jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Damdae Park | Wonsuk Chung | Byoung Koun Min | Ung Lee | Seungho Yu | Kyeongsu Kim
Source: npj Computational Materials (article, 2024-09-20)
Field: Materials Science — Materials Chemistry

Computational screening of sodium solid electrolytes through unsupervised learning All-solid-state Na-ion batteries have emerged as alternatives to all-solid-state Li-ion batteries owing to the global abundance of Na element. However, finding a commercially viable Na-ion solid-state electrolyte (SSE) remains challenging due to the relatively poor understanding of the structures effective for conduction compared to those for Li-ion SSE. In this study, we develop a screening framework based on an unsupervised machine learning technique to characterize Na-ion SSEs according to their lattice structures. Specifically, we evaluate feature vectors encoding 180 structural properties for 12,670 materials containing Na ions. Subsequently, the resulting feature vectors are clustered using hierarchical density-based spatial clustering of applications with noise (HDBSCAN), leading to the discovery of 12 groups including those with experimentally proven Na-ion superionic conductors such as NASICONs and sodium chalcogenides. Post hoc analysis of these clusters reveals that the groups with high conductivity share similar characteristics, including the existence of ion channels for Na ions and the weak interactions between Na ions and the proximate atoms. Ab initio molecular dynamics simulations confirm that the promising groups exhibit exceptional ion diffusivity compared to other groups. By employing decision tree classifiers trained to screen promising groups, we demonstrate the rapid assessment of the potential of a given material. Finally, we offer perspectives and insights for the development of novel Na-ion SSEs for all-solid-state Na-ion batteries. Unsupervised learning ; Sodium ; Computer science ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Zijing Li | Yingchuan Zhang | Tao Zhou | Guangri Jia
Source: Nexus (article, 2024-07-18)
Field: Materials Science — Materials Chemistry

Accelerating electrocatalyst design for CO2 conversion through machine learning: Interpretable models and data-driven innovations Electrocatalytic conversion of CO2 into valuable products is a promising approach toward mitigating climate change and energy crisis. However, the product diversity and multi-electron transfer pathways drive the development of numerous strategies for catalyst component and active site modifications, leading to a long journey toward rational electrocatalyst design. The integration of machine learning (ML) with experimental workload provides an opportunity to speed up the materials discovery by automatically exploiting trends and patterns from database. This review focuses on the interpretability of ML models in electrocatalyst design, and demonstrates a reliable workflow based on adequate catalytic data with refined descriptors, and satisfactory configuration of model with appropriate human intervention. Moreover, the combination of data-driven techs and cutting-edge methodologies for the discovery of catalyst is also discussed, which can serve as bridge between contemporary catalysis and quantum chemistry. This review may provoke more ML-based innovations toward the rationalization of electrocatalyst design and the discovery of novel materials for net-zero energy industries. Electrocatalyst ; Workflow ; Interpretability ; Rationalization (economics) ; Computer science ; Ontology ; Rational design ; Nanotechnology ; Machine learning ; Chemistry ; Materials science ; Database Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Wei Fang | Yucheng Zhu | Yihan Cheng | Yi-Ping Hao | Jeremy O. Richardson
Source: Journal of Chemical Theory and Computation (article, 2024-05-06)
Field: Materials Science — Materials Chemistry

Robust Gaussian Process Regression Method for Efficient Tunneling Pathway Optimization: Application to Surface Processes Simulation of surface processes is a key part of computational chemistry that offers atomic-scale insights into mechanisms of heterogeneous catalysis, diffusion dynamics, and quantum tunneling phenomena. The most common theoretical approaches involve optimization of reaction pathways, including semiclassical tunneling pathways (called instantons). The computational effort can be demanding, especially for instanton optimizations with an ab initio electronic structure. Recently, machine learning has been applied to accelerate reaction-pathway optimization, showing great potential for a wide range of applications. However, previous methods still suffer from numerical and efficiency issues and were not designed for condensed-phase reactions. We propose an improved framework based on Gaussian process regression for general transformed coordinates, which has improved efficiency and numerical stability, and we propose a descriptor that combines internal and Cartesian coordinates suitable for modeling surface processes. We demonstrate with 11 instanton optimizations in three representative systems that the improved approach makes ab initio instanton optimization significantly cheaper, such that it becomes not much more expensive than a classical transition-state theory rate calculation. Instanton ; Semiclassical physics ; Computer science ; Ab initio ; Gaussian process ; Quantum tunnelling ; Electronic structure ; Statistical physics ; Gaussian ; Quantum ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Souvik Pore | A. Pelloux | Mainak Chatterjee | Arkaprava Banerjee | Kunal Roy
Source: Journal of Hazardous Materials (article, 2024-09-03)
Field: Chemistry — Analytical Chemistry

Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305 Bioconcentration ; Bioaccumulation ; Quantitative structure–activity relationship ; Partial least squares regression ; Guideline ; Fish <Actinopterygii> ; Set (abstract data type) ; Range (aeronautics) ; Chemistry ; Environmental chemistry ; Computer science ; Engineering ; Machine learning ; Stereochemistry ; Biology ; Fishery ; Political science Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Computational Drug Discovery Methods
Authors: Thomas Plé | Olivier Adjoua | Louis Lagardère | Jean‐Philip Piquemal
Source: The Journal of Chemical Physics (article, 2024-07-25)
Field: Materials Science — Materials Chemistry

FeNNol: An efficient and flexible library for building force-field-enhanced neural network potentials Neural network interatomic potentials (NNPs) have recently proven to be powerful tools to accurately model complex molecular systems while bypassing the high numerical cost of ab initio molecular dynamics simulations. In recent years, numerous advances in model architectures as well as the development of hybrid models combining machine-learning (ML) with more traditional, physically motivated, force-field interactions have considerably increased the design space of ML potentials. In this paper, we present FeNNol, a new library for building, training, and running force-field-enhanced neural network potentials. It provides a flexible and modular system for building hybrid models, allowing us to easily combine state-of-the-art embeddings with ML-parameterized physical interaction terms without the need for explicit programming. Furthermore, FeNNol leverages the automatic differentiation and just-in-time compilation features of the Jax Python library to enable fast evaluation of NNPs, shrinking the performance gap between ML potentials and standard force-fields. This is demonstrated with the popular ANI-2x model reaching simulation speeds nearly on par with the AMOEBA polarizable force-field on commodity GPUs (graphics processing units). We hope that FeNNol will facilitate the development and application of new hybrid NNP architectures for a wide range of molecular simulation problems. Computer science ; Python (programming language) ; Modular design ; Force field (fiction) ; Artificial neural network ; Field (mathematics) ; Graphics ; Computational science ; Artificial intelligence ; Computer graphics (images) ; Programming language Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Protein Structure and Dynamics
Authors: Balázs Póta | Paramvir Ahlawat | Gábor Csányi | Michele Simoncelli
Source: arXiv (Cornell University) (preprint, 2024-08-01)
Field: Materials Science — Materials Chemistry

Thermal Conductivity Predictions with Foundation Atomistic Models Advances in machine learning have led to the development of foundation models for atomistic materials chemistry, enabling quantum-accurate descriptions of interatomic forces across chemically diverse compounds at reduced computational cost. Hitherto, the accuracy and utility of these models have been assessed relying on descriptors based on formation energies or idealized harmonic atomic vibrations. Yet, the rigorous and physically interpretable quantification of their capability to describe both realistic anharmonic atomic dynamics and technologically relevant observables remains a pressing problem. Here, we address this problem, leveraging the Wigner formulation of heat transport and the Grüneisen approach to thermal expansion to connect the atomic-physics awareness of foundation models to their utility in predicting experimentally observable thermomechanical properties, presenting standards and fine-tuning protocols needed to achieve first-principles accuracy. We apply our framework to a database of 103 solids with diverse compositions and structures, demonstrating that it overcomes the major bottlenecks of current methods for designing heat-management materials -- high cost, limited transferability, or lack of physics awareness -- and its potential to discover materials for next-gen technologies ranging from thermal insulation to neuromorphic computing. Foundation (evidence) ; Thermal conductivity ; Statistical physics ; Conductivity ; Materials science ; Physics ; Thermodynamics ; Political science ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Physical and Chemical Molecular Interactions
Authors: Reshma Devi | Keith T. Butler | Gopalakrishnan Sai Gautam
Source: npj Computational Materials (article, 2024-12-20)
Field: Materials Science — Materials Chemistry

Optimal pre-train/fine-tune strategies for accurate material property predictions Abstract A pathway to overcome limited data availability in materials science is to use the framework of transfer learning, where a pre-trained (PT) machine learning model (on a larger dataset) can be fine-tuned (FT) on a target (smaller) dataset. We systematically explore the effectiveness of various PT/FT strategies to learn and predict material properties and create generalizable models by PT on multiple properties (MPT) simultaneously. Specifically, we leverage graph neural networks (GNNs) to PT/FT on seven diverse curated materials datasets, with sizes ranging from 941 to 132,752. Besides identifying optimal PT/FT strategies and hyperparameters, we find our pair-wise PT-FT models to consistently outperform models trained from scratch on target datasets. Importantly, our MPT models outperform pair-wise models on several datasets and, more significantly, on a 2D material band gap dataset that is completely out-of-domain. Finally, we expect our PT/FT and MPT frameworks to accelerate materials design and discovery for various applications. Property (philosophy) ; Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Welding Techniques and Residual Stresses
Authors: Casey L. Olen | Andrew F. Zahrt | Sean W. Reilly | Danielle M. Schultz | Khateeta M. Emerson | David A. Candito | Xiao Wang | Neil A. Strotman | Scott E. Denmark
Source: ACS Catalysis (article, 2024-02-06)
Field: Chemistry — Inorganic Chemistry

Chemoinformatic Catalyst Selection Methods for the Optimization of Copper–Bis(oxazoline)-Mediated, Asymmetric, Vinylogous Mukaiyama Aldol Reactions A catalyst selection method for the optimization of an asymmetric, vinylogous Mukaiyama aldol reaction is described. A large library of commercially available and synthetically accessible copper–bis(oxazoline) catalysts was constructed in silico. Conformer-dependent, grid-based descriptors were calculated for each catalyst, defining a chemical feature space suitable for machine learning. Selection of a diverse subset of catalyst space produced an initial training set of 26 new bis(oxazoline) ligands that were synthesized and tested for stereoselectivity in the copper-catalyzed, vinylogous Mukaiyama aldol reaction for five substrate combinations. One ligand in the training set provided 88% average enantiomeric excess, exceeding the performance of catalysts identified through an initial optimization campaign. Supervised and unsupervised catalyst selection methods, including quantitative structure–selectivity relationship modeling, nearest neighbors analysis, and a focused analogue clustering strategy, were employed to identify an additional 12 new bis(oxazoline) ligands. The selected ligands outperformed the initial training set hit in four out of five product classes and in some cases demonstrated enantiocontrol exceeding 95% ee. The effectiveness of the unsupervised training set selection process is discussed, and the expediency of the nearest neighbor and focused analogue approaches are contrasted with the supervised quantitative structure–selectivity relationship modeling approach. Oxazoline ; Aldol reaction ; Chemistry ; Stereoselectivity ; Chemical space ; Combinatorial chemistry ; Catalysis ; Computer science ; Organic chemistry ; Drug discovery Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis ; Catalytic C–H Functionalization Methods
Authors: Christopher J. Heard | Lukáš Grajciar | Andreas Erlebach
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Migration of zeolite-encapsulated subnanometre platinum clusters <i>via</i> reactive neural network potentials The migration of atoms and small clusters is an important process in sub-nanometre scale heterogeneous catalysis, affecting activity, accessibility and deactivation through sintering. Control of migration can be partially achieved <i>via</i> encapsulation of sub-nanometre metal particles into porous media such as zeolites. However, a general understanding of the migration mechanisms and their sensitivity to particle size and framework environment is lacking. Here, we extend the time-scale and sampling of atomistic simulations of platinum cluster diffusion in siliceous zeolite frameworks, by introducing a reactive neural network potential of density functional quality. We observe that Pt atoms migrate in a qualitatively different manner from clusters, occupying the dense region of the framework and avoiding the free pore space. We also find that for cage-like zeolite CHA there exists a maximum in self diffusivity for the Pt dimer beyond which, confinement effects hinder intercage migration. By extending the quality of sampling, NNP-based methods allow for the discovery of novel dynamical processes at the atomistic scale, bringing modelling closer to <i>operando</i> experimental characterization of catalytic materials. Zeolite ; Thermal diffusivity ; Platinum ; Materials science ; Chemical engineering ; Nanotechnology ; Chemical physics ; Catalysis ; Chemistry ; Organic chemistry ; Thermodynamics ; Physics ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Guokun Yang | Shuang Jiang | Yi Luo | Song Wang | Jun Jiang
Source: The Journal of Physical Chemistry Letters (article, 2024-08-20)
Field: Materials Science — Materials Chemistry

Cross-Modal Prediction of Spectral and Structural Descriptors via a Pretrained Model Enhanced with Chemical Insights Proposing and utilizing machine learning descriptors for chemical property prediction and material screening have become a cutting-edge field in artificial intelligence-enabled chemical research. However, a single descriptor typically captures only partial features of a chemical object, resulting in an information deficiency and limiting generalizability. Obtaining a comprehensive set of descriptors is essential but challenging, especially when accessing some microlevel structural and electronic features due to technological limitations. Herein, we exploit multimodal chemical descriptors to construct an encoder-decoder machine learning framework that enables the cross-modal prediction of spectral and structural descriptors. By pretraining the model to endow it with chemical insights, the multimodal data fusion is implemented in a descriptor-encoded hidden layer. The model's capabilities are validated in the system of CO/NO adsorption on Au/Ag surfaces, demonstrating successful reciprocal prediction of infrared spectra, Raman spectra, and internal coordinates. This work provides a proof-of-concept for the feasibility of cross-modal predictions between different chemical features and will significantly reduce the machine learning model's dependence on complete physicochemical parameters and improve its multitarget prediction capabilities. Computer science ; Artificial intelligence ; Autoencoder ; Header ; Machine learning ; Pattern recognition (psychology) ; Set (abstract data type) ; Modal ; Encoder ; Field (mathematics) ; Data mining ; Deep learning ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Daniil S. Koshelev
Source: Applied Spectroscopy (article, 2024-01-28)
Field: Chemistry — Analytical Chemistry

Expert System for Fourier Transform Infrared Spectra Recognition Based on a Convolutional Neural Network With Multiclass Classification Fourier transform infrared spectroscopy (FT-IR) is a widely used spectroscopic method for routine analysis of substances and compounds. Spectral interpretation of spectra is a labor-intensive process that provides important information about functional groups or bonds present in compounds and complex substances. In this paper, based on deep learning methods of convolutional neural networks, models were developed to determine the presence of 17 classes of functional groups or 72 classes of coupling oscillations in the FT-IR spectra. Using web scanning, the spectra of 14 361 FT-IR spectra of organic molecules were obtained. Several different variants of model architectures with different sizes of feature maps have been tested. Based on the Shapley additive explanations (SHAP) and gradient-weighted class activation mapping (GradCAM) methods, visualization tools have been developed for visualizing and highlighting the areas of absorption bands manifestation for corresponding functional groups or bonds in the spectrum. To determine 17 and 72 classes, the F1-weighted metric, which is the harmonic mean of the class' precision and class' recall weighted by class' fraction, reached 93 and 88%, respectively, when using data on the position of absorption maxima in the spectrum as an additional source layer. The resulting model can be used to facilitate the routine analysis of spectra for all areas such as organic chemistry, materials science, and biology, as well as to facilitate the preparation of the obtained experimental data for publication. Infrared spectroscopy ; Pattern recognition (psychology) ; Artificial intelligence ; Spectral line ; Fourier transform ; Chemistry ; Infrared ; Fourier transform infrared spectroscopy ; Computer science ; Biological system ; Mathematics ; Optics ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jiajia Sun | Wei Lai | Jiayan Zhao | Jinhong Xue | Tong Zhu | Mingshu Xiao | Tiantian Man | Ying Wan | Hao Pei | Li Li
Source: ACS Sensors (article, 2024-08-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Rapid Identification of Drug Mechanisms with Deep Learning-Based Multichannel Surface-Enhanced Raman Spectroscopy Rapid identification of drug mechanisms is vital to the development and effective use of chemotherapeutics. Herein, we develop a multichannel surface-enhanced Raman scattering (SERS) sensor array and apply deep learning approaches to realize the rapid identification of the mechanisms of various chemotherapeutic drugs. By implementing a series of self-assembled monolayers (SAMs) with varied molecular characteristics to promote heterogeneous physicochemical interactions at the interfaces, the sensor can generate diversified SERS signatures for directly high-dimensionality fingerprinting drug-induced molecular changes in cells. We further train the convolutional neural network model on the multidimensional SAM-modulated SERS data set and achieve a discriminatory accuracy toward 99%. We expect that such a platform will contribute to expanding the toolbox for drug screening and characterization and facilitate the drug development process. Identification (biology) ; Drug discovery ; Raman scattering ; Convolutional neural network ; Computer science ; Nanotechnology ; Toolbox ; Drug development ; Drug ; Process (computing) ; Deep learning ; Materials science ; Raman spectroscopy ; Chemistry ; Artificial intelligence ; Pharmacology ; Medicine ; Biology ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced biosensing and bioanalysis techniques
Authors: Rui Xu | Tao Cheng | Chaoyu Li | Xue Yang | Junfeng Rong
Source: Journal of Power and Energy Engineering (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Properties of Ti-Based Hydrogen Storage Alloy An efficient and safe hydrogen storage method is one of the important links for the large-scale development of hydrogen in the future. Because of its low price and simple design, Ti-based hydrogen storage alloys are considered to be suitable for practical applications. In this paper, we review the latest research on Ti-based hydrogen storage alloys. Firstly, the machine learning and density functional theory are introduced to provide theoretical guidance for the optimization of Ti-based hydrogen storage alloys. Then, in order to improve the hydrogen storage performance, we briefly introduce the research of AB type and AB2 type Ti-based alloys, focusing on doping elements and adaptive after treatment. Finally, suggestions for the future research and development of Ti-based hydrogen storage alloys are proposed. Hydrogen storage ; Alloy ; Materials science ; Hydrogen ; Metallurgy ; Chemistry Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Hybrid Renewable Energy Systems
Authors: Maryam Mousavizadegan | Morteza Hosseini | Mahsa Sheikholeslami | Mohammad Reza Ganjali
Source: Microchimica Acta (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

A fluorescent sensor array based on antibiotic-stabilized metal nanoclusters for the multiplex detection of bacteria Nanoclusters ; Nanochemistry ; Fluorescence ; Multiplex ; Bacteria ; Metal ; Nanotechnology ; Chemistry ; Materials science ; Biology ; Organic chemistry ; Physics ; Bioinformatics ; Optics Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Gold and Silver Nanoparticles Synthesis and Applications