jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sandesh Ramrao Bhitre | Madhukar Navgire
Source: International Journal of Engineering Research and Science & Technology (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

AI-Augmented Analysis of FT-IR Spectroscopy Data for Nanomaterials Characterization: A Review of Current Applications and Future Directions The characterization of nanomaterials by Fourier-transform infrared (FTIR) spectroscopy generates complex, high-dimensional datasets that present significant challenges for traditional manual interpretation methods. Overlapping spectral bands, subtle chemical variations, and the need for high-throughput analysis have driven the integration of artificial intelligence (AI) and machine learning (ML) into spectroscopic workflows. This paper reviews the current state of AI applications in the analysis of FTIR data for nanomaterials. We explore how ML techniques—ranging from unsupervised learning for cluster analysis to supervised deep learning for property prediction—are being deployed to overcome the limitations of conventional chemometrics. Key applications discussed include the automated classification of graphene oxide derivatives, the analysis of nanoplastic pollutants, and the extraction of physical properties from nanoscale infrared spectra. The review also addresses critical challenges such as model interpretability, data standardization, and generalization. Finally, we outline future directions, including physics-informed neural networks, foundation models, and the development of autonomous, self-driving spectroscopic laboratories [6,7]. Characterization (materials science) ; Nanomaterials ; Current (fluid) ; Nanoscopic scale ; Key (lock) ; Artificial neural network ; Spectroscopy Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Alexander Arjun Lobo | Urvi Awasthi | Leonid Zhukov
Source: arXiv (Cornell University) (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Property-Guided Molecular Generation and Optimization via Latent Flows Molecular discovery is increasingly framed as an inverse design problem: identifying molecular structures that satisfy desired property profiles under feasibility constraints. While recent generative models provide continuous latent representations of chemical space, targeted optimization within these representations often leads to degraded validity, loss of structural fidelity, or unstable behavior. We introduce MoltenFlow, a modular framework that combines property-organized latent representations with flow-matching generative priors and gradient-based guidance. This formulation supports both conditioned generation and local optimization within a single latent-space framework. We show that guided latent flows enable efficient multi-objective molecular optimization under fixed oracle budgets with controllable trade-offs, while a learned flow prior improves unconditional generation quality. Modular design ; Prior probability ; Generative grammar ; Property (philosophy) ; Generative model ; Optimization problem ; Flow (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Rongru Zhang | Honghao Yue | Hao Sun | Miao Wang | Fei Yang | Jing Liu | Zhiwen Yu | Xing Huang
Source: Microsystems & Nanoengineering (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Wire-form shape memory alloy actuators: modeling, design, and control Wire-form shape memory alloy (WF-SMA) actuators have become integral components in numerous advanced applications ranging from robotics and aerospace to biomedicine thanks to their exceptional energy density, compact architectures and versatile actuation modes. Serving as a unique bridge between high-force actuation and material compliance, WF-SMAs enable the fabrication of intelligent soft materials and stretchable electronic systems. This work contributes a comprehensive and systematic assessment of WF-SMA actuators, including actuation modeling methodologies, typical actuator configurations, control strategies, and cutting edge applications in multiple fields. We firstly revisit the SMA actuation models with an emphasis on the theoretical foundation as well as current challenges in representing SMA's nonlinear, thermodynamic and actuation behaviors. Then, actuator design paradigms are classified according to the characteristic of the mechanical load (i.e. linear, nonlinear and differential) followed by briefly exploring the large-stroke strategies. Control approaches for manipulating WF-SMA systems are also surveyed covering a spectrum from conventional algorithms to smart strategies based on SMA-specific models, neural networks and integrated self-sensing methods. Drawing upon this assessment, we elucidate the key challenges that impede the widespread and practical application of SMA technologies, and suggest that future fabrication of WF-SMA actuators should increasingly rely on the integration of micro-nano fabrication techniques, flexible electronics, and multifunctional materials. Another promising direction for future research would be to prioritize the development of integrated modeling-design-control frameworks. Leveraging deep learning within the framework to navigate the complex nonlinearities of SMAs will directly improve operational performance and long-term reliability. Actuator ; Shape-memory alloy ; Aerospace ; SMA* ; Fabrication ; Robotics ; Soft robotics ; Smart material ; Bridge (graph theory) Shape Memory Alloy Transformations ; Soft Robotics and Applications ; Advanced Materials and Mechanics
Authors: Yu-Chien Huang | Dennis Chung-Yang Huang | Yun-Cheng Tsai
Source: ArXiv.org (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Automated Analysis of DFT Output Files for Molecular Descriptor Extraction and Reactivity Modeling Understanding the relationship between molecular structure and chemical reactivity or properties is fundamental to rational molecular design. Linear free energy relationships (LFERs), particularly Hammett analysis, have long served as powerful tools in organic chemistry. Recently, these approaches have been enhanced by incorporating computationally derived parameters, enabling broader applicability across diverse molecules and reactions. To facilitate and scale this process, we present DFTDescriptorPipeline, a fully automated workflow for extracting quantum chemical descriptors from Gaussian log files and constructing structure-property and structure-reactivity relationships using multivariate linear regression (MLR) models. We validate the workflow across four case studies, including photoswitchable molecules and catalytic reactions. In each case, the models provide interpretable results, demonstrating the versatility of this approach and its relevance to a wide range of chemical contexts. We anticipate that this platform will serve as a generalizable framework for integrating quantum chemical calculations into data-driven molecular design. Workflow ; Quantum chemical ; Relevance (law) ; Molecular descriptor ; Range (aeronautics) ; Gaussian ; Scale (ratio) ; Linear model ; Reactivity (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Radical Photochemical Reactions
Authors: Mengke Li | Helen Li | Yuyu Shi | Yanfang Meng
Source: Micromachines (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

A Novel Transformer Architecture for Scalable Perovskite Thin-Film Detection The further development of scalable fabrication for perovskite solar cells has been considerably constrained by strong process variability and the lack of a reliable real-time predictive mechanism during the thin-film formation process. Existing machine learning-based methods are incapable of capturing the inherent multi-stage kinetic characteristics and uncertainties of the perovskite crystallization process, as they rely on deterministic point prediction models and flatten time-series signals into static features, which necessitates more advanced modeling strategies. To address these challenges, an in situ process monitoring and predictive modeling framework based on a lightweight probabilistic Transformer is proposed for the scalable preparation of perovskite thin films. The strategically designed inputs, consisting of time-resolved photoluminescence (PL) and diffuse reflectance imaging signals acquired during the vacuum quenching process, enable the model to directly learn the conditional probability distribution of the final device performance metrics. Rather than producing a single predicted value, this method enables the explicit quantification of prediction uncertainty, providing statistical support for uncertainty-aware process assessment. Leveraging its advantages over feed-forward neural networks and traditional tree-based machine learning methods, the proposed Transformer architecture effectively captures the staged and non-stationary kinetic features of thin-film formation. Consequently, it exhibits higher robustness and superior uncertainty calibration capability during the early-stage prediction phase. The results demonstrate that the probabilistic Transformer-based modeling paradigm provides a viable pathway toward uncertainty-aware, data-driven process evaluation in perovskite manufacturing. This framework extends its application beyond perovskite photovoltaic device fabrication, providing a generalizable modeling strategy for real-time predictive assessment in the preparation of other complex materials governed by irreversible stochastic dynamics. Probabilistic logic ; Scalability ; Robustness (evolution) ; Photovoltaic system ; Transformer ; Process modeling ; Artificial neural network ; Perovskite (structure) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Domain Adaptation and Few-Shot Learning
Authors: Aiden Sable | Bander Linjawi | Kyle Bradbury | Jordan M. Malof | Olivier Delaire
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning inversion of interatomic force constants from single-crystal inelastic neutron scattering Machine learning bridges synthetic and experimental inelastic neutron scattering data to recover interatomic force constants under realistic measurement conditions. Inelastic scattering ; Inelastic neutron scattering ; Phonon ; Neutron scattering ; Inversion (geology) ; Neutron ; Quasielastic neutron scattering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Prof. Pavitra M Badiger | Fakruddin Ahmed Bidiwale | Mohammed Tanzil Mestri | Amaan Ansari
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Enhanced Cooling with Peltier and Proprietary Coolant for Adaptive Thermal Management of Electronic Systems Abstract. Considering the explosive proliferation of small electronic devices, effective heat management now ceases to be an optional feature but a serious design requisite. Power electronic modules, IoT platforms, and modern embedded devices often operate continuously under severe heat stress, with limited airflow and physical space. These is difficult for conventional air-cooling methods to achieve which leads to decreased performance, thermal instability, and long-term reliability problems. This work describes a hybrid adaptive cooling system using a Peltier module for thermoelectric cooling, liquid-based heat extraction, and a proprietary coolant scientifically designed and managed by sophisticated PID and PWM algorithms. The system integrates relay-based fail-safe protection, optocoupler-isolated power electronics, and Bluetooth-enabled real-time telemetry for safe and dependable operation. In parallel, a novel Internet of Things (IoT)-based thermal conductivity testing device was developed to evaluate various distilled water–ethylene glycol (DW–EG) coolant combinations scientifically using Fourier's heat conduction concept. A 70:30 DW–EG mixture provides the optimal balance of thermal conductivity, viscosity, chemical stability, electrical safety, and pump efficiency, according to experimental results. This proposed system represents an advancement of the conventional concept of cooling. The new one offers tighter temperature control, more stability, and fewer operational costs, which make it perfect for compact embedded electronics. Keywords Thermoelectric Cooling, Peltier Module, .Liquid Cooling, PID Control, PWM Control, Thermal Conductivity, Proprietary Coolant, .Embedded Systems &amp; IoT Measurement Thermoelectric cooling ; Coolant ; Thermoelectric effect ; Heat sink ; Electronics ; Reliability (semiconductor) ; Power (physics) ; Thermoelectric generator ; Water cooling Advanced Thermoelectric Materials and Devices ; Heat Transfer and Boiling Studies ; Phase Change Materials Research
Authors: Alessandro Marcomini | Philipp J. Vetter | Tommaso Calarco | Felix Motzoi | Fedor Jelezko | Matthias M. Müller
Source: arXiv (Cornell University) (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Gate Optimization via Efficient Two-Qubit Benchmarking for NV Centers in Diamond High-fidelity gate implementation requires sophisticated control pulses that steer the quantum system to undergo the desired transformation. Quantum Optimal Control allows to derive these control pulses in an open-loop fashion based on numerical simulations. However, their precision can be limited by incomplete knowledge of the system. Closed-loop optimization overcomes this limitation by incorporating feedback from measurements, provided a suitable and efficient measure of the gate performance can be defined. In this article, we present an efficient method to evaluate the performance of a two-qubit gate by preparation and measurement of only two quantum states, enabling experimental closed-loop optimization with a metric previously believed to be limited to open-loop control. We tailor the approach to nitrogen-vacancy centers in diamond and, through numerical simulations, demonstrate how the method can optimize a two-qubit gate while reducing the number of required measurements by two orders of magnitude compared to standard process tomography under realistic experimental settings. Measure (data warehouse) ; Metric (unit) ; Benchmarking ; Process (computing) ; Diamond ; Quantum gate ; Logic gate ; Quantum sensor Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Quantum and electron transport phenomena
Authors: Eunchi Kim | Thomas Kirchartz
Source: arXiv (Cornell University) (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Bayesian Parameter Estimation for Predictive Modeling of Illumination-Dependent Current-Voltage Curves Machine learning enables rapid estimation of material parameters in solar cells via neural-network-based surrogate models. However, the reliability of extracted parameters depends on underlying assumptions such as the choice of one-dimensional drift-diffusion model and selection of free material parameters. To validate the inferred parameters, we perform predictive modeling of light-intensity-dependent current-voltage (JV) characteristics. Well-known physical effects, including the influence of external resistance and recombination dynamics on illumination-dependent device performance, are reflected in parameter estimation and prediction workflow. We show that correct treatment of dark shunt resistance and emphasizing shifted current (J + Jsc) during fitting enhances prediction accuracy at low to intermediate illumination level. Additionally, we analyze the information content of various input JV curve combinations, demonstrating that including at least one illuminated JV, preferably not under high illumination due to series resistance effects, is critical for reliable parameter estimation and device performance prediction. Estimation theory ; Reliability (semiconductor) ; Bayesian probability ; Model selection ; Model parameter ; Selection (genetic algorithm) ; Estimation ; Curve fitting Machine Learning in Materials Science ; Silicon and Solar Cell Technologies ; Thin-Film Transistor Technologies
Authors: Filippo Stocco | Michele Garibbo | Noelia Ferruz
Source: Current Opinion in Structural Biology (preprint, 2026-04-13)
Field: Materials Science — Materials Chemistry

Steering generative models for protein design: Aligning and conditioning strategies Generative artificial intelligence models learn probability distributions from data and produce novel samples that capture the salient properties of their training sets. Proteins are particularly attractive for such approaches given their abundant data and the versatility of their representations, ranging from sequences to structures and functions. This versatility has motivated the rapid development of generative models for protein design, enabling the generation of functional proteins and enzymes with unprecedented success. However, because these models mirror their training distribution, they tend to sample from its most probable modes, while low-probability regions, often encoding valuable properties, remain underexplored. To address this challenge, recent work has proposed strategies for steering generative models toward user-specified properties. In this review, we survey and categorize these strategies, distinguishing approaches that modify model parameters, such as reinforcement learning or supervised fine-tuning, from those that keep the model’s parameters fixed, including conditional generation, retrieval-augmented strategies, Bayesian guidance, and tailored sampling methods. Together, these developments are beginning to enable the steering of generative models toward proteins with desired properties. • Generative AI models have reshaped the field of protein design, enabling the generation of functional proteins and enzymes with unprecedented success. • However, these models tend to mirror their training distribution, which does not necessarily align with the objectives required for protein engineering. • Recent work proposes strategies for steering generative models toward user-defined properties, and we categorize these efforts into parameter-updating alignment and parameter-fixed steering. • Parameter-updating Alignment approaches modify the model weights, either through fine-tuning or by optimizing with external oracles via rewards (reinforcement learning). • Parameter-fixed steering approaches guide the model at inference time through conditioning, retrieval, guidance, or sampling strategies. Generative grammar ; Categorization ; Generative model ; Salient ; Bayesian probability ; Bayesian inference ; Reinforcement learning ; Encoding (memory) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Machine Learning in Bioinformatics
Authors: Faqing Yang | Jack D. Evans
Source: Open MIND (preprint, 2026-01-30)
Field: Materials Science — Materials Chemistry

QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities The integration of large language models (LLMs) into materials science offers a transformative opportunity to streamline computational workflows, yet current agentic systems remain constrained by rigid, carefully crafted domain-specific tool-calling paradigms and narrowly scoped agents. In this work, we introduce QUASAR, a universal autonomous system for atomistic simulation designed to facilitate production-grade scientific discovery. QUASAR autonomously orchestrates complex multi-scale workflows across diverse methods, including density functional theory, machine learning potentials, molecular dynamics, and Monte Carlo simulations. The system incorporates robust mechanisms for adaptive planning, context-efficient memory management, and hybrid knowledge retrieval to navigate real-world research scenarios without human intervention. We benchmark QUASAR against a series of three-tiered tasks, progressing from routine tasks to frontier research challenges such as photocatalyst screening and novel material assessment. These results suggest that QUASAR can function as a general atomistic reasoning system rather than a task-specific automation framework. They also provide initial evidence supporting the potential deployment of agentic AI as a component of computational chemistry research workflows, while identifying areas requiring further development. Benchmark (surveying) ; Workflow ; Software deployment ; Function (biology) ; Transformative learning ; Automation ; Component (thermodynamics) ; Monte Carlo method Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Zhenzhen Yang | Gabriel M. Veith | Seoung‐Bum Son | Brian J. Ingram
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Accelerating Battery Materials Discovery with an Agentic AI for Automated XPS Analysis Integrating Physical Constraints and Chemometrics Bottleneck ; Battery (electricity) ; Chemometrics ; Task (project management) ; Data analysis ; Overhead (engineering) ; X-ray photoelectron spectroscopy ; Pace Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Shreyas Vinaya Sathyanarayana | Sharanabasava D. Hiremath | Shah Rahil Kirankumar | Rishikesh Panda | Rahul Jana | Riya Singh | Rida Irfan | Ashwin Murali | Bharath Ramsundar
Source: Scientific Reports (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

DeepRetro discovers retrosynthetic pathways through iterative large language model reasoning Synthesizing complex natural products is a grand challenge in organic chemistry. We present DeepRetro, a significant advancement in computational retrosynthesis that discovers viable synthetic routes for molecules previously considered too complex for automated methods. DeepRetro is a novel, open-source framework that tightly integrates large language models (LLMs), traditional retrosynthetic engines, and expert human feedback into an iterative design loop. Unlike prior approaches that rely on either template-based methods or unconstrained LLMs, our hybrid system combines the precision of templates with the generative flexibility of LLMs, governed by rigorous chemical validity checks and recursive refinement. This system dynamically explores and revises synthetic pathways, guided by algorithmic checks and expert input through an interactive interface. While DeepRetro shows strong performance on standard benchmarks, its main strength is its ability to propose novel, viable pathways for highly complex natural products. Through case studies, we demonstrate how this approach facilitates new total synthesis routes and enhances human-machine collaboration. DeepRetro serves as a working model for applying LLMs to scientific discovery, and we release it as an open-source tool to accelerate progress in drug discovery and materials design. Retrosynthetic analysis ; Flexibility (engineering) ; Natural language ; Code refactoring ; Generative grammar ; String (physics) ; Natural language generation ; Natural (archaeology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis