jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qirui Sun | Hu Zhao | Guofan Zhang | Jiaao Han | Rui Liu
Source: ChemRxiv (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

A Modular Web Platform for Porphyrin Molecular Design and UV-Vis-NIR Spectral Prediction in the Photosciences We present a modular, web-based computational platform for automated porphyrin molecular design and electronic absorption spectrum prediction, deployed at www.beyondphoton.com, which integrates molecular construction, structural optimization, orbital energy prediction, and spectral simulation into a continuous and reproducible workflow. The system is implemented as four interconnected yet independently operable Flask applications that communicate through lightweight JSON data exchange, enabling seamless data transfer while preserving transparency at each computational stage. Users can design novel porphyrin derivatives through visualized substituent selections or direct SMILES input, perform geometry optimization and structural visualization, predict frontier molecular orbital energies using semiempirical and machine learning approaches, and generate normalized UV-Vis-NIR absorption spectra based on the Gouterman four-orbital model with empirical correction and Gaussian broadening. Interactive spectral visualization, automated peak annotation, and batch-exportable data formats are provided to facilitate rapid analysis and downstream research use. By decoupling complex photophysical property predictions into traceable and interoperable modules, this platform addresses common fragmentation issues in existing computational chemistry tools and offers a scalable, useroriented solution for the research development of porphyrin photophysics and related photoscience applications. Interoperability ; Porphyrin ; Modular design ; Decoupling (probability) ; Transparency (behavior) ; Molecular orbital Machine Learning in Materials Science ; Porphyrin and Phthalocyanine Chemistry ; Metal-Catalyzed Oxygenation Mechanisms
Authors: Yunjeong Lee | Aziz Ahmed | Jihyun Lee | Kang Min Ok | Tae-Soo You
Source: Journal of Materials Chemistry A (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

From synthesis to machine learning validation for the thermoelectric Zintl phase Ba <sub> 1− <i>x</i> </sub> Eu <sub> <i>x</i> </sub> Zn <sub> 2− <i>y</i> </sub> Al <sub> <i>y</i> </sub> Sb <sub>2</sub> system New Zintl phases in the Ba 1− x Eu x Zn 2− y Al y Sb 2 system were synthesized via a machine-learning validated approach. The Eu/Al co-substitution induces atomic disordering, suppressing κ tot to 0.24 W m −1 K −1 and yielding a ZT = 0.50 at 755 K. Thermoelectric effect ; Phase (matter) ; Atomic force microscopy ; Stoichiometry ; Zintl phase ; Thermoelectric cooling Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science
Authors: Brenna Ren | Eli Krasnoff | Dhruva Bhat | Dvita Bhattacharya | Isabelle Chan | Aditi Kiran | M Rafailovich
Source: ChemRxiv (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Investigating the Impact of Bidispersity on Spin-Coated Polystyrene Thin Films Using Machine Learning The thickness of spin-coated polystyrene (PS) thin films largely influences their physical properties. While industrial applications primarily utilize polydisperse PS, existing models address only monodisperse systems. We created bidisperse PS films across varying concentrations, molecular weights, and blend ratios, evaluated monodisperse models with bidisperse data, and tested other machine learning models. We discovered that bidisperse films were systematically thinner than monodisperse films of equivalent weight-average molecular weight, with the overlap parameter (c/c*) emerging as a key predictor. Our Gaussian Process Regression achieved MAPE = 3.82% (63% improvement over monodisperse models), R2 = 0.9919, and RMSE = 75.2 Å. Dispersity ; Polystyrene ; Thin film ; Process (computing) ; Gaussian Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Rheology and Fluid Dynamics Studies
Authors: Jiaxuan Zhang | Yu Zhao | Yu Qiao | Jie Sui | Shuang Li | Jiaxin Liu | Jiayi Guo | Jianning Xu | Gang Wang | Wu Zhao | R Chen | Xinyu Zhang | Yujie Hu | Jiangni Yun | Junfeng Yan
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

All-Medium Metamaterial Absorber Derived from CoNi@rGA Magnetic Aerogels with Deep-Learning Optimization Metamaterial ; Metamaterial absorber ; Coating ; Bandwidth (computing) ; Reflection loss ; Broadband ; Electrical conductor ; Polarization (electrochemistry) ; Cloaking Electromagnetic wave absorption materials ; Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies
Authors: Jilong Wang | Yaqi Fan | Wan Zheng | Xiangxiang Shen | Xinyue Zhao | Ziwen Niu | Xintong Li | Xianchen Gong | Jingang Jiang | Zhendong Wang | Yi Luo | Yejun Guan | Xian Wei | Hao Xu | Yanhang Ma | Xiao He | Weimin Yang | Peng Wu
Source: Journal of the American Chemical Society (article, 2026-03-04)
Field: Chemistry — Inorganic Chemistry

Artificial Intelligence Predicted OSDAs Enable Direct Synthesis of Interlayer-Expanded Zeolites Zeolite crystallization is a metastable process under harsh conditions with poorly understood mechanisms, making the directed synthesis of specific frameworks challenging. Organic structure-directing agents (OSDAs) are key to framework control, but their discovery remains dominated by trial-and-error screening. Here, we develop a domain knowledge-informed machine learning model to predict OSDAs, which enables the successful synthesis of three novel zeolites, namely, ECNU-30, ECNU-34, and ECNU-40 (named after East China Normal University), validating the efficacy of the model. Traditional descriptor-based machine learning models exhibit limited predictive performance in screening OSDAs for unknown zeolite frameworks. Combining an end-to-end architecture with active learning, the ECNU-Zeoformer effectively overcomes this limitation, enabling more accurate prediction of OSDA-zeolite binding energies for selecting suitable OSDAs and superior generalizability to different framework topologies. Generalizability theory ; Zeolite ; Process (computing) ; Applicability domain ; Metastability ; Key (lock) ; Domain (mathematical analysis) Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications
Authors: Byung Chul Yeo | Hyun‐Jae Lee | Sungwoo Kang | Jung‐Hoon Lee
Source: Advanced Science (article, 2026-03-28)
Field: Materials Science — Materials Chemistry

Diffusion–Model–Driven Discovery of Ferroelectrics for Photocurrent Applications Ferroelectric materials are vital for next-generation memory and photovoltaic technologies, yet their discovery is limited to a few known prototypes. Here, we present a design framework that integrates diffusion-model-based crystal generation with multi-fidelity screening to the discovery of ferroelectrics. Using MatterGen, we generated 12 800 candidate structures and applied a pipeline combining diverse machine-learning tools and density functional theory calculations. This process revealed two promising candidates, Ca<sub>3</sub>P<sub>2</sub> and LiCdP, both insulating and switchable. The polarization value of Ca<sub>3</sub>P<sub>2</sub> is 8.9 µC cm<sup>-2</sup> while that of LiCdP reaches as high as 144.1 µC cm<sup>-2</sup> which is slightly higher than that of Sc-doped AlN, one of the highest-polarization ferroelectrics reported to date. Notably, the low-temperature crystal structure of Ca<sub>3</sub>P<sub>2</sub> has not been previously identified, and our study reveals a plausible candidate for this phase. In addition, HSE06 calculations yield bandgaps of 1.58 and 1.13 eV for Ca<sub>3</sub>P<sub>2</sub> and LiCdP, respectively, suggesting strong potential for photocurrent applications. These findings establish new promising candidates for the ferroelectric family and demonstrate the power of generative models to uncover novel functional materials. Photocurrent ; Ferroelectricity ; Pipeline (software) ; Polarization (electrochemistry) ; Density functional theory ; Yield (engineering) Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Gianmarco G. Terrones | Roland G. St. Michel | Jacob Toney | Akash Ball | Yiran Wang | Aaron Garrison | Aditya Nandy | R. C. Meyer | Freya Edholm | Changhwan Oh | Sebastian Pujet | B. K. Chu Daniel | Davut Muhammetgulyyev | Heather J. Kulik
Source: Journal of Chemical Information and Modeling (article, 2026-02-25)
Field: Chemistry — Inorganic Chemistry

molSimplify 2.0: Improved Structure Generation for Automating Discovery in Inorganic Molecular and Reticular Chemistry We provide an overview of core molSimplify code functionality and recent updates that enhance its capabilities for automated molecular and materials modeling. We describe the mol3D and atom3D classes, which store atomic and bonding information for a wide range of functions, including reading, modifying, and characterizing molecular geometries from common file formats. Enhancements to decoration and substructure addition functions enable the systematic derivatization of template molecules. We introduce a new mol2D class that enables graph-based uniqueness checks and substructure identification. Most importantly, we introduce improvements to transition metal complex (TMC) generation that eliminate steric clashes and enable structure building with ligands of higher denticity. Integration with machine learning models that predict coordinating atom identities enables truly high-throughput, <i>de novo</i> TMC generation. We describe applications of molSimplify outside of isolated TMCs, including extensions to periodic systems (e.g., metal-organic frameworks) and to metalloenzymes through the protein3D class. We demonstrate our improved combined structure prediction and generation workflow by generating structures of a database of experimentally characterized Ir complexes from only the SMILES strings of their respective ligands. We envision that recent enhancements will make the code easily extendable to other periodic materials, such as covalent organic frameworks and zeolites, or to multimetallic TMCs. Workflow ; Substructure ; Covalent bond ; Code (set theory) ; Code generation ; Class (philosophy) ; Molecule Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Chemistry and Complexes ; Machine Learning in Materials Science
Authors: Thangarasu Sasikumar | Ajahar Khan | Parya Ezati | Jun Young Kim | Jong-Whan Rhim
Source: Food Chemistry (article, 2026-03-27)
Field: Materials Science — Biomaterials

Nanomaterials for active and intelligent food packaging: Shelf-life extension, real-time sensing, and security applications Nanomaterials ; Food security ; Key (lock) Nanocomposite Films for Food Packaging ; Advanced Sensor and Energy Harvesting Materials ; Polydiacetylene-based materials and applications
Authors: Omar Abou El Kheir | Marco Bernasconi
Source: arXiv (Cornell University) (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

On the origin of in-gap states in amorphous Ge$_2$Sb$_2$Te$_5$ The localized states in the band gap of amorphous phase change alloys like Ge$_2$Sb$_2$Te$_5$ control the electrical conduction via the Poole-Frenkel mechanism. Understanding the origin of in-gap states and their evolution in time during aging of the glass is therefore important for the control of the resistance drift in phase change memory devices. Here, we use a machine learning interatomic potential to generate several models of Ge$_2$Sb$_2$Te$_5$ whose electronic structure is then analyzed within density functional theory with a hybrid functional. A detailed statistical analysis of the structural motifs on which the in-gap states are localized, reveals that the vast majority of in-gap states involve wrong bonds (homopolar or Ge-Sb bonds) often accompanied by Ge in tetrahedral configurations or overcoordinated Ge and Sb atoms. Metadynamics simulations mimicking glass aging support the picture that structural relaxations lead to the depletion of in-gap states and then to an increase of resistance. The simulations thus provide important insights for the mitigation of the resistance drift in phase change memory devices. Metadynamics ; Amorphous solid ; Phase (matter) ; Density of states ; Band gap ; Phase-change memory ; Molecular dynamics ; Polyamorphism ; Density functional theory Phase-change materials and chalcogenides ; Chemical and Physical Properties of Materials ; Advanced Physical and Chemical Molecular Interactions
Authors: Souvik Shaw | Marry Hazarika | Amit Kumar Paul
Source: ChemRxiv (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Probing Vibrational Excitation Patterns of Benzene Dimer using Machine Learning Tools In this letter, another important application of machine learning (ML) techniques is demonstrated in understanding and analyzing vibrational excitation patterns of benzene dimer complex. A total of 15000 trajectories is integrated via chemical dynamics simulations to generate the required data for the present ML applications. This work shows that, when training with the survival probability information of the complex, a Multi Layer Perceptron model can predict the same when all modes are randomly excited. A Gradient Boosting model is used to find out the contribution from modes of different frequency groups to the overall dissociation pattern. Most importantly, a Decision Tree Regression model shows its power in predicting the result obtained from a different excitation energy without using any data from that energy. Excitation ; Perceptron ; Benzene ; Boosting (machine learning) ; Gradient boosting ; Decision tree ; Dissociation (chemistry) ; Artificial neural network Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Mengsa Wang | Yuzhi Zhou | Han Wang
Source: Journal of Chemical Theory and Computation (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Relativistic Fermionic Neural Networks Based on Zeroth-Order Regular Approximation: ZORANet In this paper, we introduce ZORANet, a Fermionic neural network framework for the relativistic calculations based on scalar zeroth-order regular approximation (ZORA), which greatly expands the scope of deep neural network-based electronic structure methods. To address the numerical difficulties specific to the relativistic calculations, the electron-nucleus cusp correction scheme and an importance sampling technique have been carefully implemented to ensure training stability and reduce statistical errors. In addition, two variants, namely, scaled ZORANet and atomic ZORANet, have been developed to mitigate the notorious gauge-dependent error of ZORA. For benchmarks, we first study the hydrogen-like systems, in which both the original and scaled ZORA energies closely agree with the exact ZORA and Dirac results, respectively. Second, we calculate the atomic ionization potentials and electron affinities for the first two rows of elements. The ZORANet results show overall improvement compared to FermiNet, particularly for the second-row elements, with average deviations within chemical accuracy of the experimental values. For the dissociation energies of simple hydrides, atomic ZORANet again outperforms FermiNet, and reaches the same level of accuracy as state-of-the-art relativistic effective core potentials. Our results demonstrate that ZORANet is a highly accurate all-electron method capable of simultaneously treating electron correlation and relativistic effects, while maintaining computational costs comparable to FermiNet. Lastly, we give some prospects on the future development of ZORANet, which helps to further build it as a more integral <i>ab initio</i> relativistic quantum chemistry method. Relativistic quantum chemistry ; Artificial neural network ; Scalar (mathematics) ; Electron ; Quantum ; Ionization ; Electronic structure ; Stability (learning theory) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Jian-Feng Zhang | Ze-Feng Gao | Xiao-Qi Han | Bo Zhan | Dingshun Lv | Miao Gao | Kai Liu | Xinguo Ren | Zhong-Yi Lu | Tao Xiang
Source: arXiv (Cornell University) (preprint, 2026-03-19)
Field: Materials Science — Materials Chemistry

Bridging Crystal Structure and Material Properties via Bond-Centric Descriptors Although chemical bonding is the fundamental mechanistic bridge connecting atomic structure to macroscopic material properties, current data-driven materials science largely treats it as an implicit "black box". Existing machine learning (ML) models rely predominantly on geometric coordinates, forcing them to implicitly relearn complex quantum mechanics from scratch. This lack of intermediate physical features limits model interpretability and generalizability, particularly when training data is scarce. To solve this problem, we introduce MattKeyBond, a bond-centric materials database that explicitly maps the local electronic landscape and bonding interactions of materials. Building on this, we propose Bonding Attractivity (BA), a novel element-specific descriptor that quantifies the intrinsic capability of atoms to form covalent networks. By providing pre-calculated, energy-dimensional bonding descriptors, MattKeyBond transforms the implicit "black box" into physically interpretable features. This strategy relieves ML models from the burden of deducing physical laws from pure geometry, enabling accurate predictions even with limited data and seamlessly integrating electronic structure theory into modern AI workflows. Interpretability ; Bridging (networking) ; Crystal structure prediction ; Forcing (mathematics) ; Material properties ; Crystal structure ; Covalent bond Machine Learning in Materials Science ; Crystallography and molecular interactions ; Inorganic Chemistry and Materials
Authors: Hao An | Cheng Qian | Liping Ding | Xiao Wang | Pai Li | Sungyoub Kim | Feiqing Ding
Source: ACS Nano (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

The Role of Sulfur in Single-Walled Carbon Nanotube Growth It was broadly known that the addition of a small amount of sulfur into the reactor drastically changes the behavior of single-walled carbon nanotube (SWCNT) growth. However, due to the lack of in situ characterization technologies and the limitations of computational approaches, our understanding of the role of sulfur in SWCNT growth remains very poor. To resolve the long-term mystery of the carbon society, we employed a highly accurate machine learning force field (MLFF)-based molecular dynamics (MD) approach to explore the role of sulfur in SWCNT growth from Fe catalyst particles. We successfully grew defect-free SWCNTs on Fe catalyst particles with different sulfur contents via MLFF-MD simulations, and through systematic studies, we found that sulfur atoms are prone to passivate the surface of Fe clusters first. The sulfur-passivated Fe cluster surface is less active for carbon adsorption and SWCNT nucleation. Thus, the addition of both sulfur and carbon onto an Fe cluster during SWCNT growth leads to the formation of a sulfur-rich region and a carbon-rich region on the Fe catalyst surface, which facilitates the nucleation and growth of smaller SWCNTs from the carbon-rich regions. Consequently, more sulfur addition results in a smaller carbon-rich region and the growth of smaller SWCNTs, but an excessive amount of sulfur may poison the catalysts. This insightful understanding agrees very well with most experimental observations, and thus, the long-term mystery of the carbon society was successfully resolved by the artificial intelligence (AI)-assisted computational approach. These deep insights offer a strategy for synthesizing SWCNTs with controlled diameters through proper catalyst passivation, and they also show that the mechanism of SWCNT growth can be revealed via advanced theoretical studies powered by AI. Sulfur ; Catalysis ; Nucleation ; Carbon nanotube ; Carbon fibers ; Adsorption ; Cluster (spacecraft) Carbon Nanotubes in Composites ; Electrocatalysts for Energy Conversion ; Nanowire Synthesis and Applications
Authors: Samuel Del Fré | Gilberto A. Alou Angulo | Maurice Monnerville | Alejandro Rivero Santamaría
Source: ArXiv.org (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Data-driven construction of machine-learning-based interatomic potentials for gas-surface scattering dynamics: the case of NO on graphite Accurate atomistic simulations of gas-surface scattering require potential energy surfaces that remain reliable over broad configurational and energetic ranges while retaining the efficiency needed for extensive trajectory sampling. Here, we develop a data-driven workflow for constructing a machine-learning interatomic potential (MLIP) tailored to gas-surface scattering dynamics, using nitric oxide (NO) scattering from highly oriented pyrolytic graphite (HOPG) as a benchmark system. Starting from an initial ab initio molecular dynamics (AIMD) dataset, local atomic environments are described by SOAP descriptors and analyzed in a reduced feature space obtained through principal component analysis. Farthest point sampling is then used to build a compact training set, and the resulting Deep Potential model is refined through a query-by-committee active-learning strategy using additional configurations extracted from molecular dynamics simulations over extended ranges of incident energies and surface temperatures. The final MLIP reproduces reference energies and forces with high fidelity and enables large-scale molecular dynamics simulations of NO scattering from graphite at a computational cost far below that of AIMD. The simulations provide detailed insight into adsorption energetics, trapping versus direct scattering probabilities, translational energy loss, angular distributions, and rotational excitation. Overall, the results reproduce the main experimental trends and demonstrate that descriptor-guided sampling combined with active learning offers an efficient and transferable strategy for constructing MLIPs for gas-surface interactions. Molecular dynamics ; Scattering ; Interatomic potential ; Highly oriented pyrolytic graphite ; Ab initio ; Radius of gyration ; Potential energy ; Graphite ; Sampling (signal processing) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: H. Wang | Yuxiang Huang | Wengjun Li | Xiaokun Tian | Jing Wang | Luyao Li | Jianjun Han | Han-Xing Liu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Ceramics and Composites

Unraveling the mixed alkali-earth effects on Structure of Aluminosilicate Glass via Machine Learning Potentials Aluminosilicate ; Thermal expansion ; Ab initio ; Substrate (aquarium) ; Thermal ; Molecular dynamics ; Property (philosophy) ; Interatomic potential ; Transformation (genetics) Glass properties and applications ; Thermal Expansion and Ionic Conductivity ; High-pressure geophysics and materials