jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Can Xu | Haosen Wang | Weigang Wang | Pengfei Zheng | Hongyang Chen
Source: arXiv (Cornell University) (preprint, 2024-01-05)
Field: Materials Science — Materials Chemistry

Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation Denoising diffusion models have shown great potential in multiple research areas. Existing diffusion-based generative methods on de novo 3D molecule generation face two major challenges. Since majority heavy atoms in molecules allow connections to multiple atoms through single bonds, solely using pair-wise distance to model molecule geometries is insufficient. Therefore, the first one involves proposing an effective neural network as the denoising kernel that is capable to capture complex multi-body interatomic relationships and learn high-quality features. Due to the discrete nature of graphs, mainstream diffusion-based methods for molecules heavily rely on predefined rules and generate edges in an indirect manner. The second challenge involves accommodating molecule generation to diffusion and accurately predicting the existence of bonds. In our research, we view the iterative way of updating molecule conformations in diffusion process is consistent with molecular dynamics and introduce a novel molecule generation method named Geometric-Facilitated Molecular Diffusion (GFMDiff). For the first challenge, we introduce a Dual-Track Transformer Network (DTN) to fully excevate global spatial relationships and learn high quality representations which contribute to accurate predictions of features and geometries. As for the second challenge, we design Geometric-Facilitated Loss (GFLoss) which intervenes the formation of bonds during the training period, instead of directly embedding edges into the latent space. Comprehensive experiments on current benchmarks demonstrate the superiority of GFMDiff. Computer science ; Embedding ; Diffusion ; Diffusion process ; Algorithm ; Statistical physics ; Artificial intelligence ; Theoretical computer science ; Physics Machine Learning in Materials Science ; Cell Image Analysis Techniques
Authors: S. Shantha Kumari | Gouri Kumar Sahu | Satyanarayan Dhal | A. K. Misra
Source:  (article, 2024-12-19)
Field: Chemistry — Physical and Theoretical Chemistry

Prediction of Poisson's Ratio vs Average Ionic Radius for Various Elements of the Periodic Table Using Machine Learning Understanding the mechanical properties of materials is crucial for engineering applications. Poisson's ratio., which measures a material's lateral strain response to uniaxial stress., and the average ionic radius., defining the distance between an ion's nucleus and its outermost shell., are key parameters for material characterization. This study applies machine learning techniques to predict Poisson's ratio based on the average ionic radius across various elements. The research aims to develop a predictive model that facilitates material selection processes by leveraging a dataset of elemental properties. Poisson's ratio., ranging from -1 to Y2 for stable isotropic materials., reflects a material's elastic deformation behavior. The average ionic radius significantly influences ionic interactions., solvation., and system thermodynamics., highlighting its relevance in understanding material properties. The findings aim to bridge the gap between ionic characteristics and mechanical behavior., offering insights into advanced material design and selection. RADIUS ; Poisson distribution ; Table (database) ; Ionic radius ; Computer science ; Periodic table ; Mathematics ; Statistics ; Physics ; Data mining ; Quantum mechanics History and advancements in chemistry
Authors: William G. Fahrenholtz
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Superhard Boride Ceramics Produced by Boro-Carbothermal Reduction Boride ceramics have been proposed for use in extreme environments based on melting temperatures above 3000°C and other attractive properties such as high hardness. Inspired by the research of Professor Samsonov and others, our laboratory has investigated compositionally complex borides containing five different transition metals in an effort to identify new superhard ceramics. The most commonly studied compositionally complex boride has been (Hf,Nb,Ta,Ti,Zr)B2 with the metals present in nominally equimolar amounts. The initial search for harder materials involved substituting metals such as Cr, Mo, or W in place of Nb, which increased the hardness. Among the materials produced, (Cr,Hf,Ta,Ti,Zr)B2 had the highest hardness, which was about 48 GPa at a load of 0.49 N. Subsequently, new boride compositions were identified using a computational approach called the Disordered Enthalpy-Entropy Descriptor (DEED). Using DEED, (Cr,Mo,Ti,V,W)B2 was identified as a thermodynamically stable compound. The dense ceramic had a hardness of 48 GPa at a load of 0.49 N. Recently, machine learning predicted that (Hf,Mo,Ti,V,W)B2 should be superhard. This composition was synthesized by our group and found to have a hardness of 66 GPa at a load of 0.49 N. This presentation will provide a broad perspective on promising approaches for synthesis and densification of new borides that are identified through computational studies and produced by boro-carbothermal reduction. Boride ; Materials science ; Ceramic ; Carbothermic reaction ; Enthalpy ; Metallurgy ; Thermodynamics Boron and Carbon Nanomaterials Research ; Advanced materials and composites ; Advanced ceramic materials synthesis
Authors: Eugene L. Mirkin | Elena Savchenko | Ekaterina A. Medvedeva
Source:  (article, 2024-10-17)
Field: Chemistry — Analytical Chemistry

Algorithms for Artificial Neural Networks with Interval Target Tuning for the Task of Classifying Cammeo and Osmancik Rice Varieties This paper investigates the application of intelligent learning algorithms for artificial neural networks (ANN) with interval target tuning for the task of classifying Cammeo and Osmancik rice varieties. The data used includes 3810 rice grain samples for which seven morphological features were measured. The core concept of the method is the creation of a multi-level hierarchical network consisting of various typical ANN modules interacting in a hierarchical structure based on a specially developed algorithm. The process of network synthesis and training is conducted, and the effectiveness of the proposed approach is analyzed. The results show that this method allows achieving high classification accuracy. Task (project management) ; Computer science ; Artificial neural network ; Interval (graph theory) ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Juan Carlos Perez
Source: International Journal of Computing Programming and Database Management (article, 2024-01-01)
Field: Materials Science — Biomaterials

Development of a data driven database framework to monitor and standardize fungal chitosan fermentation using agro industrial waste inputs The development of data-driven database frameworks for monitoring and standardizing fungal chitosan fermentation processes has emerged as a crucial area of research in biotechnological advancements. Chitosan, a biopolymer derived from fungal sources, exhibits numerous applications in agriculture, medicine, and food industries due to its exceptional biocompatibility, biodegradability, and antimicrobial properties. However, the production of fungal chitosan, especially using agro-industrial waste inputs, remains inefficient and inconsistent due to variations in fermentation parameters. This study aims to establish a robust database framework that integrates real-time data monitoring, statistical analysis, and machine learning tools to optimize the fungal chitosan fermentation process. By leveraging agro-industrial waste as raw material, the framework aims to not only standardize the fermentation but also improve the yield and quality of chitosan. Data on various fermentation parameters such as pH, temperature, aeration, and substrate concentration will be collected, analyzed, and modeled to predict optimal conditions for maximum yield. The study employs various machine learning algorithms to create predictive models for fermentation optimization, focusing on process consistency and reproducibility. The outcome of this research will offer an innovative solution to the biotechnological community by enhancing the commercial viability of fungal chitosan production, while also contributing to the sustainable use of agro-industrial waste. Ultimately, this framework will support the transition towards eco-friendly and economically feasible industrial-scale chitosan production, providing a scalable model that can be applied to other bioprocesses. Fermentation ; Data-driven ; Consistency (knowledge bases) ; Raw material ; Process (computing) ; Production (economics) ; Sustainability ; Scalability Nanocomposite Films for Food Packaging ; Fungal Biology and Applications ; Biofuel production and bioconversion
Authors: Xing Su | Siwei Cui | Yifei Zhang | Hui Yang | Dongping Wu
Source: Materials (article, 2024-07-08)
Field: Materials Science — Materials Chemistry

The Impact of Microwave Annealing on MoS2 Devices Assisted by Neural Network-Based Big Data Analysis Microwave annealing, an emerging annealing method known for its efficiency and low thermal budget, has established a foundational research base in the annealing of molybdenum disulfide (MoS<sub>2</sub>) devices. Typically, to obtain high-quality MoS<sub>2</sub> devices, mechanical exfoliation is commonly employed. This method's challenge lies in achieving uniform film thickness, which limits the use of extensive data for studying the effects of microwave annealing on the MoS<sub>2</sub> devices. In this experiment, we utilized a neural network approach based on the HSV (hue, saturation, value) color space to assist in distinguishing film thickness for the fabrication of numerous MoS<sub>2</sub> devices with enhanced uniformity and consistency. This method allowed us to precisely assess the impact of microwave annealing on device performance. We discovered a relationship between the device's electrical performance and the annealing power. By analyzing the statistical data of these electrical parameters, we identified the optimal annealing power for MoS<sub>2</sub> devices as 700 W, providing insights and guidance for the microwave annealing process of two-dimensional materials. Annealing (glass) ; Materials science ; Microwave ; Molybdenum disulfide ; Optoelectronics ; Computer science ; Composite material ; Telecommunications 2D Materials and Applications ; MXene and MAX Phase Materials ; Perovskite Materials and Applications
Authors: Pinchen Xie | Yixiao Chen | E Weinan | Roberto Car
Source: arXiv (Cornell University) (preprint, 2024-10-08)
Field: Materials Science — Materials Chemistry

Thermal disorder and phonon softening in the ferroelectric phase transition of lead titanate We report a molecular dynamics study of ab initio quality of the ferroelectric phase transition in crystalline PbTiO3. We model anharmonicity accurately in terms of potential energy and polarization surfaces trained on density functional theory data with modern machine learning techniques. Our simulations demonstrate that the transition has a strong order-disorder character, in agreement with diffraction experiments, and provide fresh insight into the approach to equilibrium across the phase transition. We find that the emergence and disappearance of the macroscopic polarization is driven by dipolar switching at the nanometer scale. We also computed the infrared optical absorption spectra in both the ferroelectric and the paraelectric phases, finding good agreement with the experimental Raman frequencies. Often, the almost ideal displacive character of the soft mode detected by Raman scattering in the paraelectric phase has been contrasted with the order-disorder character of the transition suggested by diffraction experiments. We settle this issue by showing that the soft mode coexists with a strong Debye relaxation associated with thermal disordering of the dipoles. The Debye relaxation feature is centered at zero frequency and appears near the transition temperature in both the ferroelectric and the paraelectric phases. Softening ; Ferroelectricity ; Phase transition ; Lead titanate ; Condensed matter physics ; Phonon ; Materials science ; Lead (geology) ; Phase (matter) ; Thermal ; Thermodynamics ; Composite material ; Physics ; Dielectric ; Optoelectronics ; Geology ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Glass properties and applications ; Solid-state spectroscopy and crystallography
Authors: Kullberg, Jacob G.
Source: ScholarsArchive  (Brigham Young University) (article, 2024-08-07)
Field: Materials Science — Materials Chemistry

Using Neural Networks to Reconstruct Temperature From Fluorescent Data for use in Bio-Microfluidics It has been theorized that Quantum Dots (QD) can solve the temperature sensor issue with microfluidic devices. Quantum Dots are nano-sized semiconducting crystals with a radius of an excited electron-hole pair. When a QD is excited by a short wavelength light, the QD produces fluorescent light. As the temperature of the dot changes, the dot expands and shifts the fluorescent spectrum. Quantum Dots are small enough to be placed inside the microfluidic devices, and a spectrometer can measure the change in fluorescent data. This fluorescent data can be used to create fluorescent images corresponding to the QD temperature. These fluorescent images can then be used to create temperature maps. Fluorescent images created by QDs have been used with curve-fitting techniques to create temperature maps with accuracy near 1\textdegree K RMSE. When using neural networks that take advantage of the spacial properties of the image, a sub 1\textdegree K RMSE has been achieved. When the neural network architecture mimics heat diffusion and uses known physics equations, an accuracy of 0.03619\textdegree K RMSE has been reached on microfluidic chips. Fluorescence ; Quantum dot ; Excited state ; Artificial neural network ; Microfluidics ; Wavelength ; RADIUS Chemical and Physical Properties of Materials ; Scientific Research and Discoveries ; Advanced Physical and Chemical Molecular Interactions
Authors: Jialei Dai | Yutong Zhang | Shiyi Chen | Yang Liu | Peng Xiu | Yong Wang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-09-13)
Field: Chemistry — Spectroscopy

BEGAN: Boltzmann-Reweighted Data Augmentation for Enhanced GAN-Based Molecule Design in Insect Pheromone Receptors Identifying molecules that bind strongly to target proteins in rational drug design is crucial. Machine learning techniques, such as generative adversarial networks (GAN), are now essential tools for generating such molecules. In this study, we present an enhanced method for molecule generation using objective-reinforced GANs. Specifically, we introduce BEGAN (Boltzmann-Enhanced GAN), a novel approach that adjusts molecule occurrence frequencies during training based on the Boltzmann distribution exp(−∆U/τ), where ∆U represents the estimated binding free energy derived from docking algorithms and τ is a temperature-related scaling hyperparameter. This Boltzmann reweighting process shifts the generation process towards molecules with higher binding affinities, allowing the GAN to explore molecular spaces with superior binding properties. The reweighting process can also be refined through multiple iterations without altering the overall distribution shape. To validate our approach, we apply it to the design of sex pheromone analogs targeting Spodoptera frugiperda pheromone receptor SfruOR16, illustrating that the Boltzmann reweighting significantly increases the likelihood of generating promising sex pheromone analogs with improved binding affinities to SfruOR16, further supported by atomistic molecular dynamics simulations. Furthermore, we conduct a comprehensive investigation into parameter dependencies and propose a reasonable range for the hyperparameter τ. Our method offers a promising approach for optimizing molecular generation for enhanced protein binding, potentially increasing the efficiency of drug discovery pipelines. Pheromone ; Insect ; Receptor ; Chemistry ; Molecule ; Boltzmann constant ; Computer science ; Computational biology ; Biological system ; Biology ; Physics ; Biochemistry ; Ecology ; Organic chemistry ; Thermodynamics Mass Spectrometry Techniques and Applications ; Pesticide Residue Analysis and Safety ; Analytical Chemistry and Chromatography
Authors: Johan F. Storm | I.B.C.M. Rocha | F.P. van der Meer
Source: arXiv (Cornell University) (preprint, 2024-02-20)
Field: Materials Science — Materials Chemistry

A Microstructure-based Graph Neural Network for Accelerating Multiscale Simulations Simulating the mechanical response of advanced materials can be done more accurately using concurrent multiscale models than with single-scale simulations. However, the computational costs stand in the way of the practical application of this approach. The costs originate from microscale Finite Element (FE) models that must be solved at every macroscopic integration point. A plethora of surrogate modeling strategies attempt to alleviate this cost by learning to predict macroscopic stresses from macroscopic strains, completely replacing the microscale models. In this work, we introduce an alternative surrogate modeling strategy that allows for keeping the multiscale nature of the problem, allowing it to be used interchangeably with an FE solver for any time step. Our surrogate provides all microscopic quantities, which are then homogenized to obtain macroscopic quantities of interest. We achieve this for an elasto-plastic material by predicting full-field microscopic strains using a graph neural network (GNN) while retaining the microscopic constitutive material model to obtain the stresses. This hybrid data-physics graph-based approach avoids the high dimensionality originating from predicting full-field responses while allowing non-locality to arise. By training the GNN on a variety of meshes, it learns to generalize to unseen meshes, allowing a single model to be used for a range of microstructures. The embedded microscopic constitutive model in the GNN implicitly tracks history-dependent variables and leads to improved accuracy. We demonstrate for several challenging scenarios that the surrogate can predict complex macroscopic stress-strain paths. As the computation time of our method scales favorably with the number of elements in the microstructure compared to the FE method, our method can significantly accelerate FE2 simulations. Microstructure ; Artificial neural network ; Computer science ; Graph ; Artificial intelligence ; Materials science ; Theoretical computer science ; Metallurgy Machine Learning in Materials Science
Authors: Valerio Briganti | Alessandro Lunghi
Source: arXiv (Cornell University) (preprint, 2024-10-11)
Field: Materials Science — Materials Chemistry

A machine-learning framework for accelerating spin-lattice relaxation simulations Molecular and lattice vibrations are able to couple to the spin of electrons and lead to their relaxation and decoherence. Ab initio simulations have played a fundamental role in shaping our understanding of this process but further progress is hindered by their high computational cost. Here we present an accelerated computational framework based on machine-learning models for the prediction of molecular vibrations and spin-phonon coupling coefficients. We apply this method to three open-shell coordination compounds exhibiting long relaxation times and show that this approach achieves semi-to-full quantitative agreement with ab initio methods reducing the computational cost by about 80%. Moreover, we show that this framework naturally extends to molecular dynamics simulations, paving the way to the study of spin relaxation in condensed matter beyond simple equilibrium harmonic thermal baths. Lattice (music) ; Relaxation (psychology) ; Condensed matter physics ; Computer science ; Statistical physics ; Physics ; Psychology Machine Learning in Materials Science ; Quantum and electron transport phenomena ; Advanced NMR Techniques and Applications
Authors: Anastasia Zelenina | Nikita Smirnov | Peter P. Paholchuk | N. I. Busleev | Victor G. Vins | П. А. Данилов | Alexey Gorevoy | Nikita Orekhov | S. I. Kudryashov
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

High-Temperature Multi-Vacancy Mediated Diffusion of Nitrogen in Diamond: Md Simulations and Mid-Ir Laser Experiments Download ; Diamond ; Vacancy defect ; Diffusion ; Computer science ; Nitrogen ; Materials science ; World Wide Web ; Crystallography ; Chemistry ; Physics ; Thermodynamics ; Composite material Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Semiconductor materials and devices
Authors: Gustina Alfa Trisnapradika | Muhamad Akrom | Supriadi Rustad | Hermawan Kresno Dipojono | Ryo Maezono | Wilson Agerico Diño
Source: Research Square (preprint, 2024-09-13)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach for Forecasting the Efficacy of Pyridazine Corrosion Inhibitors Pyridazine ; Corrosion ; Artificial intelligence ; Computer science ; Machine learning ; Materials science ; Chemistry ; Medicinal chemistry ; Metallurgy Material Properties and Failure Mechanisms ; Corrosion Behavior and Inhibition ; Engineering Diagnostics and Reliability
Authors: Flávio Noronha | Askery Canabarro | Rafael Chaves | Rodrigo G. Pereira
Source: arXiv (Cornell University) (preprint, 2024-08-29)
Field: Materials Science — Materials Chemistry

Predicting topological invariants and unconventional superconducting pairing from density of states and machine learning Competition between magnetism and superconductivity can lead to unconventional and topological superconductivity. However, the experimental confirmation of the presence of Majorana edge states and unconventional pairing currently poses a major challenge. Here we consider a two-dimensional lattice model for a superconductor with spin-orbit coupling and exchange coupling to randomly distributed magnetic impurities. Depending on parameters of the model, this system may display topologically trivial or nontrivial edge states. We map out the phase diagram by computing the Bott index, a topological invariant defined in real space. We then use machine learning (ML) algorithms to predict the Bott index from the local density of states (LDOS) at zero energy, obtaining high-accuracy results. We also train ML models to predict the amplitude of odd-frequency pairing in the anomalous Green's function at zero energy. Once the ML models are trained using the LDOS, which is experimentally accessible via scanning tunneling spectroscopy, our method could be applied to predict the number of Majorana edge states and to estimate the magnitude of odd-frequency pairing in real materials. Pairing ; Superconductivity ; Condensed matter physics ; Physics ; Topology (electrical circuits) ; Theoretical physics ; Mathematics ; Combinatorics Machine Learning in Materials Science