jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christian Hölzer | Rick Oerder | Stefan Grimme | Jan Hamaekers
Source: Journal of Chemical Information and Modeling (article, 2024-11-20)
Field: Materials Science — Materials Chemistry

ConfRank: Improving GFN-FF Conformer Ranking with Pairwise Training Conformer ranking is a crucial task for drug discovery, with methods for generating conformers often based on molecular (meta)dynamics or sophisticated sampling techniques. These methods are constrained by the underlying force computation regarding runtime and energy ranking accuracy, limiting their effectiveness for large-scale screening applications. To address these ranking limitations, we introduce ConfRank, a machine learning-based approach that enhances conformer ranking using pairwise training. We demonstrate its performance using GFN-FF-generated conformer ensembles, leveraging the DimeNet++ architecture trained on pairs of 159 760 uncharged organic compounds from the GEOM data set with r<sup>2</sup>SCAN-3c reference level. Instead of predicting only on single molecules, this approach captures relative energy differences between conformers, leading to a significant improvement of the overall conformational ranking, outperforming GFN-FF and GFN2-xTB. Thereby, the pairwise RMSD of the relative energy difference of two conformers can be reduced from 5.65 to 0.71 kcal mol<sup>-1</sup> on the test data set, allowing to correctly identify up to 81% of all lowest lying conformers correctly (GFN-FF: 10%, GFN2-xTB: 47%). The ConfRank approach is cost-effective, allowing for scalable deployment on both CPU and GPU, achieving runtime accelerations by up to 2 orders of magnitude compared to GFN2-xTB. Out-of-sample investigations on CREST-generated conformer ensembles from the QM9 data set and conformers taken from an extended GMTKN55 data set show promising results for the robustness of this approach. Thereby, ranking correlation coefficient such as Spearman can be improved to 0.90 (GFN-FF: 0.39, GFN2-xTB: 0.84) reducing the probability of an incorrect sign flip in pairwise energy comparison from 32 to 7%. On the extended GMTKN55 subsets the pairwise MAD (RMSD) could be reduced on almost all subsets by up to 62% (58%) with an average improvement of 30% (29%). Moreover, an exemplary case study on vancomycin shows similar performance, indicating applicability to larger (bio)molecular structures. Furthermore, we motivate the usage of the pairwise training approach from a theoretical perspective, highlighting that while pairwise training can lead to a decline in single sample prediction of absolute energies for ML models, it significantly enhances conformer ranking performance. The data and models used in this study are available at https://github.com/grimme-lab/confrank. Conformational isomerism ; Pairwise comparison ; Ranking (information retrieval) ; Computer science ; Artificial intelligence ; Scalability ; Machine learning ; Chemistry ; Molecule ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ze Li | Ahmad Alkhayyat | Anupam Yadav | Majid Samavatian | Vahid Samavatian
Source: Physica Scripta (article, 2024-12-11)
Field: Materials Science — Materials Chemistry

Elastic constant analysis of orthotropic steel sheets using multitask machine learning and the impulse excitation technique Abstract This work presents a novel multitask learning approach featuring a dual convolutional neural network (CNN) system for determining the elastic constants of orthotropic rolled steel sheets. In the proposed model, resonance frequency spectra from the impulse excitation technique are converted into 2D image data. The first CNN focuses on detecting and predicting missing peak intensities, while the second CNN utilizes features from the entire spectrum image to predict elastic constants, including E 11 , E 22 , and G 12 . The input features include raw pixel data alongside three key categories for enhanced analysis: image-based features (such as the mean, median, mode, and skewness of pixel intensity distributions), spatial relations (including spatial frequency, pixel intensity correlations, and local contrast), and geometric features (such as shape descriptors and pixel connectivity). The results reveal that the optimal number of peaks (14), image resolution (121 pixels), and sample size (20 × 20 × 0.3 cm) maximize the model’s efficiency. Under these conditions, the model achieves R 2 values of 0.952, 0.902, and 0.913, and RMSE values of 1.89 GPa, 3.09 GPa, and 1.92 GPa for E 11 , E 22 , and G 12 , respectively. It is suggested that the superior prediction accuracy for E 11 is attributed to the stability of the Young’s modulus along the rolling direction, which is less variable in orthotropic materials. Furthermore, the study finds a dependency between input weight functions—including image-based features, spatial relations, and geometric features—as the material’s anisotropy changes, underscoring the importance of accounting for process variability in predictive modeling. Orthotropic material ; Pixel ; Impulse (physics) ; Convolutional neural network ; Impulse response ; Computer science ; Skewness ; Materials science ; Anisotropy ; Artificial intelligence ; Pattern recognition (psychology) ; Algorithm ; Mathematics ; Mathematical analysis ; Optics ; Physics ; Finite element method ; Statistics Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Thermography and Photoacoustic Techniques
Authors: Hiroki Sawada | Wataru Ohata | Jun Tani
Source: IEEE Transactions on Systems Man and Cybernetics Systems (article, 2024-10-24)
Field: Materials Science — Materials Chemistry

Human-Robot Kinaesthetic Interactions Based on the Free-Energy Principle The current study investigated possible human-robot kinaesthetic interactions using a variational recurrent neural network (RNN) model, called PV-RNN, which is based on the free-energy principle. Our prior robotic studies using PV-RNN showed that the nature of interactions between the top-down expectation and bottom-up inference is strongly affected by a parameter, called the meta-prior, which regulates the complexity term in free energy. The current study examines how changing the meta-prior w in the interaction phase affects the counter force generated when an experimenter attempts to induce movement pattern transitions familiar to the robot through its prior training. The study also compares the counter force generated when trained transitions are induced by a human experimenter and when untrained transitions are induced. Finally, the study examines how different levels of disturbances, pattern level, and cognitive level can be resolved internally across different layers. Our experimental results indicated that 1) the human experimenter needs more/less force to induce trained transitions when w is set with larger/smaller values; 2) the human experimenter needs more force to act on the robot when he attempts to induce untrained as opposed to trained movement pattern transitions; and 3) when the robot was disturbed in the cognitive level, the disturbance was resolved more in the higher layer as compared to the case with disturbance in the pattern level. Our analysis of time development of essential variables and values in PV-RNN during bodily interaction clarified the mechanism by which gaps in action intentions between the human experimenter and the robot can be manifested as reaction forces between them. Free energy principle ; Energy (signal processing) ; Robot ; Computer science ; Human–computer interaction ; Psychology ; Artificial intelligence ; Cognitive science ; Mathematics ; Machine learning Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies
Authors: Jiewen Zuo | Yankun Peng | Yongyu Li | Tianzhen Yin | Kuanglin Chao
Source: Food Research International (article, 2024-10-09)
Field: Chemistry — Analytical Chemistry

Nondestructive intelligent detection of total viable count in pork based on miniaturized spectral sensor Nondestructive testing ; Total Viable Count ; Computer science ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Joakim S. Jestilä | Nian Wu | Fabio Priante | Adam S. Foster
Source: Journal of Chemical Theory and Computation (article, 2024-02-26)
Field: Materials Science — Materials Chemistry

Accelerated Lignocellulosic Molecule Adsorption Structure Determination Here, we present a study combining Bayesian optimization structural inference with the machine learning interatomic potential Neural Equivariant Interatomic Potential (NequIP) to accelerate and enable the study of the adsorption of the conformationally flexible lignocellulosic molecules β-d-xylose and 1,4-β-d-xylotetraose on a copper surface. The number of structure evaluations needed to map out the relevant potential energy surfaces are reduced by Bayesian optimization, while NequIP minimizes the time spent on each evaluation, ultimately resulting in cost-efficient and reliable sampling of large systems and configurational spaces. Although the applicability of Bayesian optimization for the conformational analysis of the more flexible xylotetraose molecule is restricted by the sample complexity bottleneck, the latter can be effectively bypassed with external conformer search tools, such as the Conformer-Rotamer Ensemble Sampling Tool, facilitating the subsequent lower-dimensional global minimum adsorption structure determination. Finally, we demonstrate the applicability of the described approach to find adsorption structures practically equivalent to the density functional theory counterparts at a fraction of the computational cost. Conformational isomerism ; Bayesian optimization ; Computer science ; Adsorption ; Potential energy surface ; Molecular dynamics ; Bottleneck ; Chemistry ; Bayesian probability ; Biological system ; Molecule ; Computational chemistry ; Mathematical optimization ; Mathematics ; Artificial intelligence ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Eline Desmedt | Michiel Jacobs | Mercedes Alonso | Freija De Vleeschouwer
Source: Physical Chemistry Chemical Physics (article, 2024-11-07)
Field: Materials Science — Materials Chemistry

Deciphering nonlinear optical properties in functionalized hexaphyrins <i>via</i> explainable machine learning Over the years, several studies have aimed to elucidate why certain molecules show more enhanced nonlinear optical (NLO) properties than others. This knowledge is particularly valuable in the design of new NLO switches, where the ON and OFF states of the switch display markedly different NLO behaviors. In the literature, orbital contributions, aromaticity, planarity, and intramolecular charge transfer have been put forward as key factors in this regard. Based on our previous work on functionalized hexaphyrin-based redox switches, we aim at identifying through explainable machine learning the driving forces of the first hyperpolarizability related to the hyper-Rayleigh scattering (<i>β</i><sub>HRS</sub>) of <i>meso</i>-substituted and/or core-modified [26]- and [30]hexaphyrins. The significant correlation between <i>β</i><sub>HRS</sub> and the HOMO-LUMO energy gap can be further improved by including other orbitals as well as charge-transfer features in a 6-fold cross-validated kernel-ridge-regression model. Our Shapley additive explanations (SHAP) analysis shows that the charge transfer excitation length is more important for 30R systems, whereas the transition dipole moment between the ground and first excited state is one of the main contributors for 26R systems. We also demonstrate that, besides various hexaphyrin-based redox states, the ML model can describe to a large degree the <i>β</i><sub>HRS</sub> response of other hexaphyrins, differing in substitution pattern and topology (26D and 28M). Nonlinear optical ; Nonlinear system ; Materials science ; Nanotechnology ; Chemical engineering ; Computer science ; Chemistry ; Physics ; Engineering ; Quantum mechanics Porphyrin and Phthalocyanine Chemistry ; Molecular spectroscopy and chirality ; Nonlinear Optical Materials Studies
Authors: Amit Kumar | Balasaheb J. Nagare | R.P. Sharma | D. G. Kanhere
Source: The Journal of Chemical Physics (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Understanding melting behavior of aluminum clusters using machine learned deep neural network potential energy surfaces Deep neural network-based deep potentials (DP), developed by Tuo et al., have been used to compute the thermodynamic properties of free aluminum clusters with accuracy close to that of density functional theory. Although Jarrold and collaborators have reported extensive experimental measurements on the melting temperatures and heat capacities of free aluminum clusters, no reports exist for finite-temperature ab initio simulations on larger clusters (N > 55 atoms). We report the heat capacities and melting temperatures for 32 clusters in the size range of 48-342 atoms, computed using the multiple histogram technique. Extensive molecular dynamics (MD) simulations at twenty four temperatures have been performed for all the clusters. Our results are in very good agreement with the experimental melting temperatures for 19 clusters. Except for a few sizes, the interesting features in the heat capacities have been reproduced. To gain insight into the striking features reported in the experiments, we used structural and dynamical descriptors such as temperature-dependent mean squared displacements and the Lindemann index. Bimodal features observed in Al116 and the weak shoulder seen in Al52 are attributed to solid-solid structural transitions. In confirmation of the earlier reports, we observe that the behavior of the heat capacities is significantly influenced by the nature of the ground state geometries. Our findings show that the sharp drop in the melting temperature of the 56-atom cluster is a consequence of the change in the geometry of Al55. Mulliken population analysis of Al55 reveals that the charge-induced local electric field is responsible for the strong bonding between core and surface atoms, leading to the higher melting temperature. Our calculations do not support the lower melting temperature observed in experimental studies of Al69. Our results indicate that Al48 is in a liquid state above 600 K and does not support the high melting temperature reported in the experiment. It turns out that the accuracy of the DP model by Tuo et al. is not reliable for MD simulations beyond 750 K. We also report low-lying equilibrium geometries and thermodynamics of 11 larger clusters (N = 147-342) that have not been previously reported, and the melting temperatures of these clusters are in good agreement with the experimental ones. Cluster (spacecraft) ; Heat capacity ; Atom (system on chip) ; Thermodynamics ; Molecular dynamics ; Aluminium ; Ab initio ; Materials science ; Melting point ; Population ; Molecular physics ; Condensed matter physics ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: Lucas R. Timmerman | Shashikant Kumar | Phanish Suryanarayana | Andrew J. Medford
Source: Journal of Chemical Theory and Computation (article, 2024-07-08)
Field: Materials Science — Materials Chemistry

Overcoming the Chemical Complexity Bottleneck in on-the-Fly Machine Learned Molecular Dynamics Simulations We develop a framework for on-the-fly machine learned force field molecular dynamics simulations based on the multipole featurization scheme that overcomes the bottleneck with the number of chemical elements. Considering bulk systems with up to 6 elements, we demonstrate that the number of density functional theory calls remains approximately independent of the number of chemical elements, in contrast to the increase in the smooth overlap of the atomic positions scheme. Bottleneck ; On the fly ; Molecular dynamics ; Computer science ; Scheme (mathematics) ; Density functional theory ; Multipole expansion ; Field (mathematics) ; Statistical physics ; Fast multipole method ; Biological system ; Theoretical computer science ; Physics ; Computational chemistry ; Chemistry ; Mathematics ; Quantum mechanics ; Biology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Kawther Adeeb Hussein | Naser Shaalan | Aliaa Khauon Lafta | Janan Majeed Al Akeedi
Source: Indonesian Journal of Chemistry (article, 2024-04-01)
Field: Chemistry — Organic Chemistry

Preparation, Characterization, and Biological Activity of La(III), Nd(III), Er(III), Gd(III), and Dy(III) Complexes with Schiff Base Resulted from Reaction of 4-Antipyrinecarboxaldehyde and 2-Aminobenzothiazole The research includes the preparation of several complexes of the internal transition elements lanthanide (Ln = La, Nd, Er, Gd, and Dy) containing the 4f shell, with Schiff bases resulting from condensation reactions between 4-antipyrinecarboxaldehyde and 2-aminobenzothiazoles. Schiff's base was identified using FTIR spectra, UV-vis spectroscopy, elemental microanalysis CHNSO, nuclear magnetic resonance, mass spectrometry, and TGA thermal analysis. The complexes were studied and identified with elemental microanalysis CHNSO, FTIR spectroscopy, UV-vis spectroscopy, TGA thermal analysis, conductivity measurement, and magnetic sensitivity. The result showed that these complexes were classified as homogeneous bidentate complexes with the general formula of [Ln2(L)2(NO3)6]·6H2O. The physical measurements indicated that the prepared complexes are non-electrolyte and paramagnetic. Some compounds prepared in vitro were evaluated for their antibacterial activity against four types of pathogenic strains Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Klebsiella pneumonia, and using the agar disc spreading method for the evaluation. The results showed that some of these complexes have good antibacterial activity compared to the biological activity of the ligand. Also, the biological activity of Schiff's base and the prepared complexes were evaluated against three types of fungi (Candida albicans, Tropical fungi, and Scandal fungi), and they showed great activity against the prepared complexes. Schiff base ; Chemistry ; Characterization (materials science) ; Base (topology) ; Nuclear chemistry ; Polymer chemistry ; Materials science ; Mathematics ; Nanotechnology Inorganic and Organometallic Chemistry ; Metal complexes synthesis and properties
Authors: Niloofar Rahmani | Ahmad Mani‐Varnosfaderani
Source: Industrial Crops and Products (article, 2024-10-30)
Field: Chemistry — Analytical Chemistry

Profiling volatile organic compounds of different grape seed oil genotypes using gas chromatography-mass spectrometry and chemometric methods Chemistry ; Gas chromatography–mass spectrometry ; Mass spectrometry ; Chromatography ; Gas chromatography ; Volatile organic compound ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Weiping Liu | Yi Fang | Xiaolin Lyu | Xiangfang Peng | Zhong‐Zhen Luo | Zhigang Zou
Source: Soft Science (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Construction and application of thermogalvanic hydrogels Low-grade heat (below 373 Kelvins) is abundant and ubiquitous, yet the lack of cost-effective recovery technologies frequently impedes its effective utilization. The advent of thermogalvanic hydrogel thermocells has garnered significant attention due to their high thermopower, inherent flexibility, low cost, and scalability. Thermogalvanic hydrogels have significantly enhanced their thermoelectric performance, resulting in the development of functional materials that exhibit flexibility, stretchability, self-healing, and frost resistance. However, there are substantial challenges in developing multifunctional thermogalvanic hydrogels that combine high power density and efficiency with practical applicability. This review discusses the synthesis of the novel redox couple, improving the performance of electrolytes to increase thermopower, creating electrodes with extensive surface areas for better current density and flexibility, and optimizing thermocell structure design to improve performance further. This comprehensive review aims to propel progress toward higher performance levels and broader applications of thermogalvanic hydrogel thermocells. Flexibility (engineering) ; Self-healing hydrogels ; Scalability ; Materials science ; Nanotechnology ; Computer science ; Thermoelectric effect Advanced Thermoelectric Materials and Devices ; Perovskite Materials and Applications ; Advanced battery technologies research
Authors: laxmi singh | hemant shukla | Gopal Gupta
Source: Preprints.org (review, 2024-05-13)
Field: Materials Science — Materials Chemistry

A Comprehensive Review on Various Techniques Used for Synthesizing Graphene Quantum Dots Graphene quantum dots (GQDs) have garnered significant attention in recent years due to their unique properties and promising applications in various fields such as electronics, optoelectronics, bioimaging, and sensing. The synthesis of GQDs is a critical step in harnessing their potential, and numerous methods have been developed to produce GQDs with specific properties. This review provides an overview of the different techniques employed for synthesizing GQDs, including top-down and bottom-up approaches. Furthermore, it discusses the advantages, limitations, and potential applications of each method, offering insights into the future direction of GQD synthesis research. Quantum dot ; Graphene ; Nanotechnology ; Computer science ; Materials science Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanocluster Synthesis and Applications
Authors: Yitong Wu | Sifan Kong | Yao Qingxin | Muyun Li | Huayi Lai | Duoyu Sun | Qingyue Cai | Ze‐Lin Qiu | Honglong Ning | Yong Zhang
Source: Micromachines (article, 2024-08-26)
Field: Materials Science — Polymers and Plastics

Machine Learning-Guided Cycle Life Prediction for Electrochromic Devices Based on Deuterium and Water Mixing Solvent Electrochromic devices have demonstrated considerable potential in a range of applications, including smart windows and automotive rearview mirrors. However, traditional cycle life testing methods are time-consuming and require significant resources to process a substantial amount of generated data, which presents a significant challenge and remains an urgent issue to be addressed. To address this challenge, we proposed the use of Long Short-Term Memory (LSTM) networks to construct a prediction model of the cycle life of electrochromic devices and introduced an interpretable analysis method to further analyze the model's predictive capabilities. The original dataset used for modeling was derived from preliminary experiments conducted under 1000 cycles of six devices prepared with varying mixing ratios of heavy water (D<sub>2</sub>O). Furthermore, validation experiments confirmed the feasibility of the D<sub>2</sub>O mixing strategy, with 83% of the devices exhibiting a high initial transmittance modulation amplitude (Δ<i>T</i> = 43.95%), a rapid response time (<i>t<sub>c</sub></i> = 7 s and <i>t<sub>b</sub></i> = 8 s), and excellent cyclic stability (Δ<i>T</i> = 44.92% after 1000 cycles). This study is the first to use machine learning techniques to predict the cycle life of electrochromic devices while proposing performance enhancement and experimental time savings for inorganic all-liquid electrochromic devices. Mixing (physics) ; Electrochromism ; Solvent ; Deuterium ; Materials science ; Chemical engineering ; Chemistry ; Organic chemistry ; Engineering ; Physical chemistry ; Physics ; Nuclear physics ; Electrode Conducting polymers and applications ; Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors