jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wei Ge | Ramindu De Silva | Yanan Fan | Scott A. Sisson | Martina H. Stenzel
Source: Advanced Materials (review, 2025-02-09)
Field: Materials Science — Materials Chemistry

Machine Learning in Polymer Research Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into machine readable descriptors. However, limited and inadequately curated datasets, broad molecular weight distributions, and irregular polymer configurations pose significant challenges. Most off the shelf mathematical models often need refinement for specific applications. Addressing these challenges demand a close collaboration between chemists and mathematicians as chemists must formulate research questions in mathematical terms while mathematicians are required to refine models for specific applications. This review unites both disciplines to address dataset curation hurdles and highlight advances in polymer synthesis and modeling that enhance data availability. It then surveys ML approaches used to predict solid-state properties, solution behavior, composite performance, and emerging applications such as drug delivery and the polymer-biology interface. A perspective of the field is concluded and the importance of FAIR (findability, accessibility, interoperability, and reusability) data and the integration of polymer theory and data are discussed, and the thoughts on the machine-human interface are shared. Interoperability ; Interface (matter) ; Computer science ; Data science ; Field (mathematics) ; Nanotechnology ; Materials science ; World Wide Web Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Shibani Mohata | Poulami Majumder | Rahul Banerjee
Source: Chemical Society Reviews (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Design and structure–function interplay in covalent organic frameworks for photocatalytic CO<sub>2</sub> reduction The escalating global energy demands and the need to alleviate the rapid rise in greenhouse gases have led to colossal interest in designing efficient catalytic systems for photocatalytic CO<sub>2</sub> reduction. While inorganic semiconductors have been the frontrunners for a long time, porous photocatalysts, particularly covalent organic frameworks (COFs), are gaining traction due to their atomically precise structures, enabling tuning their structural and chemical properties. Designed using the principles of reticular chemistry, the building units of COFs can be modulated to incorporate catalytically active sites periodically using robust covalent bonds to endow them with high efficiency, selectivity, and stability. Unlike the non-porous congeners, COFs, with their high porosity and precisely defined pore channels, allow for quicker diffusion of substrates and products, enabling the utilization of deeply buried photocatalytic sites. Our approach is to comprehend the significant roadblocks that must be overcome for designing state-of-the-art catalysts for photocatalytic CO<sub>2</sub> reduction. Building upon that, we highlight the key strategies devised to design COF-based CO<sub>2</sub>RR photocatalysts. A fundamental understanding of the structure-property relationship is quintessential for utilizing the precision of COF chemistry for developing next-generation materials combining activity, selectivity, and efficiency in a single system. Throughout this review, we have taken a closer look at how the critical design aspects and molecular engineering reciprocate towards augmenting the bulk photocatalytic properties of efficiency and selectivity. Understanding molecular engineering and structure-property relationships will be conducive to developing sophisticated systems to solve global crises in this burgeoning area of research. Photocatalysis ; Reduction (mathematics) ; Nanotechnology ; Function (biology) ; Property (philosophy) ; Structure function ; Materials science ; Covalent bond ; Computer science ; Systems engineering ; Biochemical engineering ; Chemistry ; Engineering ; Physics ; Catalysis ; Mathematics ; Organic chemistry Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; CO2 Reduction Techniques and Catalysts
Authors: Janosh Riebesell | Rhys E. A. Goodall | Philipp Benner | Yuan Chiang | Bowen Deng | Gerbrand Ceder | Mark Asta | Alpha A. Lee | Anubhav Jain | Kristin A. Persson
Source: Nature Machine Intelligence (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

A framework to evaluate machine learning crystal stability predictions Abstract The rapid adoption of machine learning in various scientific domains calls for the development of best practices and community agreed-upon benchmarking tasks and metrics. We present Matbench Discovery as an example evaluation framework for machine learning energy models, here applied as pre-filters to first-principles computed data in a high-throughput search for stable inorganic crystals. We address the disconnect between (1) thermodynamic stability and formation energy and (2) retrospective and prospective benchmarking for materials discovery. Alongside this paper, we publish a Python package to aid with future model submissions and a growing online leaderboard with adaptive user-defined weighting of various performance metrics allowing researchers to prioritize the metrics they value most. To answer the question of which machine learning methodology performs best at materials discovery, our initial release includes random forests, graph neural networks, one-shot predictors, iterative Bayesian optimizers and universal interatomic potentials. We highlight a misalignment between commonly used regression metrics and more task-relevant classification metrics for materials discovery. Accurate regressors are susceptible to unexpectedly high false-positive rates if those accurate predictions lie close to the decision boundary at 0 eV per atom above the convex hull. The benchmark results demonstrate that universal interatomic potentials have advanced sufficiently to effectively and cheaply pre-screen thermodynamic stable hypothetical materials in future expansions of high-throughput materials databases. Stability (learning theory) ; Computer science ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Evert Jan Baerends | Néstor F. Aguirre | N. Austin | Jochen Autschbach | F. Matthias Bickelhaupt | Rosa E. Bulo | Chiara Cappelli | Adri C. T. van Duin | Franco Egidi | Célia Fonseca Guerra | A. Förster | Mirko Franchini | T. P. M. Goumans | Thomas Heine | Matti Hellström | Christoph R. Jacob | Lasse Jensen | Mykhaylo Krykunov | Erik van Lenthe | Artur Michalak | Mariusz P. Mitoraj | Johannes Neugebauer | Valentin Paul Nicu | Pier Philipsen | Harry Ramanantoanina | Robert Rüger | Georg Schreckenbach | Mauro Stener | Marcel Swart | J. M. Thijssen | Tomáš Trnka | Lucas Visscher | Alexei Yakovlev | Stan van Gisbergen
Source: The Journal of Chemical Physics (article, 2025-04-22)
Field: Materials Science — Materials Chemistry

The Amsterdam Modeling Suite In this paper, we present the Amsterdam Modeling Suite (AMS), a comprehensive software platform designed to support advanced molecular and materials simulations across a wide range of chemical and physical systems. AMS integrates cutting-edge quantum chemical methods, including Density Functional Theory (DFT) and time-dependent DFT, with molecular mechanics, fluid thermodynamics, machine learning techniques, and more, to enable multi-scale modeling of complex chemical systems. Its design philosophy allows for seamless coupling between components, facilitating simulations that range from small molecules to complex biomolecular and solid-state systems, making it a versatile tool for tackling interdisciplinary challenges, both in industry and in academia. The suite also emphasizes user accessibility, with an intuitive graphical interface, extensive scripting capabilities, and compatibility with high-performance computing environments. Suite ; Computer science ; Scripting language ; Software suite ; Graphical user interface ; Python (programming language) ; Software ; Computational science ; Programming language Machine Learning in Materials Science ; Protein Structure and Dynamics ; Catalysis and Oxidation Reactions
Authors: Umme Thayyiba Khatoon | Aditya Velidandi
Source: Sustainability (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

An Overview on the Role of Government Initiatives in Nanotechnology Innovation for Sustainable Economic Development and Research Progress Nanoparticle technology has emerged as a fundamental component across various industries, including electronics, renewable energy, textiles, and medical biotechnology, particularly for targeted drug delivery applications. Commercialization has profoundly impacted economic growth, especially in the pharmaceutical and electronics industries. Moreover, it has improved workforce education and training, generating millions of employment prospects associated with nanotechnology development. By 2024, the Organisation for Economic Co-operation and Development anticipates that the global market for nanotechnology products will attain a value of United States Dollar (USD) 1 trillion to USD 3 trillion, resulting in the creation of over 2 million new employments globally. The swift progression of nanoparticle technology from 2000 to 2024 is primarily propelled by substantial industrial investment in research and development, alongside collaborations with academic institutions. The National Nanotechnology Initiative in the United States (US) has significantly contributed to these developments, with federal funding exceeding USD 30 billion by 2024 since its establishment in 2001. This funding has catalyzed significant advancements in both commercial and research applications of nanotechnology. Patent data highlights this expansion, with China establishing itself as the preeminent nation in nanotechnology patents. From 2000 to 2024, China steadily raised its proportion of nanotechnology patents, accounting for almost 40% of the global total by 2024. The US, Japan, Germany, and the Republic of Korea continued to be significant contributors, together advancing the frontiers of innovation in nanotechnology. In this timeframe, the quantity of nanotechnology-related patents increased by more than 150%, demonstrating the swift growth of the sector. The regulation of nanotechnology in the US is primarily managed by the Food and Drug Administration, particularly about healthcare and biotechnology applications. As the scope of nanotechnology uses has expanded, there is an increasing demand for more extensive regulations concerning potential long-term environmental and health effects. The future trajectory of nanotechnology, both in the US and worldwide, will hinge on continuous invention, economic advancement, and the progression of governmental policy. By upholding a robust regulatory framework and promoting ongoing collaboration between academics and industry, the complete potential of nanotechnology in advancing industrial and societal progress can be actualized. Sustainable development ; Government (linguistics) ; Societal impact of nanotechnology ; Political science ; Engineering ; Business ; Engineering ethics ; Nanotechnology ; Economic growth ; Economics ; Materials science Nanoparticles: synthesis and applications
Authors: Zaheer Ud Din Babar | Vincenzo Iannotti | Giulio Rosati | Ayesha Zaheer | Raffaele Velotta | Bartolomeo Della Ventura | Ruslán Álvarez-Diduk | Arben Merkoçi
Source: Chemical Society Reviews (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

MXenes in healthcare: synthesis, fundamentals and applications Since their discovery over a decade ago, MXenes have transformed the field of "materials for healthcare", stimulating growing interest in their healthcare-related applications. These developments have also driven significant advancements in MXenes' synthesis. This review systematically examines the synthesis of MXenes and their applications in sensing and biomedical fields, underscoring their pivotal role in addressing critical challenges in modern healthcare. We describe the experimental synthesis of MXenes by combining appropriate laboratory modules with the mechanistic principles underlying each synthesis step. In addition, we provide extensive details on the experimental parameters, critical considerations, and essential instructions for successful laboratory synthesis. Various healthcare applications including sensing, biomedical imaging, synergistic therapies, regenerative medicine, and wearable devices have been explored. We further highlight the emerging trends of MXenes, <i>viz.</i>, their role as nanovehicles for drug delivery, vectors for gene therapy, and tools for immune profiling. By identifying the important parameters that define the utility of MXenes in biomedical applications, this review outlines strategies to regulate their biomedical profile, thereby serving as a valuable guide to design MXenes with application-specific properties. The final section integrates experimental research with theoretical studies to provide a comprehensive understanding of the field. It examines the role of emerging technologies, such as artificial intelligence (AI) and machine learning (ML), in accelerating material discovery, structure-property optimization, and automation. Complemented by detailed supplementary information on synthesis, stability, biocompatibility, environmental impact, and theoretical insights, this review offers a profound knowledge base for understanding this diverse family of 2D materials. Finally, we compared the potential of MXenes with that of other 2D materials to underscore the existing challenges and prioritize interdisciplinary collaboration. By synthesizing key studies from its discovery to current trends (especially from 2018 onward), this review provides a cohesive assessment of MXene synthesis with theoretical foundations and their prospects in the healthcare sector. MXenes ; Computer science ; Health care ; Profiling (computer programming) ; Nanotechnology ; Biochemical engineering ; Artificial intelligence ; Data science ; Systems engineering ; Engineering ; Materials science ; Political science MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; 2D Materials and Applications
Authors: Dylan M. Anstine | R.I. Zubatyuk | Olexandr Isayev
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff. Despite this attraction, the benefits of such efficiency are only impactful when an MLIP uniquely enables insight into a target system or is broadly transferable outside of the training dataset. In this work, we present the 2<sup>nd</sup> generation of our atoms-in-molecules neural network potential (AIMNet2), which is applicable to species composed of up to 14 chemical elements in both neutral and charged states, making it a valuable method for modeling the majority of non-metallic compounds. Using an exhaustive dataset of 2 × 10<sup>7</sup> hybrid DFT level of theory quantum chemical calculations, AIMNet2 combines ML-parameterized short-range and physics-based long-range terms to attain generalizability that reaches from simple organics to diverse molecules with "exotic" element-organic bonding. We show that AIMNet2 outperforms semi-empirical GFN2-xTB and is on par with reference density functional theory for interaction energy contributions, conformer search tasks, torsion rotation profiles, and molecular-to-macromolecular geometry optimization. Overall, the demonstrated chemical coverage and computational efficiency of AIMNet2 is a significant step toward providing access to MLIPs that avoid the crucial limitation of curating additional quantum chemical data and retraining with each new application. Generalizability theory ; Parameterized complexity ; Density functional theory ; Computer science ; Artificial neural network ; Organic molecules ; Molecule ; Quantum chemistry ; Valence (chemistry) ; Quantum ; Range (aeronautics) ; Quantum chemical ; Computational chemistry ; Chemistry ; Biological system ; Statistical physics ; Artificial intelligence ; Machine learning ; Algorithm ; Materials science ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mohammad Harun‐Ur‐Rashid | Tahmina Foyez | Suresh Babu Naidu Krishna | P. Sudhakar | Abu Bin Imran
Source: RSC Advances (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Recent advances of silver nanoparticle-based polymer nanocomposites for biomedical applications Silver nanoparticle-polymer nanocomposites (AgNP-PNCs) represent a transformative advancement in biomedical material science, integrating the potent antimicrobial properties of AgNPs with the structural versatility of polymer matrices. This synergy enables enhanced infection control, mechanical stability, and controlled drug delivery, making these nanocomposites highly suitable for applications such as wound healing, medical coatings, tissue engineering, and biosensors. Recent progress in synthesis and functionalization has led to greater control over particle morphology, dispersion, and stability, optimizing AgNP-PNCs for clinical and translational applications. However, challenges related to cytotoxicity, long-term stability, immune response, and scalability persist, necessitating systematic improvements in surface functionalization, hybridization strategies, and biocompatibility assessments. This review critically evaluates the latest advancements in AgNP-PNC development, focusing on their functionalization techniques, regulatory considerations, and emerging strategies to overcome biomedical challenges. Additionally, it discusses preclinical and translational aspects, including commercialization barriers and regulatory frameworks such as FDA and EMA guidelines, ensuring a comprehensive outlook on their clinical feasibility. By bridging the gap between innovation and practical application, this review investigates the transformative potential of AgNP-PNCs in advancing next-generation biomedical materials. Nanoparticle ; Nanocomposite ; Nanotechnology ; Materials science ; Polymer ; Silver nanoparticle ; Polymer nanocomposite ; Composite material Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Conducting polymers and applications
Authors: Zhe Cheng | Huichao Huang | Mingru Yin | Huaizheng Liu
Source: Experimental Hematology and Oncology (review, 2025-01-31)
Field: Materials Science — Biomaterials

Applications of liposomes and lipid nanoparticles in cancer therapy: current advances and prospects Liposomes and lipid nanoparticles are common lipid-based drug delivery systems and play important roles in cancer treatment and vaccine manufacture. Although significant progress has been made with these lipid-based nanocarriers in recent years, efficient clinical translation of active targeted liposomal nanocarriers remains extremely challenging. In this review, we focus on targeted liposomes, stimuli-responsive strategy and combined therapy in cancer treatment. We also summarize advances of liposome and lipid nanoparticle applications in nucleic acid delivery and tumor vaccination. In addition, we discuss limitations and challenges in the clinical translation of these lipid nanomaterials and make recommendations for the future research in cancer therapy. Liposome ; Current (fluid) ; Cancer ; Nanotechnology ; Cancer therapy ; Nanoparticle ; Medicine ; Materials science ; Engineering ; Internal medicine Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Nanoplatforms for cancer theranostics
Authors: Haiyang Peng | Da Zhang | Zhipeng Xie | Shuiqing Lu | Yichang Liu | Feng Liang
Source: Small (review, 2025-01-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Advances in Structural Design of Carbon/Magnetic Composites and their Electromagnetic Wave Absorption Applications Electromagnetic pollution protection and military stealth technologies underscore the urgent need to develop efficient electromagnetic wave-absorbing materials (EWAMs). Traditional EWAMs suffer from single absorption loss mechanisms, poor impedance matching, and weak reflection loss. To date, combining dielectric loss with magnetic loss in EWAMs have proven to be an effective approach to enhancing electromagnetic absorption performance. The structural design of composites plays a pivotal role in improving impedance matching and enhancing the attenuation of electromagnetic waves. It is widely regarded as one of the principal methods for fine-tuning electromagnetic parameters and response mechanisms. Among these, composites of carbon and magnetic materials have become a research hotspot due to their magnetoelectric synergistic effects and versatile microstructure design. Herein, the principles of electromagnetic wave absorption in terms of both the loss mechanism and impedance matching are outlined. The research progress on core-shell, skeleton, and hollow structure of carbon/magnetic composite EWAMs are summarized. The synthesis methods, absorption properties, and attenuation mechanisms of composites with these structures are described in detail. Finally, the limitations of carbon/magnetic composites in electromagnetic wave absorption are discussed, possible solutions are proposed, and future development directions for carbon/magnetic composite EWAMs are envisioned. Materials science ; Reflection loss ; Electromagnetic radiation ; Attenuation ; Impedance matching ; Composite material ; Microwave ; Absorption (acoustics) ; Dielectric loss ; Composite number ; Dielectric ; Electrical impedance ; Computer science ; Optics ; Optoelectronics ; Electrical engineering ; Telecommunications ; Physics ; Engineering Electromagnetic wave absorption materials ; Multiferroics and related materials ; Advanced Antenna and Metasurface Technologies
Authors: Xue Jiang | Weiren Wang | Shaohan Tian | Hao Wang | Turab Lookman | Yanjing Su
Source: npj Computational Materials (article, 2025-03-24)
Field: Materials Science — Materials Chemistry

Applications of natural language processing and large language models in materials discovery The transformative impact of artificial intelligence (AI) technologies on materials science has revolutionized the study of materials problems. By leveraging well-characterized datasets derived from the scientific literature, AI-powered tools such as Natural Language Processing (NLP) have opened new avenues to accelerate materials research. The advances in NLP techniques and the development of large language models (LLMs) facilitate the efficient extraction and utilization of information. This review explores the application of NLP tools in materials science, focusing on automatic data extraction, materials discovery, and autonomous research. We also discuss the challenges and opportunities associated with utilizing LLMs and outline the prospects and advancements that will propel the field forward. Computer science ; Natural language processing ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography