jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: ísak Valsson | Matthew T. Warren | Charlotte M. Deane | Aniket Magarkar | Garrett M. Morris | Philip C. Biggin
Source: Communications Chemistry (article, 2025-02-08)
Field: Materials Science — Materials Chemistry

Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data Machine learning offers great promise for fast and accurate binding affinity predictions. However, current models lack robust evaluation and fail on tasks encountered in (hit-to-) lead optimisation, such as ranking the binding affinity of a congeneric series of ligands, thereby limiting their application in drug discovery. Here, we address these issues by first introducing a novel attention-based graph neural network model called AEV-PLIG (atomic environment vector-protein ligand interaction graph). Second, we introduce a new and more realistic out-of-distribution test set called the OOD Test. We benchmark our model on this set, CASF-2016, and a test set used for free energy perturbation (FEP) calculations, that not only highlights the competitive performance of AEV-PLIG, but provides a realistic assessment of machine learning models with rigorous physics-based approaches. Moreover, we demonstrate how leveraging augmented data (generated using template-based modelling or molecular docking) can significantly improve binding affinity prediction correlation and ranking on the FEP benchmark (weighted mean PCC and Kendall's τ increases from 0.41 and 0.26 to 0.59 and 0.42). These strategies together are closing the performance gap with FEP calculations (FEP+ achieves weighted mean PCC and Kendall's τ of 0.68 and 0.49 on the FEP benchmark) while being ~400,000 times faster. Computer science ; Perturbation (astronomy) ; Machine learning ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Alex M. Ganose | Hrushikesh Sahasrabuddhe | Mark Asta | Kévin Beck | Tathagata Biswas | Alexander Bonkowski | Joana Bustamante | Xin Chen | Yuan Chiang | D. C. Chrzan | Jacob M. Clary | Orion A. Cohen | Christina Ertural | Max C. Gallant | Janine George | Sophie Gerits | Rhys E. A. Goodall | Rishabh D. Guha | Geoffroy Hautier | Matthew K. Horton | Théo Jaffrelot Inizan | Aaron D. Kaplan | Ryan Kingsbury | Matthew C. Kuner | Bryant Y. Li | Xavier Linn | Matthew J. McDermott | Rohith Srinivaas Mohanakrishnan | Aakash Ashok Naik | Jeffrey B. Neaton | Shehan M. Parmar | Kristin A. Persson | Guido Petretto | Thomas A. R. Purcell | Francesco Ricci | Benjamin S. Rich | Janosh Riebesell | Gian‐Marco Rignanese | Andrew Rosen | Matthias Scheffler | Jonathan Schmidt | Jimmy‐Xuan Shen | Alexander S. Sobolev | Ravishankar Sundararaman | Cooper Tezak | Victor Trinquet | Joel B. Varley | Derek Vigil‐Fowler | Duo Wang | David Waroquiers | Mingjian Wen | Han Yang | Hui Zheng | Jiongzhi Zheng | Zhuoying Zhu | Anubhav Jain
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Atomate2: modular workflows for materials science High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of "universal" machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science. Workflow ; Orchestration ; Interoperability ; Modular design ; Computer science ; Software engineering ; Workflow technology ; Workflow engine ; Throughput ; World Wide Web ; Database ; Programming language ; Operating system Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Cunhao Han | Xinghua Dong | Wang Zhang | Xiaoxia Huang | Linji Gong | Chunjian Su
Source: Nanomaterials (review, 2025-04-21)
Field: Materials Science — Materials Chemistry

Intelligent Systems for Inorganic Nanomaterial Synthesis Inorganic nanomaterials are pivotal foundational materials driving traditional industries' transformation and emerging sectors' evolution. However, their industrial application is hindered by the limitations of conventional synthesis methods, including poor batch stability, scaling challenges, and complex quality control requirements. This review systematically examines strategies for constructing automated synthesis systems to enhance the production efficiency of inorganic nanomaterials. Methodologies encompassing hardware architecture design, software algorithm optimization, and artificial intelligence (AI)-enabled intelligent process control are analyzed. Case studies on quantum dots and gold nanoparticles demonstrate the enhanced efficiency of closed-loop synthesis systems and their machine learning-enabled autonomous optimization of process parameters. The study highlights the critical role of automation, intelligent technologies, and human-machine collaboration in elucidating synthesis mechanisms. Current challenges in cross-scale mechanistic modeling, high-throughput experimental integration, and standardized database development are discussed. Finally, the prospects of AI-driven synthesis systems are envisioned, emphasizing their potential to accelerate novel material discovery and revolutionize nanomanufacturing paradigms within the framework of AI-plus initiatives. Nanomanufacturing ; Computer science ; Automation ; Process (computing) ; Nanotechnology ; Systems engineering ; Engineering ; Materials science Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions
Authors: Arslan Mazitov | Filippo Bigi | Matthias Kellner | Paolo Pegolo | Davide Tisi | Guillaume Fraux | Sergey N. Pozdnyakov | Philip Loche | Michele Ceriotti
Source: Nature Communications (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

PET-MAD as a lightweight universal interatomic potential for advanced materials modeling Machine-learning interatomic potentials have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of the cost. Leveraging large quantum mechanical databases and expressive architectures, recent universal models deliver qualitative accuracy across the periodic table but are often biased toward low-energy configurations. We introduce PET-MAD, a generally applicable interatomic potential trained on a dataset combining stable inorganic and organic solids, systematically modified to enhance atomic diversity. Using a moderate but thoroughly consistent level of electronic-structure theory, we assess PET-MAD's accuracy on established benchmarks and advanced simulations of six materials. Despite the small training set and lightweight architecture, PET-MAD is competitive with the state-of-the-art machine-learned interatomic potentials for inorganic solids, while also being reliable for molecules, organic materials, and surfaces. It is stable and fast, enabling the near-quantitative study of thermal and quantum mechanical fluctuations, functional properties, and phase transitions out of the box. It can be efficiently fine-tuned to deliver full quantum mechanical accuracy with a minimal number of targeted calculations. Interatomic potential ; Quantum ; Set (abstract data type) ; Table (database) ; Molecular dynamics ; Thermal ; Multiscale modeling Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Inorganic Chemistry and Materials
Authors: Stefano Falletta | Andrea Cepellotti | Anders Johansson | Chuin Wei Tan | Marc Descoteaux | Albert Musaelian | Cameron J. Owen | Boris Kozinsky
Source: Nature Communications (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Unified differentiable learning of electric response Predicting response of materials to external stimuli is a primary objective of computational materials science. However, current methods are limited to small-scale simulations due to the unfavorable scaling of computational costs. Here, we implement an equivariant machine-learning framework where response properties stem from exact differential relationships between a generalized potential function and applied external fields. Focusing on responses to electric fields, the method predicts electric enthalpy, forces, polarization, Born charges, and polarizability within a unified model enforcing the full set of exact physical constraints, symmetries and conservation laws. Through application to α-SiO<sub>2</sub>, we demonstrate that our approach can be used for predicting vibrational and dielectric properties of materials, and for conducting large-scale dynamics under arbitrary electric fields at unprecedented accuracy and scale. We apply our method to ferroelectric BaTiO<sub>3</sub> and capture the temperature dependence, frequency dependence, and time evolution of the ferroelectric hysteresis, revealing the underlying intrinsic mechanisms of nucleation and growth that govern ferroelectric domain switching. Differentiable function ; Computer science ; Computational biology ; Biology ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Force Microscopy Techniques and Applications
Authors: Guangxun Zhang | Wanchang Feng | Guangyu Du | Yi Zhang | Ya Yang | Dian Xu | Tianyi Wang | Han‐Yi Chen | Huaiguo Xue | Mohsen Shakouri | Huan Pang
Source: Advanced Materials (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

Thermodynamically‐Driven Phase Engineering and Reconstruction Deduction of Medium‐Entropy Prussian Blue Analogue Nanocrystals Prussian blue analogs (PBAs) are exemplary precursors for the synthesis of a diverse array of derivatives.Yet, the intricate mechanisms underlying phase transitions in these multifaceted frameworks remain a formidable challenge. In this study, a machine learning-guided analysis of phase transitions in a medium-entropy PBA system is delineated, utilizing an array of descriptors that encompass crystallographic phases, structural subtleties, and fluctuations in multimetal valence states. By integrating multimodal simulations with experimental validation, a thermodynamics-driven phase transformation model for medium-entropy PBA is established and accurately predicted the critical synthesis parameters. A constellation of advanced techniques-including atomic force microscopy coupled with Kelvin probe force microscopy for individual nanoparticles, X-ray absorption spectroscopy, operando ultraviolet-visible spectroscopy, in situ X-ray diffraction, theoretical calculations, and multiphysics simulations-substantiated that the iron oxide@NiCoZnFe-PBA exhibits both exceptional stability and remarkable electrochemical activity. This investigation provides profound insights into the phase transition dynamics of polymetallic complexes and propels the rational design of other thermally-induced derivatives. Prussian blue ; Materials science ; Phase transition ; Multiphysics ; Nanoparticle ; Chemical physics ; Nanocrystal ; Nanotechnology ; Spectroscopy ; Thermodynamics ; Electrochemistry ; Physical chemistry ; Chemistry ; Physics ; Electrode Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Conducting polymers and applications
Authors: K. Kadirgama | Lingenthiran Samylingam | Navid Aslfattahi | Maryam Sadat Kiai | Chee Kuang Kok | Talal Yusaf
Source: International Journal of Hydrogen Energy (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Advancements and challenges in numerical analysis of hydrogen energy storage methods: Techniques, applications, and future direction Hydrogen storage ; Computer science ; Energy storage ; Hydrogen ; Computational science ; Chemistry ; Physics ; Thermodynamics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: Adnan Younis | Edvin Idrisov | Saleh Thaker | Fathalla Hamed | El Hadi Sadki | Muhammad Z. Iqbal | Tariq Mahmood | Babar Shabbir | Qiaoliang Bao
Source: Advanced Electronic Materials (article, 2025-07-20)
Field: Materials Science — Materials Chemistry

Advances in MXene‐Based Electronics via Surface and Structural Redesigning and Beyond Abstract MXenes, a prominent class of 2D materials, offer exceptional physicochemical properties, including tunable surface chemistry, high electrical conductivity, and structural versatility, making them ideal for advanced electronic, energy, and sensing applications. This review critically examines recent progress in the surface and structural engineering of MXenes, emphasizing their impact on tailoring electronic properties and enabling multifunctional device integration. Key surface modification strategies, such as termination group control, defect regulation, heteroatom doping, and oxidation tuning, are discussed in relation to their influence on the work function, conductivity, and chemical reactivity. Concurrently, structural engineering approaches, including interlayer manipulation, hierarchical assembly, and the formation of MXene‐based composites and heterostructures, are analyzed for their roles in enhancing charge transport, mechanical robustness, and device adaptability. This review highlights how these synergistic modifications drive performance enhancements in field‐effect transistors, photodetectors, and resistive memory devices. This work offers a cohesive framework for understanding and advancing MXene functionality by integrating insights across diverse engineering strategies. The findings aim to guide future research directions and stimulate innovation in next‐generation nanoelectronics based on MXenes and related 2D materials. Materials science ; Nanotechnology ; Electronics ; Engineering ; Electrical engineering MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Eslam M. Hamed | Fun Man Fung | Sam Fong Yau Li
Source: ACS Materials Au (article, 2025-01-31)
Field: Materials Science — Materials Chemistry

Zinc Single-Atom Nanozyme As Carbonic Anhydrase Mimic for CO<sub>2</sub> Capture and Conversion Single-atom nanozymes (SANs) are a class of nanozymes with metal centers that mimic the structure of metalloenzymes. Herein, we report the synthesis of Zn-N-C SAN, which mimics the action of the natural carbonic anhydrase enzyme. The two-step annealing technique led to a metal content of more than 18 wt %. Since the metal centers act as active sites, this high metal loading resulted in superior catalytic activity. Zn-SAN showed a CO<sub>2</sub> uptake of 2.3 mmol/g and a final conversion of CO<sub>2</sub> to bicarbonate of more than 91%. CO<sub>2</sub> was converted via a biomimetic process by allowing its adsorption by the catalyst, followed by the addition of the catalyst to HEPES buffer (pH = 8) to start the CO<sub>2</sub> conversion into HCO<sub>3</sub> <sup>-</sup>. Afterward, CaCl<sub>2</sub> was added to form a white CaCO<sub>3</sub> precipitate, which was then filtered, dried, and weighed. Active carbon and MCM-41 were used as controls under the same reaction conditions. According to the findings, the CO<sub>2</sub> sequestration capacity was 42 mg of CaCO<sub>3</sub>/mg of Zn-SAN. Some amino acids (AAs) with binding affinity for Zn were able to suppress the enzymatic activity of Zn-SAN by blocking the active metal centers. This strategy was used for the detection of His, Cys, Glu, and Asp with detection limits of 0.011, 0.031, 0.029, and 0.062 μM, respectively, and hence was utilized for quantifying these AAs in commercial dietary supplements. Chemistry ; Bicarbonate ; Carbonic anhydrase ; Catalysis ; Metal ; Zinc ; Adsorption ; Active site ; Inorganic chemistry ; Carbonic anhydrase II ; Enzyme ; Metalloprotein ; Combinatorial chemistry ; Nuclear chemistry ; Organic chemistry Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Nanocluster Synthesis and Applications
Authors: Satyasankar Jana | Anbanandam Parthiban | Wendy Rusli
Source: Chemical Communications (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Polymer material innovations for a green hydrogen economy Polymeric materials are ubiquitous in modern life. Similar to many other technological applications, polymer materials are essential in advancing the green hydrogen economy, offering solutions for hydrogen production, storage, transport, and utilization. In production, polymeric proton exchange membranes in water electrolysers enable efficient green hydrogen generation using renewable energy. Polymer-based composite tanks provide lightweight, high-strength on-board storage options for vehicles, enhancing safety and reducing costs. Polymeric proton exchange membranes in fuel cells efficiently convert hydrogen into electricity. Polymers also support hydrogen infrastructure with corrosion-resistant, durable pipelines, distribution ports and as hydrogen sensors. Additionally, porous and reversible hydrogenated-to-dehydrogenated forms of polymers show promise for material-based storage systems. This review highlights the role of polymer materials, their current advancements supporting a green hydrogen economy as a solution for a low-carbon future and future research directions. Hydrogen economy ; Polymer ; Hydrogen ; Green economy ; Materials science ; Business ; Polymer science ; Chemistry ; Nanotechnology ; Chemical engineering ; Hydrogen fuel ; Engineering ; Composite material ; Organic chemistry ; Sustainable development ; Political science Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: Guojian Wu | Si Li | Linpin Luo | Yuechun Li | Wentao Zhang | Heng Wang | Sha Liu | Chenxing Du | Jianlong Wang | Jie Cheng | Yongning Wu | Yizhong Shen
Source: Nano-Micro Letters (review, 2025-04-28)
Field: Materials Science — Materials Chemistry

Exploring Single-Atom Nanozymes Toward Environmental Pollutants: Monitoring and Control As environmental pollutants pose a serious threat to socioeconomic and environmental health, the development of simple, efficient, accurate and cost-effective methods for pollution monitoring and control remains a major challenge, but it is an unavoidable issue. In the past decade, the artificial nanozymes have been widely used for environmental pollutant monitoring and control, because of their low cost, high stability, easy mass production, etc. However, the conventional nanozyme technology faces significant challenges in terms of difficulty in regulating the exposed crystal surface, complex composition, low catalytic activity, etc. In contrast, the emerging single-atom nanozymes (SANs) have attracted much attention in the field of environmental monitoring and control, due to their multiple advantages of atomically dispersed active sites, high atom utilization efficiency, tunable coordination environment, etc. To date, the insufficient efforts have been made to comprehensively characterize the applications of SANs in the monitoring and control of environmental pollutants. Building on the recent advances in the field, this review systematically summarizes the main synthesis methods of SANs and highlights their advances in the monitoring and control of environmental pollutants. Finally, we critically evaluate the limitations and challenges of SANs, and provide the insights into their future prospects for the monitoring and control of environmental pollutants. Pollutant ; Environmental pollution ; Environmental monitoring ; Environmental science ; Biochemical engineering ; Computer science ; Environmental planning ; Nanotechnology ; Environmental protection ; Environmental engineering ; Engineering ; Chemistry ; Materials science Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques ; Nanocluster Synthesis and Applications
Authors: Xiaocheng Lan | Jingguang G. Chen | Tiefeng Wang
Source: Chemical Society Reviews (review, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Recent advances in thermocatalytic acetylene selective hydrogenation Selective acetylene hydrogenation is a crucial reaction for purifying ethylene in the petroleum industry and presents a promising non-oil route for producing ethylene by integrating acetylene production from natural gas and coal. Despite significant advancements in catalyst development, achieving both high catalytic activity and ethylene selectivity remains challenging due to competing side reactions, including over-hydrogenation to ethane, C-C coupling leading to oligomers, and C-C bond cleavage resulting in coke formation. This review provides a comprehensive overview of recent progress in the development of catalysts and understanding of reaction mechanism for acetylene hydrogenation to ethylene. Firstly, benchmarks for conversion and selectivity calculation are critically discussed. Then, research on active site design is categorized into monometallic sites, disordered alloy sites, intermetallic compound (IMC) sites, and single-atom (SA) sites, with a distinction between Pd-based and non-Pd-based catalysts. This categorization highlights the active site design strategies and summarizes state-of-the-art performance metrics. Emphasis is placed on the structure-performance relationships and the role of different active metals in enhancing ethylene selectivity and catalytic activity. In addition, the roles of catalyst support and modifiers are reviewed. Finally, we discuss challenges and future research directions in mechanistic understanding and catalyst design, aiming to guide further innovations in this important field. Acetylene ; Relevance (law) ; Nanotechnology ; Chemistry ; Materials science ; Organic chemistry ; Political science Asymmetric Hydrogenation and Catalysis ; Ammonia Synthesis and Nitrogen Reduction ; Covalent Organic Framework Applications