jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jessica Lukowski | Byoung‐Kyu Cho | Antonia Zamacona Calderon | Borna Dianati | Katherine A. Stumpo | Savannah R. Snyder | Young Ah Goo
Source: PROTEOMICS (review, 2025-05-12)
Field: Chemistry — Spectroscopy

Advances in Spatial Multi‐Omics: A Review of Multi‐Modal Mass Spectrometry Imaging and Laser Capture Microdissection‐LCMS Integration Mass spectrometry has long been utilized to characterize a variety of biomolecules such as proteins, metabolites, and lipids. Most MS-based omics studies rely on bulk analysis; however, bulk approaches often overlook low-abundance molecules that may exert critical biological effects. Recently, multi-omics analyses have been driving an explosion of knowledge about how biomolecules interact within biological systems. In particular, spatial multi-omics has emerged as a groundbreaking approach for implementing multi-omic and multi-modal analyses. Broadly defined, spatial omics has the ability to analyze biomolecules within their native spatial contexts, offering transformative insights. This review focuses on mass spectrometry-based spatial omics, specifically matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). We will explore how MALDI-MSI, in combination with laser capture microdissection (LCM) and traditional liquid chromatography-mass spectrometry (LC-MS) workflow, is advancing spatially resolved multi-omics research. Mass spectrometry ; Omics ; Mass spectrometry imaging ; Proteomics ; Laser capture microdissection ; Biomolecule ; Computational biology ; Workflow ; MALDI imaging ; Chemistry ; Computer science ; Bioinformatics ; Matrix-assisted laser desorption/ionization ; Biology ; Chromatography ; Biochemistry ; Desorption Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Lukas Hörmann | Wojciech G. Stark | Reinhard J. Maurer
Source: npj Computational Materials (review, 2025-07-01)
Field: Materials Science — Materials Chemistry

Machine learning and data-driven methods in computational surface and interface science Machine learning and data-driven methods have started to transform the study of surfaces and interfaces. Here, we review how data-driven methods and machine learning approaches complement simulation workflows and contribute towards tackling grand challenges in computational surface science from 2D materials to interface engineering and electrocatalysis. Challenges remain, including the scarcity of large datasets and the need for more electronic structure methods for interfaces. Workflow ; Computer science ; Interface (matter) ; Complement (music) ; Data science ; Surface (topology) ; Artificial intelligence ; Science and engineering ; Machine learning ; Human–computer interaction ; Engineering ; Engineering ethics ; Chemistry ; Database Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Srivathsan Badrinarayanan | Rishikesh Magar | Akshay Antony | Radheesh Sharma Meda | Amir Barati Farimani
Source: Journal of Chemical Information and Modeling (article, 2025-08-28)
Field: Chemistry — Inorganic Chemistry

MOFGPT: Generative Design of Metal–Organic Frameworks using Language Models The discovery of Metal-Organic Frameworks (MOFs) with application-specific properties remains a central challenge in materials chemistry, owing to the immense size and complexity of their structural design space. Conventional computational screening techniques such as molecular simulations and density functional theory (DFT), while accurate, are computationally prohibitive at scale. Machine learning offers an exciting alternative by leveraging data-driven approaches to accelerate materials discovery. The complexity of MOFs, with their extended periodic structures and diverse topologies, creates both opportunities and challenges for generative modeling approaches. To address these challenges, we present a reinforcement learning-enhanced, transformer-based framework for the de novo design of MOFs. Central to our approach is MOFid, a chemically informed string representation encoding both connectivity and topology, enabling scalable generative modeling. Our pipeline comprises three components: (1) a generative GPT model trained on MOFid sequences, (2) MOFormer, a transformer-based property predictor, and (3) a reinforcement learning (RL) module that optimizes generated candidates via property-guided reward functions. By integrating property feedback into sequence generation, our method drives the model toward synthesizable, topologically valid MOFs with desired functional attributes. This work demonstrates the potential of large language models, when coupled with reinforcement learning, to accelerate inverse design in reticular chemistry and unlock new frontiers in computational MOF discovery. Generative grammar ; Generative model ; Natural language processing ; Computer science ; Artificial intelligence Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Corrosion Behavior and Inhibition
Authors: Bryn Davies | Stefan Szyniszewski | Marcelo A. Dias | Leo de Waal | Anastasia V. Kisil | V. P. Smyshlyaev | Shane Cooper | I. V. Kamotski | Marie Touboul | Richard V. Craster | James R Capers | S. A. R. Horsley | Robert Hewson | Matthew Santer | Ryan Murphy | Dilaksan Thillaithevan | S. J. Berry | G. J. Conduit | Jacob Earnshaw | Nicholas Syrotiuk | Olly Duncan | Łukasz Kaczmarczyk | Fabrizio Scarpa | J. B. Pendry | Marc Martí-Sabaté | Sébastien Guenneau | Daniel Torrent | Elena Cherkaev | Niklas Wellander | Andrea Alù | K. H. Madine | D. J. Colquitt | Ping Sheng | Luke G. Bennetts | Anastasiia O. Krushynska | Z. Zhang | Mohammad J. Mirzaali | Amir A. Zadpoor
Source: Journal of Physics D Applied Physics (article, 2025-04-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Roadmap on metamaterial theory, modelling and design Abstract This Roadmap surveys the diversity of different approaches for characterising, modelling and designing metamaterials. It contains articles covering the wide range of physical settings in which metamaterials have been realised, from acoustics and electromagnetics to water waves and mechanical systems. In doing so, we highlight synergies between the many different physical domains and identify commonality between the main challenges. The articles also survey a variety of different strategies and philosophies, from analytic methods such as classical homogenisation to numerical optimisation and data-driven approaches. We highlight how the challenging and many-degree-of-freedom nature of metamaterial design problems call for techniques to be used in partnership, such that physical modelling and intuition can be combined with the computational might of modern optimisation and machine learning to facilitate future breakthroughs in the field. Metamaterial ; Designtheory ; Computer science ; Architectural engineering ; Systems engineering ; Engineering ; Physics ; Human–computer interaction ; Optics Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Antenna Design and Analysis
Authors: Shafidah Shafian | Faizus Salehin | Sojeong Lee | Azlan Ismail | Shuhaida Mohamed Shuhidan | Lin Xie | Kyungkon Kim
Source: ACS Applied Energy Materials (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Development of Organic Semiconductor Materials for Organic Solar Cells via the Integration of Computational Quantum Chemistry and AI-Powered Machine Learning The development of high-efficiency and stable organic solar cells (OSCs) relies on discovering organic semiconductor materials that efficiently absorb light and generate charge. Traditional experimental methods struggle to evaluate the vast array of potential materials, leading to a shift toward computational chemistry simulations and machine learning (ML) technologies. ML, a branch of computer science, automates solutions for complex problems, making it valuable for screening and designing OSC materials. This review explores how computational chemistry and ML are used to identify promising materials and optimize their performance. It begins with an overview of photovoltaic properties influenced by organic semiconductor selection and theoretical computational chemistry methods. Recent advances in material design optimization through simulations are discussed, highlighting the creation of libraries to aid molecular design. Challenges and opportunities in integrating computational chemistry with ML are examined, followed by an exploration of the ML paradigms and their applications in OSC prediction. Case studies demonstrate the effectiveness of computational and ML techniques in OSCs research. The review concludes with insights into current advancements, future research directions, and the potential of OSCs for efficient and sustainable energy technologies, encouraging further innovation in the field. Organic semiconductor ; Organic solar cell ; Semiconductor ; Engineering physics ; Materials science ; Quantum ; Nanotechnology ; Chemistry ; Computer science ; Optoelectronics ; Engineering ; Physics ; Polymer ; Composite material ; Quantum mechanics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Dilshod Nematov | Mirabbos Hojamberdiev
Source: Computational Condensed Matter (review, 2025-09-17)
Field: Materials Science — Materials Chemistry

Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review Pipeline (software) ; Field (mathematics) ; Deep learning ; Key (lock) ; Bayesian optimization ; Materials informatics ; Artificial neural network ; Property (philosophy) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Bing Bing Wang | Dequan Meng | Chunsheng Lu | Qiaoyun Zhang | MingHao Zhao | Jianwei Zhang
Source: Journal of Applied Physics (article, 2025-01-10)
Field: Materials Science — Materials Chemistry

Physics-informed neural networks for analyzing size effect and identifying parameters in piezoelectric semiconductor nanowires Piezoelectric semiconductors (PSCs) are crucial in micro-electromechanical systems, but analyzing their size effects and accurately determining flexoelectric parameters is challenging due to the complexity of multi-scale and multi-field coupling. Physics-informed neural networks (PINNs), which merge physical laws with machine learning, provide a promising approach for solving partial differential equations and parameter inversion. In this paper, we develop a PINN model to solve a system of fourth-order partial differential equations for PSC nanowires, accounting for strain gradient and flexoelectric effects. Predictions by the model closely match results from traditional numerical methods. Additionally, with minimal labeled data, the PINN model can predict both physical solutions and material parameters, such as the flexoelectric coefficient. It is expected that PINNs offer an effective method for analyzing PSC nanowires and inverting key material properties. Piezoelectricity ; Nanowire ; Materials science ; Semiconductor ; Artificial neural network ; Nanotechnology ; Condensed matter physics ; Physics ; Computer science ; Optoelectronics ; Artificial intelligence ; Composite material Thermal properties of materials ; Advancements in Semiconductor Devices and Circuit Design ; Nanowire Synthesis and Applications
Authors: Zhenming Xu | Yixi Lin | Yinghui Xia | Yuqiao Jiang | Xiangmin Feng | Zhenhui Liu | Laifa Shen | Mingbo Zheng | Yongyao Xia
Source: Journal of Power Sources (article, 2025-02-24)
Field: Materials Science — Materials Chemistry

Machine learning molecular dynamics simulations unraveling No paddle-wheel effect in Li2B12H12 solid-state electrolyte at room temperature Electrolyte ; Molecular dynamics ; Paddle ; Dynamics (music) ; Solid-state ; Materials science ; Chemical physics ; Chemistry ; Thermodynamics ; Physical chemistry ; Composite material ; Physics ; Computational chemistry ; Electrode Hydrogen Storage and Materials ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Malte Esders | Thomas Schnake | Jonas Lederer | Adil Kabylda | Grégoire Montavon | Alexandre Tkatchenko | K. Müller
Source: Journal of Chemical Theory and Computation (article, 2025-01-10)
Field: Materials Science — Materials Chemistry

Analyzing Atomic Interactions in Molecules as Learned by Neural Networks While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data. We focus on the strength of the interactions for different atomic species, how predictions for intensive and extensive quantum molecular properties are made, and analyze the decay and many-body nature of the interactions with interatomic distance. Models that deviate too far from known physical principles produce unstable MD trajectories, even when they have very high energy and force prediction accuracy. We also suggest further improvements to the ML architectures to better account for the polynomial decay of atomic interactions. Computer science ; Set (abstract data type) ; Artificial neural network ; Quantum ; Quantum chemical ; Artificial intelligence ; Machine learning ; Biological system ; Statistical physics ; Molecule ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Wail Al Zoubi | Yujun Sheng | Iftikhar Hussain | Heo Seongjun | Mohammad R. Thalji | Nokeun Park
Source: Small (review, 2025-04-14)
Field: Materials Science — Materials Chemistry

Synthesis and Machine Learning Prediction of High Entropy Multi‐Principal Element Nanoparticles The vast compositional space of multi-principal element nanoparticles (MPENs), along with their unique properties and diverse applications, has garnered significant attention from the research community. MPENs exhibit unique properties, high configurational entropy, multi-element synergy, and long-range atomic ordering, featuring distinct sublattices of semi-metallic or metallic components. This review reports the recent approaches described in the literature, highlighting their commonalities and differences, and classifies them into general strategies. This report discusses in detail the synthesis approaches of single-phase MPENs. To integrate experimental validation with computational preselection, machine learning (ML) offers the opportunity to establish relationships between lattice structures, properties, and phase formations and how collect and analysis of experimental data. Additionally, challenges such as ML-guided uncertainty quantification and materials design are explored. Entropy (arrow of time) ; Nanoparticle ; Computer science ; High entropy alloys ; Element (criminal law) ; Artificial intelligence ; Nanotechnology ; Machine learning ; Materials science ; Phase (matter) ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films
Authors: Krutarth Khot | Boyuan Xiao | Zherui Han | Ziqi Guo | Zixin Xiong | Xiulin Ruan
Source: Journal of Applied Physics (article, 2025-03-18)
Field: Materials Science — Materials Chemistry

Phonon local non-equilibrium at Al/Si interface from machine learning molecular dynamics All electronics are equipped with metal/semiconductor junctions, resulting in resistance to thermal transport. The nanoscale phononic complexities, such as phonon local non-equilibrium and inelastic scattering, add to the computational or experimental characterization difficulty. Here, we use a neural network potential (NNP) trained by ab initio data, demonstrating near-first-principles precision more accurate than classical potentials used in molecular dynamics (MD) simulations to predict thermal transport at the Al/Si interface. The interfacial thermal conductance of 380±33MW/m2K from our NNP-MD simulations is in good agreement with the previous experimental consensus while considering the crucial physics of interfacial bonding nature, phonon local non-equilibrium, and inelastic scattering. Furthermore, we extract phonon mode insights from the NNP-MD simulations to reveal the decrease in local non-equilibrium of the longitudinal acoustic modes at the Al/Si interface. Our work demonstrates the utility of a machine learning MD to predict and extract accurate insights about interfacial thermal transport. Phonon ; Molecular dynamics ; Dynamics (music) ; Interface (matter) ; Silicon ; Statistical physics ; Materials science ; Chemical physics ; Condensed matter physics ; Computer science ; Physics ; Molecule ; Optoelectronics ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design
Authors: Guangying Jin | Ran Wei | M. Zhang | Yun Li
Source: Journal of Chemical Information and Modeling (article, 2025-01-20)
Field: Chemistry — Inorganic Chemistry

An Intelligent Prediction Model for the Synthesis Conditions of Metal–Organic Frameworks Utilizing Artificial Neural Networks Enhanced by Genetic Algorithm Optimization In the field of emerging materials, metal-organic frameworks (MOFs) have gained prominence due to their unique porous structures, showing versatility in gas adsorption, storage, separation, and liquid processes. However, their decomposition, collapse tendencies, and complex synthesis make large-scale production costly and challenging with no accurate method for predicting synthesis conditions. This work proposes an intelligent prediction model based on the structural characteristics of MOFs to forecast synthesis conditions. A genetic algorithm-optimized back-propagation (BP) neural network was developed, starting with feature selection via the minimum redundancy maximum relevance algorithm to rank feature importance. The optimal number of inputs and outputs was determined on the basis of performance, followed by genetic algorithm optimization of the BP neural network. The best initial population size and number of hidden nodes were identified. The study compared 10 models, including a genetic algorithm-optimized BP neural network and a simple BP neural network. The results revealed that the <i>R</i> coefficient of the optimized model reached 96.2%, surpassing that of conventional methods with all <i>R</i> values of approximately 85%. This approach allows for accurate prediction of MOF synthesis conditions, aiding material manufacturing in precise control over synthesis processes, improving material quality, and reducing raw material waste. Artificial neural network ; Genetic algorithm ; Computer science ; Artificial intelligence ; Optimization algorithm ; Algorithm ; Machine learning ; Mathematical optimization ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Qinghan Yu | Pai Li | Xing Ni | Youyong Li | Lu Wang
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Dynamics and kinetics exploration of the oxygen reduction reaction at the Fe–N<sub>4</sub>/C–water interface accelerated by a machine learning force field Understanding the oxygen reduction reaction (ORR) mechanism and accurately characterizing the reaction interface are essential for improving fuel cell efficiency. We developed an active learning framework combining machine learning force fields and enhanced sampling to explore the dynamics and kinetics of the ORR on Fe-N<sub>4</sub>/C using a fully explicit solvent model. Different possible reaction paths have been explored and the O<sub>2</sub> adsorption process is confirmed as the rate-determining step of the ORR at the Fe-N<sub>4</sub>/C-water interface, which needs to overcome a free energy barrier of 0.39 eV. By statistical analysis of solvent configurations for proton-coupled electron transfer (PCET) processes, it is revealed that the configurations of interface water remarkably influence the reaction efficiency. More hydrogen bonds and longer lifetimes facilitate the PCET reactions and even make them barrierless. Our theoretical framework highlights the significance of solvent configurations in determining free energy barriers, and offers new insights into the reaction mechanism of the ORR on Fe-N<sub>4</sub>/C catalysts. Interface (matter) ; Reduction (mathematics) ; Oxygen reduction reaction ; Oxygen ; Field (mathematics) ; Kinetic energy ; Fuel cells ; Dynamics (music) ; Reaction dynamics ; Mechanism (biology) ; Reaction mechanism ; Computer science ; Chemistry ; Chemical physics ; Chemical engineering ; Engineering ; Physical chemistry ; Physics ; Catalysis ; Molecule ; Mathematics ; Classical mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Tiffany C. Kaspar | Sarah Akers | Henry W. Sprueill | Arman Ter-Petrosyan | Jenna A. Bilbrey | Derek Hopkins | A. Harilal | J. Christudasjustus | Patrick T. Gemperline | R. Comès
Source: Journal of Vacuum Science & Technology A Vacuum Surfaces and Films (article, 2025-03-27)
Field: Materials Science — Surfaces, Coatings and Films

Machine-learning-enabled on-the-fly analysis of RHEED patterns during thin film deposition by molecular beam epitaxy Thin film deposition is a fundamental technology for the discovery, optimization, and manufacturing of functional materials. Deposition by molecular beam epitaxy (MBE) typically employs reflection high-energy electron diffraction (RHEED) as a real-time in situ probe of the growing film. However, the state-of-the-art for RHEED analysis during deposition requires human observation. Here, we present an approach using machine learning (ML) methods to monitor, analyze, and interpret RHEED images on-the-fly during thin film deposition. In the analysis workflow, RHEED pattern images are collected at one frame per second and featurized using a pretrained deep convolutional neural network. The feature vectors are then statistically analyzed to identify changepoints; these changepoints can be related to changes in the deposition mode from initial film nucleation to a transition regime, smooth film deposition, and in some cases, an additional transition to a rough, islanded deposition regime. The feature vectors are additionally analyzed via graph analysis and community classification. The graph is quantified as a stabilization plot, and we show that inflection points in the stabilization plot correspond to changes in the growth regime. The full RHEED analysis workflow is termed RHAAPsody and includes data transfer and output to a visual dashboard. We demonstrate the functionality of RHAAPsody by analyzing the precaptured RHEED images from epitaxial depositions of anatase TiO2 on SrTiO3(001) and show that the analysis workflow can be executed in less than 1 s. Our approach shows promise as one component of ML-enabled real-time feedback control of the MBE deposition process. Reflection high-energy electron diffraction ; Molecular beam epitaxy ; Deposition (geology) ; On the fly ; Materials science ; Thin film ; Epitaxy ; Optoelectronics ; Optics ; Nanotechnology ; Computer science ; Geology ; Physics ; Layer (electronics) ; Paleontology Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides