jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Наталя Гончарова | Johannes Hoja
Source: The Journal of Chemical Physics (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Nudged-elastic band calculations of polymorph transitions and solid-state reactions in molecular crystals The modeling of solid-state transformations, such as polymorphic transitions and chemical reactions in molecular crystals, is vital for many applications, including drug design and the development of new synthesis methods. However, a description via nudged-elastic band (NEB) calculations faces several crucial challenges. First, the automatic initial pathway generation based on a linear interpolation often fails for periodic systems, leading to unrealistic geometries and atomic collisions. Second, the necessary system sizes are typically beyond the scope of density functional theory (DFT) calculations in terms of computational cost, but the associated accuracy is vitally needed. To address these issues, we introduce a hybrid interpolation method that combines linear interpolation for cell parameters with spherical linear interpolation for molecular structures or intramolecular fragments, ensuring smooth and realistic transitions. Moreover, we train and benchmark machine-learned force fields (MLFFs) based on the SO3krates equivariant neural network architecture to accelerate NEB calculations while retaining near-DFT accuracy. We apply our approach to two polymorph transitions and a solid-state Diels-Alder reaction and show that our new interpolation method reliably produces viable initial pathways. The MLFFs are trained on PBE+MBD reference data and reproduce DFT lattice energies with a mean absolute error of 0.4 kJ mol-1 along the minimum-energy paths. These results highlight the potential of combining advanced interpolation techniques with MLFFs to enable automated, accurate, and efficient exploration of solid-state transformations in molecular crystals. Interpolation (computer graphics) ; Linear interpolation ; Anharmonicity ; Density functional theory ; Bilinear interpolation ; Benchmark (surveying) ; Intramolecular force Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Samuel Andrello | Daniel Alabi | Simon J. L. Billinge
Source: ArXiv.org (preprint, 2025-12-02)
Field: Materials Science — Materials Chemistry

Representation of Inorganic Synthesis Reactions and Prediction: Graphical Framework and Datasets While machine learning has enabled the rapid prediction of inorganic materials with novel properties, the challenge of determining how to synthesize these materials remains largely unsolved. Previous work has largely focused on predicting precursors or reaction conditions, but only rarely on full synthesis pathways. We introduce the ActionGraph, a directed acyclic graph framework that encodes both the chemical and procedural structure, in terms of synthesis operations, of inorganic synthesis reactions. Using 13,017 text-mined solid-state synthesis reactions from the Materials Project, we show that incorporating PCA-reduced ActionGraph adjacency matrices into a $k$-nearest neighbors retrieval model significantly improves synthesis pathway prediction. While the ActionGraph framework only results in a 1.34% and 2.76% increase in precursor and operation F1 scores (average over varying numbers of PCA components) respectively, the operation length matching accuracy rises 3.4 times (from 15.8% to 53.3%). We observe an interesting trade-off where precursor prediction performance peaks at 10-11 PCA components while operation prediction continues improving up to 30 components. This suggests composition information dominates precursor selection while structural information is critical for operation sequencing. Overall, the ActionGraph framework demonstrates strong potential, and with further adoption, its full range of benefits can be effectively realized. Representation (politics) ; Directed acyclic graph ; Adjacency matrix ; Graph ; Matching (statistics) ; Adjacency list ; Selection (genetic algorithm) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Gabriela Naibo | Caroline Cecchele Zanotelli | Jackson Freitas Brilhante de São José | Gustavo Pesini | Bruno Brito Lisboa | Luciano Kayser | Jean Michel Moura-Bueno | Claudimar Sidnei Fior | Tales Tiecher
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Near-Infrared Spectroscopy and Machine Learning to Estimate the Physical and Chemical Properties of Soils Cultivated with Ilex Paraguariensis Soil water ; Spectroscopy ; Infrared ; Infrared spectroscopy ; Near-infrared spectroscopy ; Environmental science ; Agricultural engineering ; Chemistry ; Agroforestry ; Soil science ; Engineering ; Physics ; Optics Heavy Metals in Plants ; Spectroscopy and Chemometric Analyses
Authors: Yi Zhao | Lei Zhang | Libo Zhang | Mengyuan Gong | Haoran Yuan
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Multi-Scale Modeling and Optimization of Co2 Hydrogenation to Light Olefins Via Deep Learning and Optimization Algorithms Scale (ratio) ; Deep learning ; Computer science ; Optimization algorithm ; Artificial intelligence ; Algorithm ; Mathematical optimization ; Mathematics ; Physics Machine Learning in Materials Science ; Carbon Dioxide Capture Technologies ; Catalysts for Methane Reforming
Authors: Gabriela Naibo | Jackson Freitas Brilhante de São José | Caroline Cecchele Zanotelli | Gustavo Pesini | Bruno Brito Lisboa | Luciano Kayser | Jean Michel Moura-Bueno | Claudimar Sidnei Fior | Tales Tiecher
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Near-Infrared Spectroscopy and Machine Learning to Estimate the Physical and Chemical Properties of Soils Cultivated with Ilex Paraguariensis Soil water ; Infrared ; Spectroscopy ; Infrared spectroscopy ; Environmental chemistry ; Environmental science ; Chemistry ; Materials science ; Soil science ; Physics ; Optics ; Organic chemistry Heavy Metals in Plants ; Spectroscopy and Chemometric Analyses
Authors: Ha Eun Kim (7872134) | Jaeseong Jin (21477144) | Hyun Woo Kim (1543174) | Won-jin Chung (1310691)
Source: Figshare (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Reaction Profile Forecasting by Artificial Data Generationfor Wittig-Type Geminal Bromofluoroolefination Machine learning (ML) is emerging as a valuable tool in organic synthesis for reaction design and prediction. In recent studies, the ML approach for reaction development using big data with many features provided the best reaction conditions for optimal yields and stereoselectivities. However, the preparation of large data sets is often challenging, especially for nonspecialists such as experimental scientists. In this study, we developed simple ML models for predicting reaction profiles of our geminal bromofluoroolefination with a minimal data set containing only readily accessible features, including <sup>13</sup>C NMR chemical shifts of the reacting sites and Verloop’s Sterimol values. Notably, the model’s efficiency was significantly enhanced through an underutilized tabular augmentation method. By fitting the sparse data points to proper sigmoidal curves, we generated augmented data sets that improved the predicting ability of the feed-forward neural network (FNN). Furthermore, the combination of this augmentation technique with a conditional tabular generative adversarial network (CTGAN) synergistically refined the model’s performance. Our achievement highlights the utility of tailored augmentation strategies as a potential solution for the limitations posed by small experimental data sets in ML-driven reaction development. Geminal ; Artificial neural network ; Set (abstract data type) ; Data set ; Simple (philosophy) ; Sigmoid function ; Data point ; Synthetic data Machine Learning in Materials Science ; Fluorine in Organic Chemistry ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Chenlong Zhao | Y.G. Wang | Shuting Cheng | Yuhang You | Yi Li | Xianwu Xiu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Collaborative Enhancement of Sers Substrate Based on Ta2o5/Agnps Coupled with Deep Learning Techniques for Microplastic Detection Substrate (aquarium) ; Nanotechnology ; Materials science ; Computer science ; Geology Conducting polymers and applications ; Polymer Surface Interaction Studies ; Advanced Sensor and Energy Harvesting Materials
Authors: Ying Wang | Zhijie Jiang | Weigang Lu | Dan Li
Source: Angewandte Chemie (article, 2025-03-11)
Field: Chemistry — Inorganic Chemistry

Machine Learning‐Assisted Exploration of Chemical Space of MOF‐5 Analogs for Enhanced C<sub>2</sub>H<sub>6</sub>/C<sub>2</sub>H<sub>4</sub> Separation Abstract Adsorptive separation using C 2 H 6 ‐selective adsorbents can produce high‐purity C 2 H 4 directly, making it an energy‐efficient separation method with the potential to replace cryogenic distillation. Although many C 2 H 6 ‐selective MOFs have been reported, developing MOFs with both large C 2 H 6 adsorption capacity and high C 2 H 6 /C 2 H 4 selectivity remains challenging. Herein, we present a machine learning–assisted molecular simulation strategy to explore the C 2 H 6 /C 2 H 4 separation capability of pcu ‐MOFs isoreticular to MOF‐5. The eXtreme gradient boosting (XGBoost) algorithm showed high accuracy in predicting the C 2 H 6 /C 2 H 4 selectivity and C 2 H 6 uptake, where Henry coefficient ratio ( S 0 ) and Henry coefficient of C 2 H 6 ( K (C 2 H 6 )) were identified as key factors. We further synthesized the top‐performing MOF termed A‐66 and experimentally verified its large C 2 H 6 adsorption capacity and excellent C 2 H 6 /C 2 H 4 separation performance. This work provides a valuable strategy for exploring the chemical space of MOF‐5 analogs and identifying promising candidates for the efficient purification of C 2 H 4 from C 2 H 6 /C 2 H 4 mixtures. Adsorption ; Selectivity ; Distillation ; Metal-organic framework ; Materials science ; Chemistry ; Process engineering ; Chemical engineering ; Chromatography ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Inorganic Fluorides and Related Compounds ; Boron and Carbon Nanomaterials Research
Authors: Ayush Kumar Pandey | Vivek Pandey | Abhishek Tewari
Source: Materials Today Physics (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Machine learning aided bandgap and defect engineering of mixed halide perovskites for photovoltaic applications Band gap ; Photovoltaic system ; Photovoltaics ; Density functional theory ; Halide ; Vacancy defect ; Stability (learning theory) ; Solar fuel ; Hamiltonian (control theory) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Ram Naresh Yadav | Luciana C. Schmidt | Ashok K. Srivastava | Md. Firoj Hossain | Vandana Singh
Source: Advances in carbohydrate chemistry and biochemistry (review, 2025-01-01)
Field: Chemistry — Organic Chemistry

Late stage O-glycosylation: A strategic gateway to complex therapeutic natural products Glycosylation ; Natural product ; Total synthesis ; Drug discovery ; Synthetic biology ; Glycosidic bond ; Glycobiology Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Microbial Natural Products and Biosynthesis
Authors: Qi Li | Bangliang Jia | Wei Zhou | Haiyan Zhang | Liangfeng Xu | Jiajia Xu | Yongsheng Ren | Shu Zhan
Source: Molecular Simulation (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

GTKNet: a transformer-based graph neural network for predicting molecular properties with KAN and multi-head attention Artificial neural network ; Transformer ; Computer science ; Artificial intelligence ; Materials science ; Chemistry ; Engineering ; Voltage ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Kausik Roy
Source: ChemRxiv (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Cross-Geometry Pretraining for Equivariant Neural Networks: Improving Molecular Property Prediction Under Computational Constraints Property (philosophy) ; Equivariant map ; Graph ; Transfer of learning ; Artificial neural network ; Focus (optics) ; Molecular graph ; Code (set theory) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Jun Yin | Wentao Li | Honghao Chen | Ju Qiu | Huasheng Feng | Xiangya Xu | Qiuyan Jin | Xiaonan Wang
Source: ChemRxiv (preprint, 2025-06-11)
Field: Materials Science — Materials Chemistry

CaTS: Toward Scalable and Efficient Transition State Screening for Catalyst Discovery Large-scale screening of materials via machine learning is emerging as an effective strategy for accelerating scientific discovery and industrial applications. Machine learning methods for transition state (TS)-based screening for catalysts remain underexplored due to the scarcity of TS datasets and the inherent challenges of TS searching tasks. Here, we present a framework for large-scale transition states screening for catalysts (CaTS), which uniquely bridges microscopic reaction kinetics and macroscopic computational efficiency by leveraging TS energy, a mechanistically rigorous yet computationally prohibitive descriptor. CaTS integrates automated structure generation with a machine learning force field-based Nudged Elastic Band (NEB) method, enabling high-throughput TS exploration at 10^4 the speed of density functional theory (DFT). First optimized and validated on a small-molecule TS database comprising 10,000 reactions (achieving sub-0.2 eV errors in TS energy prediction) and further applied to a metal-organic complex catalyst (0.16 eV MAE with only 327 training samples), CaTS achieves DFT-level accuracy at 0.01% computational cost. Scaling to over 1,000 unseen metal-organic complex structures, it identifies top candidates validated by rigorous DFT. AI-assisted analysis using ChatGPT O3 and SHAP confirms that the predictions are consistent with mechanistic heuristics, providing theoretical validation for large-scale prediction. This paradigm shift from static descriptors to kinetic-resolution screening enables industrial-scale catalyst discovery with atomistic precision. CATS ; Scalability ; Transition (genetics) ; State (computer science) ; Catalysis ; Drug discovery ; Computer science ; Business ; Chemistry ; Biology ; Bioinformatics ; Database Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Jun Yin (94374) | Wentao Li (1413067) | Honghao Chen (1675084) | Jiangjie Qiu (20974022) | Huasheng Feng (14525987) | Xiangya Xu (2003683) | Qiuyan Jin (4412839) | Xiaonan Wang (276308)
Source: Figshare (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

CaTS: Toward Scalableand Efficient Transition StateScreening for Catalyst Discovery Large-scale screening of materials via machine learning is emerging as an effective strategy for accelerating scientific discovery and industrial applications. Machine learning methods for transition state (TS)-based screening for catalysts remain underexplored due to the scarcity of TS data sets and the inherent challenges of TS searching tasks. Here, we present a framework for large-scale transition states screening for catalysts (CaTS), which uniquely bridges microscopic reaction kinetics and macroscopic computational efficiency by leveraging TS energy, a mechanistically rigorous yet computationally prohibitive descriptor. CaTS integrates automated structure generation with a machine learning force field-based nudged elastic band (NEB) method, enabling high-throughput TS exploration at 10<sup>4</sup> the speed of density functional theory (DFT). Initially optimized and validated on a small-molecule TS database comprising 10,000 reactions (achieving sub-0.2 eV errors in TS energy prediction) and further applied to a metal–organic complex catalyst (0.16 eV MAE with only 327 training samples), CaTS achieves DFT-level accuracy at 0.01% computational cost. Scaling to over 1000 unseen metal–organic complex structures, it identifies top candidates validated by rigorous DFT. AI-assisted analysis using ChatGPT o3 and SHAP confirms that the predictions are consistent with mechanistic heuristics, providing theoretical validation for large-scale prediction. This paradigm shift from static descriptors to kinetic-resolution screening enables industrial-scale catalyst discovery with atomistic precision. Scalability ; Scaling ; Density functional theory ; Catalysis ; Transition state ; State (computer science) ; Scale (ratio) ; Transition (genetics) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Strothmann, Robert | Amanpur, Mehran | Neveselý, Tomáš | Hecht, Stefan | Reuter, Karsten | Margraf, Johannes T.
Source: edoc Publication server (Humboldt University of Berlin) (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Machine learning driven design of spiropyran photoswitches This study presents the development and application of a generative machine learning model for the design of novel spiropyran photoswitches with enhanced switching speed and absorption bands with small spectral overlap between the open and closed form ( i.e. high addressability). Leveraging a scaffold decoration approach, we fine-tuned a general chemical recurrent neural network (RNN) model on a curated dataset of photoswitches. The fine-tuned model was evaluated against both the pretrained baseline and literature-reported spiropyran compounds, demonstrating superior performance in generating diverse and novel candidates. Notably, the fine-tuned model effectively mitigates common biases in decoration patterns and functional group selection observed in the literature. The study also outlines the synthesis and experimental characterization of several newly designed spiropyran photoswitches, validating the design principles derived from the generative model. These findings highlight the potential of generative models in accelerating the discovery of advanced molecular photoswitches with tailored properties. Spiropyran ; Generative model ; Generative grammar ; Artificial neural network ; Selection (genetic algorithm) ; Robotics Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Electrowetting and Microfluidic Technologies
Authors: Min Zhao | Bao Wang | Fuming Lai
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Prediction of Surface and Interface Energies Based on Winterbottom Morphology of Au/Tio2 Nanoparticles Morphology (biology) ; Nanoparticle ; Interface (matter) ; Materials science ; Nanotechnology ; Surface (topology) ; Chemical engineering ; Biological system ; Composite material ; Engineering ; Mathematics ; Geometry ; Geology ; Biology Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Ian T. Beck | Justin M. Turney | Henry F. Schaefer
Source: Molecules (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Methods in PES-Learn: Direct-Fit Machine Learning of Born–Oppenheimer Potential Energy Surfaces The release of PES-Learn version 1.0 as an open-source software package for the automatic construction of machine learning models of semi-global molecular potential energy surfaces (PESs) is presented. Improvements to PES-Learn's interoperability are stressed with new Python API that simplifies workflows for PES construction via interaction with QCSchema input and output infrastructure. In addition, a new machine learning method is introduced to PES-Learn: kernel ridge regression (KRR). The capabilities of KRR are emphasized with examination of select semi-global PESs. All machine learning methods available in PES-Learn are benchmarked with benzene and ethanol datasets from the rMD17 database to illustrate PES-Learn's performance ability. Fitting performance and timings are assessed for both systems. Finally, the ability to predict gradients with neural network models is presented and benchmarked with ethanol and benzene. PES-Learn is an active project and welcomes community suggestions and contributions. Python (programming language) ; Workflow ; Artificial neural network ; Interoperability ; Software ; Convolutional neural network ; Deep learning ; Energy (signal processing) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Xinyu Huang (679541) | Zhongjun Zhou (53993) | Huajie Song (4166074) | Zexing Qu (1684801)
Source: Figshare (article, 2025-10-18)
Field: Materials Science — Materials Chemistry

Machine Learning-BasedExcited-State Reactive ForceField: A New Approach for Modeling the Photodissociation Dynamicsof 2‑Fluorothiophenol The absence of accurate yet efficient excited-state reactive force fields has emerged as a critical bottleneck hindering advancements in photochemical dynamics. To overcome it, we develop a machine learning-based excited-state reactive force field (ML-ES-RFF) that implements an innovative divide-and-conquer strategy. Specifically, this approach systematically classifies degrees of freedom (DOFs) into chemically active and inactive ones based on their contributions in reactions. For active DOFs, which exhibit a significant anharmonicity, the potential energy surfaces are constructed using high-level quantum mechanics-based neural network potentials (QM-NNPs). In contrast, for inactive DOFs, which satisfy the harmonic approximation near equilibrium, the potential energy surfaces could be described by conventional molecular mechanics (MM) force fields. This multiscale methodology simultaneously achieves quantum chemical accuracy and remarkable computational efficiency. To validate the reliability of this approach, we applied it to study the nonadiabatic dynamics of 2-fluorothiophenol using ML-ES-RFF, successfully obtaining complete atomistic characterization of the photodissociation process. Our framework establishes a new paradigm for studying excited-state processes in complex molecular systems. Force field (fiction) ; Bottleneck ; Photodissociation ; Degrees of freedom (physics and chemistry) ; Field (mathematics) ; Molecular dynamics ; Potential energy ; Characterization (materials science) ; Quantum Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Iman Peivaste | Ahmed Makradi | Francesco Mercuri | Salim Belouettar
Source: Acta Mechanica (article, 2025-04-28)
Field: Materials Science — Materials Chemistry

HYPE-MAD: high-yield python environment for molecular automation and data generation Python (programming language) ; Solid mechanics ; Automation ; Yield (engineering) ; Programming language ; Computer science ; Software engineering ; Computer graphics (images) ; Computational science ; Engineering ; Materials science ; Physics ; Mechanical engineering ; Thermodynamics ; Metallurgy Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Molecular Junctions and Nanostructures