jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lukman Olatomiwa Agbolade | Tijjani Adam | Clement-Andi Edet | Akeem Adekunle Adewale | Abdullah Chik | Mohammed Mohammed | Subash C.B. Gopinath | Ummu Raihanah Hashim
Source: Heliyon (review, 2023-03-01)
Field: Materials Science — Materials Chemistry

Recent advances in density functional theory approach for optoelectronics properties of graphene Graphene has received tremendous attention among diverse 2D materials because of its remarkable properties. Its emergence over the last two decades gave a new and distinct dynamic to the study of materials, with several research projects focusing on exploiting its intrinsic properties for optoelectronic devices. This review provides a comprehensive overview of several published articles based on density functional theory and recently introduced machine learning approaches applied to study the electronic and optical properties of graphene. A comprehensive catalogue of the bond lengths, band gaps, and formation energies of various doped graphene systems that determine thermodynamic stability was reported in the literature. In these studies, the peculiarity of the obtained results reported is consequent on the nature and type of the dopants, the choice of the XC functionals, the basis set, and the wrong input parameters. The different density functional theory models, as well as the strengths and uncertainties of the ML potentials employed in the machine learning approach to enhance the prediction models for graphene, were elucidated. Lastly, the thermal properties, modelling of graphene heterostructures, the superconducting behaviour of graphene, and optimization of the DFT models are grey areas that future studies should explore in enhancing its unique potential. Therefore, the identified future trends and knowledge gaps have a prospect in both academia and industry to design future and reliable optoelectronic devices. Graphene ; Density functional theory ; Dopant ; Materials science ; Nanotechnology ; Heterojunction ; Band gap ; Computer science ; Doping ; Artificial intelligence ; Computational chemistry ; Chemistry ; Optoelectronics Graphene research and applications ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Xin Wei | Ding Peng | Lin Shen | Yuejie Ai | Zhanhui Lu
Source: Journal of environmental chemical engineering (article, 2023-05-09)
Field: Chemistry — Inorganic Chemistry

Analyzing of metal organic frameworks performance in CH4 adsorption using machine learning techniques: A GBRT model based on small training dataset Adsorption ; Metal-organic framework ; Methane ; Ab initio ; Core (optical fiber) ; Molecule ; Molecular descriptor ; Chemistry ; Computer science ; Materials science ; Quantitative structure–activity relationship ; Machine learning ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yi-Hung Chiu | Ya-Hsuan Liao | Jia‐Yang Juang
Source: Polymers (article, 2023-01-05)
Field: Materials Science — Materials Chemistry

Designing Bioinspired Composite Structures via Genetic Algorithm and Conditional Variational Autoencoder Designing composite materials with tailored stiffness and toughness is challenging due to the massive number of possible material and geometry combinations. Although various studies have applied machine learning techniques and optimization methods to tackle this problem, we still lack a complete understanding of the material effects at different positions and a systematic experimental procedure to validate the results. Here we study a two-dimensional (2D) binary composite system with an edge crack and grid-like structure using a Genetic Algorithm (GA) and Conditional Variational Autoencoder (CVAE), which can design a composite with desired stiffness and toughness. The fitness of each design is evaluated using the negative mean square error of their predicted stiffness and toughness and the target values. We use finite element simulations to generate a machine-learning dataset and perform tensile tests on 3D-printed specimens to validate our results. We show that adding soft material behind the crack tip, instead of ahead of the tip, tremendously increases the overall toughness of the composite. We also show that while GA generates composite designs with slightly better accuracy (both methods perform well, with errors below 20%), CVAE takes considerably less time (~1/7500) to generate designs. Our findings may provide insights into the effect of adding soft material at different locations of a composite system and may also provide guidelines for conducting experiments and Explainable Artificial Intelligence (XAI) to validate the results. Autoencoder ; Stiffness ; Toughness ; Composite number ; Finite element method ; Genetic algorithm ; Computer science ; Materials science ; Algorithm ; Ultimate tensile strength ; Structural engineering ; Artificial neural network ; Artificial intelligence ; Composite material ; Machine learning ; Engineering Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Calcium Carbonate Crystallization and Inhibition
Authors: Yi-Fan Hou | Fuchun Ge | Pavlo O. Dral
Source: Journal of Chemical Theory and Computation (article, 2023-04-06)
Field: Materials Science — Materials Chemistry

Explicit Learning of Derivatives with the KREG and pKREG Models on the Example of Accurate Representation of Molecular Potential Energy Surfaces The KREG and pKREG models were proven to enable accurate learning of multidimensional single-molecule surfaces of quantum chemical properties such as ground-state potential energies, excitation energies, and oscillator strengths. These models are based on kernel ridge regression (KRR) with the Gaussian kernel function and employ a relative-to-equilibrium (RE) global molecular descriptor, while pKREG is designed to enforce invariance under atom permutations with a permutationally invariant kernel. Here we extend these two models to also explicitly include the derivative information from the training data into the models, which greatly improves their accuracy. We demonstrate on the example of learning potential energies and energy gradients that KREG and pKREG models are better or on par with state-of-the-art machine learning models. We also found that in challenging cases both energy and energy gradient labels should be learned to properly model potential energy surfaces and learning only energies or gradients is insufficient. The models' open-source implementation is freely available in the MLatom package for general-purpose atomistic machine learning simulations, which can be also performed on the MLatom@XACS cloud computing service. Computer science ; Invariant (physics) ; Potential energy ; Kernel (algebra) ; Statistical physics ; Artificial intelligence ; Representation (politics) ; Theoretical computer science ; Machine learning ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Markus J. Buehler
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2023-01-12)
Field: Materials Science — Materials Chemistry

Predicting mechanical fields near cracks using a progressive transformer diffusion model and exploration of generalization capacity Abstract We report a deep learning method to predict high-resolution stress fields from material microstructures, using a novel class of progressive attention-based transformer diffusion models. We train the model with a small dataset of pairs of input microstructures and resulting atomic-level Von Mises stress fields obtained from molecular dynamics (MD) simulations, and show excellent capacity to accurately predict results. We conduct a series of computational experiments to explore generalizability of the model and show that while the model was trained on a small dataset that featured samples of multiple cracks, the model can accurately predict distinct fracture scenarios such as single cracks, or crack-like defects with very different shapes. A comparison with MD simulations provides excellent comparison to the ground truth results in all cases. The results indicate that exciting opportunities that lie ahead in using progressive transformer diffusion models in the physical sciences, to produce high-fidelity and high-resolution field images. Graphical abstract Materials science ; Transformer ; Generalizability theory ; High fidelity ; Fidelity ; Computer science ; Voltage ; Physics ; Acoustics ; Mathematics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Advanced Electron Microscopy Techniques and Applications
Authors: Ramya Gurunathan | Kamal Choudhary | Francesca Tavazza
Source: Physical Review Materials (article, 2023-02-24)
Field: Materials Science — Materials Chemistry

Rapid prediction of phonon structure and properties using the atomistic line graph neural network (ALIGNN) The phonon density of states (DOS) summarizes the lattice vibrational modes supported by a structure and gives access to rich information about the material's stability, thermodynamic constants, and thermal transport coefficients. Here, we present an atomistic line graph neural network (ALIGNN) model for the prediction of the phonon density of states and the derived thermal and thermodynamic properties. The model is trained on a database of over 14 000 phonon spectra included in the joint automated repository for various integrated simulations: density functional theory (jarvis-dft) database. The model predictions are shown to capture the spectral features of the phonon density of states, effectively categorize dynamical stability, and lead to accurate predictions of DOS-derived thermal and thermodynamic properties, including heat-capacity ${C}_{\mathrm{V}}$, vibrational entropy ${S}_{\mathrm{vib}}$, and the isotopic phonon-scattering rate ${\ensuremath{\tau}}_{\mathrm{i}}^{\ensuremath{-}1}$. A comparison of room temperature thermodynamic property predictions reveals that the DOS-mediated ALIGNN model provides superior predictions when compared to a direct deep-learning prediction of these material properties as well as predictions based on analytic simplifications of the phonon DOS, including the Debye or Born--von Karman models. Finally, the ALIGNN model is used to predict the phonon spectra and properties for about 40 000 additional materials listed in the jarvis-dft database, which are validated as far as possible against other open-sourced high-throughput DFT phonon databases. Phonon ; Materials science ; Density functional theory ; Debye model ; Heat capacity ; Lattice (music) ; Spectral line ; Thermodynamics ; Condensed matter physics ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; X-ray Diffraction in Crystallography
Authors: Justin Airas | Xinqiang Ding | Bin Zhang
Source: ACS Central Science (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks Implicit solvent models are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. Efforts are underway to develop accurate and transferable implicit solvent models and coarse-grained (CG) force fields in general, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to the lack of analytical expressions for the PMF and algorithmic limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent the solvation free energy and potential contrasting for parameter optimization. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside of the training data. Our study offers valuable insights for deriving systematically improvable implicit solvent models and CG force fields from a bottom-up perspective. Solvation ; Computer science ; Transferability ; Solvent models ; Force field (fiction) ; Molecular dynamics ; Implicit solvation ; Artificial neural network ; Potential of mean force ; Graph ; Statistical physics ; Theoretical computer science ; Artificial intelligence ; Machine learning ; Solvent ; Computational chemistry ; Chemistry ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Xi Chen | Muammar El Khatib | Per Lindgren | Adam P. Willard | Andrew J. Medford | Andrew A. Peterson
Source: npj Computational Materials (article, 2023-05-11)
Field: Materials Science — Materials Chemistry

Atomistic learning in the electronically grand-canonical ensemble Abstract A strategy is presented for the machine-learning emulation of electronic structure calculations carried out in the electronically grand-canonical ensemble. The approach relies upon a dual-learning scheme, where both the system charge and the system energy are predicted for each image. The scheme is shown to be capable of emulating basic electrochemical reactions at a range of potentials, and coupling it with a bootstrap-ensemble approach gives reasonable estimates of the prediction uncertainty. The method is also demonstrated to accelerate saddle-point searches, and to extrapolate to systems with one to five water layers. We anticipate that this method will allow for larger length- and time-scale simulations necessary for electrochemical simulations. Emulation ; Computer science ; Saddle point ; Grand canonical ensemble ; Ensemble learning ; Scheme (mathematics) ; Range (aeronautics) ; Microcanonical ensemble ; Canonical ensemble ; Statistical ensemble ; Ensemble forecasting ; Statistical physics ; Coupling (piping) ; Saddle ; Artificial intelligence ; Mathematics ; Mathematical optimization ; Physics ; Materials science ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Catalysis and Oxidation Reactions
Authors: Yunrui Qiu | Michael S. O’Connor | Mingyi Xue | Bojun Liu | Xuhui Huang
Source: Journal of Chemical Theory and Computation (article, 2023-06-29)
Field: Chemistry — Spectroscopy

An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes Conformational changes (i.e., dynamic transitions between pairs of conformational states) play important roles in many chemical and biological processes. Constructing the Markov state model (MSM) from extensive molecular dynamics (MD) simulations is an effective approach to dissect the mechanism of conformational changes. When combined with transition path theory (TPT), MSM can be applied to elucidate the ensemble of kinetic pathways connecting pairs of conformational states. However, the application of TPT to analyze complex conformational changes often results in a vast number of kinetic pathways with comparable fluxes. This obstacle is particularly pronounced in heterogeneous self-assembly and aggregation processes. The large number of kinetic pathways makes it challenging to comprehend the molecular mechanisms underlying conformational changes of interest. To address this challenge, we have developed a path classification algorithm named latent-space path clustering (LPC) that efficiently lumps parallel kinetic pathways into distinct metastable path channels, making them easier to comprehend. In our algorithm, MD conformations are first projected onto a low-dimensional space containing a small set of collective variables (CVs) by time-structure-based independent component analysis (tICA) with kinetic mapping. Then, MSM and TPT are constructed to obtain the ensemble of pathways, and a deep learning architecture named the variational autoencoder (VAE) is used to learn the spatial distributions of kinetic pathways in the continuous CV space. Based on the trained VAE model, the TPT-generated ensemble of kinetic pathways can be embedded into a latent space, where the classification becomes clear. We show that LPC can efficiently and accurately identify the metastable path channels in three systems: a 2D potential, the aggregation of two hydrophobic particles in water, and the folding of the Fip35 WW domain. Using the 2D potential, we further demonstrate that our LPC algorithm outperforms the previous path-lumping algorithms by making substantially fewer incorrect assignments of individual pathways to four path channels. We expect that LPC can be widely applied to identify the dominant kinetic pathways underlying complex conformational changes. Autoencoder ; Path (computing) ; Computer science ; Kinetic energy ; Metastability ; Algorithm ; Statistical physics ; Artificial intelligence ; Biological system ; Physics ; Deep learning ; Biology Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Bojun Liu | Mingyi Xue | Yunrui Qiu | Kirill A. Konovalov | Michael S. O’Connor | Xuhui Huang
Source: The Journal of Chemical Physics (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

GraphVAMPnets for uncovering slow collective variables of self-assembly dynamics Uncovering slow collective variables (CVs) of self-assembly dynamics is important to elucidate its numerous kinetic assembly pathways and drive the design of novel structures for advanced materials through the bottom-up approach. However, identifying the CVs for self-assembly presents several challenges. First, self-assembly systems often consist of identical monomers, and the feature representations should be invariant to permutations and rotational symmetries. Physical coordinates, such as aggregate size, lack high-resolution detail, while common geometric coordinates like pairwise distances are hindered by the permutation and rotational symmetry challenges. Second, self-assembly is usually a downhill process, and the trajectories often suffer from insufficient sampling of backward transitions that correspond to the dissociation of self-assembled structures. Popular dimensionality reduction methods, such as time-structure independent component analysis, impose detailed balance constraints, potentially obscuring the true dynamics of self-assembly. In this work, we employ GraphVAMPnets, which combines graph neural networks with a variational approach for Markovian process (VAMP) theory to identify the slow CVs of the self-assembly processes. First, GraphVAMPnets bears the advantages of graph neural networks, in which the graph embeddings can represent self-assembly structures in high-resolution while being invariant to permutations and rotational symmetries. Second, it is built upon VAMP theory, which studies Markov processes without forcing detailed balance constraints, which addresses the out-of-equilibrium challenge in the self-assembly process. We demonstrate GraphVAMPnets for identifying slow CVs of self-assembly kinetics in two systems: the aggregation of two hydrophobic molecules and the self-assembly of patchy particles. We expect that our GraphVAMPnets can be widely applied to molecular self-assembly. Pairwise comparison ; Computer science ; Detailed balance ; Dimensionality reduction ; Homogeneous space ; Curse of dimensionality ; Self-assembly ; Graph ; Invariant (physics) ; Statistical physics ; Theoretical computer science ; Mathematics ; Artificial intelligence ; Physics ; Geometry ; Nanotechnology Pickering emulsions and particle stabilization ; Supramolecular Self-Assembly in Materials ; Machine Learning in Materials Science
Authors: Chuanqi Xie | Weidong Zhou
Source: Foods (review, 2023-06-05)
Field: Chemistry — Analytical Chemistry

A Review of Recent Advances for the Detection of Biological, Chemical, and Physical Hazards in Foodstuffs Using Spectral Imaging Techniques Traditional methods for detecting foodstuff hazards are time-consuming, inefficient, and destructive. Spectral imaging techniques have been proven to overcome these disadvantages in detecting foodstuff hazards. Compared with traditional methods, spectral imaging could also increase the throughput and frequency of detection. This study reviewed the techniques used to detect biological, chemical, and physical hazards in foodstuffs including ultraviolet, visible and near-infrared (UV-Vis-NIR) spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging, and Raman spectroscopy. The advantages and disadvantages of these techniques were discussed and compared. The latest studies regarding machine learning algorithms for detecting foodstuff hazards were also summarized. It can be found that spectral imaging techniques are useful in the detection of foodstuff hazards. Thus, this review provides updated information regarding the spectral imaging techniques that can be used by food industries and as a foundation for further studies. Hyperspectral imaging ; Biological hazard ; Spectral imaging ; Chemical imaging ; Computer science ; Environmental science ; Remote sensing ; Nanotechnology ; Process engineering ; Materials science ; Artificial intelligence ; Engineering ; Medicine ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Chao Yang | Jingjing Qi | Anquan Wang | Jingyu Zha | Chao Liu | Shupeng Yao
Source: Materials Research Express (article, 2023-11-13)
Field: Chemistry — Inorganic Chemistry

Application of machine learning in MOFs for gas adsorption and separation Abstract Metal–organic frameworks (MOFs) with high specific surface area, permanent porosity and extreme modifiability had great potential for gas storage and separation applications. Considering the theoretically nearly infinite variety of MOFs, it was difficult but necessary to achieve high-throughput computational screening (HTCS) of high-performance MOFs for specific applications. Machine learning (ML) was a field of computer science where one of its research directions was the effective use of information in a big data environment, focusing on obtaining hidden, valid and understandable knowledge from huge amounts of data, and had been widely used in materials research. This paper firstly briefly introduced the MOFs databases and related algorithms for ML, followed by a detailed review of the research progress on HTCS of MOFs based on ML according to four classes of descriptors, including geometrical, chemical, topological and energy-based, for gas storage and separation, and finally a related outlook was presented. This paper aimed to deepen readers’ understanding of ML-based MOF research, and to provide some inspirations and help for related research. Adsorption ; Variety (cybernetics) ; Field (mathematics) ; Computer science ; Porosity ; Big data ; Metal-organic framework ; Throughput ; Nanotechnology ; Separation (statistics) ; Artificial intelligence ; Process engineering ; Machine learning ; Materials science ; Data mining ; Chemistry ; Engineering ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography