jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ge Huang | Marcus Winther Dreisler | Jacob Kæstel‐Hansen | Annette Juma Nielsen | Min Zhang | Nikos S. Hatzakis
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-04-24)
Field: Chemistry — Inorganic Chemistry

Defect-Engineered Metal-Organic Frameworks as Nanocarriers for Pharmacotherapy: Insights into Intracellular Dynamics at The Single Particle Level Abstract NanoMOFs are widely implemented in a host of assays involving drug delivery, biosensing catalysis, and bioimaging. Despite their wide use, the cell entry pathways and cell fate remain poorly understood. Here we have synthesized a new fluorescent nanoMOF integrating ATTO 655 into surface defects of colloidal nano UiO-66 that allowed us to track the spatiotemporal localization of Single nanoMOF in live cells. Density Functional Theory(DFT) reveals the stronger binding of ATTO 655 to the uncoordinated saturated Zr 6 cluster nodes compared with phosphate and Alendronate Sodium (AL). Parallelized tracking of the spatiotemporal localization of tens of thousands of nanoMOFs and analysis using machine learning platforms revealed whether nanoMOFs remain outside as well as their cellular internalization pathways. To quantitatively assess their colocalization with endo/lysosomal compartments, we developed a colocalization proxy approach relying on the nanoMOF detection of particles in one channel to the signal in the corresponding endo/lysosomal compartments channel, considering signal vs local background intensity ratio (S/B) and signal-to-noise ratio (SNR). This strategy effectively mitigates the potential inflation of colocalization values arising from the heightened expression of signals originating from endo/lysosomal compartments, it also overcomes limitations of low SNRs in the endo/lysosomal compartments marker channel, which incapacitates any trajectory-trajectory colocalization assessment. The results accurately measure the amount of nanoMOFs’ colocalization in real-time from early (EE) to late endosomes(LE) and lysosomes(LY) and emphasize the importance of understanding their intracellular dynamics based on single-particle tracking (SPT) for optimal and safe drug delivery. Nanocarriers ; Particle (ecology) ; Nanotechnology ; Intracellular ; Materials science ; Metal-organic framework ; Dynamics (music) ; Drug delivery ; Chemistry ; Psychology ; Biochemistry ; Organic chemistry ; Biology Metal-Organic Frameworks: Synthesis and Applications ; Nanoparticle-Based Drug Delivery ; Advanced Nanomaterials in Catalysis
Authors: José‐Abel Flores | A. Alvarado | Miguel A. García‐Castro | F. Martinón Sánchez | Ximena Limón Aguilar | María Fernanda Saviñon-Flores
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Organic Chemistry

Machine Learning Applied to Determine Enthalpies of Formation, Combustion, and Sublimation of 4-Methylphthalic Acid and its Related Anhydride Sublimation (psychology) ; Combustion ; Chemistry ; Thermodynamics ; Organic chemistry ; Psychology ; Physics Chemical Thermodynamics and Molecular Structure ; Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies
Authors: Carla Alexander | Jothsna Kethar | Kristina Lilova
Source: Journal of Student Research (article, 2024-11-30)
Field: Materials Science — Biomaterials

AI-Assisted Nanofibers in Neural Tissue Regeneration: Application in Traumatic Brain Injury Traumatic Brain injuries (TBI) can pose a significant challenge to neural tissue regeneration in humans, due to the Central Nervous System (CNS) limited ability to regenerate. Traditional therapeutic methods fall short in addressing the challenges associated with TBI. Some common challenges with TBI are inflammation, scar tissue formation, and presence of inhibitor factors. Advances in technology are however beginning to show promising possibilities with respect to nanotechnology that mimic the natural extracellular matrix (ECM), that allow neural cell growth and differentiation. Integration of Artificial Intelligence (AI) with advanced nanotechnology , offers promising possibilities in the enhancement of neural tissue generation. This research paper explores the use of Artificial intelligence (AI) driven approach for the fabrication of nanofibers in the treatment of TBI. Incorporation of AI and machine learning (ML) in design, composition and arrangement of nanofibers can help researchers run multiple iterations, customise nanofiber scaffolds thereby enhancing effectiveness, precision, and personalised treatment options. Machine learning models can further help predict optimal kinetic release of molecules in a controlled manner to promote neural tissue regeneration. In conclusion, The culmination of AI-driven strategies along with advanced nanomaterials offers a new scope for assisting patients with CNS injuries by restoring their neurological functioning. Traumatic brain injury ; Regeneration (biology) ; Brain tissue ; Nanofiber ; Medicine ; Neuroscience ; Biomedical engineering ; Biology ; Materials science ; Nanotechnology ; Cell biology ; Psychiatry Electrospun Nanofibers in Biomedical Applications ; Graphene and Nanomaterials Applications ; Tissue Engineering and Regenerative Medicine
Authors: Zhonghua Yang | Wenbo Ning | Wen Gu | Lihua Lu | Kunpeng Yuan
Source: ACS Applied Energy Materials (article, 2024-08-08)
Field: Materials Science — Materials Chemistry

Anomalous Electron–Phonon Coupling in Cubic Be–X (X = Co, Ni, Rh, Hf) Crystals and Insight from Fermi Surface Nesting and Phonon Bandgap The electronic contribution significantly dominates thermal transport for most pure metallic systems, while the phononic (lattice) contribution remains relatively small. We report four metallic materials, namely cubic BeCo, BeNi, BeRh, and BeHf, which are screened by our recent deep learning approach from Open Quantum Materials Database, all possessing large phonon bandgaps and phonon anomaly features. Employing first-principles calculations to solve the phonon Boltzmann transport equation, we report that at room temperature, the phononic thermal conductivity of these Be–X compounds is exceptionally high, rivaling that of diamond. Specifically, the thermal conductivities are 2117 W/mK for BeCo, 2243 W/mK for BeNi, 2368 W/mK for BeRh, and 1600 W/mK for BeHf. This remarkable thermal performance is attributed to the large phonon bandgaps and phonon anomaly features present in these materials. Furthermore, when electron–phonon coupling is accounted for, the phononic thermal conductivities of the Be–X compounds experience a reduction by approximately 2 orders of magnitude. Notably, BeHf presents an exceptional case, with its phononic thermal conductivity measured at 68 W/mK. This value is several times to an order of magnitude higher than the typical range observed in most metals and metallic systems, which generally lies between 2 and 18 W/mK. Quantitative analysis of phonon lifetime, Eliashberg spectral function, and Fermi surface show that the Be–X systems have strong electron–phonon coupling which originates from their Fermi surface nesting. However, the phonon anomalies of BeHf are shallower, which weakens the electron–phonon coupling and results in the abnormally high phononic thermal conductivity of BeHf. This research enhances our comprehension of various heat conduction phenomena in crystals, particularly offering a method to identify new materials with phonon-mediated thermal transport in metals and metallic systems for innovative applications in the future. Phonon ; Condensed matter physics ; Thermal conductivity ; Fermi surface ; Materials science ; Coupling (piping) ; Boltzmann equation ; Fermi level ; Electron ; Band gap ; Physics ; Thermodynamics ; Superconductivity ; Quantum mechanics ; Composite material Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: D. Shaw | Ran Liang | Tian Zheng | Jianzhong Qi | Joseph D. Berry
Source: ChemRxiv (preprint, 2024-03-26)
Field: Materials Science — Surfaces, Coatings and Films

Accurate and robust static hydrophobic contact angle measurements using machine learning Recent years has seen a surge in new approaches to material discovery. However, in order to validate and characterise these novel materials that are being discovered at unprecedented speeds, methods of experimental characterisation must match the pace of discovery. This is only possible through the use of high throughput autonomous approaches. % Automation addresses sources of human error To address this need, and to overcome the shortcomings in present methods, we present a machine learning (ML) approach to static contact angle measurement that is more accurate and faster than current best practice methods. The ML model was trained on a large data-set (> 7.2 million) generated via solutions of the Young-Laplace equation where the contact angle is known a priori, removing all sources of error from human input. The data-set included the effects of surface roughness, gravity, the size of drop relative to the image, reflections of the drop on the surface, and contact angles from 110° to 180°. The presented ML model (valid for contact angles >110°), in combination with a new automated image and contour processing approach, is shown to be more accurate than other methods when benchmarked against an experimental data-set, with an estimated error of 1 degree. The ML model is also two orders of magnitude faster at predicting contact angles than Young-Laplace fitting (the current best practice approach). The accuracy and speed of the presented approach provides a viable pathway towards robust and reproducible high-throughput contact angle analysis. The open-source software Conan-ML is provided for use and development of new approaches to goniometry. Contact angle ; A priori and a posteriori ; Computer science ; Set (abstract data type) ; Surface roughness ; Laplace's equation ; Algorithm ; Automation ; Throughput ; Data set ; Artificial intelligence ; Machine learning ; Simulation ; Mathematics ; Mechanical engineering ; Engineering ; Materials science ; Boundary value problem Surface Modification and Superhydrophobicity ; Electrohydrodynamics and Fluid Dynamics ; Polymer Surface Interaction Studies
Authors: An Wang | Gabriele C. Sosso
Source: arXiv (Cornell University) (preprint, 2024-08-12)
Field: Materials Science — Materials Chemistry

Graph-based Descriptors for Condensed Matter Computational scientists have long been developing a diverse portfolio of methodologies to characterise condensed matter systems. Most of the descriptors resulting from these efforts are ultimately based on the spatial configurations of particles, atoms, or molecules within these systems. Noteworthy examples include symmetry functions and the smooth overlap of atomic positions (SOAP) descriptors, which have significantly advanced the performance of predictive machine learning models for both condensed matter and small molecules. However, while graph-based descriptors are frequently employed in machine learning models to predict the functional properties of small molecules, their application in the context of condensed matter has been limited. In this paper, we put forward a number of graph-based descriptors (such as node centrality and clustering coefficients) traditionally utilised in network science, as alternative representations for condensed matter systems. We apply this graph-based formalism to investigate the dynamical properties and phase transitions of the prototypical Lennard-Jones system. We find that our graph-based formalism outperforms symmetry function descriptors in predicting the dynamical properties and phase transitions of this system. These results demonstrate the broad applicability of graph-based features in representing condensed matter systems, paving the way for exciting advancements in the field of condensed matter through the integration of network science concepts. Graph ; Computer science ; Theoretical computer science Machine Learning in Materials Science ; History and advancements in chemistry ; Advanced Chemical Sensor Technologies
Authors: Yoyo Hinuma
Source: arXiv (Cornell University) (preprint, 2024-09-09)
Field: Materials Science — Materials Chemistry

Effect of very slow O diffusion at high temperature on very fast H diffusion in the hydride ion conductor LaH2.75O0.125 Neural network potential based molecular dynamics (MD) simulations on the excellent H conductor LaH2.75O0.125 show that O starts diffusing above a critical temperature of Tc~550 K, according to the variance of atom positions regardless of the time step. The original diffusion process at temperatures below Tc has an activation barrier of 0.25 eV. Use of MD simulations with various O and La mass revealed, at above Tc, the coexistence of the 0.25 eV process and an additional diffusion process with an activation barrier of 0.20 eV. The O and La have strongly anharmonic characters. Diffusion ; Hydride ; Conductor ; Ion ; Materials science ; Chemical physics ; Condensed matter physics ; Chemistry ; Thermodynamics ; Physics ; Composite material ; Metallurgy ; Metal ; Organic chemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Nuclear Materials and Properties
Authors: Alvaro Prat | Hisham Abdel Aty | Aurimas Pabrinkis | Orestis Bastas | Tanya Paquet | Gintautas Kamuntavičius | Roy Tal
Source: ChemRxiv (preprint, 2024-10-18)
Field: Chemistry — Organic Chemistry

SE(3) Equivariant Topologies for Structure-based Drug Discovery Modeling protein-ligand interactions is a challenging task that has been approached through an array of perspectives. From physics-based computational approaches to vast deep learning pipelines, in silico methods hold promise in reducing experimental overhead in the otherwise tedious and costly drug discovery campaigns. We introduce Protein-Ligand Equivariant Transformer (ProLET), a generalizable model built upon chemically inspired SE(3) equivariant geometric deep learning. We evaluate ProLET on a wide range of established standards, including the notoriously difficult PoseBusters and Merck’s FEP benchmarks, consistently demonstrating superior performance in binding affinity prediction and pose estimation. We demonstrate its effectiveness across different stages in drug discovery, showing that ProLET can be used for lead optimization and hit identification as well as for prioritizing compounds that are selective towards a desired target. By bridging the gap between accuracy, efficiency, and generalizability, ProLET stands as a powerful and adaptive resource, signifying a step towards safe and reliable AI-driven drug discovery. Equivariant map ; Drug discovery ; Network topology ; Computer science ; Mathematics ; Pure mathematics ; Computer network ; Biology ; Bioinformatics Click Chemistry and Applications ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Mega Novita | Chong‐Geng Ma | M. Piasecki | M.G. Brik
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Exploring the Luminescent Properties of Transition Metal Ions in Solids through Theoretical Insights and the Prospects of Machine Learning This work delineates over a decade of rigorous first-principles computational investigations into compounds doped with transition metal (TM) ions, such as Sc 3+ , Ti 3+ , V 2+ , V 3+ , Cr 3+ , Mn 3+ , Mn 4+ , Fe 3+ , Co 3+ , Ni 3+ , and Cu 3+ , within host matrices including α-Al 2 O 3 , MgTiO 3 , and various fluorides, devoid of empirical parameters. Our methodological framework employs discrete variational Xα (DV-Xα) and discrete variational multielectron (DVME) techniques, enhanced by configuration-dependent and correlation corrections to refine accuracy. Notably, our analysis integrates lattice-relaxation estimations through the use of Shannon crystal radii and the Cambridge Serial Total Energy Package (CASTEP), facilitating the examination of pressure-induced effects on lattice structures. Our comprehensive studies, particularly on α-Al 2 O 3 doped with TM 3d 3 ions, have not only successfully replicated known optical characteristics but have also provided new insights into the variation of transition energies across different TM ion charges, alongside pressure-dependent behavior of multiplet energies in alignment with experimental findings. These endeavors highlight the complex interplay of dopant characteristics, including charge states and ionic radii, on the optical properties of host crystals, with precise attention to colorimetry within the International Commission on Illumination (abbreviated CIE ) color space framework to evaluate the quality and perception of emitted light. Furthermore, this work will introduce a discussion on the emerging potential of machine learning (ML) in advancing our understanding and predictive capabilities concerning the luminescence of materials. ML approaches promise to revolutionize the identification of optimal dopant-host combinations, predict novel luminescent materials, and enhance the accuracy of theoretical predictions through data-driven insights. By bridging traditional computational methods with ML, we aim to pave new pathways in the exploration of luminescent materials, potentially leading to innovative applications in lighting, display technologies, and beyond. Luminescence ; Transition (genetics) ; Ion ; Transition metal ; Nanotechnology ; Chemical physics ; Computer science ; Cognitive science ; Materials science ; Chemistry ; Psychology ; Optoelectronics ; Organic chemistry Machine Learning in Materials Science ; Luminescence Properties of Advanced Materials
Authors: Abhinav S. Raman | Annabella Selloni
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Electrochemistry

Large Scale Molecular Dynamics Simulations of Carboxylic Acid-Modifiedanatase Tio2(101)-Water Interfaces Using Ab-Initio Deepneural Network Potentials Molecular dynamics ; Ab initio ; Scale (ratio) ; Carboxylic acid ; Chemical physics ; Dynamics (music) ; Computational chemistry ; Chemistry ; Statistical physics ; Materials science ; Physics ; Organic chemistry ; Quantum mechanics Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Noah Kleinschmidt | Thomas Lemmin
Source: ChemRxiv (preprint, 2024-07-01)
Field: Materials Science — Materials Chemistry

BuildAMol: A versatile Python toolkit for fragment-based molecular design In recent years computational methods for molecular modeling have become a prime focus of computational biology and cheminformatics. Many dedicated systems exist for modeling specific classes of molecules such as proteins or small drug-like ligands. These are often heavily tailored toward the automated gen- eration of molecular structures based on some meta-input by the user and are not intended for expert-driven structure assembly. Dedicated manual or semi- automated assembly software tools exist for a variety of molecule classes but are limited in the scope of structures they can produce. In this work we present BuildAMol, a highly flexible and extendable, general-purpose fragment-based molecular assembly toolkit. Written in Python and featuring a well-documented, user-friendly API, BuildAMol empowers researchers with a framework for detailed manual or semi-automated construction of diverse molecular models. Unlike specialized software, BuildAMol caters to a broad range of applications. We demonstrate its versatility across various use cases, encompassing generating metal complexes or the modeling of dendrimers or integrated into a drug discov- ery pipeline. By providing a robust foundation for expert-driven model building, BuildAMol holds promise as a valuable tool for the continuous integration and advancement of powerful deep learning techniques. Python (programming language) ; Fragment (logic) ; Computer science ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Wen Luo | Yangyi Shen | Chengfan Fu | - - | Qiang Huang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Exploring the Co2 Conversion Activated by Plasma Photocatalysis Via Machine Learning Photocatalysis ; Plasma ; Computer science ; Materials science ; Business ; Environmental science ; Chemistry ; Catalysis ; Physics ; Nuclear physics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Jichao Fan | Ruiyang Chen | Minhan Lou | Haoyu Xie | Nina Hong | Yingheng Tang | Weilu Gao
Source: arXiv (Cornell University) (preprint, 2024-06-19)
Field: Materials Science — Materials Chemistry

A programmable wafer-scale chiroptical heterostructure of twisted aligned carbon nanotubes and phase change materials The ability to design and dynamically control chiroptical responses in solid-state matter at wafer scale enables new opportunities in various areas. Here we present a full stack of computer-aided designs and experimental implementations of a dynamically programmable, unified, scalable chiroptical heterostructure containing twisted aligned one-dimensional (1D) carbon nanotubes (CNTs) and non-volatile phase change materials (PCMs). We develop a software infrastructure based on high-performance machine learning frameworks, including differentiable programming and derivative-free optimization, to efficiently optimize the tunability of both excitonic reciprocal and linear-anisotropy-induced nonreciprocal circular dichroism (CD) responses. We experimentally implement designed heterostructures with wafer-scale self-assembled aligned CNTs and deposited PCMs. We dynamically program reciprocal and nonreciprocal CD responses by inducing phase transitions of PCMs, and nonreciprocal responses display polarity reversal of CD upon sample flipping in broadband spectral ranges. All experimental results agree with simulations. Further, we demonstrate that the vertical dimension of heterostructure is scalable with the number of stacking layers and aligned CNTs play dual roles - the layer to produce CD responses and the Joule heating electrode to electrically program PCMs. This heterostructure platform is versatile and expandable to a library of 1D nanomaterials and electro-optic materials for exploring novel chiral phenomena and photonic and optoelectronic devices. Wafer ; Carbon nanotube ; Heterojunction ; Materials science ; Scale (ratio) ; Phase (matter) ; Nanotechnology ; Phase change ; Carbon fibers ; Optoelectronics ; Engineering physics ; Composite material ; Engineering ; Chemistry ; Physics Phase-change materials and chalcogenides ; Liquid Crystal Research Advancements ; Advanced Materials and Mechanics