jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thomas D. Swinburne | Clovis Lapointe | Mihai‐Cosmin Marinica
Source: Nature Communications (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Score matching the descriptor density of states for model-agnostic free energy estimation Vibrational free energy estimation is a cornerstone of atomic simulation, essential to predict finite-temperature material properties. Expressing the free energy as a function of interatomic potential parameters is actively sought in modern workflows for uncertainty quantification or inverse design. However, to achieve meV/atom accuracy, existing schemes conduct slow, sequential sampling with fixed potential parameters. We present a solution, an efficient model-agnostic free energy estimator which is meV/atom accurate over a broad, multi-element parameter range. For a broad class of machine learning potentials we show the free energy is the Legendre transform of an entropy function, accurately estimated via score-matching. Sampling requires 10× less effort than a single traditional estimate, tensor compression ensures lightweight storage, and inference is instantaneous. We demonstrate targeting of phase boundaries in back-propagation, fine-tuning the α - γ transition temperature in a Fe model from 2030 K to 1063 K. Extensions to a range of high-dimensional integration tasks are discussed. Estimator ; Entropy (arrow of time) ; Energy (signal processing) ; Range (aeronautics) ; Tensor (intrinsic definition) ; Inference ; Thermodynamic integration ; Function (biology) ; Inverse ; Sampling (signal processing) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Jacob
W. Toney (22854413) | Roland G. St. Michel (22854416) | Aaron G. Garrison (22854419) | Ilia Kevlishvili (4633084) | Heather J. Kulik (1373331)
Source: Figshare (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Identifying DynamicMetal–Ligand CoordinationModes with Ensemble Learning Knowledge of how a ligand coordinates a metal is essential for mechanistic and data-driven studies of transition metal complexes (TMCs), but most analyses assume a single binding interaction for a given metal–ligand pair. In catalysis, many ligands engage in dynamic, hemilabile coordination modes, challenging this assumption. In this work, we curate data sets of hemilabile and nonhemilabile ligands from experimentally characterized structures in the Cambridge Structural Database, analyze trends in observed coordination modes, and introduce four exhaustive and mutually exclusive types of hemilability. Using these labeled data sets, we train graph neural networks to carry out classification of hemilabile ligands with high accuracy, precision, and recall and develop an ensemble algorithm that predicts primary and alternative chemically plausible coordination modes from SMILES strings in an end-to-end fashion. We demonstrate the utility of our algorithm by generating novel TMCs in predicted coordination modes and calculating the corresponding energy difference due to changes in coordination (i.e., Δ<i>E</i><sub>c</sub>) with density functional theory. Comparing our novel TMCs in multiple poses against an energetic criterion from experimentally observed TMCs confirms the plausibility of our alternative poses. We anticipate that our open-source workflows will accelerate organometallic discovery in experimental and virtual screening campaigns by proposing realistic metal–ligand coordination. Workflow ; Graph ; Ensemble learning ; Artificial neural network ; Precision and recall ; Energy (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organometallic Complex Synthesis and Catalysis
Authors: Yuming Gao | Chenjiang Guo | Juncheng Hong | Haoyu Yan | Jinbiao Zhang | Zhe Wang | Dawei Tang | Bo Jiang
Source: Advanced Science (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Universal and Interpretable Descriptor to Design Dual‐atom Catalysts for Multi‐Type C–C Coupling with Ultrahigh C <sub>2+</sub> Yield Dual-atom catalysts (DACs) have demonstrated superior performance in the C─C coupling processes to generate multi-carbon (C<sub>2+</sub>) products due to the multiple metal sites of DACs. However, the rational design of DACs that exhibit high activity and specific selectivity for the C─C coupling process in the CO<sub>2</sub> reaction remains a significant challenge due to the intricate inter-metal interaction. Herein, a universal and interpretable descriptor is proposed for the emerging metal-nitrogen-carbon (M<sub>1</sub>-M<sub>2</sub>-N<sub>4</sub>-C) dual-atom catalysts in the CH<sup>*</sup>─CH<sup>*</sup> coupling process, effectively evaluating the activity and selectivity for various C─C coupling (i.e., CH<sup>*</sup>─CH<sup>*</sup>, CO<sup>*</sup>─CO<sup>*</sup>, CHO<sup>*</sup>─CO<sup>*</sup>, CH<sup>*</sup>─CH<sub>2</sub> <sup>*</sup>). This defined descriptor, which consists of inherent atomic properties (electronegativity, d electron number), proves to facilitate high-throughput screening of more than 400 graphene-based DACs. Through this screening process, potential catalysts such as FeZr@NC are predicted to have superior activity and selectivity for C─C coupling with a C<sub>2+</sub> yield of 22.6%, outperforming the state-of-the-art catalysts by a factor of 3 to 30. Importantly, this descriptor can also be extended to evaluate the activity and selectivity of DACs for other C─C coupling processes (i.e., CO<sup>*</sup>─CO<sup>*</sup>, CHO<sup>*</sup>─CO<sup>*</sup>, and CH<sup>*</sup>─CH<sub>2</sub> <sup>*</sup>), with very small discrepancies between the prediction and computational results. By systematically integrating machine learning (ML) with physical insights, this work offers practical guidelines for the construction of universal descriptors for DACs based on inherent atomic properties and the logical design of advanced catalysts in C─C coupling. Selectivity ; Coupling (piping) ; Catalysis ; Yield (engineering) ; Process (computing) ; Work (physics) ; Rational design Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Ammonia Synthesis and Nitrogen Reduction
Authors: Paul Katzberger (12118440) | Lea M. Hauswirth (21070507) | Antonia S. Kuhn (21070510) | Gregory A. Landrum (1450801) | Sereina Riniker (1450804)
Source: Figshare (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

Rapid Access to SmallMolecule Conformational Ensemblesin Organic Solvents Enabled by Graph Neural Network-Based ImplicitSolvent Model Understanding and manipulating the conformational behavior of a molecule in different solvent environments is of great interest in the fields of drug discovery and organic synthesis. Molecular dynamics (MD) simulations with solvent molecules explicitly present are the gold standard to compute such conformational ensembles (within the accuracy of the underlying force field), complementing experimental findings and supporting their interpretation. However, conventional methods often face challenges related to computational cost (explicit solvent) or accuracy (implicit solvent). Here, we showcase how our graph neural network (GNN)-based implicit solvent (GNNIS) approach can be used to rapidly compute small molecule conformational ensembles in 39 common organic solvents reproducing explicit-solvent simulations with high accuracy. We validate this approach using nuclear magnetic resonance (NMR) measurements, thus identifying the conformers contributing most to the experimental observable. The method allows the time required to accurately predict conformational ensembles to be reduced from days to minutes while achieving results within one <i>k</i><sub>B</sub><i>T</i> of the experimental values. Conformational isomerism ; Small molecule ; Molecule ; Molecular dynamics ; Artificial neural network ; Solvent ; Conformational ensembles ; Graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: C. Straub | Simon Mundinar | Jessica Klonnek | Christophe Pixius | Andreas Roßkopf
Source:  (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Modeling Nickel-Silicidation Using Physics-Informed Machine Learning We simulate nickel silicidation in one and two space dimensions via physics-informed machine learning. Our machine learning models are solely trained on the governing physical laws in the form of a reaction-diffusion system, without requiring measurement or simulation data. In 1D, our model yields accurate predictions across a parametric temperature range. The 2D process is well approximated away from irregular domain features. Compared to classical state of the art simulations, our models achieve speedups of three orders of magnitude. We further discuss potential extensions to the approach, including the incorporation of measurement data for calibration purposes and enabling broader applicability to process optimization tasks. Calibration ; Process (computing) ; Domain (mathematical analysis) ; Parametric statistics ; State (computer science) ; Parametric model ; Computational learning theory ; Measure (data warehouse) ; Key (lock) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Silicon and Solar Cell Technologies
Authors: Yuki Kusano | Hiroyuki Yoshida
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Automated analysis of low-energy inverse photoelectron spectra using machine learning Gradient boosting ; Smoothing ; Random forest ; Spectral line ; Boosting (machine learning) ; Semiconductor ; Inverse ; Data point Electron and X-Ray Spectroscopy Techniques ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: Shicheng Dong (3376460) | Wenhao Wang (515852) | Jun Zhu (84054)
Source: Figshare (article, 2025-09-03)
Field: Chemistry — Organic Chemistry

Design ofBorole- and Silylene-Based Frustrated LewisPairs for Dinitrogen Activation Promoted by Aromaticity: A CombinedDFT and Machine Learning Study In recent years, while main-group elements from the <i>s</i>- and <i>p</i>-block have emerged in the field of N<sub>2</sub> activation, silylenesdespite their remarkable successes in the activation of diverse small moleculesremain unreported for N<sub>2</sub> activation. Herein, we design “silylene-borole” frustrated Lewis pairs (FLPs) by combining silylene moieties with boron components and conduct comprehensive density functional theory (DFT) calculations to thoroughly investigate their potential for N<sub>2</sub> activation. Screening silylene moieties via machine learning suggests that a smaller Δ<i>E</i><sub>ST</sub> value will benefit N<sub>2</sub> activation thermodynamically. Interestingly, all these designed novel FLPs were found to be kinetically and thermodynamically favorable for N<sub>2</sub> activation, exhibiting low reaction barriers (ranging from 16.9 to 22.5 kcal/mol) and significant exergonicity, with Δ<i>G</i> values ranging from −32.3 to −36.7 kcal/mol. The notable thermodynamic stability of the products, enhanced by aromaticity, coupled with low activation barriers, represents a pivotal advancement in the development of FLP-chemistry for metal-free N<sub>2</sub> activation. Silylene ; Frustrated Lewis pair ; Density functional theory ; Lewis acids and bases ; Stability (learning theory) ; Boron Organoboron and organosilicon chemistry ; Synthesis and characterization of novel inorganic/organometallic compounds ; Boron and Carbon Nanomaterials Research
Authors: Salome Gruchola | Peter Keresztes Schmidt | Andreas Riedo | Marek Tulej | P. Wurz
Source:  (preprint, 2025-03-15)
Field: Chemistry — Spectroscopy

Composition Analysis of an Allende Chondrule using a Space-Prototype Laser Ablation Ionization Mass Spectrometer The Allende meteorite, which fell in northern Mexico in 1969, is one of the most significant meteorites ever studied. As a carbonaceous chondrite, it represents some of the oldest and most primitive material in the solar system, dating back over 4.5 billion years. It provides insights into the early solar nebula and the processes of planetary formation. Through chemical composition analysis of the meteorite&amp;#8217;s refractory inclusions, a deeper understanding of the building blocks of planets and the chemical evolution of our solar system can be gained [1].In this contribution, we present the chemical composition analysis of a chondrule from the Allende meteorite. A space-prototype Laser Ablation Ionization Mass Spectrometer (LIMS) [2] was used to map a selected chondrule, and the element abundance of more than 19 elements was retrieved and quantitatively studied. The chondrule itself was identified as a porphyritic olivine, depleted in volatiles compared to the surrounding matrix. SEM-EDX and Raman spectroscopy were used for cross-validation.Unsupervised machine learning (ML) was used to dimensionality reduce and cluster the pre-processed LIMS data to find distinct groups of different chemical compositions. This allowed the separation of the compositionally different materials present in the studied sample and allowed for their comparison [3]. The retrieved element maps suggest the presence of two rims around the chondrule, and their possible formation times and processes will be discussed in this contribution. Furthermore, another approach to reduce the dimensionality of the acquired LIMS data based on image segmentation will be presented, together with a discussion of the benefits and feasibility of applying unsupervised ML on board a spacecraft.&amp;#160;[1] Neuland, M. B. et al., 2021, doi:10.1016/j.pss.2021.105251.[2] Riedo, A. et al., 2012, doi:10.1002/jms.3104.[3] Gruchola, S. et al., 2024, doi:10.3847/PSJ/ad90b6. Allende meteorite ; Laser ablation ; Mass spectrometry ; Laser ; Chondrule ; Ionization ; Spectrometer ; Ablation ; Materials science ; Physics ; Chemistry ; Optics ; Analytical Chemistry (journal) ; Astrobiology ; Meteorite ; Aerospace engineering ; Chromatography ; Ion ; Engineering ; Chondrite ; Organic chemistry Analytical Chemistry and Chromatography
Authors: Olivier Taboureau | Guillaume Ollitrault | Marco Marzo | Alessandra Roncaglioni | Emilio Benfenati | Enrico Mombelli
Source: Preprints.org (preprint, 2025-06-26)
Field: Chemistry — Spectroscopy

QSAR Models for Oral Bioavailability and Volume of Distribution and Their Application to the Mapping of the TK Space of Endocrine Disruptors Toxicokinetics (TK) properties are essential in the framework of chemical risk assessment and drug discovery. Indeed, a TK profile provides information about the fate of chemicals in the human body. In this context, Quantitative Structure-Activity Relationship (QSAR) models are convenient computational tools to predict TK properties. Here, we developed QSAR models for the prediction of two TK properties: oral bioavailability and volume of distribution at steady state (VDss). We collected and curated two large sets of 1712 and 1591 chemicals for respectively oral bioavailability and VDss and compared regression and classification (binary and multiclass) models with the application of several machine learning algorithms. The best predictive performance of the models for regression (R) prediction is characterized by a Q²F3 equal to 0.34 with the R-CatBoost model, for oral bioavailability, and a Geometric Mean Fold Error (GMFE) equal to 2.35 for VDss with the R-RF model. The models were then applied on a list of potential endocrine disrupting chemicals (EDC), highlighting chemicals with a high probability of posing a concern on human health due to their TK profile. Based on the results obtained, insights about structural determinants of TK properties for EDCs are also further discussed. Bioavailability ; Endocrine system ; Quantitative structure–activity relationship ; Distribution (mathematics) ; Volume of distribution ; Space (punctuation) ; Chemistry ; Medicine ; Computer science ; Mathematics ; Internal medicine ; Pharmacology ; Stereochemistry ; Pharmacokinetics ; Mathematical analysis Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Drug Solubulity and Delivery Systems
Authors: Minseok Moon (22350720) | Seungwoo Hwang (214101) | Jaesun Kim (18697405) | Yutack Park (18697402) | Changho Hong (9448556) | Seungwu Han (1802209)
Source: Figshare (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Unveiling DefectMotifs in Amorphous GeSe Using MachineLearning Interatomic Potentials Ovonic threshold switching (OTS) selectors are pivotal in nonvolatile memory devices due to their nonlinear electrical characteristics and polarity-dependent threshold voltages. However, the atomic-scale origins of the defect states responsible for these behaviors remain unclear. In this study, we systematically investigate defects in amorphous GeSe using molecular dynamics simulations accelerated by machine learning interatomic potentials (MLIPs). We first benchmark several MLIP architectures, including descriptor-based potentials and graph neural network (GNN)-based potentials. Our results demonstrate that capturing higher-order interactions, at least four-body correlations, and medium-range structural order is essential for accurately representing amorphous GeSe structures. Our analysis indicates that GNN architectures with multiple interaction layers effectively capture higher-order correlations and medium-range order, thereby preventing spurious defects easily introduced by less descriptive MLIPs. Utilizing the optimized GNN model, we identify two distinct defect motifs across 20 independent 960-atom amorphous GeSe structures: aligned Ge chains associated with defect states near the conduction band, and overcoordinated Ge chains linked to defect states near the valence band. Moreover, we establish correlations between electronic defect levels and specific structural featuresnamely, the average alignment of bond angles in aligned chains and the degree of local Peierls distortion around overcoordinated Ge atoms. These insights provide a theoretical framework for interpreting experimental observations and deepening the understanding of defect-driven OTS phenomena in amorphous GeSe. Amorphous solid ; Spurious relationship ; Valence (chemistry) ; Molecular dynamics ; Artificial neural network ; Distortion (music) ; Interatomic potential Phase-change materials and chalcogenides ; Thin-Film Transistor Technologies ; Advanced Memory and Neural Computing
Authors: Hamza Guelbi | Sami Chatti | Mohamed Ali Terres
Source: MATEC Web of Conferences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Optimization of V-Bending for Grade 4A Titanium: A Combined Experimental and Artificial Intelligence Approach Cold bending of Grade 4A titanium bone plates is a crucial process requiring optimization to prevent crack formation. This study integrates experimental analysis, numerical simulations, and artificial intelligence modeling. Tensile tests were performed to determine the mechanical properties of titanium, validating a finite element model for V-bending simulation. A design of experiments was conducted to assess the influence of bending parameters on key responses: equivalent plastic strain (PEEQ) and springback. Results highlight the predominant role of material thickness and die shoulder distance. To enhance prediction accuracy and process optimization, neural networks using the Bayesian regularization algorithm were applied. This approach contributes to improving the design and manufacturing of orthopedic implants. Finite element method ; Artificial neural network ; Bending ; Process (computing) ; Regularization (linguistics) ; Ultimate tensile strength ; Bayesian probability ; Design of experiments Titanium Alloys Microstructure and Properties ; Manufacturing Process and Optimization ; Material Selection and Properties
Authors: Y. Yotsumoto | Kanta Ono
Source: Vacuum and Surface Science (article, 2025-02-09)
Field: Materials Science — Materials Chemistry

Autonomous Robotic Experiments for Powder X-ray Diffraction In recent years, the automation of materials research has become a crucial challenge. Powder X-ray diffraction (PXRD) is essential for characterizing material properties, but current methods face issues with reproducibility and efficiency. To address this, we have developed an autonomous experimental system using robotics and machine learning. This article introduces the configuration and workflow of the developed system. Additionally, we discuss the performance evaluation of this system and compare it with conventional manual experiments. Powder diffraction ; X-ray ; Diffraction ; Materials science ; Computer science ; Computer graphics (images) ; Computer vision ; Optics ; Crystallography ; Physics ; Chemistry X-ray Diffraction in Crystallography
Authors: Wei He | Xiaodong Li | Xinyu Tian | Peifu Han | Kexin Jin | Shuang Wang | Tao Song
Source:  (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

TriPercept: A Unified Fine-Grained Structural Perception Model for Molecular Property Prediction Accurate prediction of molecular properties is a fundamental component of Artificial Intelligence-driven Drug Design (AIDD). In molecular property prediction, even slight structural variations, such as atomic arrangement or bond length, could impact molecular properties. Existing methods primarily focus on coarse-grained molecular modeling on SMILES, 2D, or 3D graphs. However, this paradigm struggles to adequately capture complex internal molecular interactions and overlooks finer-grained structural details, such as atom-bond-spatial positions. In this paper, we propose a molecular property prediction model, TriPercept, based on unified fine-grained representation learning. Specifically, TriPercept integrates atomic, topological, and geometric features, fully exploiting their intrinsic complementarity. We introduce three dedicated encoders to separately learn features at the atomic, bond, and spatial distance levels, thus addressing the neglect of complex structural details. Additionally, we design a structure-aware graph neural network to integrate these features and enhance structural consistency modeling with a contrastive and self-supervised based pretraining strategy. To evaluate the performance of TriPercept, we conduct systematic experiments on six classification datasets and six regression datasets. The results demonstrate that TriPercept performs excellently on most datasets. Visualization experiments further verify that TriPercept not only confirms the effectiveness of multi-level structural integration but also clearly reveals its interpretability in focusing on chemically relevant features and capturing complex structural relationships. The data and code are available at https://github.com/TiAW-Go/TriPercept. Interpretability ; Property (philosophy) ; Consistency (knowledge bases) ; Component (thermodynamics) ; Representation (politics) ; Molecular graph ; Encoding (memory) ; Encoder ; Visualization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks