jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuqing Zhang | Yuan Liu | Zhiyu Xie | Yang Lei | Zhongyuan Liu | Mengzhou Yang | Runze Zhang | Qilong Kou | Lin Cheng | Wenping Wang | Xiaogang Jin
Source: arXiv (Cornell University) (preprint, 2024-05-27)
Field: Chemistry — Organic Chemistry

DreamMat: High-quality PBR Material Generation with Geometry- and Light-aware Diffusion Models 2D diffusion model, which often contains unwanted baked-in shading effects and results in unrealistic rendering effects in the downstream applications. Generating Physically Based Rendering (PBR) materials instead of just RGB textures would be a promising solution. However, directly distilling the PBR material parameters from 2D diffusion models still suffers from incorrect material decomposition, such as baked-in shading effects in albedo. We introduce DreamMat, an innovative approach to resolve the aforementioned problem, to generate high-quality PBR materials from text descriptions. We find out that the main reason for the incorrect material distillation is that large-scale 2D diffusion models are only trained to generate final shading colors, resulting in insufficient constraints on material decomposition during distillation. To tackle this problem, we first finetune a new light-aware 2D diffusion model to condition on a given lighting environment and generate the shading results on this specific lighting condition. Then, by applying the same environment lights in the material distillation, DreamMat can generate high-quality PBR materials that are not only consistent with the given geometry but also free from any baked-in shading effects in albedo. Extensive experiments demonstrate that the materials produced through our methods exhibit greater visual appeal to users and achieve significantly superior rendering quality compared to baseline methods, which are preferable for downstream tasks such as game and film production. Geometry ; Diffusion ; Quality (philosophy) ; Computer science ; Mathematics ; Physics ; Thermodynamics Photopolymerization techniques and applications
Authors: Siyang Lu | Jing Zhu | Zhi‐Cheng Tan | Xinzhe Shi | Wei Wu | Lianqing Zhu
Source: IEEE Transactions on Terahertz Science and Technology (article, 2024-12-09)
Field: Chemistry — Spectroscopy

Terahertz-Intra-Particle Diffusion Model for Adsorption of Ethanol in Graphene-Based Materials In this article, the terahertz-intraparticle diffusion model (THz-IPD) based on the intraparticle diffusion model and terahertz amplitude is established to assess diffusion adsorption rates and describe the mass transfer mechanism controlled by intraparticle diffusion. The diffusion adsorption process for four graphene-based materials with ethanol was analyzed by terahertz time-domain spectroscopy. Diffusion adsorption rates for four graphene-based materials were evaluated using a terahertz-pseudo second-order kinetic model and a terahertz-double-exponential model, revealing that intraparticle diffusion is one of the rate-limiting factors for adsorption. THz-IPD fitting found that intraparticle diffusion is not the only controlling factor. Hydrophobic interaction and hydrogen bonds are the decisive factors for the adsorption rates of hydrophobic and hydrophilic graphene-based materials. The results were supported by Brunner--Emmet--Teller (BET) surface area analysis, scanning electron microscope/energy spectrometer, and Fourier transform infrared methods. Terahertz radiation ; Graphene ; Adsorption ; Diffusion ; Materials science ; Particle (ecology) ; Chemical engineering ; Nanotechnology ; Optoelectronics ; Chemistry ; Thermodynamics ; Physics ; Physical chemistry Spectroscopy and Laser Applications ; Gas Sensing Nanomaterials and Sensors ; Terahertz technology and applications
Authors: Yue Wang | Xi Xu | Zhuo‐Xu Cui | Haifeng Wang | Yihang Zhou | Dong Liang | Hairong Zheng | Yanjie Zhu
Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (article, 2024-11-26)
Field: Materials Science — Materials Chemistry

Accelerating Chemical Exchange Saturation TransferImaging using a Diffusion Model Motivation: Chemical exchange saturation transfer (CEST) magnetic resonance (MR) imaging is slow, and rapid radial undersampling significantly compromises the image quality of CEST data. Goal(s): Our goal is to enhance the image performance of CEST reconstruction under higher radial undersampling. Approach: A diffusion model is introduced to obtain prior information from MRI data, and its performance is evaluated on CEST data under radial sampling. Results: The proposed method generated high-quality CEST source images in healthy human data, outperforming iGRASP. Impact: The proposed method has achieved rapid imaging of CEST data, providing high-quality CEST source images . Saturation (graph theory) ; Diffusion ; Computer science ; Materials science ; Thermodynamics ; Physics ; Mathematics Lanthanide and Transition Metal Complexes
Authors: Jianqiang Zheng | Ziqi Xu | Huang Jh | Xiaochuan Chen | Yuetong Li | Yanjie Wei | Huiling Zhang
Source:  (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

CCVAN: A Conditional Molecular Generation Model based on conditional VAE and Wasserstein GAN Molecular generation plays a vital role in advancing drug discovery, materials science, and chemical exploration. In this study, we have effectively integrated the Conditional Variational Autoencoder (CVAE) with Wasserstein Generative Adversarial Networks (WGAN) and applied this combined framework to molecular generation tasks. This novel approach has resulted in the development of a molecular generation framework with conditional generation capabilities, which we designate as CCVAN. The model first encodes the data into a latent vector using CVAE, then shares a decoder with the generator of WGAN to reconstruct molecular SMILES, training the discriminator with both the generated and real molecular SMILES to achieve the goal of conditionally generating molecules. Compared to existing methods, CCVAN can generate molecules with specific properties as needed and performs well in terms of effectiveness, novelty, SA_score and QED. Case studies also show that CCVAN can generate high-binding energy molecules for both ligand-based and structure-based drug design. Overall, the flexibility and effectiveness of CCVAN make it a valuable tool in accelerating the compound discovery process. Regular conditional probability ; Conditional random field ; Computer science ; Artificial intelligence ; Posterior probability Machine Learning in Materials Science ; Topic Modeling
Authors: Sean Peyres | Jian Wang | Drew Weber Hollyfield | Matthew P. Confer | Necip B. Üner | Rohit Bhargava | Jeffrey S. Moore | Mohan Sankaran
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Chemistry — Electrochemistry

Plasma Electrochemistry in Non-Aqueous Solvents and Application to Organic Synthesis Low-temperature, atmospheric-pressure plasmas in contact with liquids have attracted interest for various chemical applications including the synthesis of colloidal nanoparticles, degradation of organic pollutants, and conversion of abundant feedstocks. Compared to other chemical approaches, plasma-liquid electrochemical processes do not require a catalyst material, are electrified, and produce unique reactive species such as solvated electrons, one of the strongest chemical reducing species. Here, we present a study of plasma-liquid electrochemistry in non-aqueous solvents and apply such a system to organic synthesis. The majority of studies to date of plasma-liquid electrochemistry have focused on water. For some applications, including organic synthesis, non-aqueous solvents are required. However, the compatibility of plasmas with these solvents and the chemistry has not been thoroughly investigated. We focused on methanol and the well-known pinacol coupling reaction. 1 Parametric studies were performed on methyl-4-formylbenzoate (MFB) as the substrate. The solvent had important effects including the generation of side products through parasitic radical formation. By adding water, the selectivity to the desired pinacol product could be improved. A reaction-diffusion model and thermodynamic calculations at the composite correlated G3(MP2)B3 molecular orbital theory level to support experimental measurements will also be discussed, as well as the potential application of this approach to other organic reactions. 1 J. Wang et al., J. Am. Chem. Soc. 2023, 14, 19, 10470-10474. Electrochemistry ; Aqueous solution ; Organic synthesis ; Organic solvent ; Plasma ; Chemistry ; Organic chemistry ; Chemical engineering ; Electrode ; Engineering ; Physical chemistry ; Physics ; Catalysis Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Ionic liquids properties and applications
Authors: Andreas E. Robertson | Adam P. Generale | Surya R. Kalidindi | Conlain Kelly | Michael Buzzy
Source:  (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Big Microstructure Datasets for Materials Informatics: Using Statistically Conditioned Generative Models to Curate Big Datasets Big data ; Generative grammar ; Computer science ; Informatics ; Generative model ; Data science ; Artificial intelligence ; Data mining ; Engineering Machine Learning in Materials Science
Authors: Amal Alsowat | Raghad Allahyani | Reham Alsulami | Shaima Almaqati | Tarik Alafif | Mohammed Hawsawi | Hamed Alsaidi | Tahani Alsubait
Source:  (article, 2024-12-11)
Field: Chemistry — Physical and Theoretical Chemistry

Learning to Generate and Predict New Conditional Small Organic Molecules Organic molecules are essential in various fields, including drug discovery, pharmaceuticals, materials science, and agrochemicals. However, synthesizing new organic molecules in laboratories is both costly and time-intensive. This paper introduces a novel approach for generating and predicting new conditional small organic molecules. The approach utilizes a Generative Adversarial Network (GAN), specifically a Wasserstein GAN (WGAN), to generate new molecular structures, and a Vision Transformer (ViT) to classify these generated molecules. To ensure the organic compounds generated are chemically feasible, the WGAN is conditioned with specific parameters, such as bond types and elemental composition. A generated dataset, named GenOrgo, includes 400 new molecules categorized into four classes: Homocycles, Heterocycles, Oxygen- and Nitrogen-containing Acyclics, and Hydrocarbons. The ViT is then evaluated on the GenOrgo dataset, demonstrating its effectiveness in accurately predicting the categories of generated organic molecules. The code and the dataset are available at github.com/RaghadAllehyani/WGAN. Computer science ; Organic molecules ; Artificial intelligence ; Machine learning ; Molecule ; Chemistry Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: André Brasil Vieira Wyzykowski
Source:  (preprint, 2024-03-12)
Field: Chemistry — Spectroscopy

[Poster Board #1935] Advancing efficient molecular structure extraction: A novel computational framework leveraging equivariant Transformers and diffusion models for enhanced drug discovery Drug discovery ; Computer science ; Transformer ; Equivariant map ; Extraction (chemistry) ; Nanotechnology ; Computational biology ; Engineering ; Materials science ; Chemistry ; Electrical engineering ; Bioinformatics ; Mathematics ; Biology ; Chromatography Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: André Brasil Vieira Wyzykowski
Source:  (preprint, 2024-02-29)
Field: Chemistry — Spectroscopy

[Poster Board #1935] Advancing efficient molecular structure extraction: A novel computational framework leveraging equivariant Transformers and diffusion models for enhanced drug discovery Equivariant map ; Drug discovery ; Computer science ; Transformer ; Engineering ; Mathematics ; Bioinformatics ; Biology ; Electrical engineering ; Pure mathematics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Theodoros Kouzelis | Manos Plitsis | Mihalis A. Nicolaou | Yannis Panagakis
Source: arXiv (Cornell University) (preprint, 2024-08-29)
Field: Materials Science — Materials Chemistry

Enabling Local Editing in Diffusion Models by Joint and Individual Component Analysis Recent advances in Diffusion Models (DMs) have led to significant progress in visual synthesis and editing tasks, establishing them as a strong competitor to Generative Adversarial Networks (GANs). However, the latent space of DMs is not as well understood as that of GANs. Recent research has focused on unsupervised semantic discovery in the latent space of DMs by leveraging the bottleneck layer of the denoising network, which has been shown to exhibit properties of a semantic latent space. However, these approaches are limited to discovering global attributes. In this paper we address, the challenge of local image manipulation in DMs and introduce an unsupervised method to factorize the latent semantics learned by the denoising network of pre-trained DMs. Given an arbitrary image and defined regions of interest, we utilize the Jacobian of the denoising network to establish a relation between the regions of interest and their corresponding subspaces in the latent space. Furthermore, we disentangle the joint and individual components of these subspaces to identify latent directions that enable local image manipulation. Once discovered, these directions can be applied to different images to produce semantically consistent edits, making our method suitable for practical applications. Experimental results on various datasets demonstrate that our method can produce semantic edits that are more localized and have better fidelity compared to the state-of-the-art. Component (thermodynamics) ; Diffusion ; Joint (building) ; Computer science ; Engineering ; Physics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis
Authors: Gülsen Asman | Sevil Uyanik
Source: Polymer Bulletin (article, 2024-09-11)
Field: Materials Science — Biomaterials

A new drug delivery coated matrix membrane system for theophylline delivery Theophylline ; Drug delivery ; Delivery system ; Membrane ; Matrix (chemical analysis) ; Drug ; Chemistry ; Nanotechnology ; Materials science ; Pharmacology ; Chromatography ; Medicine ; Biochemistry Nanoparticle-Based Drug Delivery ; Drug Solubulity and Delivery Systems ; Nanomaterials for catalytic reactions
Authors: Hammed Abiodun Balogun | Jair M. Cowie-Williams | Yi Ren | Reza Panah | Dimi Katsoulis | Jay Rose | Akriti Sarswat | Krista S. Walton | William J. Koros | Ryan P. Lively
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Membrane Separation of Cyclic Siloxanes from Silicone Fluid Silicone ; Separation (statistics) ; Membrane ; Materials science ; Chemical engineering ; Chemistry ; Composite material ; Computer science ; Engineering Silicone and Siloxane Chemistry ; Membrane Separation and Gas Transport ; Extraction and Separation Processes
Authors: Or Madar | Ohad Fried
Source: arXiv (Cornell University) (preprint, 2024-12-19)
Field: Materials Science — Materials Chemistry

Tiled Diffusion Image tiling -- the seamless connection of disparate images to create a coherent visual field -- is crucial for applications such as texture creation, video game asset development, and digital art. Traditionally, tiles have been constructed manually, a method that poses significant limitations in scalability and flexibility. Recent research has attempted to automate this process using generative models. However, current approaches primarily focus on tiling textures and manipulating models for single-image generation, without inherently supporting the creation of multiple interconnected tiles across diverse domains. This paper presents Tiled Diffusion, a novel approach that extends the capabilities of diffusion models to accommodate the generation of cohesive tiling patterns across various domains of image synthesis that require tiling. Our method supports a wide range of tiling scenarios, from self-tiling to complex many-to-many connections, enabling seamless integration of multiple images. Tiled Diffusion automates the tiling process, eliminating the need for manual intervention and enhancing creative possibilities in various applications, such as seamlessly tiling of existing images, tiled texture creation, and 360$^\circ$ synthesis. Diffusion ; Computer science ; Business ; Physics ; Thermodynamics Quasicrystal Structures and Properties
Authors: Gang Xiong | Meihe Yu | Yingli Ren | Baoyi Ren | Meiyan Guo | Fu Ding | Li-Xin You | Ya‐Guang Sun
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Hydrophobic Hypercrosslinked Polymers Prepared by Secondary Pore-Forming Method as Excellent Toluene Adsorbents Toluene ; Adsorption ; Polymer ; Chemical engineering ; Materials science ; Polymer chemistry ; Chemistry ; Organic chemistry ; Polymer science Covalent Organic Framework Applications
Authors: Gang Xiong | Meihe Yu | Yingli Ren | Baoyi Ren | Meiyan Guo | Fu Ding | Li-Xin You | Ya‐Guang Sun
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Hydrophobic Hypercrosslinked Polymers Prepared by Secondary Pore-Forming Method as Excellent Toluene Adsorbents Toluene ; Adsorption ; Polymer ; Chemical engineering ; Materials science ; Chemistry ; Organic chemistry ; Polymer chemistry ; Polymer science ; Engineering Covalent Organic Framework Applications
Authors: In‐Su Park | Dahee Seo | Jongsu Baek | Byung Jin Cho | Wan Sik Hwang | Min Ju Kim
Source: Nanotechnology (article, 2024-07-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

First demonstration of Sn diffusion into gallium oxide from poly-tetraallyl tin deposited by initiated chemical vapor deposition by thermal treatment Gallium oxide (Ga<sub>2</sub>O<sub>3</sub>) is attracting attention as a next-generation semiconductor material for power device because it has a wide energy band gap and high breakdown electric field. We deposited a Sn polymer, poly-tetraallyl tin, on Ga<sub>2</sub>O<sub>3</sub>samples using a disclosed initiated chemical vapor deposition (iCVD) process. The Sn dopant of the Sn polymer layer is injected into the Ga<sub>2</sub>O<sub>3</sub>through a heat treatment process. Diffusion model of Sn into the Ga<sub>2</sub>O<sub>3</sub>is proposed through secondary ion mass spectroscopy analysis and bond dissociation energy. The fabricated device exhibited typical n-type field-effect transistor (FET) behavior. Ga<sub>2</sub>O<sub>3</sub>Sn-doping technology using iCVD will be applied to 3D structures and trench structures in the future, opening up many possibilities in the Ga<sub>2</sub>O<sub>3</sub>-based power semiconductor device manufacturing process. Materials science ; Tin ; Chemical vapor deposition ; Gallium ; Diffusion ; Deposition (geology) ; Gallium oxide ; Tin oxide ; Thermal ; Oxide ; Chemical engineering ; Metallurgy ; Nanotechnology ; Thermodynamics Ga2O3 and related materials ; ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors
Authors: Takashi Ohnishi | Keiichi WATANUKI
Source: Sekkei Kougaku, Shisutemu Bumon Kouenkai kouen rombunshuu/Sekkei Kogaku, Shisutemu Bumon Koenkai koen ronbunshu (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Evaluation of magnetic susceptibility of trace paramagnetic materials by time series data during generation of high-gradient magnetic force using GAN The cathode material of lithium-ion batteries contains magnetic foreign material, which is separated by magnetic separating. However, because the cathode material and magnetic foreign material are weakly magnetic, they are all adsorbed by a high magnetic force separator, resulting in inadequate separation. There is a method to separate them by adjusting the magnetic force by clarifying the difference in magnetic susceptibility, but it is difficult to measure the magnetic force because of the small quantity of samples. Vibration of the measuring instrument also caused a lot of variation in the measured values, resulting in a large error margin. A system was needed to measure a small amount of magnetism from the small quantity of samples, remove the noise, and correct it to derive a value with a small error. In this study, the magnetic field analysis by the finite element method is used to form a uniform high-gradient magnetic field to measure the slight weight change caused by the magnetic field, and the measurement accuracy is improved by suppressing the noise during the measurement of the electronic balance with Generative adversarial network (GAN) from the aging data to suppress the variation, and the magnetic susceptibility is evaluated from a small amount of sample. Paramagnetism ; Series (stratigraphy) ; TRACE (psycholinguistics) ; Magnetic susceptibility ; Materials science ; Condensed matter physics ; Nuclear magnetic resonance ; Physics ; Geology Magnetic Properties and Applications ; Geomagnetism and Paleomagnetism Studies
Authors: V.E. Li | Yu Sun
Source:  (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

Allo-DiffHopping: A Computational Approach for Allosteric Scaffold Hopping Using Conditional E(3) Equivariant Diffusion Models Fragment-based drug design presents significant computational challenges, necessitating advanced methods for efficient molecular generation. Diffusion generative models have shown promise in this domain; however, prior approaches have overlooked the critical role of pocket-ligand interactions. In response, we introduce "Allo-DiffHopping," an innovative computational model employing conditional E(3) equivariant diffusion for allosteric scaffold hopping. This method processes an arbitrary number of fragments and anchor points to construct new molecules via scaffold hopping. The generative process is conditioned on detailed pocket-ligand interactions, including binding affinity and 3D structural data. These interactions are essential in fragment-based drug design. Our evaluation demonstrates that Allo-DiffHopping produces linkers with better binding affinity to proteins, outperforming baseline models in compound validity, synthetic accessibility, Vina score, and SCRDKit metrics. Additionally, in allosteric batch tests, Allo-DiffHopping exhibits potential for generating high-performance inhibitors, highlighting its applicability and effectiveness in computational drug design. Computer science ; Diffusion ; Equivariant map ; Statistical physics ; Allosteric regulation ; Algorithm ; Biological system ; Mathematics ; Physics ; Nuclear magnetic resonance ; Thermodynamics ; Pure mathematics ; Biology Block Copolymer Self-Assembly ; Surface Chemistry and Catalysis