jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dong-Jin Kim | Xiaoyu Wang | Santiago Vargas | Peichen Zhong | David A. King | Théo Jaffrelot Inizan | Bingqing Cheng
Source: Journal of Chemical Theory and Computation (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recently developed the Latent Ewald Summation (LES) method, which infers electrostatic interactions, polarization, and Born effective charges (BECs), just by learning from energy and force training data. Here, we present LES as a standalone library, compatible with any short-range MLIP, and demonstrate its integration with methods such as MACE, NequIP, Allegro, CACE, CHGNet, and UMA. We benchmark LES-enhanced models on distinct systems, including bulk water, polar dipeptides, and gold dimer adsorption on defective substrates, and show that LES not only captures correct electrostatics but also improves accuracy. Additionally, we scale LES to large and chemically diverse data by training MACELES-OFF on the SPICE set containing molecules and clusters, making a universal MLIP with electrostatics for organic systems, including biomolecules. MACELES-OFF is more accurate than its short-range counterpart (MACE-OFF) trained on the same data set, predicts dipoles and BECs reliably, and has better descriptions of bulk liquids. By enabling efficient long-range electrostatics without directly training on electrical properties, LES paves the way for electrostatic foundation MLIPs. Electrostatics ; Set (abstract data type) ; Dipole ; Benchmark (surveying) ; Charge (physics) ; Ewald summation ; Electric potential energy ; Molecular dynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Ling Bai | Ziting Yang | Jie Wen | Zifeng Mai | Bin Liu | Duan-Yang Liu | Penghong Ci | Liyuan Liu | Yiyang Xie | Ziqi Zhou | Yali Yu | Zhongming Wei
Source: Chip (article, 2025-03-30)
Field: Materials Science — Materials Chemistry

Polarization reversal enhanced intelligent recognition in two-dimensional MoTe2/GeSe heterostructure Wide-spectral and polarization-sensitive photodetectors are vital for applications in imaging, communication, and intelligent sensing. Although two-dimensional (2D) materials have shown great promise in enhancing the performance of these devices, conventional methods for spectral discrimination often rely on complex designs, such as external filters or multisensor systems, increasing system cost and complexity. Developing simplified devices that integrate spectral and polarization detection remains a key challenge. Here, we demonstrated a 2D MoTe 2 /GeSe-based photodetector with wide-spectral photoresponse (400 to 1064 nm) and polarization sensitivity, achieving a responsivity of 1.35 A W −1 and a polarization ratio of 2.23 under 808 nm illumination. The device exhibited a unique 90° polarization reversal between green (532 nm) and red (808 nm), providing a novel mechanism for spectral discrimination. First-principles calculations reveal the polarization reversal phenomenon based on the heterostructure's optical anisotropy. Furthermore, integration with a convolutional neural network enables intelligent traffic signal recognition using polarization-sensitive images. This work highlights the potential of MoTe 2 /GeSe heterostructures for next-generation photodetectors, offering compact, multifunctional solutions with integrated spectral and polarization discrimination capabilities. Heterojunction ; Polarization (electrochemistry) ; Materials science ; Optoelectronics ; Condensed matter physics ; Physics ; Chemistry ; Physical chemistry 2D Materials and Applications ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Vikas Bhatnagar | Adesh Kumar
Source: Energy Storage (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

A Comprehensive Review of Phase Change Memory for Neuromorphic Computing: Advancements, Challenges, and Future Directions ABSTRACT The human brain functions as a highly efficient control center, inspiring the field of neuromorphic computing, which seeks to replicate its structure and behavior through hardware systems. Neuromorphic computing integrates processing and memory functions using artificial neurons and synapses designed with electronic circuits, enabling parallel, energy‐efficient data handling. One of the leading technologies supporting this paradigm is phase change memory (PCM), a non‐volatile memory that stores data through reversible transitions between amorphous (high resistance) and crystalline (low resistance) states of chalcogenide materials, particularly Ge 2 Sb 2 Te 5 (GST225). PCM exhibits fast read/write speeds, excellent data retention, and scalability, making it ideal for neuromorphic architectures. This review highlights recent advancements in PCM for neuromorphic computing, including innovations in doping strategies and device engineering. Notable developments include arsenic‐doped ovonic threshold switches (OTS) for enhanced selector performance, monolayer Sb 2 Te 3 for atomic‐scale devices, and heater‐all‐around (HAA) 3D architectures for reduced energy consumption. Integration with machine learning tools enables precise atomistic modeling, accelerating material and device optimization. Furthermore, emerging variants like ovonic unified memory (OUM) and interfacial PCM (IPCM) offer unique performance advantages. While PCM promises significant benefits, key challenges such as resistance drift, endurance limits, and thermal crosstalk must be addressed. The global neuromorphic computing market is poised for exponential growth, driven by innovations in materials, algorithms, and architectures. The PCM and neuromorphic computing represent a transformative leap toward intelligent, adaptive, and energy‐efficient computing systems. Neuromorphic engineering ; Phase-change memory ; Memristor ; Resistive random-access memory ; Efficient energy use ; Artificial neural network ; Non-volatile memory ; Reconfigurability ; In-Memory Processing Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Neural Networks and Reservoir Computing
Authors: Georganna Benedetto | Patrick Damacet | Elissa O. Shehayeb | Gbenga Fabusola | Cory M. Simon | Katherine A. Mirica
Source: ACS Sensors (article, 2025-10-06)
Field: Materials Science — Polymers and Plastics

Metal–Organic Framework-Based Chemiresistive Array Paired with Machine Learning Algorithms for the Detection and Differentiation of Toxic Gases The development of low-power, sensitive, and selective gas sensors capable of detecting and differentiating toxic gases is pivotal for safety and environmental monitoring. This paper describes a chemiresistive sensor array comprising a series of three conductive hexahydroxytriphenylene-based metal-organic frameworks (MOFs) (M<sub>3</sub>(HHTP)<sub>2</sub> (M = Ni, Cu, Zn)) capable of detecting and differentiating parts-per-million (ppm) levels of carbon monoxide (CO), ammonia (NH<sub>3</sub>), sulfur dioxide (SO<sub>2</sub>), hydrogen sulfide (H<sub>2</sub>S), and nitric oxide (NO), as well as binary mixtures of SO<sub>2</sub> and H<sub>2</sub>S in dry nitrogen at room temperature. This capability arises from variations in the identity of the linking metal and the framework packing pattern across the materials in the array. To visualize the response pattern of the sensor array and map it to a predicted gas composition, principal component analysis and random forest classification are employed. Both machine learning techniques confirm the ability to discriminate CO, NH<sub>3</sub>, SO<sub>2</sub>, H<sub>2</sub>S, and NO analytes as well as binary SO<sub>2</sub>/H<sub>2</sub>S mixtures at ppm concentrations using the response of the array. Moreover, a feature importance method applied to the classifier assigns importance scores to each sensor in the array to quantify the impact of individual materials on analyte discrimination. Spectroscopic investigations provide insight into how the structural features of the MOFs influence sensing performance and ascertain material-analyte interactions governing sensing selectivity for SO<sub>2</sub>/H<sub>2</sub>S binary mixtures. Analyte ; Hydrogen sulfide ; Sensor array ; Principal component analysis ; Classifier (UML) ; Binary classification ; Carbon monoxide ; Binary number ; Random forest Conducting polymers and applications ; Gas Sensing Nanomaterials and Sensors ; Analytical Chemistry and Sensors
Authors: Xianyong Shen | Shutao Ma | Chunyuan Song | Yuezhen He | Maoguo Li
Source: Analytical Chemistry (article, 2025-09-06)
Field: Materials Science — Materials Chemistry

Kinetic and Mechanistic Discrepancies of Single/Dual-Atom Nanozymes Drive a Triple-Channel Sensing Array for Machine Learning-Assisted Antioxidant Discrimination Current colorimetric sensing arrays for antioxidant detection often struggle with discrimination due to cross-reactive signals from individual nanozymes. These signals are typically modulated by external factors such as pH or chromogenic substrates, offering limited kinetic and mechanistic diversity. To overcome this, we present a novel triple-channel colorimetric sensing array utilizing two distinct single-atom nanozymes (Cu SA and Fe SA) and one dual-atom nanozyme (CuFe DA). Our approach leverages the inherent kinetic and mechanistic differences between these atomically dispersed catalysts. We observed measurably distinct oxidase-like activities through variations in their Michaelis-Menten constants (Km<i>Km</i>) and specific activities. Furthermore, detailed mechanistic studies revealed differences in their active metal sites, leading to varied reactive oxygen species (ROS) production. This intrinsic functional divergence creates unique "fingerprint" responses for improved antioxidant differentiation. When integrated with machine learning algorithms (Principal Component Analysis and Hierarchical Cluster Analysis), our array successfully identified and quantified six common antioxidants: ascorbic acid, glutathione, cysteine, tea polyphenol, gallic acid, and tannin. The array exhibited excellent sensitivity, with a low detection limit of 2.01 μM for cysteine. This research offers a robust strategy for developing high-performance sensing arrays by exploiting fundamental atomic-scale kinetic and mechanistic variations, holding significant promise for food safety and health monitoring applications. Ascorbic acid ; Chromogenic ; Antioxidant ; Substrate (aquarium) ; Kinetic energy ; Detection limit ; Gallic acid Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Mohammed Ghazwani | Umme Hanı
Source: Ain Shams Engineering Journal (article, 2025-05-30)
Field: Chemistry — Analytical Chemistry

Data driven analysis of tablet design via machine learning for evaluation of impact of formulations properties on the disintegration time This study investigated the use of advanced machine learning techniques to model disintegration time for solid dosage oral formulations. The input features encompass molecular properties, physical attributes, excipient compositions, and formulation characteristics. An Isolation Forest algorithm is employed for outlier detection, while a Standard Scaler normalization technique ensures consistent feature scaling. Feature selection is conducted using Conditional Mutual Information (CMI) to identify the most informative predictors. The study compares three regression models: Local Polynomial Regression (LPR), Gaussian Process Regression (GPR), and Deep Gaussian Process Regression (DGPR). Results indicate that DGPR outperforms others, obtaining the highest R 2 scores and the lowest error rates across training, validation, and testing phases. Interpretability of the DGPR model is enhanced using SHAP (SHapley Additive exPlanations), providing insights into feature importance and their effects on predictions. Additionally, the Hunter-Prey Optimization algorithm is utilized to optimize hyperparameters, demonstrating its efficacy in balancing exploration and exploitation. This research shows that DGPR can accurately model complex relationships in pharmaceutical datasets and provides both predictive accuracy and interpretability. Computer science ; Machine learning ; Artificial intelligence ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Statistical Process Monitoring ; Anomaly Detection Techniques and Applications
Authors: Chenxin Zhang | Yanyan Chen | Qian Wang | Puru Jena
Source: Nano Letters (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Phonon Localization and a Boson-Peak-Like Anomaly in Twisted Penta-PdSe<sub>2</sub> Bilayer With the emergence of twistronics, twisting has been employed to manipulate phonon-phonon interactions in 2D-layered systems. However, due to the complexity of twisted structures, twisting-induced phonon localization and Boson-peak-like (BP-like) anomalies have not yet been reported. In this work, using machine-learning-supported molecular dynamics, we study the impact of interlayer twisting on the phonon properties in bilayer pentagonal PdSe<sub>2</sub> (penta-PdSe<sub>2</sub>) with multiple twist angles. We find that twisting reduces the in-plane thermal conductivity by up to 78.62%, while significantly enhancing the phonon coherent thermal conductivity contribution from 21.75% to 85.40%. Furthermore, by analyzing the vibrational density of states and localized shear strain distribution, we demonstrate that twisting introduces the inhomogeneous shear modulus distribution, causing phonon localization and a BP-like anomaly in twisted penta-PdSe<sub>2</sub> bilayers. These findings show that twisting becomes a new variable for producing a BP-like anomaly, different from the conventional strategies by using disorder, defects, or doping. Condensed matter physics ; Anomaly (physics) ; Bilayer ; Phonon ; Physics ; Boson ; Materials science ; Chemistry ; Quantum mechanics ; Membrane 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Topological Materials and Phenomena
Authors: Sunli Cong | Jun Sun | Bing Zhang | Lei Shi | Xin Zhou | Xiaohong Wu
Source: Microchemical Journal (article, 2025-02-06)
Field: Chemistry — Analytical Chemistry

A new method to identify the adulteration levels of Lonicerae Flos in Lonicerae Japonicae Flos using fluorescence hyperspectral imaging combined with optimized convolutional neural network Flos ; Hyperspectral imaging ; Convolutional neural network ; Fluorescence ; Chemistry ; Chromatography ; Artificial intelligence ; Computer science ; Biochemistry ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Meat and Animal Product Quality
Authors: Suswanth Poluru | Aarjoo Jaimin | Nitin Kotkunde | Swadesh Kumar Singh | Amit Kumar | Ashutosh Panchal | Prabhu Gnanasambandam
Source: Materials & Design (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Constitutive modelling and microstructural analysis of 92W-5Co-3Ni alloy subjected to high strain rate testing at elevated temperatures • Texture variation at a higher strain rate can activate the formation of Adiabatic Shear Bands. • Adiabatic Shear Bands have been noticed at high strain rates above 2700 s −1 . • Arrhenius’s model captured accurate dynamic flow-softening behaviour. • Random Forest model displayed the best flow stress prediction behaviour. The mechanical response of 92 W-5Co-3Ni alloy was analysed at dynamic strain rates ranging from 1800 s −1 to 4200 s −1 and temperatures from 323 K to 873 K. The marginal strain hardening, followed by significant strain softening, was seen in flow stress behaviour. The rise in the yield strength at room temperature and higher strain rates was mainly due to the increased fraction of low-angle grain boundaries and kernel average misorientation. The severe elongation of tungsten grains was noticed for all strain rates. The Adiabatic Shear Bands (ASBs) were noticed above strain rates of 2700 s −1 with an average shear band’s width of 45 µm. Moreover, the average size of the ASB shear band’s width was reduced to 30 µm for higher strain rates (4200 s −1 ), which exhibited the enhancement of the self-sharpening capacity of the kinetic energy penetrator. Subsequently, the Arrhenius constitutive model and machine-learning-based Random Forest model (RF) were developed to predict flow stress. The Arrhenius model accurately predicted the flow stress behaviour with a correlation coefficient (R) of 0.9766 and an average absolute error (AARE) of 2.033 %. However, the RF model demonstrated the best prediction of flow stress behaviour with an R of 0.9912 and an AARE of 1.145 %. Materials science ; Alloy ; Strain rate ; Strain (injury) ; Constitutive equation ; Microstructure ; Metallurgy ; Composite material ; Structural engineering ; Finite element method High-Velocity Impact and Material Behavior ; Nuclear Materials and Properties ; Metallurgy and Material Forming
Authors: Yu Wang | Jiahao Xu | Zemin Ren | Yuan Li | Dingkuo Liu | Fufeng Liu | Wenjie Jing
Source: Chemical Engineering Journal (article, 2025-07-22)
Field: Materials Science — Materials Chemistry

Bridging nanozyme sensor array and machine learning for accurate multi-category antibiotics intelligent identification Bridging (networking) ; Identification (biology) ; Computer science ; Artificial intelligence ; Engineering ; Machine learning ; Computer security ; Biology Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Lei Guo | Peisi Xie | X. Y. Shen | Thomas Ka‐Yam Lam | Lingli Deng | Chengyi Xie | Xiangnan Xu | Chris K.C. Wong | Jingjing Xu | Jiacheng Fang | Xiaoxiao Wang | Zhuang Xiong | Shangyi Luo | Jianing Wang | Jiyang Dong | Zongwei Cai
Source: Advanced Science (article, 2025-01-07)
Field: Chemistry — Spectroscopy

Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI Mass spectrometry imaging (MSI) provides valuable insights into metabolic heterogeneity by capturing in situ molecular profiles within organisms. One challenge of MSI heterogeneity analysis is performing an objective segmentation to differentiate the biological tissue into distinct regions with unique characteristics. However, current methods struggle due to the insufficient incorporation of biological context and high computational demand. To address these challenges, a novel deep learning-based approach is proposed, GraphMSI, which integrates metabolic profiles with spatial information to enhance MSI data analysis. Our comparative results demonstrate GraphMSI outperforms commonly used segmentation methods in both visual inspection and quantitative evaluation. Moreover, GraphMSI can incorporate partial or coarse biological contexts to improve segmentation results and enable more effective three-dimensional MSI segmentation with reduced computational requirements. These are facilitated by two optional enhanced modes: scribble-interactive and knowledge-transfer. Numerous results demonstrate the robustness of these two modes, ensuring that GraphMSI consistently retains its capability to identify biologically relevant sub-regions in complex practical applications. It is anticipated that GraphMSI will become a powerful tool for spatial heterogeneity analysis in MSI data. Computer science ; Segmentation ; Robustness (evolution) ; Context (archaeology) ; Spatial analysis ; Mass spectrometry imaging ; Artificial intelligence ; Data mining ; Mass spectrometry ; Remote sensing ; Chemistry ; Biology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Rocel Gualberto | M. Manjaiah | Persia Ada N. de Yro | Jubert Pasco | Harveen Bongao | Thomas McCarthy | Mark Cabading | Clodualdo Aranas
Source: Journal of Materials Research and Technology (article, 2025-01-21)
Field: Materials Science — Materials Chemistry

Investigating the high-strain rate response of additively manufactured 420 stainless steel through material constitutive modelling This work explores the high-temperature, dynamic deformation behavior of AISI 420 stainless steel (420SS) produced through laser powder bed fusion (LPBF). The material exhibits a complex dual-phase microstructure, influencing its dynamic mechanical response. The investigation employs the Split-Hopkinson Pressure Bar (SHPB) technique utilizing strain rates of 1000 s −1 and 1500 s −1 , and temperatures ranging from 298 K to 798 K. The results demonstrate that the flow stresses of LPBF-fabricated 420SS are insensitive to strain rate; however, they decrease with increasing temperature. To model these behaviors, modified Johnson-Cook, Hensel-Spittel, and modified Hensel-Spittel constitutive models, supplemented by an Artificial Neural Network (ANN)-based predictive approach, were developed. Comparative analysis of the phenomenological models indicates that the formulated constitutive equations predict flow stress values within an acceptable average absolute relative error (AARE) in the range of 8–11%. In contrast, the ANN model demonstrated superior precision and accuracy (with AARE of around 1%) over the constitutive equations in predicting the hot flow behavior. To further understand the stress/strain response and the errors generated by the models, microstructural analysis of deformed samples was performed, which revealed differences between low and high temperature dynamic tests. The melt pool structure was preserved after impact for low temperature test, which was not the case for the high temperature condition. Moreover, the dynamic deformation led to the transformation of retained austenite into martensite, leading to a peak stress of 1600 MPa. Materials science ; Constitutive equation ; Strain rate ; Strain (injury) ; Metallurgy ; Composite material ; Finite element method ; Structural engineering High-Velocity Impact and Material Behavior ; Additive Manufacturing Materials and Processes ; Metallurgy and Material Forming
Authors: Zhengjian Cao | Xiaoqiang Bian | Jing Chen | Jianye Zhang | Wei Fu
Source: International Journal of Hydrogen Energy (article, 2025-06-10)
Field: Chemistry — Inorganic Chemistry

Predicting hydrogen storage in metal-organic frameworks using a novel hybrid machine learning model Hydrogen storage ; Computer science ; Metal-organic framework ; Process engineering ; Hydrogen ; Materials science ; Chemistry ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Hydrogen Storage and Materials