jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Elias Polak | Heng Zhao | Stefan Vuckovic
Source: Nature Communications (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Real-space machine learning of correlation density functionals Machine learning (ML) plays a pivotal role in extending the reach of quantum chemistry methods for simulating both molecules and materials. However, leveraging ML to overcome the limitations of human-designed density functional approximations (DFAs), the primary workhorse for quantum simulations, remains a major challenge due to their severely limited transferability to unseen chemical systems. Here, we demonstrate how transferability is achieved using real-space ML, where energies are learned point by point in space through energy densities. Central to our real-space learning strategy is the derivation and implementation of correlation energy densities from regularized perturbation theory. This enables two key advances toward constructing highly transferable DFAs, grounded in the Møller-Plesset adiabatic connection framework, for correlation energies defined with respect to the Hartree-Fock reference. First, we introduce the Local Energy Loss, whose data efficiency (expanding each system's single energy into thousands of data points) dramatically enhances transferability when combined with a physically informed ML model. Second, we formulate a real-space, machine-learned, and regularized extension of Spin-Component-Scaled second-order Møller-Plesset perturbation theory, yielding transferable DFAs that effectively mitigate the self-interaction errors common to traditional DFAs. Transferability ; Adiabatic process ; Correlation ; Perturbation (astronomy) ; Quantum ; Perturbation theory (quantum mechanics) ; Energy (signal processing) ; Density functional theory Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Kwangnam Kim | Matthew P. Kroonblawd | Nir Goldman
Source: Journal of Chemical Theory and Computation (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Similarity Metric for Data Optimization and Efficient Training of Reactive Machine Learning Force Fields for Hydrocarbon Radiolysis Radiolysis is a common approach to sterilize polymers, chemically modify them for upcycling, and accelerate their decomposition for recycling purposes. Reactive molecular dynamics (MD) simulations provide a powerful tool to generate atomic-level trajectories of the reactive processes and quantify radiolytic chemical degradation pathways. For this, machine learning (ML) surrogate models for reactive force fields with quantum mechanical accuracy are now widely used, which require ML training data sets that can provide information on atomic environments for target chemical systems. However, radiolysis chemistry can be highly complex and diverse, which poses significant challenges for generating training data to parametrize ML models. In this regard, we developed a method for optimizing the training data set using a cosine similarity metric to help guide training set selection for radiolysis of polyethylene, a model hydrocarbon polymer, as well as to enhance the transferability of our reactive ML force field (MLFF) to a variety of molecular and polymeric systems. Our approach performs atom-by-atom comparisons between local atomic environments to pinpoint important data points associated with rare and localized events, such as radiolysis damage within structures. We apply this approach to train the Chebyshev Interaction Model for Efficient Simulation (ChIMES) MLFF model, which expresses the atomic interaction potentials in terms of linear combinations of many-body Chebyshev polynomials. We first show that our method can reduce our training set size by ∼70% while improving overall accuracy compared to more standard MD model fitting approaches. We then validate our optimum model against diverse hydrocarbon simulation data, including simple alkanes and systems with unsaturated carbon bonds, over a wide range of thermodynamic conditions. Finally, we use our ChIMES model to perform MD simulations of radiolytic damage with large-scale systems that help avoid system size effects. Overall, our approach yields an MD force field that retains most of the accuracy of the underlying quantum method while yielding many orders of improvement in computational efficiency. Our efforts will have impact on future hydrocarbon polymer radiolysis studies, where the chemical details of the polymer-radiation interactions can have a strong effect on the resulting products observed in experiments. Radiolysis ; Metric (unit) ; Set (abstract data type) ; Similarity (geometry) ; Chebyshev filter ; Force field (fiction) ; Field (mathematics) ; Decomposition Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Artificial Intelligence in Healthcare and Education
Authors: Netra Prova Baruah | Suprakash Rabha | Lipi B. Mahanta | Nimisha Sarma | Emee Das | Anasuya Bharadwaj | Bhaswati Devi | Manisha Goswami | Devasish Chowdhury | Arundhuti Devi
Source: Journal of Alloys and Compounds (article, 2025-02-20)
Field: Chemistry — Electrochemistry

Plasmon-activated transition metal sonophotocatalytic structural motifs for acenaphthene degradation interpreted using machine learning analysis Acenaphthene ; Transition metal ; Degradation (telecommunications) ; Plasmon ; Materials science ; Photocatalysis ; Nanotechnology ; Chemistry ; Chemical engineering ; Optoelectronics ; Photochemistry ; Computer science ; Engineering ; Telecommunications ; Catalysis ; Organic chemistry Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; TiO2 Photocatalysis and Solar Cells
Authors: Sherif Abdulkader Tawfik
Source: Journal of Chemical Information and Modeling (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Computational Material Science Has a Data Problem We present an overdue questioning of the computational material science data: Is it suitable for training machine learning models? By examining the energy above the convex hull (<i>E</i><sub><i>h</i></sub>), the electronic bandgap, and the formation energy data in the Materials Project dataset, we find that <i>E</i><sub><i>h</i></sub> is an unsteady quantity, as are DFT-computed voltages, because the present materials in the database do not constitute sufficient representation of the chemical spaces that are necessary to account for crystal decomposition. We also show that there are discrepancies in the reported electronic bandgap values in the Materials Project database and that the formation energy data can potentially shift due to changing the optimization parameters that reduce the energy of the structure below the value deposited in the database. Computer science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Zheng-Yang Hu | Zhiqiang He | Lin Chen | Cheng Shang | Zhi‐Pan Liu
Source: ACS Catalysis (article, 2025-04-15)
Field: Chemistry — Inorganic Chemistry

In Situ Selective Site-Blocking Dictates Benzene Partial Hydrogenation to Cyclohexene on Zn-Promoted Ru-Based Catalysts The partial hydrogenation of benzene to cyclohexene (CHE) represents a long-standing challenge in catalysis. Although Zn-promoted Ru-based catalysts were patented decades ago for achieving a top record of 50% CHE yield, the active sites and mechanistic origins of selectivity, specifically why the unsaturated C–C bonds in benzene are targeted over those in CHE, have remained unresolved despite four decades of research. Here, we employ large-scale machine-learning-driven global optimization to explore millions of Ru and Ru–Zn structure candidates and a complete benzene hydrogenation reaction network under realistic reaction conditions, that is, 50 atm H2 pressures, aqueous Zn2+ solution. We reveal that (i) Zn2+ ions selectively deposit on corrugated Ru surfaces, for example, Ru(101̅0) and (101̅1), where they inhibit deep hydrogenation by blocking too active sites; (ii) partial hydrogenation occurs exclusively on close-packed Ru(0001) terraces covered with one monolayer of chemisorbed H atoms. Microkinetics simulation further confirms that the presence of Zn enhances the CHE yield from 0% to 50% by poisoning corrugated surfaces in Ru nanoparticles. Building on these atomistic insights, we propose that introducing tensile strain 2% into the Ru lattice can elevate the CHE yield to 60% with 100% CHE selectivity, highlighting the potential for tailored catalyst supports to maximize performance. Catalysis ; Cyclohexene ; In situ ; Benzene ; Chemistry ; Ruthenium ; Photochemistry ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Catalysts for Methane Reforming ; Catalysis for Biomass Conversion
Authors: Chengchun Liu | Fanyang Mo
Source: Accounts of Chemical Research (article, 2025-12-22)
Field: Chemistry — Spectroscopy

Automation and AI-Powered Prediction in Chromatographic Separation ConspectusChromatography remains one of the most versatile separation technologies in chemistry, spanning thin-layer chromatography (TLC) for rapid analysis, column chromatography (CC) for purification, gas chromatography (GC) for volatile analytes, and high-performance liquid chromatography (HPLC) for precise and enantioselective separations. Despite its centrality, the development of chromatographic methods has long relied on empirical trial-and-error and tacit practitioner knowledge, making reproducibility and systematic optimization difficult. The convergence of laboratory automation and artificial intelligence (AI) is now reshaping this landscape. Automated platforms generate large-scale, standardized data sets, while machine-learning models capture quantitative relationships between molecular structures, chromatographic conditions, and retention outcomes. Crucially, by embedding mechanistic constraints─such as polarity-driven adsorption, programmed heating effects, or stereochemical recognition─models transcend black-box prediction and deliver interpretable insights into separation mechanisms. Our research illustrates how chromatography can be transformed into a predictive science through the integration of automation, machine learning, and cross-method transfer. Robotic TLC and CC systems provide reproducible polarity data that inform predictive models and even transferable equations linking TLC <i>R</i><sub><i>f</i></sub> values to CC retention volumes. Multimodal frameworks extend these principles to GC, combining molecular features with heating programs to predict retention under dynamic conditions. For HPLC enantioseparation, chirality-aware graph neural networks capture subtle stereochemical differences and, when coupled with uncertainty quantification, yield separation probabilities that mirror experimental decision-making. This Account focuses on the development of a unified framework for AI-assisted chromatography, highlighting advances in data acquisition, feature engineering, algorithmic design, and cross-scale modeling. Together, these developments chart a path toward universal chromatographic predictors─tools that are accurate, interpretable, and transferable across methods. By closing the loop with automated experimentation, they lay the foundation for predictive and programmable chromatography capable of accelerating discovery and enhancing reproducibility across the chemical sciences. Automation ; Artificial neural network ; Chromatographic separation ; Gas chromatography ; Column (typography) ; Separation (statistics) ; Graph Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Chromatography in Natural Products
Authors: Anjali R. Shelake | Vijay C. Karade | Manickam Selvaraj | Mohammed A. Assiri | Dipti D. More | Amruta D. Patil | Mukund G. Mali | Santosh S. Sutar | Rutuja U. Amate | Chan‐Wook Jeon | Tae Geun Kim | Tukaram D. Dongale
Source: Journal of Power Sources (article, 2025-11-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-guided optimization, predictive modeling, and experimental validation of MXene-based supercapacitors Supercapacitor ; Random forest ; Artificial neural network ; Curse of dimensionality ; Capacitive sensing ; Cyclic voltammetry ; Gradient boosting ; Boosting (machine learning) ; Key (lock) ; Linear regression Supercapacitor Materials and Fabrication ; MXene and MAX Phase Materials ; Advanced Memory and Neural Computing
Authors: Erkin Şeker | Shan Shi | Ian McCue
Source: MRS Bulletin (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

Realizing nanostructure-enabled applications through dealloyed materials Nanostructure ; Nanotechnology ; Materials science ; Biochemical engineering ; Engineering Nanoporous metals and alloys ; Supercapacitor Materials and Fabrication ; Polyoxometalates: Synthesis and Applications
Authors: Andrew R. Ickes | Jordan P. Liles | Niginia Borlinghaus | Jeremy Henle | Rafal Swiatowiec | Naveen Kumar Kaushik | Wilfried M. Braje | Kaid C. Harper | Shashank Shekhar | Matthew S. Sigman
Source: Journal of the American Chemical Society (article, 2025-07-30)
Field: Chemistry — Inorganic Chemistry

Leveraging Data Science to Elucidate Ligand Features for Pd-Catalyzed Enantioretentive <i>N</i>-Arylations of Cyclic α-Substituted Amines in Aqueous Media The combination of high-throughput experimentation (HTE) and data science offers a promising solution for the optimization of challenging chemical reactions, although current data science approaches to deal with highly skewed data sets resulting from typical HTE campaigns are limited. One such attractive yet challenging reaction is the preparation of chiral tertiary anilines via palladium-catalyzed <i>N</i>-arylation of enantioenriched, α-substituted secondary amines. While enantioenriched tertiary anilines are highly valuable motifs in medicinal chemistry, their synthesis remains challenging to accomplish in high yields and enantiospecificity. Herein, we disclose a method for the enantioretentive <i>N</i>-arylation of cyclic secondary amines. After an extensive HTE campaign, a novel phosphorinane ligand for palladium, 'NiniPhos,' was found to demonstrate high yields (up to 96%) and enantiospecificity (96 to >99% es) across a range of amines with diverse aryl halides under aqueous conditions. Despite screening more than 120 ligands, only 31 demonstrated catalytic activity (>10% yield), resulting in a highly skewed data set. By applying data science-driven hotspot analysis and machine learning (linear support vector machines), the structural features of ligands leading to the activity cliff were identified, pinpointing di<i>-tert</i>-butylphosphines and phosphorinanes as privileged ligand classes in this reaction. This workflow offers a generalizable strategy for extracting mechanistic insights from skewed data sets, which can be leveraged for reaction design and optimization. Chemistry ; Aqueous medium ; Catalysis ; Ligand (biochemistry) ; Aqueous solution ; Combinatorial chemistry ; Cyclic amines ; Organic chemistry ; Biochemistry ; Receptor Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis ; Machine Learning in Materials Science
Authors: Shaswat Mohanty | Yifan Wang | Wei Cai
Source: Advanced Theory and Simulations (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Generalizability of Graph Neural Network Force Fields for Predicting Solid‐State Properties Abstract Machine‐learned force fields (MLFFs) promise to offer a computationally efficient alternative to ab initio simulations for complex molecular systems. However, ensuring their generalizability beyond training data is crucial for their wide application in studying solid materials. This work investigates the ability of a graph neural network (GNN)‐based MLFF, trained on Lennard–Jones Argon, to describe solid‐state phenomena not explicitly included during training. The MLFF's performance is assessed in predicting phonon density of states (PDOS) for a perfect face‐centered cubic (FCC) crystal structure at both zero and finite temperatures. Additionally, vacancy migration rates and energy barriers are evaluated in an imperfect crystal using direct molecular dynamics (MD) simulations and the string method. Notably, vacancy configurations are absent from the training data. These results demonstrate the MLFF's capability to capture essential solid‐state properties with good agreement to reference data, even for unseen configurations. Data engineering strategies are further discussed to enhance the generalizability of MLFFs. The proposed set of benchmark tests and workflow for evaluating MLFF performance in describing perfect and imperfect crystals pave the way for reliable application of MLFFs in studying complex solid‐state materials. Generalizability theory ; Computer science ; Artificial neural network ; Imperfect ; Ab initio ; Molecular dynamics ; Statistical physics ; Graph ; Algorithm ; Materials science ; Artificial intelligence ; Theoretical computer science ; Physics ; Computational chemistry ; Chemistry ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Giulia Sormani | Álex Rodríguez | Ali Hassanali
Source: Journal of Chemical Theory and Computation (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Opportunities and Challenges in Unsupervised Learning: The Case of Aqueous Electrolyte Solutions Machine learning has emerged as a powerful tool in atomistic simulations, enabling the identification of complex patterns in molecular systems, limiting human intervention and bias. However, the practical implementation of these methods presents significant technical challenges, particularly in the selection of hyperparameters and in the physical interpretability of machine-learned descriptors. In this work, we systematically investigate these challenges by applying an unsupervised learning protocol to a fundamental problem in physical chemistry, namely, how ions perturb the local structure of water. Using the Smooth Overlap of Atomic Positions (SOAP) descriptors, we demonstrate how the intrinsic dimension (ID) serves as a guide for selecting hyperparameters and interpreting the structural complexity. Furthermore, we construct a high-dimensional free-energy landscape encompassing all water environments surrounding different ions. This analysis reveals how the physical properties of ions are intricately reflected in their hydration shells, shaping the landscape through specific connections between different minima. Our findings highlight the difficulty in balancing algorithmic automation with the need of employing both physical and chemical intuition, particularly for the construction of meaningful descriptors and for the interpretation of final results. By critically assessing the methodological hurdles associated with unsupervised learning, we provide a roadmap for researchers looking to harness these techniques for studying electrolyte and aqueous solutions in general. Interpretability ; Computer science ; Artificial intelligence ; Machine learning ; Energy landscape ; Data science ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; Spectroscopy and Quantum Chemical Studies
Authors: Amir Mohseni Armaki | Yaqi Guo | Majid Ahmadi | Roan Streefland | Patrick S. Bäuerlein | J.M.C. Mol | Siddhant Kumar | Peyman Taheri
Source: Nature Communications (article, 2025-12-03)
Field: Chemistry — Electrochemistry

Ion sensing based on frequency-dependent physico-chemical processes at electrode/electrolyte interfaces Ions play a fundamental role in solid-liquid interface processes, whether as essential or undesirable components, highlighting the need for precise and quantitative real-time monitoring. Electrochemical sensors are identified as promising tools, particularly for field-deployable applications. However, conventional electrochemical sensing is inherently restricted to redox-active species and is often single use, constraining its scope. This study presents electrochemical impedance spectroscopy as an alternative for ion detection, utilizing physico-chemical interactions at the electrode-electrolyte interface. We introduce a first-principles model that describes the interfacial impedance behavior and shows how ion specific processes shape the impedance response. Based on this framework, an extensive dataset is compiled, and a machine learning model is trained to predict electrolyte composition with consistent accuracy, demonstrating detection limits at the parts-per-billion level. The findings indicate that this method has considerable potential as a real-time method for ion sensing, providing a perspective on selectivity and sensitivity beyond traditional electrochemical approaches. This work could serve as a foundation for advanced models of impedance behavior, and development of impedance-based sensors with applicability in complex environments, including biological fluids and industrial liquids. Dielectric spectroscopy ; Electrical impedance ; Electrolyte ; Interface (matter) ; Ion ; Electrochemistry Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies ; Chemical and Physical Properties in Aqueous Solutions
Authors: Jakob Blahusch | Konstantin S. Jakob | Johannes T. Margraf | Karsten Reuter | Bettina V. Lotsch
Source: Chemistry of Materials (article, 2025-07-03)
Field: Materials Science — Materials Chemistry

Predict before You Precipitate: Learning Templating Effects in Hybrid Antimony and Bismuth Halides Hybrid organic–inorganic (HOI) antimony and bismuth halides exhibit diverse structural features and have been studied intensely for their promising electronic and optical properties. There are well-explored structure–property relations for these materials. However, a thorough understanding of the synthesis routes and templating effects is lacking, turning their targeted synthesis into an open challenge. In this study, we assemble a literature data set of established HOI material candidates and train an explainable machine learning classification model to explore the templating effects in more detail. With a classification accuracy upward of 70%, our model is effective in predicting HOI structure types based on the reactants and points out several structural and electrostatic design features for the organic cation that influence the inorganic substructure most strongly. We further demonstrate the validity of our classifier on 9 newly synthesized members of this materials class and propose incremental learning routes to expand the model in future research. Antimony ; Bismuth ; Halide ; Materials science ; Precipitation ; Inorganic chemistry ; Nanotechnology ; Chemical engineering ; Chemistry ; Metallurgy ; Physics ; Engineering ; Meteorology Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Electronic and Structural Properties of Oxides