jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pengxin Pu (14691508) | Xin Song (465237) | Hu Ding (196375) | Yuan Deng (177795) | Haisong Feng (8990867) | Xin Zhang (35492)
Source: Figshare (article, 2025-04-21)
Field: Materials Science — Materials Chemistry

Fast-Track to Catalyst Stability: Machine LearningOptimized Predictions for M1/M2‑N<sub>6</sub>‑Gra Catalysts Graphene-based dual-atom catalysts M1/M2-N<sub>6</sub>-Gra have shown significant potential in various reactions, although their stabilities are debated. Therefore, developing an efficient and accurate approach to screen thermodynamically stable M1/M2-N<sub>6</sub>-Gra is significant. Herein, we designed a rational machine learning (ML) scheme based on 143 DFT calculated samples to predict the formation energies (<i>E</i><sub><i>f</i></sub>) of 1134 possible M1/M2-N<sub>6</sub>-Gra. A well performing multilayer perceptron model with test set <i>R</i><sup>2</sup> <i>=</i> 0.98 was obtained after feature engineering, model training, data supplementation, and transfer learning. This model successfully screened 604 thermodynamic stable M1/M2-N<sub>6</sub>-Gra with <i>E</i><sub><i>f</i></sub> < 0 eV. Feature importance, predictions distribution, and energy decomposition revealed that the coordination number significantly influences <i>E</i><sub><i>f</i></sub>, with cohesive energy dominating low-coordination catalysts and binding energy between metal and substrate being more critical in higher-coordination catalysts. This work highlights the potential of ML and developed effective approaches to screen thermodynamically stable catalysts and reveals the laws of stability for various materials. Catalysis ; Stability (learning theory) ; Work (physics) ; Decomposition ; Feature (linguistics) ; Substrate (aquarium) ; Energy (signal processing) ; Multilayer perceptron ; Perceptron Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Electrocatalysts for Energy Conversion
Authors: Shen Wang (464529) | Yining Liu (764439) | Weiren Zhao (4444675) | Yang Li (7082)
Source: Figshare (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

Optimizing Model LearningPerformance on a ChallengingHeck Reaction Yield Data Set The development of machine learning (ML) in organic synthesis is limited due to the lack of available data sets. The ML-compatible literature data set for Heck reaction yields, named HeckLit, has been established. With 10,002 cases, the data set spans multiple reaction subclasses and covers a larger chemical space compared to high-throughput experimentation data sets, including nearly 3.6 × 10<sup>12</sup> accessible cases. HeckLit accelerates the advancement of ML-driven synthesis research; however, it suffers from the same dilemma as other literature-based data sets, sparse distribution and high-yield preference, leading to limited model learning ability on the test set, with <i>R</i><sup>2</sup> = 0.318. Thus, feature distribution smoothing (FDS) and subset splitting training strategy (SSTS) are utilized to tackle this issue. Despite no improvement when using FDS, SSTS boosted the <i>R</i><sup>2</sup> to 0.380. This optimization approach relies on the subset division. Therefore, we suggest a criterion for splitting. The SSTS opens up a new avenue for tackling the challenge of learning from large-scale data sets. Data set ; Set (abstract data type) ; Smoothing ; Feature (linguistics) ; Training set ; Synthetic data ; Statistical learning Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Juan García de la Concepción | Pedro Cintas | R. Fernando Martínez
Source: Molecules (article, 2025-06-27)
Field: Chemistry — Organic Chemistry

Understanding Bio-Orthogonal Strain-Driven Sydnone Cycloadditions: Data-Assisted Profiles and the Search for Linear Relationships In the realm of click-type reactions and their application to bioorthogonal chemistry in living organisms, metal-free [3+2] cycloadditions involving mesoionic rings and strained cycloalkynes have gained increasing attention and potentiality in recent years. While there has been a significant accretion of experimental data, biological assays, and assessments of reaction mechanisms, some pieces of the tale are still missing. For instance, which structural and/or stereoelectronic effects are actually interlocked and which remain unplugged. With the advent of data-driven methods, including machine learning simulations, quantitative estimations of relevant observables and their correlations will explore better the chemical space of these transformations. Here we unveil a series of linear relationships, such as Hammett-type correlations, as well as deviations of linearity, using the case study of phenylsydnone (and its 4-aryl-substituted derivatives) with a highly reactive bicyclo[6.1.0]nonyne carbinol. Through accurate estimation of activation barriers and prediction of rate constants, our findings further increase the significance of integrating strain release and electronic effects in organic reactivity. Moreover, such results could pave the way to use mesoionics cycloadditions as probes for measuring the extent of delocalization-assisted strain release, which can be applied to related reactions involving dipoles and strained rings. Chemistry ; Computational chemistry ; Chemical space ; Reactivity (psychology) ; Bioorthogonal chemistry ; Mesoionic ; Photobiology ; Click chemistry ; Biological system ; Combinatorial chemistry ; Drug discovery ; Medicinal chemistry ; Physics Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Molecular Junctions and Nanostructures
Authors: Asuna Hagiya | T. Sekiya | Tetsuo Asakura | Takashi Kataoka | Keiji Komatsu | Keisuke Sato
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Thallium Substitution Effects on Co Valence States and Room-Temperature Ferromagnetism in Co-Doped In2o3: Experimental and Machine-Learning Insights Valence (chemistry) ; Ferromagnetism ; Thallium ; Doping ; Condensed matter physics ; Substitution (logic) ; Materials science ; Substitution effect ; Analytical Chemistry (journal) ; Chemistry ; Metallurgy ; Physics ; Computer science ; Environmental chemistry ; Economics ZnO doping and properties ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Hyeonbin Moon | Songho Lee | Wabi Demeke | Byungki Ryu | Seunghwa Ryu
Source: ArXiv.org (preprint, 2025-06-09)
Field: Materials Science — Materials Chemistry

Physics-Informed Neural Operators for Generalizable and Label-Free Inference of Temperature-Dependent Thermoelectric Properties Accurate characterization of temperature-dependent thermoelectric properties (TEPs), such as thermal conductivity and the Seebeck coefficient, is essential for reliable modeling and efficient design of thermoelectric devices. However, their nonlinear temperature dependence and coupled transport behavior make both forward simulation and inverse identification difficult, particularly under sparse measurement conditions. In this study, we develop a physics-informed machine learning approach that employs physics-informed neural networks (PINN) for solving forward and inverse problems in thermoelectric systems, and neural operators (PINO) to enable generalization across diverse material systems. The PINN enables field reconstruction and material property inference by embedding governing transport equations into the loss function, while the PINO generalizes this inference capability across diverse materials without retraining. Trained on simulated data for 20 p-type materials and evaluated on 60 unseen materials, the PINO model demonstrates accurate and label-free inference of TEPs using only sparse field data. The proposed framework offers a scalable, generalizable, and data-efficient approach for thermoelectric property identification, paving the way for high-throughput screening and inverse design of advanced thermoelectric materials. Thermoelectric effect ; Inference ; Generalization ; Inverse problem ; Artificial neural network ; Property (philosophy) ; Field (mathematics) ; Nonlinear system Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Model Reduction and Neural Networks
Authors: Jerome Workman | Howard Mark
Source: Spectroscopy (article, 2025-09-22)
Field: Chemistry — Analytical Chemistry

From Classical Regression to AI and Beyond: The Chronicles of Calibration in Spectroscopy: Part II The analysis of chemical data has undergone a profound transformation, from early basic statistical methods into the modern era of machine learning (ML) and artificial intelligence (AI). This progression is particularly evident in the field of spectroscopy, where multivariate analysis techniques such as regression, principal component analysis (PCA), and partial least squares (PLS) laid the foundation for today’s more advanced or automated ML calibration modeling techniques. This Chemometrics in Spectroscopy column traces the historical and technical development of these methods, emphasizing their application in calibrating spectrophotometers for prediction of measured sample chemical or physical properties—particularly in near-infrared (NIR), infrared (IR), Raman, and atomic spectroscopy—and explores how AI and deep learning are reshaping the spectroscopic landscape. In the previous installment of this two-part series we have taken a look back into the history of chemometrics. In this second part of this series we will peer into the future for an estimation of where chemometrics might be going. Chemometrics ; Partial least squares regression ; Calibration ; Principal component analysis ; Field (mathematics) ; Multivariate statistics ; Series (stratigraphy) ; Column (typography) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Thorben Prein (17858201) | Elton Pan (14516136) | Janik Jehkul (22692710) | Steffen Weinmann (22692713) | Elsa Olivetti (461698) | Jennifer L. M. Rupp (8736402)
Source: Figshare (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Language Models EnableData-Augmented Synthesis Planningfor Inorganic Materials Inorganic synthesis planning currently relies primarily on heuristic approaches or machine learning models trained on limited data sets, which constrains its generality. We demonstrate that language models (LMs) without task-specific fine-tuning can recall synthesis conditions reported in the scientific literature. Off-the-shelf models, such as GPT-4.1, Gemini 2.0 Flash, and Llama 4 Maverick achieve a Top-1 precursor prediction accuracy of up to 53.8% and a Top-5 performance of 66.8% on a held-out set of 1000 reactions. They also predict calcination and sintering temperatures with mean absolute errors of <126 °C, matching or surpassing specialized regression models. Ensembling these LMs further enhances predictive accuracy and reduces inference cost per prediction by up to 70%. Given the broad, cross-domain knowledge of LMs, we evaluate whether they enable knowledge transfer by training a transformer, SyntMTE, on 28,548 LM-generated reaction recipes. Compared to a model trained on literature-reported data, we find that a model trained solely on LM-generated data exhibits competitive performance (only 6% worse). Conversely, a model trained on both the LM-generated and literature-reported data improves performance by up to 4%. In a case study on Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> solid-state electrolytes, we demonstrate that SyntMTE reproduces the experimentally observed dopant-dependent sintering trends. Our hybrid workflow enables scalable and data-efficient inorganic synthesis planning. Workflow ; Scalability ; Inference ; Heuristic ; Training set ; Matching (statistics) ; Set (abstract data type) ; Data modeling Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Battery Materials and Technologies
Authors: Xiaowei Xie | John L. Weber | Mats Svensson | Ryne C. Johnston | Edward Harder | Leif D. Jacobson
Source: ChemRxiv (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Accurate Hydration Free Energy Calculations for Diverse Organic Molecules With a Machine Learning Force Field Force field (fiction) ; Ab initio ; Solvation ; Workflow ; Free energy perturbation ; Ab initio quantum chemistry methods ; Field (mathematics) ; Potential energy ; Quantum Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Lizano-VIlalobos, Andres Francisco
Source: Civil War Book Review (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

An automatable machine learning-based optimal control framework for colloidal self-assembly Controlling colloidal self-assembly is essential for manufacturing materials with well-defined structural properties. However, developing effective control protocols is challenging, requiring domain expertise and significant computational resources---from identifying suitable state representations to designing control policies. In this dissertation, we introduce a fully data-driven framework for automating the optimal control of two-dimensional colloidal self-assembly, reducing reliance on domain knowledge. The framework integrates machine learning-based state representation with deep reinforcement learning (DQN), and is demonstrated on an experimentally validated Brownian Dynamics (BD) model of a quadrupole electric field-mediated assembly system. To construct effective state descriptors, we first apply an unsupervised learning pipeline that combines convolutional autoencoders, UMAP, and HDBSCAN to extract and cluster latent features from particle configuration images. This clustering is further refined using Siamese neural networks (SiNNs), improving cluster-based classification accuracy from 72.26\% to 92.23\%. We then train supervised classifiers---convolutional neural networks (CNNs) and graph neural networks (GNNs)---to map new configurations to structural states, using either images or particle graphs as inputs. Validation against pre-labeled data shows that these machine learning-based representations capture structural subtleties more effectively than traditional order parameters such as $\psi_6$. Using these learned state representations, we train Deep Q-Network (DQN) controllers to drive the system toward target states. When tested in BD simulations, CNN-based DQN policies achieved a 99\% success rate relative to the CNN-defined target cluster, but only a 48\% actual success rate upon visual inspection, due to classification errors propagating through the control process. In contrast, GNN-based DQN policies achieved a 97\% actual success rate, demonstrating the value of incorporating topological information into state representations. Comparative analysis also confirms that $\psi_6$ is not an ideal descriptor for this system, whereas GNN-derived representations improve both state classification and control performance. Overall, this framework provides a modular and generalizable approach for closed-loop control of self-assembling materials. By eliminating the need for hand-crafted order parameters and analytical process models, it offers a pathway toward fully automated control pipelines applicable to broader micro- and nano-particle assembly systems. Cluster analysis ; Artificial neural network ; Reinforcement learning ; Pipeline (software) ; Unsupervised learning ; Convolutional neural network ; Optimal control ; Representation (politics) ; State (computer science) Pickering emulsions and particle stabilization ; Micro and Nano Robotics ; Modular Robots and Swarm Intelligence
Authors: Junnan Song | Qingjie Sun | André G. Skirtach | Bogdan V. Parakhonskiy
Source: Advances in Colloid and Interface Science (review, 2025-11-30)
Field: Materials Science — Materials Chemistry

Machine learning in nanoscience and beyond: Workflows, data processing, XAI and ITAP metrics, language-based models Adaptation (eye) ; Big data ; Scale (ratio) ; Interface (matter) ; Key (lock) ; Transfer of learning ; Preprocessor ; Applications of artificial intelligence Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Catalysis and Oxidation Reactions
Authors: Xiang Huang | Weiye Hou | Jie Zhang | Jian Gu | Qin Jin | Hongbo Wu | Zhe Zhang
Source: Physica E Low-dimensional Systems and Nanostructures (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Defect-strain synergy tunes Au d-band center and triggers spontaneous O2 activation on goldene Dissociation (chemistry) ; Adsorption ; Monolayer ; Molecular dynamics ; Vacancy defect ; Activation barrier ; Oxygen ; Tensile strain ; Molecule Graphene research and applications ; Gold and Silver Nanoparticles Synthesis and Applications ; 2D Materials and Applications
Authors: Yunhan Zhang | Jianfeng Cai | Hongjian Chen | Xuming Lv | Bowen Huang
Source: Metals (article, 2025-08-30)
Field: Materials Science — Materials Chemistry

Development of a TaN-Ce Machine Learning Potential and Its Application to Solid–Liquid Interface Simulations This study develops a machine learning potential (MLP) based on the Moment Tensor Potential (MTP) method for the TaN-Ce system. This potential is employed to investigate the interfacial structure and wetting behavior between liquid Ce and solid TaN. Molecular dynamics (MDs) simulations reveal that liquid Ce exhibits significant wetting on the TaN surface at high temperatures. The interfacial region undergoes pre-melting and component interdiffusion, forming an amorphous transition layer. Nitrogen atoms display high diffusivity, leading to surface mass loss, while tantalum atoms demonstrate excellent thermal stability and penetration resistance. These findings provide theoretical support for the design of interfacial materials and corrosion control in high-temperature metallurgy. Interface (matter) ; Computer science ; Human–computer interaction ; Materials science ; Operating system Machine Learning in Materials Science ; Semiconductor materials and devices ; Ferroelectric and Negative Capacitance Devices
Authors: Haoxuan Liu | Haile Yan | Nan Jia | Bo Yang | Zongbin Li | Xiang Zhao | Liang Zuo
Source: Advanced Science (article, 2025-07-14)
Field: Materials Science — Materials Chemistry

Metallic Bond Induces Soft Phonon Mode and Anharmonicity in Heusler Alloy The fruitful functional performances of metastable Heusler alloys originate from the martensitic transformation driven by the transverse acoustic soft phonon mode, regulated by anharmonicity. However, the origins of the soft mode and the anharmonicity in these alloys remain unclear. In this work, using ab-initio calculations combined with self-consistent phonon theory (SCPH) and compressed sensing techniques in machine learning, a link among chemical bonding, long-range interaction, soft mode, and anharmonicity is established using Ni<sub>2</sub>MnGa as a model system. The long-range interaction rooted in metallic bonding between spin-down d-orbitals of Ni─Ni induces phonon softening and significant anharmonicity. This finding offers new insights into the role of metallic bonding and long-range interactions in martensitic transformation, providing a fresh perspective for the design and optimization of phase transition functional alloys. Anharmonicity ; Phonon ; Condensed matter physics ; Materials science ; Metastability ; Soft modes ; Diffusionless transformation ; Density functional theory ; Ab initio ; Alloy ; Martensite ; Softening ; Chemistry ; Computational chemistry ; Physics ; Microstructure ; Metallurgy ; Quantum mechanics ; Optoelectronics ; Composite material ; Ferroelectricity Shape Memory Alloy Transformations ; Heusler alloys: electronic and magnetic properties ; Advanced Thermoelectric Materials and Devices
Authors: Shuangli Yue | Dong-Hui Xu | Li Zhang | Gang Jiang | Mingli Yang | Xian Wang
Source: Physical review. B./Physical review. B (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Enabling and enhancing sliding ferroelectricity in homobilayers via atomic intercalation Sliding ferroelectricity (FE) in two-dimensional (2D) materials offers a promising route to ultrathin, low-power, nonvolatile devices, yet in pristine homobilayers it is typically weak and confined to a small subset of known 2D materials. This limitation prompts a central question: Can sliding FE be reliably induced and substantially enhanced in otherwise nonferroelectric homobilayers through a controllable, experimentally feasible intervention? Here, we combine high-throughput first-principles calculations with machine learning to establish atomic intercalation as a versatile strategy for activating and amplifying sliding FE. Notably, we identify pristine InP and GaP as record performers, exhibiting out-of-plane polarizations up to 21.0 pC/m, approximately ten times greater than experimentally synthesized BN and over 45 times higher than $\mathrm{Mo}{\mathrm{S}}_{2}$, while maintaining moderate switching barriers of just 13.6 and 32.0 meV/f.u. Intercalation with Cu, Ag, and Au expands the pool from 106 pristine sliding FE systems to 591, including 212 activated from initially nonferroelectric states. To accelerate exploration of this enlarged configurational space, we develop a descriptor-free graph neural network incorporating message passing and global attention, enabling direct polarization prediction from atomic configurations. This integrated computational--machine learning framework delivers a robust and tunable pathway for designing high-performance sliding ferroelectrics and a blueprint for targeted discovery of functional 2D materials. Ferroelectricity ; Intercalation (chemistry) ; Polarization (electrochemistry) ; Non-volatile memory ; Bridging (networking) ; Blueprint 2D Materials and Applications ; Graphene research and applications ; Topological Materials and Phenomena
Authors: Jiewei Cheng (18561547) | Penghu Du (21662031) | Qiang Sun (225630) | Puru Jena (1282011)
Source: Figshare (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

DenselyPacked Hydrogen in Porous Hydridic FrameworksSimulated by Hybrid Grand Canonical Monte Carlo and Molecular Dynamics By integrating machine learning potential with grand canonical Monte Carlo and molecular dynamics, we present an effective method capable of accurately accounting for the dense packing of molecular hydrogen in porous materials. This method quantitatively accounts for the storage capacity of hydrogen and the observation of penta-dihydrogen clusters in recently synthesized porous γ-Mg(BH<sub>4</sub>)<sub>2</sub> at 25 K [Oh et al. <i>Nature Chemistry</i> <b>2024</b>, <i>16</i>, 809]. Equally important, we show that γ-Mg(AlH<sub>4</sub>)<sub>2</sub>, formed by replacing BH<sub>4</sub> with AlH<sub>4</sub>, outperforms the hydrogen storage capacity of γ-Mg(BH<sub>4</sub>)<sub>2</sub> at <i>higher</i> temperature with a hydrogen capacity of 5.9 wt % and 36.3 g/L at 70 K and 100 bar (or 3.3 wt % and 19.8 g/L at 160 K and 100 bar). This methodology is versatile and can be applied to porous materials for capturing other gases, such as CO<sub>2</sub>, CO, N<sub>2</sub>O, SH<sub>2</sub> and CH<sub>4</sub>. Monte Carlo method ; Hydrogen storage ; Porosity ; Hydrogen ; Molecular dynamics ; Bar (unit) ; Porous medium ; Hydrogen molecule Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics
Authors: Benben Liu | Yizhuo Gu | Ruiqi Guo | Shaokai Wang | Min Li
Source: Composites Science and Technology (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Multilayer design and multi-objective optimization of neutron shielding composites by means of MCNP simulation and machine learning Electromagnetic shielding ; Neutron ; Neutron radiation ; Bilayer ; Epoxy ; Monte Carlo method ; Matrix (chemical analysis) ; Neutron scattering Radiation Shielding Materials Analysis ; Electromagnetic wave absorption materials ; Graphite, nuclear technology, radiation studies