jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tao Li | Zhen Xu | Yongliang Huang | Weisai Zu | Haohua Huo
Source: Journal of the American Chemical Society (article, 2025-02-19)
Field: Chemistry — Organic Chemistry

Enantioselective Alkyl–Acyl Radical Cross-Coupling Enabled by Metallaphotoredox Catalysis Radical-radical cross-coupling (RCC) offers a promising approach for carbon-carbon bond formation in organic synthesis, particularly for creating complex, three-dimensional molecules. However, achieving both cross- and enantioselectivity in RCC reactions has remained a significant challenge. Here, we report a novel metallaphotoredox platform that enables highly enantioselective decarboxylative coupling of carboxylic acid derivatives with aldehydes. Our strategy leverages independent control over radical generation and subsequent enantioselective bond formation through fine-tuning of a common photocatalyst and a simple chiral bis(oxazoline) nickel catalyst. This redox-neutral protocol requires no exogenous oxidants or reductants and demonstrates broad substrate scope and functional group compatibility in the synthesis of enantioenriched α-aryl and α-amino ketones. The α-amino ketone products can be readily transformed into valuable β-amino alcohols, streamlining access to these important motifs. Furthermore, we showcase the potential of this approach for more challenging enantioselective C(sp<sup>3</sup>)-C(sp<sup>3</sup>) alkyl-alkyl RCC reactions. This unified platform for enantioselective alkyl-acyl radical cross-coupling represents a significant advance in asymmetric catalysis and underscores the potential for metallaphotoredox catalysis to exploit new mechanisms to solve long-standing synthetic problems. Chemistry ; Enantioselective synthesis ; Alkyl ; Catalysis ; Coupling (piping) ; Organic chemistry ; Combinatorial chemistry Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Oxidative Organic Chemistry Reactions
Authors: Martin Koller | Dustin Heeney | Anindya Mukherjee
Source: Biodegradation (review, 2025-08-01)
Field: Materials Science — Biomaterials

Biodegradability of polyhydroxyalkanoate (PHA) biopolyesters in nature: a review In the search for sustainable alternatives and substitutes to overcome plastic pollution, polyhydroxyalkanoates (PHA) stand out as the gold standard. The very fact that PHA are microbially produced from renewable carbon sources, biodegraded by microbial action, and possess the beneficial properties of over 50% of the world's plastics has caught the attention of a wide range of producers, converters, brand owners, and policy makers with a view to replace conventional fossil-based plastics with these natural materials. PHA are readily biodegraded by the enzymatic toolbox of living organisms, aligning with the principle of natural circularity. Over 150 different monomeric building blocks of PHA have been identified, leading to a wide variety of naturally accessible PHA biopolyesters with diverse properties that include thermoplastic and crosslinkable polymers for single use and durable uses for packaging and personal care and as paints, coatings and adhesives, and as fibers for fabrics and textiles. The type of monomer and microstructure, as well as the environment, play important roles in their production and biodegradation. This comprehensive paper reviews the degradability of commercially available and other PHA types with varying microstructures in fresh water, sea water, soil, as well as in home and industrial composting and anaerobic conditions. Unlike previous reviews the authors integrate information from diverse biodegradation studies and provide a holistic view and understanding of the biodegradability of the PHA biopolymer family in nature and in industrial environments. Polyhydroxyalkanoates ; Biodegradation ; Cupriavidus necator ; Bioplastic ; Chemistry ; Bacteria ; Waste management ; Biology ; Organic chemistry ; Engineering biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Carbon dioxide utilization in catalysis
Authors: Sebastian Musslick | Laura Bartlett | Suyog Chandramouli | Marina Dubova | Fernand Gobet | Thomas L. Griffiths | Jessica Hullman | Ross D. King | J. Nathan Kutz | Christopher G. Lucas | Suhas Mahesh | Franco Pestilli | Sabina J. Sloman | William R. Holmes
Source: Proceedings of the National Academy of Sciences (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Automating the practice of science: Opportunities, challenges, and implications Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: where do the greatest opportunities lie for automation in scientific practice?; What are the current bottlenecks of automating scientific practice?; and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. Automation ; Scope (computer science) ; Engineering ethics ; Frontier ; Domain (mathematical analysis) ; Data science ; Best practice ; Computer science ; Knowledge management ; Management science ; Engineering ; Political science Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Scientific Computing and Data Management
Authors: Junhyeong Kim | Jae‐Yong Kim | Jungmin Kim | Yun Hyeong | Berkay Neşeli | Jong-Bum You | Joonsup Shim | Jonghwa Shin | Hyo‐Hoon Park | Hamza Kurt
Source: Nanophotonics (review, 2025-01-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects Nanophotonics, which explores significant light-matter interactions at the nanoscale, has facilitated significant advancements across numerous research fields. A key objective in this area is the design of ultra-compact, high-performance nanophotonic devices to pave the way for next-generation photonics. While conventional brute-force, intuition-based forward design methods have produced successful nanophotonic solutions over the past several decades, recent developments in optimization methods and artificial intelligence offer new potential to expand these capabilities. In this review, we delve into the latest progress in the inverse design of nanophotonic devices, where AI and optimization methods are leveraged to automate and enhance the design process. We discuss representative methods commonly employed in nanophotonic design, including various meta-heuristic algorithms such as trajectory-based, evolutionary, and swarm-based approaches, in addition to adjoint-based optimization. Furthermore, we explore state-of-the-art deep learning techniques, involving discriminative models, generative models, and reinforcement learning. We also introduce and categorize several notable inverse-designed nanophotonic devices and their respective design methodologies. Additionally, we summarize the open-source inverse design tools and commercial foundries. Finally, we provide our perspectives on the current challenges of inverse design, while offering insights into future directions that could further advance this rapidly evolving field. Nanophotonics ; Computer science ; Artificial intelligence ; Nanotechnology ; Materials science Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Neural Networks and Reservoir Computing
Authors: Aleksey Vishnyakov
Source: Materials (review, 2025-01-24)
Field: Materials Science — Materials Chemistry

Machine Learning in Computational Design and Optimization of Disordered Nanoporous Materials This review analyzes the current practices in the data-driven characterization, design and optimization of disordered nanoporous materials with pore sizes ranging from angstroms (active carbon and polymer membranes for gas separation) to tens of nm (aerogels). While the machine learning (ML)-based prediction and screening of crystalline, ordered porous materials are conducted frequently, materials with disordered porosity receive much less attention, although ML is expected to excel in the field, which is rich with ill-posed problems, non-linear correlations and a large volume of experimental results. For micro- and mesoporous solids (active carbons, mesoporous silica, aerogels, etc.), the obstacles are mostly related to the navigation of the available data with transferrable and easily interpreted features. The majority of published efforts are based on the experimental data obtained in the same work, and the datasets are often very small. Even with limited data, machine learning helps discover non-evident correlations and serves in material design and production optimization. The development of comprehensive databases for micro- and mesoporous materials with low-level structural and sorption characteristics, as well as automated synthesis/characterization protocols, is seen as the direction of efforts for the immediate future. This paper is written in a language readable by a chemist unfamiliar with the data science specifics. Nanoporous ; Characterization (materials science) ; Mesoporous material ; Porosity ; Materials science ; Sorption ; Porous medium ; Volume (thermodynamics) ; Material Design ; Nanotechnology ; Computer science ; Work (physics) ; Artificial intelligence ; Machine learning ; Mechanical engineering ; Engineering ; Adsorption ; Composite material ; Physics ; Chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Mesoporous Materials and Catalysis
Authors: Harshit Sharma | Gaurav Arora | Manoj Kumar Singh | Vinod Ayyappan | Papiya Bhowmik | Sanjay Mavinkere Rangappa | Suchart Siengchin
Source: Discover Applied Sciences (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

Review of machine learning approaches for predicting mechanical behavior of composite materials In recent years, machine learning (ML) has emerged as a powerful tool for predicting the mechanical behavior of composite materials, offering a faster, more cost-effective alternative to traditional testing and simulation methods. This review explores how various ML techniques, including random forests, support vector machines, artificial neural networks, and deep learning models, are used to forecast key material properties such as tensile strength, hardness, fracture toughness, and fatigue life. From a broad spectrum of recent studies, the article highlights how ML models are trained on experimental and simulation data to explore complex relationships between processing parameters, material compositions, and resulting performance. The review also compares the accuracy, limitations, and suitability of different algorithms and emphasizes the growing importance of hybrid models, feature selection, and data quality. Finally, the future potential of ML in multiscale modeling, design optimization, and real-world applications, while also addressing practical challenges like data scarcity, model interpretability, and the need for standardized validation protocols, has been discussed. By summarizing current progress and challenges, the article supports informed decision-making in adopting ML for composite materials research and application. Artificial neural network ; Support vector machine ; Feature (linguistics) ; Experimental data ; Random forest ; Deep learning ; Key (lock) ; Feature engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Mineral Processing and Grinding
Authors: Yuanqing Wang | Kenichiro Takaba | Michael S. Chen | Marcus Wieder | Yuzhi Xu | Tong Zhu | John Z. H. Zhang | Arnav Nagle | Kuang Yu | Xinyan Wang | Daniel J. Cole | Joshua A. Rackers | Kyunghyun Cho | Joe G. Greener | Peter Eastman | Stefano Martiniani | Mark E. Tuckerman
Source: Applied Physics Reviews (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

On the design space between molecular mechanics and machine learning force fields A force field as accurate as quantum mechanics (QMs) and as fast as molecular mechanics (MMs), with which one can simulate a biomolecular system efficiently enough and meaningfully enough to get quantitative insights, is among the most ardent dreams of biophysicists—a dream, nevertheless, not to be fulfilled any time soon. Machine learning force fields (MLFFs) represent a meaningful endeavor in this direction, where differentiable neural functions are parametrized to fit ab initio energies and forces through automatic differentiation. We argue that, as of now, the utility of the MLFF models is no longer bottlenecked by accuracy but primarily by their speed, as well as stability and generalizability—many recent variants, on limited chemical spaces, have long surpassed the chemical accuracy of 1 kcal/mol—the empirical threshold beyond which realistic chemical predictions are possible—though still magnitudes slower than MM. Hoping to kindle exploration and design of faster, albeit perhaps slightly less accurate MLFFs, in this review, we focus our attention on the technical design space (the speed-accuracy trade-off) between MM and ML force fields. After a brief review of the building blocks (from a machine learning-centric point of view) of force fields of either kind, we discuss the desired properties and challenges now faced by the force field development community, survey the efforts to make MM force fields more accurate and ML force fields faster, and envision what the next generation of MLFF might look like. Molecular mechanics ; Space (punctuation) ; Computer science ; Classical mechanics ; Physics ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Nangamso Nathaniel Nyangiwe
Source: Next Materials (review, 2025-04-28)
Field: Materials Science — Materials Chemistry

Applications of density functional theory and machine learning in nanomaterials: A review The development and creation of nanomaterials carry enormous prospects in advancing technology in electronics, energy storage and medicine. The high degree of complexity and diversity in nanomaterials presents a real challenge in their theoretical and experimental studies. Density Functional Theory (DFT) is emerging as a powerful computational tool to model, understand, and predict material properties at a quantum mechanical level for nanomaterials. This review highlights the considerable use of DFT in elucidating the electronic, structural, and catalytic attributes of various nanomaterials. Also, this review considers developments between DFT and machine learning (ML)-based techniques that have paved the way for accelerated discoveries and design of novel nanomaterials. In fact, the ML algorithm has built models based on data from DFT, which predicts with high accuracy the properties of materials at reduced computational costs to expand vast areas of emerging chemistries. Major advances in this new hybrid approach include the development of ML models to predict band gaps, adsorption energies, and reaction mechanisms. The review discusses open topics regarding the future efforts to integrate DFT and ML focusing on model interpretability, data quality and broadened applicability to increasingly complex systems. The review concludes by discussing key advancements, such as those of machine learning interatomic potentials, graph-based models for structure property mapping and generative AI for materials design. Density functional theory ; Nanomaterials ; Nanotechnology ; Computer science ; Cognitive science ; Materials science ; Psychology ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Nanomaterials in Catalysis ; 2D Materials and Applications
Authors: Hanzo Tsubota | Andrei Jitianu | Go Kawamura
Source: ACS Materials Letters (article, 2025-02-17)
Field: Materials Science — Polymers and Plastics

Recent Advances in High-Entropy Oxides for Photocatalytic Applications This paper provides a comprehensive overview of high-entropy oxides (HEOs) and their applications in photocatalysis. The unique functionality of HEOs stems from their distinct crystal and electronic structures, achieved through the substitution of one cation with multiple cations in the lattice. This structural innovation has attracted significant attention, with increasing studies since 2019. To date, approximately 38 papers on HEOs in photocatalysis have been reported, indicating that this research is still in its early stages. Some HEOs exhibit photocatalytic performance comparable to that of conventional oxides like TiO2 and SrTiO3, highlighting their potential. This Perspective discusses the synthesis methods, crystal structures, and photocatalytic properties of HEOs, emphasizing their advantages through specific examples. Additionally, it proposes future research directions, including the development of innovative materials and prediction of properties using first-principles calculations and machine learning, to advance the application of HEOs in photocatalysis. Photocatalysis ; Materials science ; Nanotechnology ; Statistical physics ; Environmental science ; Chemistry ; Physics ; Organic chemistry ; Catalysis Transition Metal Oxide Nanomaterials ; Catalytic Processes in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Ruben Goeminne | Véronique Van Speybroeck
Source: Journal of the American Chemical Society (article, 2025-01-17)
Field: Chemistry — Inorganic Chemistry

Ab Initio Predictions of Adsorption in Flexible Metal–Organic Frameworks for Water Harvesting Applications Metal-organic frameworks such as MOF-303 and MOF-LA2-1 have demonstrated exceptional performance for water harvesting applications. To enable a reticular design of such materials, an accurate prediction of the adsorption properties with chemical accuracy and fully accounting for the flexibility is crucial. The computational prediction of water adsorption properties in MOFs has become standard practice, but current methods lack the predictive power needed to design new materials. Limitations stem from the way the interatomic potential is described and the inadequate consideration of the framework flexibility. Herein, we showcase a methodology to obtain chemically accurate adsorption isotherms that fully account for framework flexibility. The method relies on very accurate and efficiently trained machine learning potentials and transition matrix Monte Carlo simulations to account for framework flexibility. For MOF-303, quantitatively accurate adsorption isotherms are obtained, provided an accurately benchmarked electronic structure method is used to train the machine learning potential, and local and global framework flexibility is accounted for. The broader applicability is shown through the study of MOF-333 and MOF-LA2-1. Analysis of the water density profiles in the MOFs gives insight into the factors governing the shape and origin of the isotherm. An optimal water harvester should have initial seeding sites with intermediate adsorption strength to prevent detrimental low-pressure water uptake. To increase the working capacity, linker extension strategies can be used while maintaining the initial seeding sites, as was done in MOF-LA2-1. The methodology can be applied to other guest molecules and MOFs, enabling the future design of MOFs with specific adsorption properties. Chemistry ; Ab initio ; Adsorption ; Metal-organic framework ; Ab initio quantum chemistry methods ; Computational chemistry ; Nanotechnology ; Chemical physics ; Molecule ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Enhanced Oil Recovery Techniques ; Solar-Powered Water Purification Methods
Authors: Yu Han | Chi Ding | Junjie Wang | Hao Gao | Jiuyang Shi | Shaobo Yu | Qiuhan Jia | Shuning Pan | Jian Sun
Source: Nature Computational Science (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

Efficient crystal structure prediction based on the symmetry principle Crystal structure prediction ; Computer science ; Benchmarking ; Symmetry (geometry) ; Graph ; Theoretical computer science ; Metastability ; Crystal structure ; Algorithm ; Artificial intelligence ; Mathematics ; Crystallography ; Physics ; Geometry ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Navid Rabiee
Source: TrAC Trends in Analytical Chemistry (article, 2025-01-23)
Field: Materials Science — Materials Chemistry

Wearable MOF biosensors: A new frontier in real-time health monitoring Wearable computer ; Frontier ; Computer science ; Nanotechnology ; Embedded system ; Materials science ; Geography Advanced Nanomaterials in Catalysis ; Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Pengyu Zhang | Yinghui Sun | Jiacheng Sun | Shitong Wang | Rongming Wang | Junying Zhang
Source: Advanced Functional Materials (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

Progress in 2D Material‐Based Infrared Photodetectors for Intelligent Vision Applications Abstract Infrared (IR) photodetectors based on narrow‐bandgap 2D materials and heterojunctions have shown great promise in constructing IR sensing systems, including optical communication, security monitoring, thermal imaging, and astronomy exploration. In recent years, significant progress has been made in developing performance enhancement strategies for 2D material‐based IR photodetectors and integrating them with artificial neural networks, paving the way for sophisticated intelligent IR applications. This review offers a detailed overview of recent advancements in enhancing IR photodetection capabilities and fostering related intelligent applications. First, a concise overview of the underlying photodetection mechanisms and key performance metrics of 2D photodetectors designed for operation in the IR region is illustrated. Next, strategies for enhancing sensitivity and light absorption of IR photodetectors, including defect engineering, heterostructure construction, and optical field enhancement, are discussed. Then, recent advances and applications of 2D material‐based photodetectors are summarized, with a particular focus on innovations that enable intelligent, real‐time sensing and processing capabilities for IR applications. Finally, the review highlights the challenges and provides a forward‐looking perspective on the development of advanced intelligent IR photodetectors. Materials science ; Photodetector ; Infrared ; Optoelectronics ; Nanotechnology ; Remote sensing ; Engineering physics ; Systems engineering ; Optics ; Engineering 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Semiconductor Detectors and Materials
Authors: Durga Madhab Mahapatra | Ashish Kumar | Rajesh Kumar | Navneet Kumar Gupta | Baranitharan Ethiraj | Lakhveer Singh
Source: Coordination Chemistry Reviews (article, 2025-01-24)
Field: Materials Science — Materials Chemistry

Artificial intelligence interventions in 2D MXenes-based photocatalytic applications MXenes ; Chemistry ; Photocatalysis ; Psychological intervention ; Nanotechnology ; Astrobiology ; Organic chemistry ; Psychology ; Catalysis MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Advanced Photocatalysis Techniques