jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jigyasa Nigam | Sergey N. Pozdnyakov | Kevin K. Huguenin-Dumittan | Michele Ceriotti
Source: APL Machine Learning (article, 2024-02-08)
Field: Materials Science — Materials Chemistry

Completeness of atomic structure representations In this paper, we address the challenge of obtaining a comprehensive and symmetric representation of point particle groups, such as atoms in a molecule, which is crucial in physics and theoretical chemistry. The problem has become even more important with the widespread adoption of machine-learning techniques in science, as it underpins the capacity of models to accurately reproduce physical relationships while being consistent with fundamental symmetries and conservation laws. However, some of the descriptors that are commonly used to represent point clouds— notably those based on discretized correlations of the neighbor density that power most of the existing ML models of matter at the atomic scale—are unable to distinguish between special arrangements of particles in three dimensions. This makes it impossible to machine learn their properties. Atom-density correlations are provably complete in the limit in which they simultaneously describe the mutual relationship between all atoms, which is impractical. We present a novel approach to construct descriptors of finite correlations based on the relative arrangement of particle triplets, which can be employed to create symmetry-adapted models with universal approximation capabilities, and have the resolution of the neighbor discretization as the sole convergence parameter. Our strategy is demonstrated on a class of atomic arrangements that are specifically built to defy a broad class of conventional symmetric descriptors, showing its potential for addressing their limitations. Completeness (order theory) ; Computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Manuel Grumet | Clara von Scarpatetti | Tomáš Bučko | David A. Egger
Source: The Journal of Physical Chemistry C (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra Raman spectroscopy is an important characterization tool with diverse applications in many areas of research. We propose a machine learning (ML) method for predicting polarizabilities with the goal of providing Raman spectra from molecular dynamics trajectories at a reduced computational cost. A linear-response model is used as a first step, and symmetry-adapted ML is employed for the higher-order contributions as a second step. We investigate the performance of the approach for several systems, including molecules and extended solids. The method can reduce the training-set sizes required for accurate dielectric properties and Raman spectra in comparison to a single-step ML approach. Raman spectroscopy ; Delta ; Dielectric ; Artificial intelligence ; Materials science ; Spectral line ; Pattern recognition (psychology) ; Computer science ; Physics ; Optics ; Optoelectronics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Machine Learning and ELM
Authors: Minkai Xu
Source: TIB Data Manager (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Learning neural generative dynamics for molecular conformation generation We study how to generate molecule conformations (i.e., 3D structures) from a molecular graph. Traditional methods, such as molecular dynamics, sample conformations via computationally expensive simulations. Recently, machine learning methods have shown great potential by training on a large collection of conformation data. Challenges arise from the limited model capacity for capturing complex distributions of conformations and the difficulty in modeling long-range dependencies between atoms. Inspired by the recent progress in deep generative models, in this paper, we propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph. We propose a method combining the advantages of both flow-based and energy-based models, enjoying: (1) a high model capacity to estimate the multimodal conformation distribution; (2) explicitly capturing the complex long-range dependencies between atoms in the observation space. Extensive experiments demonstrate the superior performance of the proposed method on several benchmarks, including conformation generation and distance modeling tasks, with a significant improvement over existing generative models for molecular conformation sampling. Computer science ; Molecular dynamics ; Generative grammar ; Probabilistic logic ; Generative model ; Graph ; Artificial intelligence ; Range (aeronautics) ; Machine learning ; Theoretical computer science ; Computational chemistry ; Chemistry ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yanze Wang | Tinghao Zhang | Wenjing Zhao | Weidong Xu | Zhongbin Wu | Yung Doug Suh | Yuezhou Zhang | Xiaowang Liu | Wei Huang
Source: Angewandte Chemie International Edition (article, 2024-10-29)
Field: Materials Science — Materials Chemistry

Machine Learning‐Guided Discovery of Copper(I)‐Iodide Cluster Scintillators for Efficient X‐ray Luminescence Imaging Developing efficient scintillators with environmentally friendly compositions, adaptable band gaps, and robust chemical stability is crucial for modern X-ray radiography. While copper(I)-iodide cluster crystals show promise, the vast design space of inorganic cores and organic ligands poses challenges for conventional approaches. In this study, we present machine learning-guided discovery of copper(I)-iodide cluster scintillators for efficient X-ray luminescence imaging. Our findings reveal that combining base learning models with fused features enhances model generalization, achieving an impressive determination coefficient of 0.88. By leveraging this approach, we obtain a high-performance Cu(I)-I cluster scintillator, named copper iodide-(1-Butyl-1,4-diazabicyclo[2.2.2]octan-1-ium)<sub>2</sub>, which exhibit radioluminescence 56 times stronger than that of PbWO<sub>4</sub>, and enables a detection limit for X-rays of 19.6 nGy<sub>air</sub> s<sup>-1</sup>. Furthermore, we demonstrate the versatility of these scintillators by incorporating them as microfillers in the fabrication of flexible composite scintillators for X-ray imaging, achieving a static resolution of 20 lp mm<sup>-1</sup> and demonstrating promising performance for dynamic X-ray imaging. Scintillator ; Luminescence ; Copper ; Cluster (spacecraft) ; Iodide ; X-ray ; Materials science ; Optoelectronics ; Physics ; Computer science ; Chemistry ; Optics ; Inorganic chemistry ; Metallurgy ; Detector ; Operating system Machine Learning in Materials Science ; Radiation Detection and Scintillator Technologies ; Medical Imaging Techniques and Applications
Authors: Fen Zhang | Hao Shi | Yali Yu | Shuo Liu | Duanyang Liu | Xinyun Zhou | Le Yuan | Jiaqi Shi | Qinglin Xia | Zhongming Wei | Jun He | Mianzeng Zhong
Source: Advanced Optical Materials (article, 2024-02-26)
Field: Materials Science — Materials Chemistry

Dynamic Band‐Alignment Modulation in MoTe<sub>2</sub>/SnSe<sub>2</sub> Heterostructure for High Performance Photodetector Abstract For two‐dimensional (2D) layered material heterojunctions, dynamic modulation of band alignments allows for the design of devices with flexible multi‐functional applications. In this paper, a device structure is presented based on a MoTe 2 /SnSe 2 field‐effect transistor. By applying a bias voltage to the electrostatic gate, the gate voltage is adjusted from negative to positive, causing the heterojunction to transition from type‐III band alignment to type‐II band alignment. The working mechanism and device performance of the heterojunctions with different band alignments are investigated. The device exhibited outstanding detection range (from ultraviolet to infrared), detectivity (2.42 × 10 9 Jones), and speed (1.3 ms). Under a positive gate voltage, a higher ratio of light current to dark current (2×10 3 ) is achieved. To further demonstrate the potential of the high‐performance devices, their reliability is confirmed through their performance in image recognition using deep learning. Heterojunction ; Materials science ; Photodetector ; Optoelectronics ; Modulation (music) ; Transistor ; Biasing ; Voltage ; Dark current ; Infrared ; Optics ; Electrical engineering ; Physics 2D Materials and Applications ; Perovskite Materials and Applications ; MXene and MAX Phase Materials
Authors: Edoardo Cignoni | Divya Suman | Jigyasa Nigam | Lorenzo Cupellini | Benedetta Mennucci | Michele Ceriotti
Source: ACS Central Science (article, 2024-02-29)
Field: Materials Science — Materials Chemistry

Electronic Excited States from Physically Constrained Machine Learning Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly with physically grounded operations. We present an example of an integrated modeling approach in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those on which it is trained and allows for dramatic computational savings by indirectly targeting the outputs of well-converged calculations while using a parametrization corresponding to a minimal atom-centered basis. These results emphasize the merits of intertwining data-driven techniques with physical approximations, improving the transferability and interpretability of ML models without affecting their accuracy and computational efficiency and providing a blueprint for developing ML-augmented electronic-structure methods. Computer science ; Interpretability ; Electronic structure ; Parametrization (atmospheric modeling) ; Hamiltonian (control theory) ; Context (archaeology) ; Quantum ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Rehan Ishtiaq | Nallain Zahra | Sara Iftikhar | Fazila Rubab | Khawar Sultan | Ather Abbas | Sze–Mun Lam | Zeeshan Haider Jaffari | Ki Young Park
Source: Journal of environmental chemical engineering (article, 2024-02-17)
Field: Materials Science — Materials Chemistry

Adsorption of Cr(VI) ions onto fluorine-free niobium carbide (MXene) and machine learning prediction with high precision Adsorption ; Niobium ; MXenes ; Carbide ; Langmuir adsorption model ; Hydrothermal circulation ; Fluorine ; Chemical engineering ; Chemistry ; Materials science ; Wastewater ; Inorganic chemistry ; Nanotechnology ; Metallurgy ; Physical chemistry ; Environmental engineering MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Graphene research and applications
Authors: Yuning Wang | Xiaoyu Xu | Yuzheng Fang | Shouzhi Yang | Qirui Wang | Wanshan Liu | Juxiang Zhang | Dingyitai Liang | Wei Zhai | Kun Qian
Source: ACS Nano (article, 2024-01-08)
Field: Chemistry — Spectroscopy

Self-Assembled Hyperbranched Gold Nanoarrays Decode Serum United Urine Metabolic Fingerprints for Kidney Tumor Diagnosis Serum united urine metabolic analysis comprehensively reveals the disease status for kidney diseases in particular. Thus, the precise and convenient acquisition of metabolic molecular information from united biofluids is vitally important for clinical disease diagnosis and biomarker discovery. Laser desorption/ionization mass spectrometry (LDI-MS) presents various advantages in metabolic analysis; however, there remain challenges in ionization efficiency and MS signal reproducibility. Herein, we constructed a self-assembled hyperbranched black gold nanoarray (HyBrAuNA) assisted LDI-MS platform to profile serum united urine metabolic fingerprints (S-UMFs) for diagnosis of early stage renal cell carcinoma (RCC). The closely packed HyBrAuNA afforded strong electromagnetic field enhancement and high photothermal conversion efficacy, enabling effective ionization of low abundant metabolites for S-UMF collection. With a uniform nanoarray, the platform presented excellent reproducibility to ensure the accuracy of S-UMFs obtained in seconds. When it was combined with automated machine learning analysis of S-UMFs, early stage RCC patients were discriminated from the healthy controls with an area under the curve (AUC) > 0.99. Furthermore, we screened out a panel of 9 metabolites (4 from serum and 5 from urine) and related pathways toward early stage kidney tumor. In view of its high-throughput, fast analytical speed, and low sample consumption, our platform possesses potential in metabolic profiling of united biofluids for disease diagnosis and pathogenic mechanism exploration. Biomarker discovery ; Reproducibility ; Urine ; Renal cell carcinoma ; Biomarker ; Diagnostic biomarker ; Chemistry ; Cancer research ; Medicine ; Proteomics ; Oncology ; Chromatography ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Biosensors and Analytical Detection
Authors: Congli Cui | Weixu Qiao | Dong Li | Lijun Wang
Source: Journal of Colloid and Interface Science (article, 2024-09-17)
Field: Chemistry — Analytical Chemistry

Dual cross-linked magnetic gelatin/carboxymethyl cellulose cryogels for enhanced Congo red adsorption: Experimental studies and machine learning modelling Carboxymethyl cellulose ; Gelatin ; Adsorption ; Congo red ; Cellulose ; Chemical engineering ; Chemistry ; Polymer science ; Polymer chemistry ; Materials science ; Organic chemistry ; Sodium ; Engineering Dye analysis and toxicity
Authors: Zhengxiang Sun | Rui Wang | V.E. Matulis | В. Н. Корчак
Source: Molecules (article, 2024-03-14)
Field: Materials Science — Materials Chemistry

Structure, Synthesis, and Catalytic Performance of Emerging MXene-Based Catalysts As traditional fossil fuel energy development faces significant challenges, two-dimensional layered materials have become increasingly popular in various fields and have generated widespread research interest. MXene is an exceptional catalytic material that is typically integrated into functional composite materials with other substances to enhance its catalytic-reaction performance. Improving the thermal stability, electrical conductivity, and electrochemical activity, as well as enhancing the specific surface structure, can make the material an excellent catalyst for photoelectrocatalysis and energy-regeneration reactions. The article mainly outlines the structural characteristics, preparation methods, and applications of MXene in the field of catalysis. This text highlights the latest progress and performance comparison of MXene-based catalytic functional materials in various fields such as electrochemical conversion, photocatalysis, renewable energy, energy storage, and carbon capture and conversion. It also proposes future prospects and discusses the current bottlenecks and challenges in the development of MXene-based catalytic materials. Catalysis ; Materials science ; Renewable energy ; Nanotechnology ; Photocatalysis ; Electrochemistry ; Electrochemical energy conversion ; Electrochemical energy storage ; Fossil fuel ; Energy transformation ; Biochemical engineering ; Supercapacitor ; Chemistry ; Waste management ; Engineering MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Advanced Memory and Neural Computing
Authors: R. Sreedhara | B.R. Radha Krushna | G.R. Mamatha | S.C. Sharma | S. Padmavathi | Swagatika Kamila | Augustine George | C. Krithika | R. Sudarmani | K. Manjunatha | Sheng Yun Wu | H. Nagabhushana
Source: Materials Research Bulletin (article, 2024-08-25)
Field: Materials Science — Materials Chemistry

Influence of carbon dots integrated in Pr3+ doped gahnite nanophosphor for thermal sensing, data fortification and fingerprint visualization analysis through YOLOv8x deep learning embedded model Visualization ; Doping ; Materials science ; Fingerprint (computing) ; Mineralogy ; Computer science ; Optoelectronics ; Artificial intelligence ; Chemistry Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials ; Polydiacetylene-based materials and applications
Authors: Ke Xu | Yujie Cui | Bin Guan | Qin Lin-lin | Dihao Feng | Abudumijiti Abuduwayiti | Yimu Wu | Hao Li | Hongfei Cheng | Zhao Li
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Nanozymes with biomimetically designed properties for cancer treatment Nanozymes, as a type of nanomaterials with enzymatic catalytic activity, have demonstrated tremendous potential in cancer treatment owing to their unique biomedical properties. However, the heterogeneity of tumors and the complex tumor microenvironment pose significant challenges to the <i>in vivo</i> catalytic efficacy of traditional nanozymes. Drawing inspiration from natural enzymes, scientists are now using biomimetic design to build nanozymes from the ground up. This approach aims to replicate the key characteristics of natural enzymes, including active structures, catalytic processes, and the ability to adapt to the tumor environment. This achieves selective optimization of nanozyme catalytic performance and therapeutic effects. This review takes a deep dive into the use of these biomimetically designed nanozymes in cancer treatment. It explores a range of biomimetic design strategies, from structural and process mimicry to advanced functional biomimicry. A significant focus is on tweaking the nanozyme structures to boost their catalytic performance, integrating them into complex enzyme networks similar to those in biological systems, and adjusting functions like altering tumor metabolism, reshaping the tumor environment, and enhancing drug delivery. The review also covers the applications of specially designed nanozymes in pan-cancer treatment, from catalytic therapy to improved traditional methods like chemotherapy, radiotherapy, and sonodynamic therapy, specifically analyzing the anti-tumor mechanisms of different therapeutic combination systems. Through rational design, these biomimetically designed nanozymes not only deepen the understanding of the regulatory mechanisms of nanozyme structure and performance but also adapt profoundly to tumor physiology, optimizing therapeutic effects and paving new pathways for innovative cancer treatment. Nanotechnology ; Computer science ; Cancer ; Materials science ; Medicine ; Internal medicine Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Karaneh Eftekhari | Bogdan V. Parakhonskiy | Dmitry Grigoriev | André G. Skirtach
Source: Materials (review, 2024-02-24)
Field: Materials Science — Materials Chemistry

Advances in Nanoarchitectonics: A Review of “Static” and “Dynamic” Particle Assembly Methods Particle assembly is a promising technique to create functional materials and devices from nanoscale building blocks. However, the control of particle arrangement and orientation is challenging and requires careful design of the assembly methods and conditions. In this study, the static and dynamic methods of particle assembly are reviewed, focusing on their applications in biomaterial sciences. Static methods rely on the equilibrium interactions between particles and substrates, such as electrostatic, magnetic, or capillary forces. Dynamic methods can be associated with the application of external stimuli, such as electric fields, magnetic fields, light, or sound, to manipulate the particles in a non-equilibrium state. This study discusses the advantages and limitations of such methods as well as nanoarchitectonic principles that guide the formation of desired structures and functions. It also highlights some examples of biomaterials and devices that have been fabricated by particle assembly, such as biosensors, drug delivery systems, tissue engineering scaffolds, and artificial organs. It concludes by outlining the future challenges and opportunities of particle assembly for biomaterial sciences. This review stands as a crucial guide for scholars and professionals in the field, fostering further investigation and innovation. It also highlights the necessity for continuous research to refine these methodologies and devise more efficient techniques for nanomaterial synthesis. The potential ramifications on healthcare and technology are substantial, with implications for drug delivery systems, diagnostic tools, disease treatments, energy storage, environmental science, and electronics. Nanotechnology ; Biomaterial ; Electronics ; Computer science ; Materials science ; Systems engineering ; Engineering ; Electrical engineering Pickering emulsions and particle stabilization ; Micro and Nano Robotics ; Advanced Materials and Mechanics
Authors: Taek‐Seung Kim | C. R. O'Connor | C. Reece
Source: Nature Communications (article, 2024-03-07)
Field: Materials Science — Materials Chemistry

Interrogating site dependent kinetics over SiO2-supported Pt nanoparticles A detailed knowledge of reaction kinetics is key to the development of new more efficient heterogeneous catalytic processes. However, the ability to resolve site dependent kinetics has been largely limited to surface science experiments on model systems. Herein, we can bypass the pressure, materials, and temperature gaps, resolving and quantifying two distinct pathways for CO oxidation over SiO<sub>2</sub>-supported 2 nm Pt nanoparticles using transient pressure pulse experiments. We find that the pathway distribution directly correlates with the distribution of well-coordinated (e.g., terrace) and under-coordinated (e.g., edge, vertex) CO adsorption sites on the 2 nm Pt nanoparticles as measured by in situ DRIFTS. We conclude that well-coordinated sites follow classic Langmuir-Hinshelwood kinetics, but under-coordinated sites follow non-standard kinetics with CO oxidation being barrierless but conversely also slow. This fundamental method of kinetic site deconvolution is broadly applicable to other catalytic systems, affording bridging of the complexity gap in heterogeneous catalysis. Kinetics ; Catalysis ; Nanoparticle ; Heterogeneous catalysis ; Chemistry ; Chemical kinetics ; Materials science ; Nanotechnology ; Chemical physics ; Chemical engineering ; Physics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion