jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bowen Li | Jin Xiao | Ya Gao | John Z. H. Zhang | Tong Zhu
Source: Journal of Chemical Information and Modeling (article, 2025-02-20)
Field: Materials Science — Materials Chemistry

Transition State Searching Accelerated by Neural Network Potential Understanding transition states is pivotal in the design of efficient chemical processes and catalysts. However, identifying transition states is challenging due to the resource-intensive and iterative nature of current computational methods. This study integrates neural network potentials with physical models to enhance the transition state prediction. Different neural network potentials and transition states locating algorithms are benchmarked. By combining NequIP with the energy-weighted Climbing Image-Nudged Elastic Band (EW-CI-NEB) method, we achieved highly accurate transition state predictions, significantly surpassing semiempirical methods in accuracy and greatly outpacing density functional theory in efficiency. Additionally, the transferability of the model was evaluated using a NequIP model trained on a refined subset of the dataset, and the model's performance was further improved through active learning. This method can directly search for transition states in given reactions or serve as an efficient tool for generating initial guesses of transition state structures, significantly reducing manual effort. State (computer science) ; Transition (genetics) ; Artificial neural network ; Computer science ; Artificial intelligence ; Chemistry ; Algorithm Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Neural Networks and Applications
Authors: Paul Fuchs | Weilong Chen | Stephan Thaler | Julija Zavadlav
Source: Journal of Chemical Theory and Computation (article, 2025-07-23)
Field: Materials Science — Materials Chemistry

chemtrain-deploy: A Parallel and Scalable Framework for Machine Learning Potentials in Million-Atom MD Simulations Machine Learning Potentials (MLPs) have advanced rapidly and show great promise to transform Molecular Dynamics (MD) simulations. However, most existing software tools are tied to specific MLP architectures, lack integration with standard MD packages, or are not parallelizable across GPUs. To address these challenges, we present chemtrain-deploy, a framework that enables the model-agnostic deployment of MLPs in LAMMPS. chemtrain-deploy supports any JAX-defined semilocal potential, allowing users to exploit the functionality of LAMMPS and perform large-scale MLP-based MD simulations on multiple GPUs. It achieves state-of-the-art efficiency and scales to systems containing millions of atoms. We validate its performance and scalability using graph neural network architectures, including MACE, Allegro, and PaiNN, applied to a variety of systems such as liquid-vapor interfaces, crystalline materials, and solvated peptides. Our results highlight the practical utility of chemtrain-deploy for real-world, high-performance simulations and provide guidance for MLP architecture selection and future design. Scalability ; Computer science ; Atom (system on chip) ; Computational science ; Artificial intelligence ; Data science ; Parallel computing ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Haoyang Fu | Ke Li | Qingze Chen | Bijun Tang | Zhongyi Deng | Ziyang Toh | Runliang Zhu | Shuzhou Li
Source: Angewandte Chemie International Edition (article, 2025-04-16)
Field: Materials Science — Materials Chemistry

Accelerated Design of Fenton‐Like Copper Single‐Atom Catalysts by Adaptive Learning with Genetic Programming Traditional trial-and-error methods for optimizing catalyst synthesis are time-consuming and costly, exploring only a small fraction of the vast combinatorial space. Machine learning (ML) offers a promising alternative but still has the limitation of relying on well-selected initial datasets, which the recent development of active learning (AL) could be addressed. Here, we novelly integrate an AL-derived algorithm, the adaptive learning genetic algorithm (ALGA), into experimental workflows to optimize the synthesis of Fenton-like single-atom catalysts (SACs). Our results show that the closed-loop ALGA framework effectively learns from limited and sparse datasets, greatly reducing the research cycle compared to traditional ML and AL frameworks. By iteratively retaining better-performing genetic information and proactively expanding the search space through mutation and crossover, ALGA identifies the highest-performing Fenton-like Cu SACs with less than 90 experiments. The maximum phenol degradation rate k-value (0.147 min<sup>-1</sup>) achieved within the ALGA framework is approximately three times higher than that of the initial dataset and surpasses the reported best Fenton-like Cu SACs. Our successful implementation of ALGA signifies an advancement in SACs synthesis assisted by the AL-derived algorithm, offering a guiding methodology for the exploration of other functional materials. Workflow ; Computer science ; Genetic algorithm ; Crossover ; Genetic programming ; Degradation (telecommunications) ; Atom (system on chip) ; Copper ; Catalysis ; Biological system ; Materials science ; Biochemical engineering ; Mathematical optimization ; Machine learning ; Chemistry ; Mathematics ; Biology ; Engineering ; Parallel computing Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Nanomaterials for catalytic reactions
Authors: Yuan Yan | Kaiyun Chen | Minglei Sun | Yinchang Ma | Peiyao Wang | Junkai Deng | Xixiang Zhang | Jefferson Zhe Liu
Source: Science Advances (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Deciphering the stability of two-dimensional III-V semiconductors: Building blocks and their versatile assembly Two-dimensionalization unlocks the unique and superior physical properties of materials, but extending it to nonlayered crystals is challenging. Using density functional theory and machine learning, we unveil a universal rule for creating stable two-dimensional counterparts of traditional high-performance III-V semiconductors, i.e., the versatile assembly of building blocks originating from orbital hybridization and electron transfers adhering to the electron counting rule. Akin to LEGO construction, the various building blocks are arranged in different configurations, introducing diverse two-dimensional structures with higher energetic stability than previous structures. Regression analysis reveals the energies of these structures as a linear superposition of the energies of their building blocks, further confirming the LEGO concept. Notably, the predicted two-dimensional GaSb exhibits a hole mobility (~10<sup>8</sup> square centimeters per volt per second) that far surpasses that of graphene (2 × 10<sup>5</sup> square centimeters per volt per second). This study highlights the expansion of nonlayered materials into two dimensions and the potential of two-dimensional confinement in traditional materials. Semiconductor ; Stability (learning theory) ; Superposition principle ; Square (algebra) ; Physics ; Computer science ; Statistical physics ; Nanotechnology ; Materials science ; Geometry ; Mathematics ; Quantum mechanics ; Machine learning 2D Materials and Applications ; Advanced Semiconductor Detectors and Materials ; Nanowire Synthesis and Applications
Authors: Joel M. Bowman | Chen Qu | Riccardo Conte | Apurba Nandi | Paul L. Houston | Qi Yu
Source: The Journal of Chemical Physics (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

A perspective marking 20 years of using permutationally invariant polynomials for molecular potentials This Perspective is focused on permutationally invariant polynomials (PIPs). Since their introduction in 2004 and first use in developing a fully permutationally invariant potential for the highly fluxional cation CH5+, PIPs have found widespread use in developing machine learned potentials (MLPs) for isolated molecules, chemical reactions, clusters, condensed phase, and materials. More than 100 potentials have been reported using PIPs. The popularity of PIPs for MLPs stems from their fundamental property of being invariant with respect to permutations of like atoms; this is a fundamental property of potential energy surfaces. This is achieved using global descriptors and, thus, without using an atom-centered approach (which is manifestly fully permutationally invariant). PIPs have been used directly for linear regression fitting of electronic energies and gradients for complex energy landscapes to chemical reactions with numerous product channels. PIPs have also been used as inputs to neural network and Gaussian process regression methods and in many-body (atom-centered, water monomer, etc.) applications, notably for gold standard potentials for water. Here, we focus on the progress and usage of PIPs since 2018, when the last review of PIPs was done by our group. Invariant (physics) ; Computer science ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Yan Yu | Wenjing Lu | Xiaobin Yao | Yan Jiang | J. Li | Meng Yang | Xingjiu Huang | Xianghu Tang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-01-26)
Field: Chemistry — Analytical Chemistry

Machine learning-integrated surface-enhanced Raman spectroscopy analysis of multicomponent dye mixtures Chemistry ; Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Spectroscopy ; Surface (topology) ; Nanotechnology ; Chemical engineering ; Analytical Chemistry (journal) ; Raman scattering ; Chromatography ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Chenyang Wei | Yutong Shi | Wenbo Mu | Hongyuan Zhang | Rui Qin | Yijun Yin | Gangqiang Yu | Tiancheng Mu
Source: ACS Sustainable Chemistry & Engineering (article, 2025-04-04)
Field: Materials Science — Materials Chemistry

Large Language Models Assisted Materials Development: Case of Predictive Analytics for Oxygen Evolution Reaction Catalysts of (Oxy)hydroxides This study explores the transformative role of artificial intelligence (AI) and machine learning (ML) in materials science, leveraging large language models (LLMs) such as OpenAI’s ChatGPT. Focusing on (oxy)hydroxides as oxygen evolution reaction (OER) catalysts, we demonstrate how LLMs streamline data extraction, significantly reducing reliance on traditional, time-intensive methods. Using few-shot training and strategic prompting, ChatGPT achieved an extraction accuracy of approximately 0.9. The curated data set was then used to predict OER performance via the PyCaret library to evaluate various ML algorithms and a high-accuracy XGBoost regression model with accuracies above 0.9 is subsequently established. Further analysis using SHAP and Python Symbolic Regression (PySR) identified key descriptors-electrochemical double-layer capacitance, transition metal composition, support material, and d-electron count-as critical factors, consistent with established electrochemical principles. Additionally, SHAP’s extreme values for Cu and Zn suggest unconventional catalytic roles, potentially linked to Cu2O-facilitated NiOOH formation and Zn-induced electronic modulation, demonstrating the power of data-driven analysis in uncovering hidden mechanisms. To enhance literature-based insights, Microsoft’s GraphRAG technology was employed for in-depth chemical information retrieval. Overall, this study introduces an innovative, end-to-end ML framework powered by ChatGPT, promoting broader AI adoption in scientific research and bridging computational intelligence with experimental sciences. Catalysis ; Analytics ; Oxygen evolution ; Chemistry ; Computer science ; Chemical engineering ; Materials science ; Organic chemistry ; Data science ; Engineering ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Xiangyu Meng | Yujiao Xie | Li Sun | Xiaotian Wang | Aiguo Wu | Jie Lin
Source: Rare Metals (article, 2025-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic field‐charge transfer synergy boosted SERS in noble metal–semiconductor nanohybrids for environmental and bio‐detection Abstract Noble metal–semiconductor nanohybrids synergistically integrate the merits of electromagnetic field and chemical enhancement mechanisms, endowing exceptional surface‐enhanced Raman scattering (SERS) performance through the electromagnetic field‐charge transfer synergy. This review systematically outlines recent advancements in the design and application of noble metal–semiconductor nanohybrid systems, emphasizing the distinct roles of diverse semiconductors in enhancing SERS activity and applications. Specifically, metal oxides contribute abundant oxygen vacancies for charge transfer, metal sulfides exhibit narrow band gaps to broaden light‐harvesting capacity, and metal–organic frameworks (MOFs) offer ultrahigh surface areas to enhance analyte adsorption. Through tailored interface engineering, semiconductor substrates enable band alignment to facilitate photo‐induced charge transfer (PICT), providing structural support to improve stability and supply uniform anchoring sites for the homogeneous dispersion of noble metal nanoparticles. Such advanced nanocomposites exhibit transformative potential in interdisciplinary fields, spanning fundamental molecular‐level interfacial studies to practical applications in environmental pollutant monitoring and ultrasensitive biosensing platforms. Especially in the field of bio‐detection, SERS provides a revolutionary tool for cell detection and real‐time diagnosis due to its ultrahigh sensitivity, molecular fingerprint characteristics, and multichannel detection advantages. With the development of nanotechnology and intelligence, SERS is expected to become an important technology for early detection and treatment monitoring of diseases. Furthermore, this review critically puts forward the future challenges in SERS technology, particularly selectivity and long‐term stability in complex environments, and proposes that integrating SERS with various techniques such as optical imaging, magnetic resonance imaging, and machine learning is the key to further promoting its development in bio‐detection. Materials science ; Noble metal ; Semiconductor ; Nanotechnology ; Field (mathematics) ; Charge (physics) ; Metal ; Optoelectronics ; Metallurgy ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Fabian L. Thiemann | Camille Scalliet | Erich A. Müller | Angelos Michaelides
Source: Proceedings of the National Academy of Sciences (article, 2025-02-28)
Field: Materials Science — Materials Chemistry

Defects induce phase transition from dynamic to static rippling in graphene Two-dimensional (2D) materials display nanoscale dynamic ripples that significantly impact their properties. Defects within the crystal lattice are the elementary building blocks to tailor the material's morphology. While some studies have explored the link between defective structures and rippling dynamics in 2D materials, a comprehensive understanding of this relationship has yet to be achieved. Here, we address this using machine learning-driven molecular dynamics simulations. Specifically, we find that above a critical concentration of defects, free-standing graphene sheets undergo a dynamic transition from freely propagating to static ripples. Our computational approach captures the dynamics with atomic resolution, and reveals that the transition is driven by elastic interactions between defects. The strength of these interactions is found to vary across defect types and we identify a unifying set of principles driving the dynamic-to-static transition in 2D materials. Our work not only rationalizes puzzling experimental results for defective 2D materials, but also paves the way to design two-dimensional devices with tailored rippling dynamics. These insights could lay the foundations for a class of disorder-based catalytic and interfacial materials. Rippling ; Graphene ; Materials science ; Molecular dynamics ; Phase transition ; Nanotechnology ; Nanoscopic scale ; Chemical physics ; Computer science ; Physics ; Condensed matter physics ; Chemistry ; Computational chemistry Graphene research and applications ; Nanopore and Nanochannel Transport Studies ; Thermal properties of materials
Authors: Yanzhou Ji | Xiaoqian Fu | Mahdieh Safyari | Chenyang Yao | Fei Shuang | Xuequn Cheng | Xuesong Yin | Xiaogang Li | Masoud Moshtaghi | Chaofang Dong | Poulumi Dey
Source: Nature Communications (article, 2025-12-02)
Field: Materials Science — Metals and Alloys

Tailoring precipitates for enhanced hydrogen trapping in aluminum alloys Enhancing the hydrogen embrittlement (HE) resistance of alloys caters to the urgent needs of engineering safety and long-distance hydrogen transportation. Highly dense precipitates in the alloys act as H traps, however, some of them cannot strongly trap H thus failing to prevent its accumulation at the critical regions. Experimentally, it is challenging to expeditiously identify and generate phases causing strengthening and acting as strong H traps. Here, we demonstrate a computation-based design strategy to generate precipitates strongly trapping H. Based on the quantum machine learning Al-Sc-Cu potential, the optimal processing parameters of strong H trapping phases are determined, even though they are metastable in nature. Elemental mapping in electron microscope and atom probe tomography confirms the presence of Cu in Al<sub>3</sub>Sc and its strong interaction with H. Hence, we envisage the proposed strategy will accelerate the design of HE-resistant microstructures of various technologically relevant materials via identification of desirable phases. Hydrogen embrittlement ; Microstructure ; Trapping ; Hydrogen ; Metastability ; Aluminium ; Atom probe ; Embrittlement Hydrogen embrittlement and corrosion behaviors in metals ; Hydrogen Storage and Materials ; Aluminum Alloy Microstructure Properties
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 Cohen | Christina Ertural | Max C. Gallant | Janine George | Sophie Gerits | Rhys E. A. Goodall | Rishabh D. Guha | Geoffroy Hautier | Matthew K. Horton | 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 | Kristin A. Persson | Guido Petretto | Thomas A. R. Purcell | Francesco Ricci | Benjamin 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: ChemRxiv (preprint, 2025-01-22)
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. Modular design ; Workflow ; Computer science ; Software engineering ; Programming language ; Database Machine Learning in Materials Science
Authors: Lijia Xu | Yuhang Ran | Ming-Jia Li | Peng Huang | Amjad Farid | Hui Huang | Yongpeng Zhao
Source: Chemical Engineering Journal (article, 2025-04-17)
Field: Chemistry — Electrochemistry

Constructing cobalt-based heterogeneous composites in porous charge transfer network enabled by the spiral structure for efficient detection of Cd2+ and Pb2+ Porosity ; Cobalt ; Spiral (railway) ; Charge (physics) ; Composite material ; Materials science ; Transfer (computing) ; Chemical engineering ; Computer science ; Mechanical engineering ; Physics ; Metallurgy ; Engineering Electrochemical Analysis and Applications ; Conducting polymers and applications ; Gas Sensing Nanomaterials and Sensors
Authors: M. M. Abdelhamied | Soumya Rajpal | Prashant Mishra | Christine Kranz | Boris Mizaikoff
Source: TrAC Trends in Analytical Chemistry (article, 2025-10-07)
Field: Chemistry — Analytical Chemistry

Molecularly imprinted polymers for biomarker detection: Advances in design, modeling, and sensing strategies Molecularly imprinted polymers (MIPs) are highly attractive for biomarker detection by offering highly stable, selective, and tailorable synthetic receptors that overcome key limitations of traditional biorecognition elements. As the demand for rapid, sensitive, and multiplexed diagnostic tools intensifies, MIPs have gained considerable attention for their ability to recognize structurally diverse and low-abundance biomarkers. We critically evaluate next-generation imprinting techniques, such as solid-phase synthesis for the development of high-affinity nanoMIPs, electrospinning for producing high-surface-area fibrous MIPs, and 3D-imprinting for creating structured MIPs with improved kinetic properties. Special attention is given to innovative strategies that significantly enhance MIP functionality: multi-template imprinting for the simultaneous detection of multiple analytes, snapshot imprinting for capturing complex epitopes, hybrid systems that integrate MIPs with aptamers or antibodies for synergistic effects, and boronate-affinity imprinting methods for selective glycoprotein recognition. Additionally, we discuss how these cutting-edge design strategies are being further empowered by computational modeling and seamlessly integrated into novel transducer platforms, bridging the gap between materials innovation and functional device performance. We also address the remaining challenges that must be overcome to enable widespread clinical translation and real-world deployment of MIP-based biosensors. • Recent advancements in MIP design strategies. • Multiscale modelling and machine learning in MIP development. • Developments in MIP-based sensing platforms. • MIP-based biomolecule detection. Molecularly imprinted polymer ; Molecular imprinting ; Aptamer ; Imprinting (psychology) ; Microfluidics Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications