Gini impurity higher than 0.025, were extracted. The feature importance showed that the grain size is the most effective microstructural parameter for controlling the SC in Mg-based materials, and according to the relative Accumulated Local Effects (ALE) plot, calculated to show the relationship between KAM and grain size, it was found that SC occurs with a lower probability in the fine range of grain size. All findings from the ML-based model built in the present work were experimentally confirmed through EBSD observations. Materials science ; Electron backscatter diffraction ; Random forest ; Artificial intelligence ; Machine learning ; Multilayer perceptron ; Grain size ; Artificial neural network ; Computer science ; Metallurgy ; Microstructure Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics
Authors: Zixun Zhang | Heyang Chen | Tian‐Ran Wei | Kunpeng Zhao | Xun Shi
Source: Materials Today Energy (article, 2022-06-11)
Field: Materials Science — Materials Chemistry

Data-driven discovery of high-performance multicomponent solid solution thermoelectric materials Thermoelectric effect ; Materials science ; Thermoelectric materials ; Solid solution ; Nanotechnology ; Thermodynamics ; Metallurgy Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science