jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qimeng Sun | Junbo Yang | Shizhao Wang | Wei Shen | Jianping Shi | Yi Song
Source: Optics & Laser Technology (article, 2024-01-30)
Field: Materials Science — Materials Chemistry

Spectroscopic ellipsometry to characterize the Transition-Metal dichalcogenides single crystals doping concentration Materials science ; Ellipsometry ; Transition metal ; Doping ; Metal ; Condensed matter physics ; Analytical Chemistry (journal) ; Nanotechnology ; Optoelectronics ; Thin film ; Chemistry ; Physics ; Metallurgy 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Advanced biosensing and bioanalysis techniques
Authors: Debarati Roy | Sevinj Guluzade | Vusala Nabi Jafarova
Source: Research Square (preprint, 2024-04-17)
Field: Materials Science — Materials Chemistry

First-principles and deep learning frameworks to predict the electronic and magnetic properties of V-doped SiC nanotube Abstract In this study based on Density Functional Theory (DFT) and Local Spin Density Approximation (LDA) methods within Hubbard U corrections have been theoretically studied electronic and magnetic properties of single wall silicon carbide nanotube doped by vanadium. These properties were simulated for cases that single or double silicon atoms of the SiC nanotube replaced with V atoms. Using Deep Learning (DL) Algorithms are the boon to provide prediction of quantum-confined electronic structure calculations, however first-principles simulation methods more accurate. ML based regression model shows the accuracy and prediction model for the quantum-confined nanotube. Among the various neural network algorithms, tri-layered and medium neural netwok algorithms provide more accuracy and less error rate for this molecular nanotube. The comparison between ML based approach and DFT based procedure reveals the similarity and accuracy of the proposed algorithm. The first-principles calculated energy spin-up and spin-down band gap values for single wall chiral (6,0) SiC:V nanotube systems are about of 0.6 and 1.4 eV, respectively. Although the undoped SiC system is a nonmagnetic, the V-doped SiC nanotube induces magnetism and total magnetic moment of this magnetic material equal to ~ 1.001 µ B . Density of states calculations indicated that the magnetization of SiC:V single wall nanotube mainly come from the 2 p orbitals of carbon atoms and 3 d orbitals of V dopant. From the total energy calculations for ferromagnetic and antiferromagnetic phases for V-doped SiCNT systems obtained that the ferromagnetic phase more stable. Nanotube ; Materials science ; Density functional theory ; Antiferromagnetism ; Condensed matter physics ; Magnetism ; Ferromagnetism ; Magnetic moment ; Magnetization ; Doping ; Band gap ; Carbon nanotube ; Nanotechnology ; Computational chemistry ; Physics ; Quantum mechanics ; Optoelectronics ; Chemistry ; Magnetic field Boron and Carbon Nanomaterials Research ; Semiconductor materials and interfaces ; Graphene research and applications
Authors: Sheuly Ghosh | Katharina Ueltzen | Janine George | Jörg Neugebauer | Fritz Körmann
Source: Research Square (preprint, 2024-03-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chemical ordering and magnetism in face-centered cubic CrCoNi alloy Magnetism ; Cubic crystal system ; Alloy ; Face (sociological concept) ; Condensed matter physics ; Materials science ; Crystallography ; Chemistry ; Physics ; Metallurgy ; Sociology ; Social science Magnetic Properties of Alloys ; Rare-earth and actinide compounds ; Metallurgical and Alloy Processes
Authors: Sriram Susarla
Source: Journal of Student Research (article, 2024-08-31)
Field: Chemistry — Spectroscopy

A Novel ML Approach to Detect Possible Protein Allosteric Sites for Drug Discovery In recent years, allosteric sites on proteins, located away from the primary active binding site, have gained prominence as promising drug targets because of the significant control they provide over biological pathways. The process of identifying allosteric sites through methods such as x-ray crystallography is often time-consuming, where the protein structure and the structure of potential allosteric-site binding molecules are analyzed to discover the allosteric site. Thus, machine learning models have been applied to expedite the process of allosteric drug discovery by contributing to allosteric site identification. In this study, I investigated different machine learning models and evaluated their performance and found that the best models were the random forest classifier and a 5-layer neural network. The random forest had an accuracy of 98.89%, a precision score of 98.20%, a recall score of 99.69%, an F1 score of 98.94%, and an AUC of .9991. The neural network had an accuracy of 97.70%, a precision score of 99.15%, a recall score of 96.04%, an F1 score of 97.57%, and an AUC of .9939. The high accuracy of these models along with their efficiency in running could potentially help the pharmaceutical industry identify these allosteric sites which would help with the allosteric drug manufacturing process. Allosteric regulation ; Drug discovery ; Drug ; Computational biology ; Chemistry ; Computer science ; Pharmacology ; Biology ; Biochemistry ; Receptor Analytical Chemistry and Chromatography ; Receptor Mechanisms and Signaling ; Monoclonal and Polyclonal Antibodies Research
Authors: Lucien Dupuy | Neepa T. Maitra
Source: arXiv (Cornell University) (preprint, 2024-07-15)
Field: Materials Science — Materials Chemistry

Exciting DeePMD: Learning excited state energies, forces, and non-adiabatic couplings We extend the DeePMD neural network architecture to predict electronic structure properties necessary to perform non-adiabatic dynamics simulations. While learning the excited state energies and forces follows a straightforward extension of the DeePMD approach for ground-state energies and forces, how to learn the map between the non-adiabatic coupling vectors (NACV) and the local chemical environment descriptors of DeePMD is less trivial. Most implementations of machine-learning-based non-adiabatic dynamics inherently approximate the NACVs, with an underlying assumption that the energy-difference-scaled NACVs are conservative fields. We overcome this approximation, implementing the method recently introduced by Richardson [J. Chem. Phys. 158 011102 (2023)], which learns the symmetric dyad of the energy-difference-scaled NACV. The efficiency and accuracy of our neural network architecture is demonstrated through the example of the methaniminium cation CH$_2$NH$_2^+$. Excited state ; Adiabatic process ; State (computer science) ; Physics ; Atomic physics ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Neural Networks and Reservoir Computing
Authors: Huiping Su | Hongbo Gao | Xinmiao Wang | Xi He | Da Shen
Source: CAAI Artificial Intelligence Research (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Variational Quantum Monte Carlo Solution of the Many-Electron Schrödinger Equation Based on Deep Neural Networks The solution of Schrödinger equation generates the quantisation properties of the system, which can completely describe the quantum behaviour of microscopic particles in a physical system. However, solving the Schrödinger equation is a non-deterministic polynomial time (NP)-hard problem. Numerical methods for solving the Schrödinger equation require careful selection of basis configurations, and the complete basis set has high accuracy but its complexity increases exponentially in the number of particles. Therefore, it is crucial to find an effective numerical method. To solve this problem, this paper present a deep learning architecture, VMCNet, using the powerful computational efficiency of neural networks to improve the speed of numerical computation. Moreover, this paper proposes a suitable wavefunction ansatz, witch determines the accuracy of neural networks, that achieves more accurate energy solutions of the many-electron Schrödinger equation compared to the conventional single-determinant variational molecular Monte Carlo. We introduce non-local and local blocks to represent quantum mechanical interactions, which contain more physical information about the particle system. The experimental results show that VMCNet can cover more than 93% of the correlation energy of the main group elements (Be–Ne) for the monoatomic system and more than 90% of the correlation energy for the diatomic system. Quantum Monte Carlo ; Monte Carlo method ; Statistical physics ; Artificial neural network ; Schrödinger equation ; Quantum ; Electron ; Hybrid Monte Carlo ; Deep neural networks ; Physics ; Monte Carlo molecular modeling ; Computer science ; Applied mathematics ; Quantum mechanics ; Mathematics ; Artificial intelligence ; Markov chain Monte Carlo ; Statistics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Erika Paola Fonseca Parra | Jihad Oumerri | Ana Arteni Andrea | Jean‐Luc Six | Steven P. Armes | Khalid Ferji
Source: ChemRxiv (preprint, 2024-09-24)
Field: Materials Science — Materials Chemistry

Neural Network-Driven Exploration of Solvophilic Block Size Effects in Polymerization-Induced Self-Assembly: From 2D To 3D Comprehensive Pseudo-Phase Diagram Phase diagram ; Polymerization ; Phase (matter) ; Block (permutation group theory) ; Block diagram ; Materials science ; Diagram ; Computer science ; Chemistry ; Engineering ; Geometry ; Mathematics ; Organic chemistry ; Polymer ; Composite material Machine Learning in Materials Science
Authors: Hao Li | Da Long | Yuan Li | Yonghong Tian | Xinchang Wang | Fanyang Mo
Source: arXiv (Cornell University) (preprint, 2024-01-07)
Field: Chemistry — Spectroscopy

Deep peak property learning for efficient chiral molecules ECD spectra prediction Chiral molecule assignation is crucial for asymmetric catalysis, functional materials, and the drug industry. The conventional approach requires theoretical calculations of electronic circular dichroism (ECD) spectra, which is time-consuming and costly. To speed up this process, we have incorporated deep learning techniques for the ECD prediction. We first set up a large-scale dataset of Chiral Molecular ECD spectra (CMCDS) with calculated ECD spectra. We further develop the ECDFormer model, a Transformer-based model to learn the chiral molecular representations and predict corresponding ECD spectra with improved efficiency and accuracy. Unlike other models for spectrum prediction, our ECDFormer creatively focused on peak properties rather than the whole spectrum sequence for prediction, inspired by the scenario of chiral molecule assignation. Specifically, ECDFormer predicts the peak properties, including number, position, and symbol, then renders the ECD spectra from these peak properties, which significantly outperforms other models in ECD prediction, Our ECDFormer reduces the time of acquiring ECD spectra from 1-100 hours per molecule to 1.5s. Spectral line ; Molecule ; Computer science ; Chemistry ; Artificial intelligence ; Biological system ; Physics ; Quantum mechanics ; Organic chemistry Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Seunghee Han | Byeong Gwan Lee | Dae Woon Lim | Jihan Kim
Source: arXiv (Cornell University) (preprint, 2024-06-18)
Field: Materials Science — Materials Chemistry

Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks Recently, metal-organic frameworks (MOFs) have demonstrated their potential as solid-state electrolytes in proton exchange membrane fuel cells. However, the number of MOFs reported to exhibit proton conductivity remains limited, and the mechanisms underlying this phenomenon are not fully elucidated, complicating the design of proton-conductive MOFs. In response, we developed a comprehensive database of proton-conductive MOFs and applied machine learning techniques to predict their proton conductivity. Our approach included the construction of both descriptor-based and transformer-based models. Notably, the transformer-based transfer learning (Freeze) model performed the best with a mean absolute error (MAE) of 0.91, suggesting that the proton conductivity of MOFs can be estimated within one order of magnitude using this model. Additionally, we employed feature importance and principal component analysis to explore the factors influencing proton conductivity. The insights gained from our database and machine learning model are expected to facilitate the targeted design of proton-conductive MOFs. Proton ; Metal-organic framework ; Conductivity ; Artificial intelligence ; Metal ; Materials science ; Computer science ; Machine learning ; Chemistry ; Physics ; Nuclear physics ; Physical chemistry ; Metallurgy ; Adsorption Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Greta M. Jacobson | Lixue Cheng | Vignesh C. Bhethanabotla | Jiace Sun | Anne B. McCoy
Source: ChemRxiv (preprint, 2024-12-30)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches for Developing Potential Surfaces: Applications to OH^-(H$_2$O)$_n$ ($n=1-3$) Complexes An approach for obtaining high-level ab initio potential surfaces is described. The approach takes advantage of machine learning strategies in a two-step process. In the first, the molecular-orbital based machine learning (MOB-ML) model uses Gaussian process regression to learn the correlation energy at the CCSD(T) level using the molecular orbitals obtained from Hartree-Fock calculations. In this work, the MOB-ML approach is expanded to use orbitals obtained using a smaller basis set, aug-cc-pVDZ, as features for learning the correlation energies at the complete basis set (CBS) limit. This approach is combined with the development of a neural-network potential, where the sampled geometries and energies that provide the training data for the potential are obtained using a diffusion Monte Carlo (DMC) calculation, which was run using the MOB-ML model. Protocols are developed to make full use of the structures that are obtained from the DMC calculation in the training process. These approaches are used to develop potentials for OH-(H2O) and H3O+(H2O), which are used for subsequent DMC calculations. The results of these calculations are compared to those performed using previously reported potentials. Overall, the results of the two sets of DMC calculations are in good agreement for these very floppy molecules. Potentials are also developed for OH-(H2O)2 and OH-(H2O)3, for which there are not available potential surfaces. The results of DMC calculations for these ions are compared to those for the correspondingOH-(H2O)2 and OH-(H2O)3 ions. It is found that the level of delocalization of the shared proton is similar for a hydroxide or hydronium ion bound to the same number of water molecules. This finding is consistent with the experimental observation that these sets of ions have similar spectra. Basis set ; Atomic orbital ; Ab initio ; Basis (linear algebra) ; Molecular orbital ; Ion ; Kriging ; Diffusion Monte Carlo ; Test set ; Gaussian ; Chemistry ; Monte Carlo method ; Statistical physics ; Molecule ; Machine learning ; Computational chemistry ; Physics ; Computer science ; Density functional theory ; Mathematics ; Quantum mechanics ; Hybrid Monte Carlo ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Greta M. Jacobson | Lixue Cheng | Vignesh C. Bhethanabotla | Jiace Sun | Anne B. McCoy
Source: ChemRxiv (preprint, 2024-12-30)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches for Developing Potential Surfaces: Applications to OH-(H2O)n (n=1-3) Complexes An approach for obtaining high-level ab initio potential surfaces is described. The approach takes advantage of machine learning strategies in a two-step process. In the first, the molecular-orbital based machine learning (MOB-ML) model uses Gaussian process regression to learn the correlation energy at the CCSD(T) level using the molecular orbitals obtained from Hartree-Fock calculations. In this work, the MOB-ML approach is expanded to use orbitals obtained using a smaller basis set, aug-cc-pVDZ, as features for learning the correlation energies at the complete basis set (CBS) limit. This approach is combined with the development of a neural-network potential, where the sampled geometries and energies that provide the training data for the potential are obtained using a diffusion Monte Carlo (DMC) calculation, which was run using the MOB-ML model. Protocols are developed to make full use of the structures that are obtained from the DMC calculation in the training process. These approaches are used to develop potentials for OH-(H2O) and H3O+(H2O), which are used for subsequent DMC calculations. The results of these calculations are compared to those performed using previously reported potentials. Overall, the results of the two sets of DMC calculations are in good agreement for these very floppy molecules. Potentials are also developed for OH-(H2O)2 and OH-(H2O)3, for which there are not available potential surfaces. The results of DMC calculations for these ions are compared to those for the correspondingOH-(H2O)2 and OH-(H2O)3 ions. It is found that the level of delocalization of the shared proton is similar for a hydroxide or hydronium ion bound to the same number of water molecules. This finding is consistent with the experimental observation that these sets of ions have similar spectra. Computer science ; Chemistry Machine Learning in Materials Science
Authors: Yuheng Wang | Amirreza Kazemi | Taotao Jing | Zhengming Ding | Like Li | Shengfeng Yang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Rapid Prediction of Grain Boundary Network Evolution in Nanomaterials Utilizing a Generative Machine Learning Approach Generative grammar ; Nanomaterials ; Boundary (topology) ; Artificial intelligence ; Computer science ; Machine learning ; Materials science ; Nanotechnology ; Mathematics Machine Learning in Materials Science ; Neural Networks and Applications ; Carbon Nanotubes in Composites
Authors: Ming Cheng | Wen Li | Wei Qi | Yuchao Dun | Victor W.-C. Chang | Zeyu Liu | Lei Jin | Guoke Wei
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Explainable Machine-Learning Optimization of H-BN Nanosheets with Surface Defects for Enhanced Hydrophobicity Surface (topology) ; Materials science ; Nanotechnology ; Chemical engineering ; Engineering ; Mathematics High voltage insulation and dielectric phenomena ; Membrane Separation Technologies
Authors: Ming Cheng | Wen Li | Wei Qi | Yuchao Dun | Victor W.-C. Chang | Zeyu Liu | Lei Jin | Guoke Wei
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Explainable Machine-Learning Optimization of H-Bn Nanosheets with Surface Defects for Enhanced Hydrophobicity Surface (topology) ; Materials science ; Nanotechnology ; Chemical engineering ; Computer science ; Engineering ; Mathematics ; Geometry High voltage insulation and dielectric phenomena
Authors: Lorenzo Branca | A. Pallottini
Source: arXiv (Cornell University) (preprint, 2024-02-19)
Field: Chemistry — Spectroscopy

Emulating the interstellar medium chemistry with neural operators Galaxy formation and evolution critically depend on understanding the complex photo-chemical processes that govern the evolution and thermodynamics of the InterStellar Medium (ISM). Computationally, solving chemistry is among the most heavy tasks in cosmological and astrophysical simulations. The evolution of such non-equilibrium photo-chemical network relies on implicit, precise, computationally costly, ordinary differential equations (ODE) solvers. Here, we aim at substituting such procedural solvers with fast, pre-trained, emulators based on neural operators. We emulate a non-equilibrium chemical network up to H$_2$ formation (9 species, 52 reactions) by adopting the DeepONet formalism, i.e. by splitting the ODE solver operator that maps the initial conditions and time evolution into a tensor product of two neural networks. We use $\texttt{KROME}$ to generate a training set spanning $-2\leq \log(n/\mathrm{cm}^{-3}) \leq 3.5$, $\log(20) \leq\log(T/\mathrm{K}) \leq 5.5$, $-6 \leq \log(n_i/n) &lt; 0$, and by adopting an incident radiation field $\textbf{F}$ sampled in 10 energy bins with a continuity prior. We separately train the solver for $T$ and each $n_i$ for $\simeq 4.34\,\rm GPUhrs$. Compared with the reference solutions obtained by $\texttt{KROME}$ for single zone models, the typical precision obtained is of order $10^{-2}$, i.e. the $10 \times$ better with a training that is $40 \times$ less costly with respect to previous emulators which however considered only a fixed $\mathbf{F}$. The present model achieves a speed-up of a factor of $128 \times$ with respect to stiff ODE solvers. Our neural emulator represents a significant leap forward in the modeling of ISM chemistry, offering a good balance of precision, versatility, and computational efficiency. Interstellar medium ; Astrobiology ; Chemistry ; Nanotechnology ; Astrophysics ; Physics ; Materials science ; Galaxy Spectroscopy and Laser Applications
Authors: Omid Sobhani | Hamid Toliati | Furkan Elmaz | Shahab Pormoradi Gerdposhteh | Benedict Carius | Kevin Mets | Siegfried Mercelis
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

A Hybrid Predictive Modelling Approach for Catalyzed Polymerization Reactors Polymerization ; Catalysis ; Process engineering ; Computer science ; Chemical engineering ; Materials science ; Chemistry ; Engineering ; Organic chemistry ; Polymer Machine Learning in Materials Science ; Fault Detection and Control Systems ; Advanced Control Systems Optimization