jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xi Chen | Xiaofeng Zhou | Jiawei Zhou | Tengyu Xie | Yaning Li | Yuxuan Yan | Jing Huang | Chen Zibo | Dan Ma | Peilong Lu
Source:  (article, 2025-12-31)
Field: Chemistry — Spectroscopy

&lt;b&gt;De novo design of dual-topology membrane transporters&lt;/b&gt; The transport of molecules across biological membranes is essential for life, allowing cells to acquire nutrients, remove waste, maintain cellular homeostasis and communicate with their environment. Although there have been advances in de novo design of functional transmembrane proteins, designing synthetic transporters that robustly and selectively transport specific small molecules across membranes has remained a significant challenge. In this study, we present the de novo design of dual-topology membrane transporters that achieve substrate-specific transport through a rationally programmed conformational cycle. By integrating symmetric backbone assembly with deep learning–guided sequence optimization, we designed 3-TM proteins that insert in opposite orientations and assemble into antiparallel dimers, forming a putative central substrate binding site that enables alternating access to either side of the membrane. These designed transporters mediate selective uptake of small-molecule dyes in both living cells and artificial liposomes, driven by substrate concentration gradients, resembling those of natural uniporters. Cryo-EM structures reveal high fidelity to the design models, and functional assays corroborate the dual-topology architecture and mechanism of action. Here we show that functional, dynamic membrane transporters can be built from the ground up with atomic-level precision—providing insights into the evolutionary origins of transporters and opening new avenues for applications, including targeted drug delivery and metabolic pathway engineering. Transmembrane protein ; Transporter ; Membrane ; Antiparallel (mathematics) ; Protein design ; Membrane transport ; Membrane transport protein ; Small molecule ; Transport protein ; Transmembrane domain Molecular Sensors and Ion Detection ; ATP Synthase and ATPases Research ; Machine Learning in Bioinformatics
Authors: Jiangnan Cui | Yang Gao | Qian Wang | Xuan Li | Keren Xu | Zhenyu Huang | Jingsong Zhang | Chunman Zuo
Source: Journal of Computer Science and Technology (article, 2025-05-01)
Field: Chemistry — Spectroscopy

Advanced Cross-Graph Cycle Attention Model for Dissecting Complex Structures in Mass Spectrometry Imaging Computer science ; Mass spectrometry ; Graph ; Theory of computation ; Theoretical computer science ; Algorithm ; Chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Machine Learning in Materials Science
Authors: Martin, Margaret R. | Hassoun, Soha
Source: arXiv (Cornell University) (preprint, 2025-11-11)
Field: Materials Science — Materials Chemistry

General Intelligence-based Fragmentation (GIF): A framework for peak-labeled spectra simulation Despite growing reference libraries and advanced computational tools, progress in the field of metabolomics remains constrained by low rates of annotating measured spectra. The recent developments of large language models (LLMs) have led to strong performance across a wide range of generation and reasoning tasks, spurring increased interest in LLMs' application to domain-specific scientific challenges, such as mass spectra annotation. Here, we present a novel framework, General Intelligence-based Fragmentation (GIF), that guides pretrained LLMs through spectra simulation using structured prompting and reasoning. GIF utilizes tagging, structured inputs/outputs, system prompts, instruction-based prompts, and iterative refinement. Indeed, GIF offers a structured alternative to ad hoc prompting, underscoring the need for systematic guidance of LLMs on complex scientific tasks. Using GIF, we evaluate current generalist LLMs' ability to use reasoning towards fragmentation and to perform intensity prediction after fine-tuning. We benchmark performance on a novel QA dataset, the MassSpecGym QA-sim dataset, that we derive from the MassSpecGym dataset. Through these implementations of GIF, we find that GPT-4o and GPT-4o-mini achieve a cosine similarity of 0.36 and 0.35 between the simulated and true spectra, respectively, outperforming other pretrained models including GPT-5, Llama-3.1, and ChemDFM, despite GPT-5's recency and ChemDFM's domain specialization. GIF outperforms several deep learning baselines. Our evaluation of GIF highlights the value of using LLMs not only for spectra simulation but for enabling human-in-the-loop workflows and structured, explainable reasoning in molecular fragmentation. Fragmentation (computing) ; Benchmark (surveying) ; Workflow ; Spurious relationship ; Similarity (geometry) ; Field (mathematics) ; Domain (mathematical analysis) Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Xing Li | Siying Wang | Changheng Chen | Runze Li | Tong Peng | Xuan Tian | Yuan Zhou | Junwei Min | Yingming Lai | Miao Liu | Chongfeng Guo | Jinyang Liang | Chen Bai | Baoli Yao
Source: Ultrafast Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Upconversion Photoluminescence Lifetime Imaging via Multi-Prior Physics-Enhanced Deep Learning Upconversion photoluminescence lifetime imaging has emerged as a transformative analytical tool by leveraging the temporal signatures of luminescent probes to resolve molecular interactions. While compressed ultrafast photography offers distinct advantages for high-speed imaging, its utility in quantitative applications, such as food safety, remains constrained by reconstruction artifacts, particularly spatial distortion and edge detail loss. To address these limitations, we present a method of single-shot compressed upconversion photoluminescence lifetime imaging (sCUPLI) empowered by a multi-prior physics-enhanced neural (mPEN) network. This framework synergizes physical models of photoluminescence dynamics and imaging processing, subpixel information loss of the compressed encoding, extended sampling priors, sparsity constraints, and deep learning to achieve precise quantification of upconversion photoluminescence lifetimes. To demonstrate its potential in beverage additive detection, we apply mPEN–sCUPLI to analyze synthetic colorants using rare-earth-doped upconversion nanoprobes. The system excels at resolving subtle optical heterogeneities in beverage matrices, achieving superior spatiotemporal resolution and artifact suppression compared to conventional methods. It attains a spatial resolution of 90.5 lp/mm at a frame rate of 33,000 fps, achieving a spatial resolution improvement of approximately 3.56 times. Quantitative analysis shows that mPEN–sCUPLI reconstruction improves the average peak signal-to-noise ratio by 4 dB and enhances the sharpness and fidelity of imaging by 1.85 times. Given its ability to achieve high-fidelity and high-throughput photoluminescence lifetime imaging from single-shot 2-dimensional measurements, the proposed mPEN–sCUPLI approach is expected to be widely adopted in food safety detection applications. Photoluminescence ; Photon upconversion ; Subpixel rendering ; Image resolution ; Deep learning ; Compressed sensing ; Spectral imaging ; Ultrashort pulse Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Advanced Optical Sensing Technologies
Authors: Wenjin Li | Yongwei Tang | Qingbo Liu | Ziyang Yu | Shiheng Liang | Zhihong Lu | Rui Xiong | Pan Zhang
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Unveiling the impact of intrinsic defects on thermal conductivity in CuInTe <sub>2</sub> using neural network potential Defect engineering is a powerful strategy for tailoring the thermoelectric performance of materials. While experiments indicate that intrinsic point defects influence the thermal properties of CuInTe<sub>2</sub>, the underlying physical mechanisms remain unclear. In this study, we systematically investigate the effects of intrinsic defects and higher-order phonon interactions on the lattice thermal conductivity <i>κ</i><sub>L</sub> of CuInTe<sub>2</sub> using neural network potentials combined with the phonon Boltzmann transport equation. Our results reveal that V<sub>Cu</sub> vacancies significantly reduce the <i>κ</i><sub>L</sub> of CuInTe<sub>2</sub>, whereas Cu<sub>In</sub> antisite defects conversely increase it. Further analysis demonstrates that the <i>κ</i><sub>L</sub> reduction in V<sub>Cu</sub>-containing CuInTe<sub>2</sub> is primarily due to decreased phonon group velocity, as well as increased phonon scattering phase space caused by the upward shift of low-frequency optical branches. Meanwhile, the unexpected <i>κ</i><sub>L</sub> enhancement induced by Cu<sub>In</sub> antisites is attributed to reduced phonon linewidths resulting from a decreased lattice distortion coefficient <i>η</i>. Furthermore, including four-phonon (4ph) scattering significantly reduces <i>κ</i><sub>L</sub> in all cases, with reductions of 21.0%, 26.7%, and 20.1% for pristine CuInTe<sub>2</sub>, V<sub>Cu</sub>-containing, and Cu<sub>In</sub>-containing CuInTe<sub>2</sub>, respectively, highlighting the substantial impact of 4ph scattering on their phonon thermal transport. This work provides fundamental insights into the roles of intrinsic point defects and higher-order phonon interactions in the thermal transport properties of CuInTe<sub>2</sub>. Phonon ; Thermal conductivity ; Phonon scattering ; Scattering ; Thermoelectric effect ; Thermal ; Crystallographic defect ; Boltzmann equation Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Chalcogenide Semiconductor Thin Films
Authors: Reasmey P. Tan | Qing-Yu Xie | Xiang Fan | Kaiwang Zhang
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Four-phonon scattering-induced ultralow thermal conductivity in Ba <sub>3</sub> X <sub>2</sub> (X = P, As) monolayers: promising high-performance thermoelectric candidates at room temperature Two-dimensional thermoelectric materials hold great promise for efficient energy harvesting and self-powered electronic devices. In this study, we systematically investigate the thermoelectric properties of Ba<sub>3</sub>X<sub>2</sub> (X = P, As) monolayers at 300 K by combining first-principles calculations with a machine-learning-assisted four-phonon scattering framework. The results reveal that Ba<sub>3</sub>X<sub>2</sub> monolayers overcome the conventional limitation imposed by narrow band gaps and exhibit outstanding thermoelectric performance at room temperature. Specifically, both materials demonstrate excellent electron mobilities (∼10<sup>4</sup> cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup>) and favorable power factors, reaching as high as 0.089 and 0.027 W m<sup>-1</sup> K<sup>-2</sup> for Ba<sub>3</sub>P<sub>2</sub> and Ba<sub>3</sub>As<sub>2</sub>, respectively. Due to the pronounced anharmonicity arising from anti-bonding states and weak interatomic forces, the inclusion of four-phonon scattering results in a marked decrease in the lattice thermal conductivity (<i>κ</i><sub>L</sub>). The <i>κ</i><sub>L</sub> is remarkably suppressed to ultralow levels, with Ba<sub>3</sub>P<sub>2</sub> and Ba<sub>3</sub>As<sub>2</sub> exhibiting values of 2.27 W m<sup>-1</sup> K<sup>-1</sup> (2.56 W m<sup>-1</sup> K<sup>-1</sup>) and 0.95 W m<sup>-1</sup> K<sup>-1</sup> (0.98 W m<sup>-1</sup> K<sup>-1</sup>), respectively. These values represent reductions of 25.8% (28.8%) and 44.7% (34.4%) relative to predictions, considering only three-phonon scattering processes. Moreover, <i>κ</i><sub>L</sub> exhibits a nonmonotonic response to biaxial strain, with Ba<sub>3</sub>P<sub>2</sub> displaying a nearly linear decrease within the 0.5% to 3% strain range, indicating exceptional tunability of thermal transport. In summary, the room-temperature <i>ZT</i> values reach ∼2.62 for Ba<sub>3</sub>P<sub>2</sub> and ∼4.0 for Ba<sub>3</sub>As<sub>2</sub> outperforming many two-dimensional thermoelectric materials. These findings underscore the pivotal role of four-phonon scattering in tailoring low-dimensional thermal transport and highlight Ba<sub>3</sub>X<sub>2</sub> monolayers as highly promising candidates for high-performance thermoelectric applications at room temperature. Phonon ; Thermoelectric effect ; Materials science ; Phonon scattering ; Scattering ; Anharmonicity ; Condensed matter physics ; Monolayer ; Thermoelectric materials ; Thermal conductivity ; Seebeck coefficient ; Atmospheric temperature range ; Nanotechnology ; Thermodynamics ; Physics ; Optics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; 2D Materials and Applications
Authors: Yunyi Li | Chenxi Yu | Jinhua Zhang | Hebin Shao | Xin Zheng | Fei Liu | Xihui Bian | Peixue Ling | Weilu Tian
Source: Microchemical Journal (article, 2025-11-29)
Field: Chemistry — Analytical Chemistry

Intelligent quantification of Regaloside B in Lilii Bulbus extracts utilizing near-infrared spectroscopy coupled with a multi-channel attention fusion network Interpretability ; Partial least squares regression ; Artificial neural network ; Support vector machine ; Fusion ; Pattern recognition (psychology) ; Sensor fusion ; Component (thermodynamics) ; Feature selection ; Convolutional neural network Spectroscopy and Chemometric Analyses ; Biological and pharmacological studies of plants ; Phytochemical Studies and Bioactivities
Authors: Rohit Sinha | Kolati Heman Sudeep | Arjun Kaithavalappil | Saikat Sarkar
Source: Journal of the Mechanics and Physics of Solids (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

Fracture analysis of nonlocally homogenised solids via stochastic gradient estimates embedded in physics-informed neural networks Quantum nonlocality ; Stiffness ; Fracture (geology) ; Displacement (psychology) ; Displacement field ; Artificial neural network ; Nucleation ; Fracture mechanics ; RADIUS Nonlocal and gradient elasticity in micro/nano structures ; Model Reduction and Neural Networks ; Numerical methods in engineering
Authors: Yong Hao | Jingjing Peng | Xinyu Chen | Chuangfeng Huai
Source: Vibrational Spectroscopy (article, 2025-12-19)
Field: Chemistry — Analytical Chemistry

Model upgrading method based on domain-adversarial neural networks (DANN) for near-infrared spectroscopy analysis Upgrade ; Preprocessor ; Artificial neural network ; Calibration ; Feature (linguistics) ; Limiting ; Standardization ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Margaret R. Martin | Soha Hassoun
Source: PubMed (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

General Intelligence-based Fragmentation (GIF): A framework for peak-labeled spectra simulation. Despite growing reference libraries and advanced computational tools, progress in the field of metabolomics remains constrained by low rates of annotating measured spectra. The recent developments of large language models (LLMs) have led to strong performance across a wide range of generation and reasoning tasks, spurring increased interest in LLMs' application to domain-specific scientific challenges, such as mass spectra annotation. Here, we present a novel framework, General Intelligence-based Fragmentation (GIF), that guides pretrained LLMs through spectra simulation using structured prompting and reasoning. GIF utilizes tagging, structured inputs/outputs, system prompts, instruction-based prompts, and iterative refinement. Indeed, GIF offers a structured alternative to ad hoc prompting, underscoring the need for systematic guidance of LLMs on complex scientific tasks. Using GIF, we evaluate current generalist LLMs' ability to use reasoning towards fragmentation and to perform intensity prediction after fine-tuning. We benchmark performance on a novel QA dataset, the MassSpecGym QA-sim dataset, that we derive from the MassSpecGym dataset. Through these implementations of GIF, we find that GPT-4o and GPT-4o-mini achieve a cosine similarity of 0.36 and 0.35 between the simulated and true spectra, respectively, outperforming other pretrained models including GPT-5, Llama-3.1, and ChemDFM, despite GPT-5's recency and ChemDFM's domain specialization. GIF outperforms several deep learning baselines. Our evaluation of GIF highlights the value of using LLMs not only for spectra simulation but for enabling human-in-the-loop workflows and structured, explainable reasoning in molecular fragmentation. Fragmentation (computing) ; Benchmark (surveying) ; Workflow ; Spurious relationship ; Similarity (geometry) ; Domain (mathematical analysis) ; Field (mathematics) Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Jinshi Cui | Ao Li
Source:  (article, 2025-09-15)
Field: Chemistry — Analytical Chemistry

Rapid detection of enzyme activities in tomato leaves using visible-near infrared hyperspectral imaging integrated with image segmentation Tomatoes (<i>Solanum lycopersicum</i> L.) are a globally consumed vegetable, rich in essential nutrients. Enzymatic activity in tomato leaves serves as a key indicator of plant health, stress response, and disease resistance. However, traditional enzyme measurement methods are destructive and time-consuming. This study employed hyperspectral imaging (HSI) combined with image segmentation to non-destructively assess three critical enzymes: peroxidase (POD), phenylalanine ammonia-lyase (PAL), and &alpha;-amylase (&alpha;-AMS). Hyperspectral images of 125 tomato leaf samples (370–1715 nm) were analyzed using a novel segmentation algorithm to extract reflectance data. Chemical assays provided reference enzyme levels (POD: 5.108 mU/g; PAL: 7.347 U/g; &alpha;-AMS: 35.856 U/g). Genetic algorithm (GA) and successive projection algorithm (SPA) optimized wavelength selection, with GA-PLS (partial least squares regression) emerging as the top-performing model. Three alternative prediction models were developed by combining GA-selected spectra with support vector machine (SVM), particle swarm optimization-backpropagation neural network (PSO-BP), and random forest (RF) algorithms. It was found that the GA-PLS model had better prediction performance for the three enzymes in the near infrared band of leaves with R<sub>p</sub>(average coefficient of determination for the three enzymes) and RMSEP(average root means square error prediction of the three enzymes) of 0.873 and 1.489, respectively. Results confirmed that near-infrared leaf spectra provided superior enzyme prediction compared to stems. The proposed segmentation method improved accuracy over manual techniques. This study demonstrates HSI’s potential for rapid, non-destructive phytochemical analysis, offering valuable applications in precision agriculture and food quality control. Hyperspectral imaging ; Segmentation ; Particle swarm optimization ; Pattern recognition (psychology) ; Projection (relational algebra) ; Partial least squares regression ; Mean squared error ; Artificial neural network Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Grier
M. Jones (16511045) | Konstantinos D. Vogiatzis (1460164)
Source: Figshare (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Capturing ElectronCorrelation with Machine Learningthrough a Data-Driven CASPT2 Framework Multireference perturbation theory methods, such as complete active space second-order perturbation theory (CASPT2), are often employed to recover the missing electron correlation from multiconfigurational zeroth-order wave functions. Here, we introduce the data-driven CASPT2 (DDCASPT2) method to capture dynamic electron correlation using features generated from lower-level electronic structure methods, such as Hartree–Fock and complete active space self-consistent field (CASSCF) theory. We examine the effects of system size, basis set size, and the number of two-electron excitations using a small, but diverse, set of molecules. We also provide insights into our physics-based feature set using SHapley Additive exPlanation (SHAP) analysis, a feature analysis method based on cooperative game theory. In this paper, we utilize these insights to introduce a DDCASPT2 method, which provides a machine-learning-based alternative to traditional single- and multistate CASPT2 for capturing dynamical electron correlation with near-CASPT2 quality accuracy. Correlation ; Electronic correlation ; Perturbation (astronomy) ; Perturbation theory (quantum mechanics) ; Complete active space ; Set (abstract data type) ; Feature (linguistics) ; Space (punctuation) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Martino De Carlo | Francesco De Leonardis | Vittorio M. N. Passaro
Source: Photonics Research (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Non-volatile optoelectronic memory based on a non-periodic grating Non-volatile optical memories represent a promising solution in data storage because of their high-speed, energy efficient, and durable data retention. Indeed, the interest of the research community in optical phase-change materials (O-PCMs) shows the urgent need for optical solutions in the non-volatile storage applications. Although several non-volatile optical memories using O-PCMs have been presented in the literature, their scalability is usually limited. In this article we show a non-volatile optoelectronic memory based on an electrically erasable programmable read-only grating (EEPROG) for the first time to our knowledge. A footprint per bit inversely proportional to the available bandwidth at the tunable source is demonstrated. Moreover, we show that to read the stored data in the EEPROG memory it is necessary only to sample the reflectivity spectrum of the EEPROG at a few wavelengths in a number equal to the number of bits stored in the memory. With 60 nm of available bandwidth at the tunable source (around 1500 nm), we demonstrate that one bit is stored in a footprint of 9.1 μm ×4.2 μm of the EEPROG memory. Higher available bandwidths would reduce the footprint per bit. The advantage with respect to the state of the art is the scalability of this memory, which represents one of the major bottlenecks of optical non-volatile memories. Indeed, the presented device allows a sequence of bits to be stored sequentially along the same waveguided structure, thus introducing a new level of scalability for optical non-volatile memories. The waveguiding device includes a Si 3 N 4 wire and a thin layer of Sb 2 S 3 as PCM, enabling non-volatile operations. The approach presented for storing and reading data is completely novel and could pave the way for a new series of non-volatile memories useful in several fields, including neuromorphic neural networks. Scalability ; Bandwidth (computing) ; Grating ; Footprint ; Memory footprint ; Computer data storage ; 3D optical data storage ; Optical switch Phase-change materials and chalcogenides ; Chemical and Physical Properties of Materials ; Nonlinear Optical Materials Studies