jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pei Ge | Linfeng Zhang | Huan Lei
Source: The Journal of Chemical Physics (article, 2023-01-23)
Field: Materials Science — Materials Chemistry

Machine learning assisted coarse-grained molecular dynamics modeling of meso-scale interfacial fluids A hallmark of meso-scale interfacial fluids is the multi-faceted, scale-dependent interfacial energy, which often manifests different characteristics across the molecular and continuum scale. The multi-scale nature imposes a challenge to construct reliable coarse-grained (CG) models, where the CG potential function needs to faithfully encode the many-body interactions arising from the unresolved atomistic interactions and account for the heterogeneous density distributions across the interface. We construct the CG models of both single- and two-component polymeric fluid systems based on the recently developed deep coarse-grained potential [Zhang et al., J. Chem. Phys. 149, 034101 (2018)] scheme, where each polymer molecule is modeled as a CG particle. By only using the training samples of the instantaneous force under the thermal equilibrium state, the constructed CG models can accurately reproduce both the probability density function of the void formation in bulk and the spectrum of the capillary wave across the fluid interface. More importantly, the CG models accurately predict the volume-to-area scaling transition for the apolar solvation energy, illustrating the effectiveness to probe the meso-scale collective behaviors encoded with molecular-level fidelity. Molecular dynamics ; Scaling ; Statistical physics ; Scale (ratio) ; Solvation ; Chemical physics ; Length scale ; Materials science ; Physics ; Molecule ; Chemistry ; Mechanics ; Computational chemistry ; Mathematics Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Bryce Hedelius | Damon Tingey | Dennis Della Corte
Source: Journal of Chemical Theory and Computation (article, 2023-12-27)
Field: Materials Science — Materials Chemistry

TrIP─Transformer Interatomic Potential Predicts Realistic Energy Surface Using Physical Bias Accurate interatomic energies and forces enable high-quality molecular dynamics simulations, torsion scans, potential energy surface mappings, and geometry optimizations. Machine learning algorithms have enabled rapid estimates of the energies and forces with high accuracy. Further development of machine learning algorithms holds promise for producing universal potentials that support many different atomic species. We present the Transformer Interatomic Potential (TrIP): a chemically sound potential based on the SE(3)-Transformer. TrIP's species-agnostic architecture, which uses continuous atomic representation and homogeneous graph convolutions, encourages parameter sharing between atomic species for more general representations of chemical environments, maintains a reasonable number of parameters, serves as a form of regularization, and is a step toward accurate universal interatomic potentials. TrIP achieves state-of-the-art accuracies on the COMP6 benchmark with an energy prediction of just 1.02 kcal/mol MAE. We introduce physical bias in the form of Ziegler-Biersack-Littmark-screened nuclear repulsion and constrained atomization energies. An energy scan of a water molecule demonstrates that these changes improve long- and near-range interactions compared to other neural network potentials. TrIP also demonstrates stability in molecular dynamics simulations, demonstrating reasonable exploration of Ramachandran space. Computer science ; Potential energy surface ; Molecular dynamics ; Interatomic potential ; Ramachandran plot ; Statistical physics ; Algorithm ; Physics ; Molecule ; Quantum mechanics ; Protein structure Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Kohei Motojima | Abhijit Sen | Yoichi M. A. Yamada | Hiromasa Kaneko
Source: Journal of Chemical Information and Modeling (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Catalyst Design and Feature Engineering to Improve Selectivity and Reactivity in Two Simultaneous Cross-Coupling Reactions Highly active catalysts are required in numerous industrial fields; therefore, to minimize costs and development time, catalyst design using machine learning has attracted significant attention. This study focused on a reaction system where two types of cross-coupling reactions, namely, Buchwald-Hartwig type cross-coupling (BHCC) and Suzuki-Miyaura type cross-coupling (SMCC) reactions, occur simultaneously. Constructing a machine-learning model that considers all experimental conditions is essential to accurately predict the product yield for both the BHCC and the SMCC reactions. The objective of this study was to establish explanatory variables <i>x</i> that considered all experimental conditions within the reaction system involving simultaneous cross-couplings and to design catalysts that achieve the target yield and the development of novel reactions. To accomplish this, Bayesian optimization was combined with established variables <i>x</i> to design new catalysts and enhance reaction selectivity. Moreover, the catalyst design in this study successfully pioneered new reactions involving Cu, Rh, and Pt catalysts in a reaction system that did not previously react with transition metals other than Ni or Pd. Catalysis ; Yield (engineering) ; Selectivity ; Coupling reaction ; Coupling (piping) ; Reactivity (psychology) ; Combinatorial chemistry ; Bayesian optimization ; Chemistry ; Reaction conditions ; Computer science ; Feature (linguistics) ; Biological system ; Materials science ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Lei Zhang | Yu Guan | Ni Wang | Fei Ge | Yan Zhang | Yuping Zhao
Source: Scientific Reports (article, 2023-08-31)
Field: Chemistry — Analytical Chemistry

Identification of growth years for Puerariae Thomsonii Radix based on hyperspectral imaging technology and deep learning algorithm Puerariae Thomsonii Radix (PTR) is not only widely used in disease prevention and treatment but is also an important raw material as a source of starch and other food. The growth years of PTR are closely related to its quality. The rapid and nondestructive identification of growth year is essential for the quality control of PTR and other traditional Chinese medicines. In this study, we proposed a convolutional neural network (CNN)-based classification framework in conjunction with hyperspectral imaging (HSI) technology for the rapid identification of the growth years of PTRs. Traditional treatment methods (i.e., multiplicative scatter correction, standard normal variate, and Savitzky-Golay smoothing) combined with machine learning algorithms (i.e., random forest, logistic regression, naive Bayes, and eXtreme gradient boost) were used as baseline models. Among them, the F1-score of CNN-based models based on PTRs' outer surfaces was over 90%, outperforming all the other baseline models. These results showed that it was feasible to use a deep learning algorithm in conjunction with HSI technology to identify the growth years of PTR. This method provides a fast, nondestructive, and simple method of identifying the growth years of PTR. It can be easily applied to other scenarios, such as for the identification of the locality or years of growth for other traditional Chinese herbs. Algorithm ; Artificial intelligence ; Computer science ; Hyperspectral imaging ; Random forest ; Smoothing ; Identification (biology) ; Convolutional neural network ; Radix (gastropod) ; Deep learning ; Pattern recognition (psychology) ; Machine learning Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Phytochemicals and Antioxidant Activities
Authors: Lei Li | Ryan A. Ciufo | Jiyoung Lee | Chuan Zhou | Bo Lin | Jae‐Young Cho | Naman Katyal | Graeme Henkelman
Source: Computer Physics Communications (article, 2023-08-18)
Field: Materials Science — Materials Chemistry

Atom-centered machine-learning force field package Python (programming language) ; Computer science ; Fortran ; Computational science ; Force field (fiction) ; Algorithm ; Artificial intelligence ; Machine learning ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Kishalay Das | Pawan Goyal | Seung‐Cheol Lee | Satadeep Bhattacharjee | Niloy Ganguly
Source: arXiv (Cornell University) (preprint, 2023-06-09)
Field: Materials Science — Materials Chemistry

CrysMMNet: Multimodal Representation for Crystal Property Prediction Machine Learning models have emerged as a powerful tool for fast and accurate prediction of different crystalline properties. Exiting state-of-the-art models rely on a single modality of crystal data i.e. crystal graph structure, where they construct multi-graph by establishing edges between nearby atoms in 3D space and apply GNN to learn materials representation. Thereby, they encode local chemical semantics around the atoms successfully but fail to capture important global periodic structural information like space group number, crystal symmetry, rotational information, etc, which influence different crystal properties. In this work, we leverage textual descriptions of materials to model global structural information into graph structure and learn a more robust and enriched representation of crystalline materials. To this effect, we first curate a textual dataset for crystalline material databases containing descriptions of each material. Further, we propose CrysMMNet, a simple multi-modal framework, which fuses both structural and textual representation together to generate a joint multimodal representation of crystalline materials. We conduct extensive experiments on two benchmark datasets across ten different properties to show that CrysMMNet outperforms existing state-of-the-art baseline methods with a good margin. We also observe that fusing the textual representation with crystal graph structure provides consistent improvement for all the SOTA GNN models compared to their own vanilla versions. We have shared the textual dataset, that we have curated for both the benchmark material databases, with the community for future use. Leverage (statistics) ; Representation (politics) ; Computer science ; Graph ; Benchmark (surveying) ; Crystal (programming language) ; Theoretical computer science ; Artificial intelligence ; Cartography ; Geography Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; X-ray Diffraction in Crystallography
Authors: Zhang Wu | Yun Peng | Hui Liu | Cheng Zhi Huang | Jun Zhou
Source: Analytical Chemistry (article, 2023-10-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High Confidence Single Particle Analysis with Machine Learning Single particle analysis can effectively determine the heterogeneity between particles based on the local information on a single particle, which is utilized extensively for monitoring chemical reactions and biological activities. However, the study of obtaining ensemble reaction information at the single particle level, which can obtain both the structural and functional heterogeneity of particles as well as the ensemble reaction information, is challenging because the selection of a single particle mainly depends on experience, which will lead to a certain randomness when analyzing the ensemble reaction with single particles. Using machine learning, it is demonstrated that the proposed intelligent single particle analysis strategy can provide single particle and ensemble analyses with high confidence. Convolutional neural network and Gaussian mixture model were utilized to develop a machine learning model for resonance scattering imaging analysis of plasmonic nanoparticles. It can identify the scattered light of single particles and select representative or diverse particles. When single particle scattering imaging is used to obtain ensemble information on the reaction, the error caused by the selection of individual particles can be significantly reduced by selecting representative particles. In addition, the real situation of the reaction can be better revealed by selecting diverse particles. These results indicate that the intelligent single particle analysis strategy has great potential for imaging analysis and biological sensing. Particle (ecology) ; Single particle analysis ; Ensemble learning ; Randomness ; Chemistry ; Selection (genetic algorithm) ; Biological system ; Artificial intelligence ; Computer science ; Statistics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Protein Interaction Studies and Fluorescence Analysis
Authors: Changsheng Zhu | Guangzhao Li | José Naranjo | Wenjing Dong | Lijun Wang
Source: Computational Materials Science (article, 2023-10-13)
Field: Materials Science — Materials Chemistry

Optimization of RF to alloy elastic modulus prediction based on cuckoo algorithm Elastic modulus ; Cuckoo ; Materials science ; Cuckoo search ; Bulk modulus ; Alloy ; Algorithm ; Density functional theory ; Random forest ; Modulus ; Computer science ; Machine learning ; Composite material ; Physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition
Authors: Haoran Long | Hao Liu | Xiaoyu Wang | Bowen Wang | Ruixue Bai | Yali Yu | Kaiyao Xin | Liyuan Liu | Yingqiang Xu | Jing Zhang | Fagang Jiang | Xinghua Wang | Zhongming Wei | Juehan Yang
Source: Advanced Functional Materials (article, 2023-07-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Vision Based on an Ultra‐Wide Bandgap 2D Semiconductor AsSbO<sub>3</sub> Abstract Facing the future development trend of miniaturization and intelligence of electronic devices, solar‐blind photodetectors based on ultrawide‐bandgap 2D semiconductors have the advantages of low dark current, and high signal‐to‐noise ratio, as well as the features of micro‐nanometer miniaturization and multi‐functionalization of 2D material devices, which have potential applications in the photoelectric sensor part of high‐performance machine vision systems. This study reports a 2D oxide semiconductor, AsSbO 3 , with an ultrawide bandgap (4.997 eV for monolayer and 4.4 eV for multilayer) to be used to fabricate highly selective solar‐blind UV photodetectors, of which the dark current as low as 100 fA and rejection ratio of UV‐C and UV‐A reaches 7.6 × 10 3 . Under 239 nm incident light, the responsivity is 105 mA W −1 and the detectivity is 7.58 × 10 12 Jones. Owing to the remarkable anisotropic crystal structure, AsSbO 3 also shows significant linear dichroism and nonlinear optical properties. Finally, a simple machine vision system is simulated by combining the real‐time imaging function in solar‐blind UV with a convolutional neural network. This study enriches the material system of ultrawide‐bandgap 2D semiconductors and provides insight into the future development of high‐performance solar‐blind UV optoelectronic devices. Materials science ; Photodetector ; Optoelectronics ; Responsivity ; Band gap ; Semiconductor ; Miniaturization ; Dark current ; Night vision ; Optics ; Nanotechnology ; Physics Ga2O3 and related materials ; Gas Sensing Nanomaterials and Sensors ; 2D Materials and Applications
Authors: Marko Melander | Tongwei Wu | Karoliina Honkala
Source: ChemRxiv (preprint, 2023-02-28)
Field: Materials Science — Materials Chemistry

Constant inner potential DFT for modelling electrochemical systems under constant potential and bias Electrochemical interfaces and reactions play a decisive role in \textit{e.g.} clean energy conversion but understanding their complex chemistry remains an outstanding challenge. Constant potential or grand canonical ensemble (GCE) simulations are indispensable for unraveling the properties of electrochemical processes as a function of the electrode potential. Currently, GCE calculations at the density functional theory (DFT) level are carried out by fixing the Fermi level of the simulation cell. Here we show that this method is inadequate for modelling semiconductor electrodes, outer sphere reactions, and a biased two-electrode cell; for these systems the Fermi level obtained from DFT calculations does not reflect the experimentally controlled electrode potential or describe the thermodynamic independent variable in GCE-DFT. To remedy this deficiency, we developed and implemented a constant inner potential (CIP) method as a more robust and general approach to carry out GCE-DFT simulations of electrochemical systems under constant potential or bias conditions. In CIP-DFT the electrode inner potential and hence the thermodynamically relevant electron bath electrochemical are directly controlled which makes the method widely applicable in simulating electrochemical interfaces. We demonstrate that the CIP and Fermi level GCE-DFT approaches are equivalent for metallic electrodes and inner-sphere reactions but CIP is also applicable to systems for which the constant Fermi level approach fails. A key advantage of CIP is that, unlike the Fermi level method, it does not require any electronic structure information. This is because only the inner potential of the systems is needed, CIP is also more compatible with classical force field or machine learning potentials. Altogether, the CIP approach emerges as a general and efficient GCE-DFT method to simulate (photo)electrochemical interfaces from first principles. Electrochemistry ; Density functional theory ; Electrode ; Fermi level ; Electrode potential ; Constant (computer programming) ; Standard electrode potential ; Electrochemical potential ; Fermi energy ; Fermi Gamma-ray Space Telescope ; Chemistry ; Chemical physics ; Materials science ; Atomic physics ; Physics ; Electron ; Physical chemistry ; Condensed matter physics ; Computational chemistry ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Electronic and Structural Properties of Oxides
Authors: Sreejith Remanan Pushpa | Rajeev K. Sukumaran | S. Savithri
Source: Biomass Conversion and Biorefinery (article, 2023-11-13)
Field: Chemistry — Analytical Chemistry

Rapid quantification of lignocellulose composition in rice straw varieties using artificial neural networks and FTIR spectroscopic data Partial least squares regression ; Artificial neural network ; Biorefinery ; Cellulose ; Lignin ; Bioprocess ; Chemometrics ; Straw ; Biological system ; Hemicellulose ; Coefficient of determination ; Pulp and paper industry ; Mathematics ; Raw material ; Agronomy ; Computer science ; Chemistry ; Machine learning ; Engineering ; Statistics ; Biology ; Chemical engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; GABA and Rice Research
Authors: Riping Liu | Xiuyuan Zhu | Jiayao Duan | Junxin Chen | Zhuang Xie | Chaoyue Chen | Xi Xie | Yanxi Zhang | Wan Yue
Source: Angewandte Chemie (article, 2023-12-12)
Field: Materials Science — Polymers and Plastics

Versatile Neuromorphic Modulation and Biosensing based on N‐type Small‐molecule Organic Mixed Ionic‐Electronic Conductors Abstract The ion/chemical‐based modulation feature of organic mixed ionic‐electronic conductors (OMIECs) are critical to advancing next generation bio‐integrated neuromorphic hardware. Despite achievements with polymeric OMIECs in organic electrochemical neuronal synapse (OENS). However, small molecule OMIECs based OENS has not yet been realized. Here, for the first time, we demonstrate an effective materials design concept of combining n‐type fused all‐acceptor small molecule OMIECs with subtle side chain optimization that enables robustly and flexibly modulating versatile synaptic behavior and sensing neurotransmitter in solid or aqueous electrolyte, operating in accumulation modes. By judicious tuning the ending side chains, the linear oligoether and butyl chain derivative gNR‐Bu exhibits higher recognition accuracy for a model artificial neural network (ANN) simulation, higher steady conductance states and more outstanding ambient stability, which is superior to the state‐of‐art n‐type OMIECs based OENS. These superior artificial synapse characteristics of gNR‐Bu can be attributed to its higher crystallinity with stronger ion bonding capacities. More impressively, we unprecedentedly realized n‐type small‐molecule OMIECs based OENS as a neuromorphic biosensor enabling to respond synaptic communication signals of dopamine even at sub‐μM level in aqueous electrolyte. This work may open a new path of small‐molecule ion‐electron conductors for next‐generation ANN and bioelectronics. Neuromorphic engineering ; Bioelectronics ; Materials science ; Ionic bonding ; Electrolyte ; Nanotechnology ; Biosensor ; Molecule ; Ion ; Chemistry ; Artificial neural network ; Computer science ; Electrode ; Organic chemistry Conducting polymers and applications ; Advanced Memory and Neural Computing ; Luminescence and Fluorescent Materials
Authors: Hui Xu | Zeyuan Li | Zhaofu Zhang | Sheng Liu | Shengnan Shen | Yuzheng Guo
Source: Nanomaterials (article, 2023-04-13)
Field: Materials Science — Materials Chemistry

High-Accuracy Neural Network Interatomic Potential for Silicon Nitride In the field of machine learning (ML) and data science, it is meaningful to use the advantages of ML to create reliable interatomic potentials. Deep potential molecular dynamics (DEEPMD) are one of the most useful methods to create interatomic potentials. Among ceramic materials, amorphous silicon nitride (SiN<sub>x</sub>) features good electrical insulation, abrasion resistance, and mechanical strength, which is widely applied in industries. In our work, a neural network potential (NNP) for SiN<sub>x</sub> was created based on DEEPMD, and the NNP is confirmed to be applicable to the SiN<sub>x</sub> model. The tensile tests were simulated to compare the mechanical properties of SiN<sub>x</sub> with different compositions based on the molecular dynamic method coupled with NNP. Among these SiN<sub>x</sub>, Si<sub>3</sub>N<sub>4</sub> has the largest elastic modulus (<i>E</i>) and yield stress (<i>σ</i><sub>s</sub>), showing the desired mechanical strength owing to the largest coordination numbers (CN) and radial distribution function (RDF). The RDFs and CNs decrease with the increase of <i>x</i>; meanwhile, <i>E</i> and <i>σ</i><sub>s</sub> of SiN<sub>x</sub> decrease when the proportion of Si increases. It can be concluded that the ratio of nitrogen to silicon can reflect the RDFs and CNs in micro level and macro mechanical properties of SiN<sub>x</sub> to a large extent. Materials science ; Interatomic potential ; Silicon nitride ; Molecular dynamics ; Silicon ; Elastic modulus ; Ceramic ; Amorphous solid ; Ultimate tensile strength ; Radial distribution function ; Nitride ; Composite material ; Nanotechnology ; Optoelectronics ; Computational chemistry ; Crystallography ; Layer (electronics) ; Chemistry Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Semiconductor materials and devices
Authors: S.A. Mousavi | Abbas Montazeri
Source: Computational Materials Science (article, 2023-07-15)
Field: Materials Science — Materials Chemistry

Predicting mechanical properties of defective h-BN nanosheets using Data-Driven models Materials science ; Ultimate tensile strength ; Artificial neural network ; Molecular dynamics ; Random forest ; Boron nitride ; Modulus ; Computer science ; Artificial intelligence ; Biological system ; Nanotechnology ; Composite material ; Chemistry ; Computational chemistry Graphene research and applications ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research