jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhihao Deng | Chang Liu | Haigang Lin | Yingsheng Zhang
Source: ChemRxiv (preprint, 2023-04-03)
Field: Materials Science — Materials Chemistry

PairNet: Predicting Correlation Energies withCorrelated Features We proposed a molecular orbital based machine learning model for predicting accurate CCSD(T) correlation energies. The model, named as PairNet, shows excellent transferability on several public data sets using features inspired by pair natural orbitals(PNOs). Though additional computational costs were required to calculate PNOs, the improvement reveals the direct and physical inspired features are essential for a successful deployment of machine learning models in computational chemistry. Transferability ; Atomic orbital ; Computer science ; Correlation ; Software deployment ; Artificial intelligence ; Machine learning ; Molecular orbital ; Molecule ; Physics ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Weiyang Qiu | Cheng He | Genrang Zheng | YI Qiao-ling | Guo Chen
Source: Advanced Theory and Simulations (article, 2023-02-22)
Field: Materials Science — Materials Chemistry

Activation Function Dependence of Data‐Driven Spectra Prediction of Nanostructures Abstract Accompanied by the fast growth of data and computing power, Deep Learning has developed at a tremendous speed. Other related areas including material science, physics, chemistry, medical science, and engineering also benefit from this well‐proven powerful tool. Activation function is an essential part of Deep Neural Networks, which has attracted a lot of attention, but there are few researches based on nanophotonics problems. In this work, comprehensive research is made on the effectiveness of several widely used activation functions. This research shows that among those fancy activation functions that are investigated, Tanhshrink performs the best, which can predict the spectra with a Root Mean Square Error &lt;0.005 for over 99% of these randomly generated instances. Other traditional activation functions like Tanh and Sigmoid also show excellent outcomes in competition with novel ones. This work shows that although deep learning has already been a powerful tool to solve physics problems, there is still a lot of fundamental work to be done to achieve its maximum potential. Sigmoid function ; Deep learning ; Function (biology) ; Computer science ; Artificial neural network ; Activation function ; Work (physics) ; Artificial intelligence ; Competition (biology) ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Machine Learning and ELM
Authors: Elena Gelžinytė | Mario Öeren | Matthew Segall | Gábor Cśanyi
Source: ChemRxiv (preprint, 2023-12-13)
Field: Materials Science — Materials Chemistry

Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications to bond dissociation energy prediction, for example, in the context of cytochrome P450 (CYP) metabolism. The transferability of the MACE potential was validated on the COMP6 dataset, containing only closed-shell molecules, where it reaches better accuracy than the readily available general ANI-2x potential. MACE achieves similar accuracy on two CYP metabolism-specific datasets, which include open- and closed-shell structures. This model enables us to calculate the aliphatic C-H bond dissociation energy (BDE), which allows us to compare reaction energies of hydrogen abstraction, which is the rate-limiting step of the aliphatic hydroxylation reaction catalysed by CYPs. On the “CYP 3A4” dataset, MACE achieves a BDE RMSE of 1.37 kcal/mol and better prediction of BDE ranks than alternatives - the semi-empirical AM1 and GFN2-xTB methods and the ALFABET model by St. John et al.1 that predicts bond dissociation enthalpies. Finally, we highlight the smoothness of the MACE potential over paths of sp3C-H bond elongation and show that a minimal extension is enough for the MACE model to start finding reasonable minimum energy paths of methoxy radical-mediated hydrogen abstraction. Altogether, this work lays the ground for further extensions of scope in terms of chemical elements, (CYP-mediated) reaction classes and modelling the full reaction paths, not only bond dissociation energies. Chemistry ; Dissociation (chemistry) ; Hydrogen atom abstraction ; Bond-dissociation energy ; Hydrogen bond ; Molecule ; Mace ; Computational chemistry ; Computer science ; Hydrogen ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Ahmed M. Salaheldin | Doris Segets
Source: Chemical Engineering Journal (article, 2023-08-14)
Field: Materials Science — Materials Chemistry

Controlled mixing during colloidal quantum dot synthesis: A proxy-concept based on equivalent parameters Scalability ; Computer science ; Design of experiments ; Mixing (physics) ; Context (archaeology) ; Process (computing) ; Quality by Design ; Process optimization ; Process engineering ; Particle size ; Materials science ; Mathematics ; Physics ; Engineering Quantum Dots Synthesis And Properties ; Semiconductor Quantum Structures and Devices
Authors: Stiv Llenga | Ganna Gryn’ova
Source: ChemRxiv (preprint, 2023-03-20)
Field: Materials Science — Materials Chemistry

Matrix of Orthogonalized Atomic Orbital Coefficients Representation for Radicals and Ions Chemical (molecular, quantum) machine learning relies on representing molecules in unique and informative ways. Here, we present matrix of orthogonalized atomic orbital coefficients (MAOC) as a quantum-inspired molecular and atomic representation containing both structural (composition and geometry) and electronic (charge and spin multiplicity) information. MAOC is based on a cost-effective localization scheme that represents localized orbitals via a predefined set of atomic orbitals. The latter can be constructed from such small atom-centered basis sets as pcseg-0 and STO-3G in conjunction with guess (non-optimized) electronic configuration of the molecule. Importantly, MAOC is suitable for representing monatomic, molecular, and periodic systems, and can distinguish compounds with identical compositions and geometries but distinct charges and spin multiplicities. Using principal component analysis, we constructed a more compact but equally powerful version of MAOC – PCX-MAOC. To test the performance of full and reduced MAOC and several other representations (CM, SOAP, SLATM, and SPAHM), we used a kernel ridge regression machine learning model to predict frontier molecular orbital energy levels and ground state single-point energies for chemically diverse neutral and charged, closed- and open-shell molecules from an extended QM7b dataset, as well as two new datasets, N-HPC-1 (N-heteropolycycles) and REDOX (nitroxyl and phenoxyl radicals, carbonyl and cyano compounds). MAOC affords accuracy that is either similar or superior to other representations for a range of chemical properties and systems. Atomic orbital ; Molecular orbital ; Chemistry ; Molecule ; Quantum chemistry ; Molecular graph ; Slater-type orbital ; Computational chemistry ; Chemical physics ; Physics ; Molecular physics ; Molecular orbital theory ; Quantum mechanics ; Electron ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Raja Sekhar Sankuri | Nagesh Bhattu Sristy | Sri Phani Krishna Karri
Source:  (article, 2023-06-08)
Field: Materials Science — Materials Chemistry

Performance Analysis and Anomaly detection of Power Distribution Insulators using Deep Learning Techniques In the field of Machine Learning, Deep Learning(DL) is a rapidly growing area and it uses Convolutional Neural Networks(CNN) for image classification as it gives the most accurate results. CNN has various pre-trained architectures available for every type of problem. A variety of insulator parts with various forms and construction materials are found in the overhead power transmission lines. These Electrical assets are continually exposed to environmental factors and operational circumstances, which could result in size, color, and structure variations. This work offers a reliable image classification methodology that seeks to identify insulator classes effectively regardless of the degree of degradation. Here, we used various classical classifiers and identified the insulator defect pieces. In order to use a previously trained CNN as a feature extractor for training new classifiers, it must first undergo a step of fine-tuning. To determine which type of classifier provided the best non-conforming component discrimination performance, a comparison study was done.By identifying non-conforming component classes with an overall accuracy of 96%, the suggested methodology significantly improved classification performance. Computer science ; Convolutional neural network ; Extractor ; Artificial intelligence ; Classifier (UML) ; Deep learning ; Contextual image classification ; Feature extraction ; Pattern recognition (psychology) ; Machine learning ; Insulator (electricity) ; Artificial neural network ; Anomaly detection ; Image (mathematics) ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Line Inspection Robots ; Advanced Neural Network Applications
Authors: Tim Bechtel | Daniel T. Speckhard | Jonathan Godwin | Claudia Draxl
Source: arXiv (Cornell University) (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

Band-gap regression with architecture-optimized message-passing neural networks Graph-based neural networks and, specifically, message-passing neural networks (MPNNs) have shown great potential in predicting physical properties of solids. In this work, we train an MPNN to first classify materials through density functional theory data from the AFLOW database as being metallic or semiconducting/insulating. We then perform a neural-architecture search to explore the model architecture and hyperparameter space of MPNNs to predict the band gaps of the materials identified as non-metals. The parameters in the search include the number of message-passing steps, latent size, and activation-function, among others. The top-performing models from the search are pooled into an ensemble that significantly outperforms existing models from the literature. Uncertainty quantification is evaluated with Monte-Carlo Dropout and ensembling, with the ensemble method proving superior. The domain of applicability of the ensemble model is analyzed with respect to the crystal systems, the inclusion of a Hubbard parameter in the density functional calculations, and the atomic species building up the materials. Artificial neural network ; Computer science ; Dropout (neural networks) ; Hyperparameter ; Message passing ; Monte Carlo method ; Machine learning ; Ensemble forecasting ; Artificial intelligence ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Shaswat Mohanty | James Stevenson | Andrea Browning | Leif D. Jacobson | Karl Leswing | Mathew D. Halls | Mohammad Atif Faiz Afzal
Source: ChemRxiv (preprint, 2023-01-05)
Field: Materials Science — Materials Chemistry

Development of Scalable and Generalizable Machine Learned Force Field for Polymers Understanding and predicting the properties of polymers is vital to developing tailored polymer molecules for desired applications. Classical force fields may fail to capture key properties, for example, the transport properties of certain polymer systems such as polyethylene glycol. As a solution, we present an alternative potential energy surface, a charge recursive neural network (QRNN) model trained on DFT calculations made on smaller atomic clusters that generalizes well to oligomers comprising larger atomic clusters or longer chains. We demonstrate the validity of the polymer QRNN workflow by modeling the oligomers of ethylene glycol. We apply two rounds of active learning (addition of new training clusters based on current model performance) and implement a novel model training approach that uses partial charges from a semi-empirical method. Our developed QRNN model for polymers produces stable molecular dynamics (MD) simulation trajectory and captures the dynamics of polymer chains as indicated by the striking agreement with experimental values. Our model allows working on much larger systems than allowed by DFT simulations, at the same time providing a more accurate force field than classical force fields which provides a promising avenue for large-scale molecular simulations of polymeric systems. Polymer ; Scalability ; Molecular dynamics ; Force field (fiction) ; Computer science ; Field (mathematics) ; Statistical physics ; Trajectory ; Materials science ; Chemical physics ; Nanotechnology ; Scaling ; Computational chemistry ; Chemistry ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Software Engineering Research
Authors: Sakshi Yadav | Kacper J. Lachowski | Huat Thart Chiang | Lilo D. Pozzo | Jim De Yoreo | Shuai Zhang
Source: Angewandte Chemie (article, 2023-09-13)
Field: Materials Science — Biomaterials

Mechanisms of Biomolecular Self‐Assembly Investigated Through <i>In Situ</i> Observations of Structures and Dynamics Abstract Biomolecular self‐assembly of hierarchical materials is a precise and adaptable bottom‐up approach to synthesizing across scales with considerable energy, health, environment, sustainability, and information technology applications. To achieve desired functions in biomaterials, it is essential to directly observe assembly dynamics and structural evolutions that reflect the underlying energy landscape and the assembly mechanism. This review will summarize the current understanding of biomolecular assembly mechanisms based on in situ characterization and discuss the broader significance and achievements of newly gained insights. In addition, we will also introduce how emerging deep learning/machine learning‐based approaches, multiparametric characterization, and high‐throughput methods can boost the development of biomolecular self‐assembly. The objective of this review is to accelerate the development of in situ characterization approaches for biomolecular self‐assembly and to inspire the next generation of biomimetic materials. Characterization (materials science) ; Nanotechnology ; Computer science ; Mechanism (biology) ; In situ ; Biochemical engineering ; Materials science ; Chemistry ; Engineering ; Physics Supramolecular Self-Assembly in Materials ; Diatoms and Algae Research ; Polydiacetylene-based materials and applications
Authors: Y. Cho | Y.H. Kim | Sylvia K. Choi | J. Park | Sung‐Ho Bae | K.I. Hahn | Young‐Je Son | A. Navin | A. Lemasson | M. Rejmund | D. Ramos | D. Ackermann | Ablaihan Utepov | C. Fourgeres | J. C. Thomas | J. Goupil | G. Frémont | G. de France | Yutaka Watanabe | Y. Hirayama | S. C. Jeong | T. Niwase | H. Miyatake | P. Schury | M. Rosenbusch | Kijoon Chae | C. Kim | Subin Kim | G. Gu | Min Ja Kim | P. R. John | A. N. Andreyev | W. Korten | F. Recchia | G. de Angelis | Rafael Pérez Vidal | K. Rezynkina | Ji Soo Ha | F. Didierjean | P. Marini | Deby Treasa | I. Tsekhanovich | J. Dudouet | S. Bhattacharyya | G. Mukherjee | R. Banik | S. Bhattacharya | M. Mukai
Source: Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms (article, 2023-05-26)
Field: Chemistry — Spectroscopy

Paricle identification at VAMOS++ with machine learning techniques Multi-nucleon transfer reaction between 136Xe beam and 198Pt target was performed using the VAMOS++ spectrometer at GANIL to study the structure of n-rich nuclei around N=126. Unambiguous charge state identification was obtained by combining two supervised machine learning methods, deep neural network (DNN) and positional correction using a gradient-boosting decision tree (GBDT). The new method reduced the complexity of the kinetic energy calibration and outperformed the conventional method improving the charge state resolution by 8%. Boosting (machine learning) ; Computer science ; Artificial neural network ; Artificial intelligence ; Identification (biology) ; Nucleon ; Decision tree ; Machine learning ; Physics ; Nuclear physics Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications ; Nuclear physics research studies
Authors: Janghoon Ock | Chakradhar Guntuboina | Amir Barati Farimani
Source: arXiv (Cornell University) (preprint, 2023-09-01)
Field: Materials Science — Materials Chemistry

Catalyst Property Prediction with CatBERTa: Unveiling Feature Exploration Strategies through Large Language Models Efficient catalyst screening necessitates predictive models for adsorption energy, a key property of reactivity. However, prevailing methods, notably graph neural networks (GNNs), demand precise atomic coordinates for constructing graph representations, while integrating observable attributes remains challenging. This research introduces CatBERTa, an energy prediction Transformer model using textual inputs. Built on a pretrained Transformer encoder, CatBERTa processes human-interpretable text, incorporating target features. Attention score analysis reveals CatBERTa's focus on tokens related to adsorbates, bulk composition, and their interacting atoms. Moreover, interacting atoms emerge as effective descriptors for adsorption configurations, while factors such as bond length and atomic properties of these atoms offer limited predictive contributions. By predicting adsorption energy from the textual representation of initial structures, CatBERTa achieves a mean absolute error (MAE) of 0.75 eV-comparable to vanilla Graph Neural Networks (GNNs). Furthermore, the subtraction of the CatBERTa-predicted energies effectively cancels out their systematic errors by as much as 19.3% for chemically similar systems, surpassing the error reduction observed in GNNs. This outcome highlights its potential to enhance the accuracy of energy difference predictions. This research establishes a fundamental framework for text-based catalyst property prediction, without relying on graph representations, while also unveiling intricate feature-property relationships. Encoder ; Computer science ; Property (philosophy) ; Graph ; Observable ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Kiho Matsubara | Kei Takahashi | Takeshi Matsuda | Yuji Ueki | Noriaki Seko | Ryohei Kakuchi
Source: ChemRxiv (preprint, 2023-07-13)
Field: Chemistry — Organic Chemistry

GFN-xTB Based Computations Provide Comprehensive Insights into Emulsion Radiation-Induced Graft Polymerization In this article, a deep insight into emulsion radiartion-induced graft polymerization (RIGP) was obtained by computing explicit solvation free energies, conformational entropy, monomer radius and dipole moments with the state-of-the-art Conformer-Rotamer Ensemble Sampling Tool (CREST) package primalily at semiempirical GFN-xTB level. By leveraging the robustness of the CREST package, above parameters provided dynamic nature of methacrylate monoers with the consideration of realistic emulsion conditions. With the chemical and physical importance of the above results, CREST-determined explanatory variables sufficiently led to the building of the prediction models for the RIGP of methacrylate monomers. The machine learning model building resulted in effective reactivity predictions and unveiled important factors for the radiation-induced graft polymerization in a chemically interpretable fashion. Emulsion polymerization ; Monomer ; Conformational isomerism ; Polymerization ; Methacrylate ; Materials science ; Dipole ; Polymer ; Polymer chemistry ; Computational chemistry ; Chemistry ; Composite material ; Molecule ; Organic chemistry Advanced Polymer Synthesis and Characterization ; TiO2 Photocatalysis and Solar Cells ; Photopolymerization techniques and applications
Authors: Kiho Matsubara | Kei Takahashi | Takeshi Matsuda | Yuji Ueki | Noriaki Seko | Ryohei Kakuchi
Source: ChemRxiv (preprint, 2023-08-28)
Field: Chemistry — Organic Chemistry

GFN-xTB Based Computations Provide Comprehensive Insights into Emulsion Radiation-Induced Graft Polymerization In this article, a deep insight into emulsion radiartion-induced graft polymerization (RIGP) was obtained by computing explicit solvation free energies, conformational entropy, monomer radius and dipole moments with the state-of-the-art Conformer-Rotamer Ensemble Sampling Tool (CREST) package primalily at semiempirical GFN-xTB level. By leveraging the robustness of the CREST package, above parameters provided dynamic nature of methacrylate monoers with the consideration of realistic emulsion conditions. With the chemical and physical importance of the above results, CREST-determined explanatory variables sufficiently led to the building of the prediction models for the RIGP of methacrylate monomers. The machine learning model building resulted in effective reactivity predictions and unveiled important factors for the radiation-induced graft polymerization in a chemically interpretable fashion. Emulsion polymerization ; Monomer ; Conformational isomerism ; Polymerization ; Methacrylate ; Materials science ; Dipole ; Polymer ; Polymer chemistry ; Computer science ; Chemistry ; Composite material ; Organic chemistry ; Molecule Advanced Polymer Synthesis and Characterization ; TiO2 Photocatalysis and Solar Cells ; Machine Learning in Materials Science
Authors: Zheng Cheng | Jiapeng Liu | Tong Jiang | Mohan Chen | Fu‐Zhi Dai | Zhifeng Gao | Guolin Ke | Zifeng Zhao | Qi Ou
Source: Advanced Optical Materials (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Automatic Screen‐out of Ir(III) Complex Emitters by Combined Machine Learning and Computational Analysis (Advanced Optical Materials 18/2023) In article number number 2301093, Zifeng Zhao, Qi Ou, and co-workers carry out an automatic high-throughput virtual screening for light-emitting Ir(III) complexes based on accurately benchmarked quantum chemistry methodology and an advanced molecular representation learning technique. Promising candidates that have not been previously reported are screened out, for both lighting and displaying purposes. Virtual screening ; Materials science ; Throughput ; Computer science ; Representation (politics) ; Nanotechnology ; Telecommunications ; Chemistry ; Molecular dynamics ; Computational chemistry Machine Learning in Materials Science