jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Poteshman, Abigail N. | Ricci, Francesco | Neaton, Jeffrey B.
Source: arXiv (Cornell University) (preprint, 2025-11-23)
Field: Materials Science — Materials Chemistry

High-throughput computation of electric polarization in solids via Berry flux diagonalization Electric polarization in the absence of an externally applied electric field is a key property of polar materials, but the standard interpolation-based ab initio approach to compute polarization differences within the modern theory of polarization presents challenges for automated high-throughput calculations. Berry flux diagonalization [J. Bonini et. al, Phys. Rev. B 102, 045141 (2020)] has been proposed as an efficient and reliable alternative, though it has yet to be widely deployed. Here, we assess Berry flux diagonalization using ab initio calculations of a large set of materials, introducing and validating heuristics that ensure branch alignment with a minimal number of intermediate interpolated structures. Our automated implementation of Berry flux diagonalization succeeds in cases where prior interpolation-based workflows fail due to band-gap closures or branch ambiguities. Benchmarking with ab initio calculations of 176 candidate ferroelectrics, we demonstrate the efficacy of the approach on a broad range of insulating materials and obtain accurate effective polarization values with fewer interpolated structures than prior automated interpolation-based workflows. Our real-space heuristics that can predict gauge stability a priori from ionic displacements enable a general automated framework for reliable polarization calculations and efficient high-throughput screening of chemically and structurally diverse polar insulators. These results establish Berry flux diagonalization as a robust and efficient method to compute the effective polarization of solids and to accelerate the data-driven discovery of functional polar materials. Ab initio ; A priori and a posteriori ; Polarization (electrochemistry) ; Heuristics ; Computation ; Polar ; Correctness 2D Materials and Applications ; Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science
Authors: Wen-Chien Miao (18135317) | Fu-He Hsiao (12207382) | Yun-Han Chang (21974443) | You-Huei Jhang (21974446) | Chih-Hsien Cheng (8693457) | Hsin-Chieh Yu (21974449) | Gong-Ru Lin (2415364) | Chun-Liang Lin (406448) | Chi-Wai Chow (17340361) | Yu-Heng Hong (6634811) | Hao-Chung Kuo (1403419)
Source: Figshare (article, 2025-08-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Compact Polarization-Multiplexed Optical Transmittersthrough the Monolithic Integration of Metasurface and Photonic CrystalSurface-Emitting Lasers The exponential growth of data traffic in artificial intelligence ecosystems and interconnected networks demands radical innovations in optical communication technologies. Herein, we demonstrate a monolithically integrated metasurface-photonic-crystal-surface-emitting laser (meta-PCSEL) that represents a paradigm shift toward compact, self-contained optical transmitters with unprecedented polarization control. The proposed device simultaneously emits two circularly polarized beams with opposite handedness at specific diffraction angles, enabling polarization-division multiplexing without bulky external optics by monolithically combining a 940 nm PCSEL with a carefully engineered metasurface. Such a meta-PCSEL maintains impressive performance metrics with modulation bandwidths exceeding 2.5 GHz and data transmission rates up to 1.3 Gbit/s per polarization channel. The integrated device exhibits enhanced thermal stability compared to conventional PCSELs, with more consistent spectral characteristics across a wide temperature range. This work enables next-generation free-space optical (FSO) communications requiring compact size, polarization diversity, and thermal stability for various applications. Polarization (electrochemistry) ; Laser ; Optical communication ; Multiplexing ; Photonics ; Diffraction ; Transmission (telecommunications) ; Semiconductor laser theory Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Photonic and Optical Devices
Authors: Poteshman, Abigail N. | Ricci, Francesco | Neaton, Jeffrey B.
Source: ArXiv.org (article, 2025-11-23)
Field: Materials Science — Materials Chemistry

High-throughput computation of electric polarization in solids via Berry flux diagonalization Electric polarization in the absence of an externally applied electric field is a key property of polar materials, but the standard interpolation-based ab initio approach to compute polarization differences within the modern theory of polarization presents challenges for automated high-throughput calculations. Berry flux diagonalization [J. Bonini et. al, Phys. Rev. B 102, 045141 (2020)] has been proposed as an efficient and reliable alternative, though it has yet to be widely deployed. Here, we assess Berry flux diagonalization using ab initio calculations of a large set of materials, introducing and validating heuristics that ensure branch alignment with a minimal number of intermediate interpolated structures. Our automated implementation of Berry flux diagonalization succeeds in cases where prior interpolation-based workflows fail due to band-gap closures or branch ambiguities. Benchmarking with ab initio calculations of 176 candidate ferroelectrics, we demonstrate the efficacy of the approach on a broad range of insulating materials and obtain accurate effective polarization values with fewer interpolated structures than prior automated interpolation-based workflows. Our real-space heuristics that can predict gauge stability a priori from ionic displacements enable a general automated framework for reliable polarization calculations and efficient high-throughput screening of chemically and structurally diverse polar insulators. These results establish Berry flux diagonalization as a robust and efficient method to compute the effective polarization of solids and to accelerate the data-driven discovery of functional polar materials. Ab initio ; A priori and a posteriori ; Polarization (electrochemistry) ; Heuristics ; Computation ; Polar ; Correctness 2D Materials and Applications ; Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science
Authors: Huayan Pu | Fengwei Liu | Tingting Qiang | Fanfei Ma | Jing Gan | Hao Chen
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Marine polysaccharide-based edible film as an alternative to plastic packaging: preparations, applications and recent advances Active packaging ; Food packaging ; Food products ; Food processing ; Plastic packaging ; Marine conservation Nanocomposite Films for Food Packaging
Authors: Ki Han Kim | J. Park | Khang June Lee | Jiwon Seo | Yeong Kwon Kim | Junhwan Choi | Moon-Sik Seo | Byung Chul Jang
Source: Advanced Science (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

MoS <sub>2</sub> Channel‐Enhanced High‐Density Charge Trap Flash Memory and Machine Learning‐Assisted Sensing Methodologies for Memory‐Centric Computing Systems Driven by the shift of artificial intelligence (AI) workloads to edge devices, there is a growing demand for nonvolatile memory solutions that offer high-density, low-power consumption, and reliability. However, well-established 3D NAND Flash using polycrystalline Si (Poly-Si) channel encounters bottlenecks in increasing bit density due to short-channel effects and cell-current limitations. This study investigates molybdenum disulfide (MoS<sub>2</sub>) as an alternative channel material for 3D NAND Flash cells. MoS<sub>2</sub>'s low bandgap facilitates hole-injection-based erase, achieving a broader memory window at moderate voltages. Furthermore, adopting a low-k (≈2.2) tunneling layer improves the gate-coupling ratio, reducing program/erase voltages and enhancing reliability, with endurance up to 10<sup>4</sup> cycles and retention of 10<sup>5</sup> s. Comprehensive analyses, including thickness-dependent MoS<sub>2</sub> electrical measurements, temperature-dependent conduction studies, and Technology Computer-Aided Design (TCAD) simulations, elucidate the relationship between channel thickness and reliability metrics such as endurance and retention. Furthermore, deep reinforcement learning-driven Berkeley Short-channel IGFET Model (BSIM) parameter calibration enables seamless integration of the MoS<sub>2</sub> model with a fabricated page-buffer chip, allowing circuit-level verification of sensing margins. This methodology can be applicable to new channel materials for next-generation memory devices. These results demonstrate that MoS<sub>2</sub>-based nonvolatile memory effectively meets high-density, low-power, and reliable storage needs, presenting a promising solution for AI-centric edge computing. Flash memory ; Trap (plumbing) ; Computer science ; Flash (photography) ; Non-volatile memory ; Charge trap flash ; Channel (broadcasting) ; Materials science ; Nanotechnology ; Embedded system ; Computer architecture ; Computer hardware ; Engineering ; Physics ; Telecommunications 2D Materials and Applications ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Yun Xiao | Xiaoxiao Han | Xin Zhang | Wenxiao Pan | Qiao Xue | Xian Liu | Jianjie Fu | Richard H. Roettger | Aiqian Zhang
Source: Journal of Agricultural and Food Chemistry (article, 2025-07-24)
Field: Chemistry — Analytical Chemistry

Integrating Non-Targeted Mass Spectrometry and Machine Learning for the Classification of Organic and Conventionally Grown Agricultural Products: A Case Study on Tomatoes Rising demand for organic agricultural products has made the verification of their authenticity a critical concern. Traditional classification approaches for mass spectrometry using full-scan high-resolution mass spectrometry data often emphasize feature selection, which can lead to information loss and limited model robustness. Here, an artificial intelligence-based classification framework was developed to differentiate between organically and conventionally grown agricultural products, utilizing mass spectrometry data. Tomatoes were used as a representative case. The proposed method, incorporating 1000 mass-to-charge ratio (<i>m</i>/<i>z</i>) binning and an Adaptive Boosting model, achieved high predictive performance, with an Area Under the Receiver Operating Characteristic Curve exceeding 0.990 and an accuracy of 0.997. Beyond model performance, interpretability is enhanced through Shapley Additive Explanations-based importance analysis, followed by structural annotation of key <i>m</i>/<i>z</i> fragments. Notably, Gibberellin A4 and (<i>Z</i>)-resveratrol were identified as potential chemical markers of organically grown tomatoes. This enables the identification of chemically meaningful ions associated with different cultivation practices. The proposed framework provides a scalable and interpretable solution for the authentication of organic agricultural products. Mass spectrometry ; Agriculture ; Organic farming ; Chemistry ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Seongmin Kim | In‐Saeng Suh
Source: Advanced Intelligent Discovery (article, 2025-09-19)
Field: Materials Science — Materials Chemistry

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data‐driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high‐performance functional materials. Surrogate‐based active learning, continually improving its surrogate model by iteratively including high‐quality data points, has emerged as a cost‐effective data‐driven approach. Furthermore, it can be coupled with quantum computing to enhance optimization processes, especially when paired with a special form of surrogate model (i.e., quadratic unconstrained binary optimization), formulated by factorization machine (FM). However, current practices often overlook the variability in design space sizes when determining the initial data size for optimization. In this work, we investigate the optimal initial data sizes required for efficient convergence across various design space sizes. By employing averaged piecewise linear regression, we identify initiation points where convergence begins, highlighting the crucial role of employing adequate initial data in achieving efficient optimization. These results contribute to the efficient optimization of functional materials by ensuring faster convergence and reducing computational costs in FM‐based active learning. Convergence (economics) ; Piecewise ; Factorization ; Surrogate model ; Data point ; Optimization problem ; Optimal design ; Active learning (machine learning) Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Yisimayili Tuersun | Pingjun Luo | Yixin Chen | Xu Huang | Mingdeng Huang | Sheng Chu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Preparation and Surface Modification of Vertically Structured Indium/Graphite Elastomer Thermal Pads for Effective Heat Dssipation Graphite ; Materials science ; Elastomer ; Indium ; Surface modification ; Thermal ; Composite material ; Surface (topology) ; Mechanical engineering ; Metallurgy ; Engineering ; Physics ; Geometry ; Thermodynamics Thermal properties of materials ; Synthesis and properties of polymers ; Electronic Packaging and Soldering Technologies
Authors: Seongmin Kim | In‐Saeng Suh
Source: ArXiv.org (preprint, 2025-06-03)
Field: Materials Science — Materials Chemistry

Optimization of Functional Materials Design with Optimal Initial Data in Surrogate-Based Active Learning The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-effective data-driven approach. Furthermore, it can be coupled with quantum computing to enhance optimization processes, especially when paired with a special form of surrogate model ($i.e.$, quadratic unconstrained binary optimization), formulated by factorization machine. However, current practices often overlook the variability in design space sizes when determining the initial data size for optimization. In this work, we investigate the optimal initial data sizes required for efficient convergence across various design space sizes. By employing averaged piecewise linear regression, we identify initiation points where convergence begins, highlighting the crucial role of employing adequate initial data in achieving efficient optimization. These results contribute to the efficient optimization of functional materials by ensuring faster convergence and reducing computational costs in surrogate-based active learning. Convergence (economics) ; Piecewise ; Optimization problem ; Surrogate model ; Optimal design ; Data point ; Active learning (machine learning) ; Quadratic equation Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Multi-Objective Optimization Algorithms
Authors: H. Moreno | G. Acero | Ederson Aguiar | Miguel Ángel Ramírez | E. Longo | A.Z. Simões
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Disclosing the Resistive Switching and Polar Properties of Nd-Doped Bi4ti3o12 Thin Films Doping ; Polar ; Materials science ; Optoelectronics ; Resistive touchscreen ; Thin film ; Engineering physics ; Nanotechnology ; Electrical engineering ; Physics ; Engineering Magnetic and transport properties of perovskites and related materials ; Advanced Memory and Neural Computing ; Atomic and Subatomic Physics Research
Authors: Garima Garima
Source:  (article, 2025-11-09)
Field: Materials Science — Biomaterials

Next-Generation Strategies for Overcoming Physiological and Cellular Barriers to Achieve Effective Targeted Drug Delivery Targeted drug delivery remains one of the most significant challenges in modern therapeutics due to complex physiological and cellular barriers that limit drug bioavailability and site-specific accumulation. Biological obstacles such as the gastrointestinal environment, reticuloendothelial system (RES), blood–brain barrier (BBB), tumor microenvironment, cellular membranes, and intracellular trafficking pathways significantly reduce therapeutic efficacy. Recent advances in nanotechnology, biomaterials engineering, molecular targeting, and smart delivery systems have enabled the development of next-generation strategies designed to overcome these barriers. This review discusses major physiological and cellular obstacles to drug delivery and highlights innovative approaches including stimuli-responsive nanocarriers, ligand-mediated targeting, biomimetic systems, cell-penetrating peptides, exosome-based delivery, microenvironment-responsive platforms, and AI-driven formulation design. The integration of precision medicine with advanced delivery systems is expected to transform targeted therapeutics and improve clinical outcomes. Drug delivery ; Targeted drug delivery ; Drug ; Drug development ; Biocompatible material ; Drug carrier Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Barrier Structure and Function Studies
Authors: Minghui Sun | Nianlong Zou | Yang Li | Chong Wang | Wenhui Duan | Yong Xu
Source: Physical review. B./Physical review. B (article, 2025-10-08)
Field: Materials Science — Materials Chemistry

Band-resolved automatic differentiation method for first-principles quantum response calculations The accurate calculation of Bloch state derivatives is numerically challenging yet indispensable for evaluating the quantum response functions, such as how materials interact with light. Here, the authors develop a band-resolved automatic differentiation approach for efficiently calculating quantum response properties, eliminating the need for manual analytical derivations and time-consuming full-band summation. This work incorporates the quantum response calculation into a broader paradigm of differentiable programming, unlocking new pathways for data-driven exploration and design of advanced quantum materials. Quantum ; Work (physics) ; Quantum operation ; State (computer science) ; Quantum process ; Quantum algorithm ; Quantum error correction ; Differentiable function ; Quantum system Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Strong Light-Matter Interactions
Authors: Dipak M. Purohit | Ravi Kumar Patel | Krupal Morker
Source: Journal of international research in medical and pharmaceutical sciences (article, 2025-10-18)
Field: Chemistry — Organic Chemistry

Comprehensive Review of Genotoxic Impurities: Current Trend, Challenges and Control Strategies for Pharmaceutical Drug Products Genotoxic impurities (GIs) in pharmaceuticals pose a significant risk due to their potential to induce DNA damage, mutations, and carcinogenesis even at trace levels. Regulatory frameworks, including ICH M7 (R1), FDA, and EMA guidelines, have established stringent impurity assessment and control strategies based on the Threshold of Toxicological Concern (TTC) approach. This review explores the sources of GIs, including synthetic process-related byproducts, degradation products, excipient interactions, and environmental contaminants. The mechanisms of genotoxicity, encompassing DNA alkylation, chromosomal aberrations, and oxidative stress, are discussed alongside structural alerts for impurity risk prediction. Advanced analytical techniques such as LC-MS/MS, GC-MS, NMR, and in silico modeling (DEREK, TOPKAT, MCASE) facilitate impurity detection and risk assessment. Control strategies, including process optimization, solvent selection, purification techniques, and green chemistry approaches, are key to mitigating impurity formation. Future directions emphasize harmonization of global regulatory limits, AI-driven predictive toxicology, and next-generation analytical methodologies for improved impurity management. This review provides a comprehensive scientific framework for genotoxic impurity risk assessment, control, and regulatory compliance, ensuring drug safety and quality. Drug ; Harmonization ; Pharmaceutical industry ; Process (computing) ; Risk assessment ; Control (management) ; Drug development ; Pharmaceutical drug Synthesis and Characterization of Heterocyclic Compounds ; Computational Drug Discovery Methods
Authors: Xingru Chen
Source: MedScien (article, 2025-10-23)
Field: Materials Science — Biomaterials

Advances in Brain Cancer Therapy: Exploration of Small Molecules and Nanomedicine Brain cancer remains one of the most challenging malignancies of the central nervous system, with high recurrence rates and poor prognosis posing a serious threat to patient survival. Current therapeutic approaches, including surgery, radiotherapy, and chemotherapy, have improved survival to some extent but remain limited by the blood–brain barrier (BBB), tumor heterogeneity, and drug resistance. In recent years, small-molecule drugs and nanomedicine-based delivery systems have shown great potential in overcoming these barriers. Small molecules such as temozolomide (TMZ) and targeted agents play important roles in combination therapies, yet issues of BBB penetration and resistance persist. Nanomedicine platforms—including liposomes, polymeric nanoparticles, micelles, and exosomes—enable enhanced drug accumulation at tumor sites and controlled release, offering new strategies to break through the limitations of conventional therapies. However, clinical translation faces challenges related to safety, regulatory approval, and high costs. Looking ahead, emerging technologies such as artificial intelligence–driven drug discovery, CRISPR-based gene editing, liquid biopsy for early detection, and theranostics (therapy combined with diagnostics) hold promise for more precise and personalized treatment of brain cancer. This review summarizes the current therapeutic strategies and limitations of brain cancer, highlights advances in small-molecule and nanomedicine approaches, and discusses challenges and future perspectives for their clinical translation. Nanomedicine ; Temozolomide ; Glioblastoma ; Drug delivery ; Brain cancer ; Cancer ; Drug ; Drug discovery Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Nanoplatforms for cancer theranostics