jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cheng‐Chu Chung | Ruipeng Li | Gabriel M. Veith | Honghu Zhang | Fernando Camino | Ming Lu | Nikhil Tiwale | Sheng Zhang | Kevin G. Yager | Yu‐chen Karen Chen‐Wiegart
Source: Small (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Accelerating Discovery of Solid‐State Thin‐Film Metal Dealloying for 3D Nanoarchitecture Materials Design through Laser Thermal Gradient Treatment (Small 35/2025) 3D Nanoarchitecture Materials In article number 2501739, Yu-chen Karen Chen-Wiegart and co-workers present a high-throughput strategy for discovering tailored nanostructures in thin-film solid-state metal dealloying. A laser-induced temperature gradient enables systematic exploration of thermally driven, complex nano-structural evolution across an extended gradient sample. Coupled with machine-learning predictions and advanced multimodal characterization, this approach accelerates the nano-architected materials design along and deepens mechanistic insights. Materials science ; Thin film ; Laser ; Solid-state ; Thermal ; Metal ; Nanotechnology ; Metallurgy ; Engineering physics ; Optics Nanoporous metals and alloys
Authors: Yachmenev, Andrey | Vogt, Emil | Corral, Álvaro Fernández | Saleh, Yahya
Source: DESY Publication Database (PUBDB) (Deutsches Elektronen-Synchrotron) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Taylor-mode automatic differentiation for constructing molecular rovibrational Hamiltonian operators We present an automated framework for constructing Taylor series expansions of rovibrational kinetic and potential energy operators for arbitrary molecules, internal coordinate systems, and molecular frame embedding conditions. Expressing operators in a sum-of-products form allows for computationally efficient evaluations of matrix elements in product basis sets. Our approach uses automatic differentiation tools from the Python machine learning ecosystem, particularly the JAX library, to efficiently and accurately generate high-order Taylor expansions of rovibrational operators. Rotational–vibrational spectroscopy ; Taylor series ; Hamiltonian (control theory) ; Embedding ; Automatic differentiation ; Algebra over a field ; Operator theory ; Python (programming language) ; Series expansion Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular spectroscopy and chirality
Authors: Zhao Fan | Michael Whittaker | Mark Asta
Source: Physical Review Materials (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

Efficient machine learning interatomic potentials robust for liquid and multiple solid polymorphs of NaF and KF Achieving atomic-level understanding of crystallization of molten salts is of importance to a wide range of technological applications. Recent work [Fan , ] revealed that crystal nucleation in molten LiF salt is a multistage process according to the molecular-dynamics (MD) simulations based on an atomic cluster expansion (ACE) machine-learning interatomic potential (MLIP). In order to understand the influence of increasing cation size on nucleation pathways and nucleation rates of molten fluoride salts, here we develop two new ACE MLIPs for NaF and KF. The two ACE MLIPs feature DFT-SCAN-level accuracy for liquid and multiple solid polymorphs over a wide temperature (0–2000 K) and pressure (0–100 GPa) range, and also reproduce well a number of experimental data for solid and liquid equilibrium properties. The efficiency of the two ACE MLIPs enable million-atom-scale or microsecond-scale MD simulations. The two general-purpose ACE MLIPs are expected to be useful for atomistic simulations for different purposes, in addition to studying crystallization of molten NaF and KF salts. Nucleation ; Crystallization ; Molten salt ; Work (physics) ; Interatomic potential ; Fluoride ; Cluster (spacecraft) ; Crystal (programming language) Machine Learning in Materials Science ; High-pressure geophysics and materials ; Inorganic Fluorides and Related Compounds
Authors: John C. Faver | Flora Sundersingh | Lauren A. Viarengo‐Baker | Ying‐Chu Chen | Katelyn Billings | Patrick Riley | Ching-Hsuan Tsai | Christopher S. Kollmann
Source: ChemRxiv (preprint, 2025-04-16)
Field: Chemistry — Physical and Theoretical Chemistry

DNA-Encoded Chemical Library Screening with Target Titration Analysis: DELTA DNA-Encoded chemical Libraries (DELs) enable highly efficient screening of billions of small molecules for binding to a target of interest and can provide valuable training data for use in machine learning models for virtual screening. However, DEL screening data is notoriously noisy due to variance in the synthetic yield of library members, the DNA amplification and sequencing process, the influence of the DNA tags on binding, and other factors. Here we show an analysis from a split-sample DEL screening strategy against Bruton’s tyrosine kinase (BTK) which includes a panel of affinity selections against the target protein at varying concentrations and a probabilistic model to estimate both the binding affinity and relative input concentrations of library members. We evaluated the model by comparing model predictions to SPR measurements of resynthesized compounds conjugated to DNA and found that this methodology yielded improved ranking of library members by binding affinity compared to enrichment metrics alone. Additionally, the method successfully recovered a library member with potent binding affinity that would not have been detected in a standard DEL selection. Titration ; Computational biology ; Delta ; DNA ; Chemistry ; Biology ; Biochemistry ; Engineering ; Inorganic chemistry Various Chemistry Research Topics
Authors: Yonghao Zhu | Zhi Wang | Jun Luo | Lin‐Wang Wang
Source: ArXiv.org (preprint, 2025-07-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Origin of Suppressed Ferroelectricity in k-Ga$_2$O$_3$: Interplay Between Polarization and Lattice Domain Walls The large discrepancy between experimental and theoretical remanent polarization and coercive field limits the applications of wide-band-gap ferroelectric materials. Here, using a machine-learning potential trained on ab-initio molecular dynamics data, we identify a new mechanism of the interplay between polarization domain wall (PDW) and lattice domain wall (LDW) in ferroelectric k-phase gallium oxide (Ga2O3), which reconciles predictions with experimental observations. Our results reveal that the reversal of out-of-plane polarization is achieved through in-plane sliding and shear of the Ga-O sublayers. This pathway creates strong anisotropy in PDW propagation, and crucially leads to topologically forbidden PDW propagation across the 120 degree LDWs observed in synthesized samples. The resulting stable network of residual domain walls bypasses slow nucleation and suppresses the observable polarization and coercive field. These insights highlight the potential for tailoring the ferroelectric response in k-Ga2O3 from lattice-domain engineering. Ferroelectricity ; Polarization (electrochemistry) ; Anisotropy ; Nucleation ; Coercivity ; Domain wall (magnetism) ; Lattice (music) Ga2O3 and related materials ; Semiconductor materials and devices ; Electronic and Structural Properties of Oxides
Authors: Zetian Mao (21525875) | Chuan-Shen Hu (10211306) | Jiawen Li (1454653) | Chen Liang (196452) | Diptesh Das (16224680) | Masato Sumita (672347) | Kelin Xia (4825065) | Koji Tsuda (86274)
Source: Figshare (article, 2025-08-09)
Field: Materials Science — Materials Chemistry

Molecule GraphNetworks with Many-Body EquivariantInteractions Message passing neural networks have demonstrated significant efficacy in predicting molecular interactions. Introducing equivariant vectorial representations augments expressivity by capturing geometric data symmetries, thereby improving model accuracy. However, two-body bond vectors in opposition may cancel each other out during message passing, leading to the loss of directional information on their shared node. In this study, we develop <b>E</b>quivariant <b>N</b>-body <b>I</b>nteraction <b>Net</b>works (ENINet) that explicitly integrates <i>l</i> = 1 equivariant many-body interactions to enhance directional symmetric information in the message passing scheme. We provided a mathematical analysis demonstrating the necessity of incorporating many-body equivariant interactions and generalized the formulation to <i>N</i>-body interactions. Experiments indicate that integrating many-body equivariant representations enhances prediction accuracy across diverse scalar and tensorial quantum chemical properties. Equivariant map ; Scalar (mathematics) ; Message passing ; Topology (electrical circuits) ; Graph ; Graph theory Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Sajid Muhaimin Choudhury | M. Karim | Md. Redwanul Karim | Naved Sadat Yamin
Source:  (article, 2025-05-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Integrating Deep Learning and Topology Optimization for Next-Generation Nanophotonic Devices Nanophotonic design has revolutionized various domains, including imaging and highspeed communication, by enabling unprecedented control over light-matter interactions. Despite these advancements, there is substantial potential to further improve the design and functionality of nanophotonic structures through the integration of deep learning techniques. Our previous work demonstrated the application of deep learning for designing four-state broadband metasurfaces utilizing phase change materials (PCMs). By exploiting the enhanced cavity length modulation effects from cascaded Fabry-Perot cavities and controlling the reflection phase across the four states, we achieved metasurfaces capable of combining amplitude and phase-dependent properties. These functionalities included achromatic beam deflection, wavelength-selective beam splitting, achromatic focusing, and broadband absorption, all within the visible spectrum. We also showcased a reconfigurable metasurface capable of dynamically controlling the polarization and directionality of quantum emitter emissions by leveraging the unique optical properties of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{Sb}_{2} ~\mathrm{S}_{3}$</tex>-based PCMs. This reconfigurability opens new avenues for developing versatile optical devices for quantum and classical applications. Building upon these achievements, we propose integrating deep learning with topology optimization to further enhance the performance and design process of nanophotonic devices. This novel framework, termed Deep Learning-Assisted Topology Optimization (DLATO), provides a systematic platform for creating highly efficient nanophotonic structures tailored for advanced imaging and communication applications. By combining the predictive capabilities of deep learning with the robust design principles of topology optimization, DLATO holds the promise of accelerating innovation and broadening the scope of achievable functionalities in nanophotonics, paving the way for next-generation optical technologies. Reconfigurability ; Nanophotonics ; Deep learning ; Broadband ; Scalability ; Achromatic lens ; Topology optimization Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Plasmonic and Surface Plasmon Research
Authors: Djamel Ghernaout Ghernaout | Messaouda Boumaaza | Ahmed Belaadi | Noureddine Elboughdiri | Mhamed Benaissa | Ibrahim M.H. Alshaikh
Source: Journal of Natural Fibers (article, 2025-12-29)
Field: Materials Science — Polymers and Plastics

Predicting Water Absorption in <i>Yucca Treculeana</i> L./HDPE Biocomposites: Optimization Using GA-ANN and RSM Techniques This study utilized cellulose fibers from waste leaves of Yucca treculeana L. to reinforce a high-density polyethylene (HDPE) matrix, aligning with a sustainable production approach that recovers agricultural waste. After complete extraction, the fibers underwent a mild chemical treatment (3% sodium bicarbonate for 4 hr) to improve bonding with the polymer matrix and remove surface contaminants. The primary aim was to examine how different fiber percentages (10, 15, 20, and 30%) affect the dynamic behavior of biocomposites, with particular focus on their absorption kinetics and associated diffusion mechanisms. The absorption characteristics were comprehensively analyzed and modeled using two innovative techniques: response surface methodology (RSM) and artificial neural networks (ANN), enhanced by genetic algorithm optimization. Results indicate that the ANN method surpasses RSM in accuracy and robustness, achieving very high correlation coefficients (0.9979 in training, 0.9777 in testing, and 0.9986 in validation) between experimental results and model predictions. This demonstrates the potential for natural fiber/HDPE composites in manufacturing industrial biocomposites, utilizing plant waste, and supporting more environmentally friendly production processes. Response surface methodology ; Absorption of water ; Cellulose fiber ; Natural fiber ; Cellulose ; Absorption (acoustics) ; Sodium bicarbonate ; Environmentally friendly Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Polymer crystallization and properties
Authors: K. M. Karthick Raghunath | K. Radha | T. Mohanraj | Chandrasekar Venkatachalam
Source: ECS Journal of Solid State Science and Technology (article, 2025-07-24)
Field: Materials Science — Materials Chemistry

Q-Learning Driven Metaheuristic for Tailoring the Solid State Properties of Carbon Nanotube-Graphene Nanocomposites for Automotive and Aerospace Applications The implementation of deep learning strategies has transformed composite material design and allowed the optimization of carbon fibre with epoxy resin beyond previous limits for greater performance results. Developing dependable structure-property-performance links continues to present a major obstacle because these systems operate through intricate combinations between nanotech advances and manufacturing simulations. This study presents a multi-faceted methodology employing a reinforcement learning (RL) approach to optimize carbon fiber and epoxy resin-based composites, incorporating over 10,000 samples of carbon nanotube, graphene, and polymer matrix combinations, thus aiming to achieve specific performance criteria such as tensile strength and thermal conductivity. Utilizing Langmuir-Blodgett self-assembly and Electrospinning methodologies, the research integrates novel nanotechnology to fabricate and test the designed composites. Combining RL with advanced manufacturing and finite element analysis simulation techniques provides a holistic view of the structure-property-performance relations in composite materials. Through iterative optimization, the expected outcomes indicate a potential improvement in mechanical properties by 15%–25% and thermal performance by 10%–20%. Simulated thermal expansion coefficient distributions show that the proposed model consistently achieves values in the range of 1.8 to 3.0 in terms of frequency and reliability. The outcomes of this research have significant implications for aerospace and automotive sectors. Materials science ; Carbon nanotube ; Graphene ; Aerospace ; Nanocomposite ; Automotive industry ; Nanotechnology ; Composite material ; Aerospace engineering ; Engineering Graphene research and applications ; Graphene and Nanomaterials Applications ; Manufacturing Process and Optimization
Authors: Hui Wang | Dongmei Bi | Qingqing Qian | Lei Pan | Shanjian Liu | Weiming Yi
Source: Journal of Analytical and Applied Pyrolysis (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Multi-objective optimization framework for nitrogen-containing compounds generation in nitrogen-enriched pyrolysis: Integrating transfer learning and experimental validation Pyrolysis ; Nitrogen ; Chemical engineering ; Chemistry ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: M. Sepliarsky | F. Aquistapace | F. Di Rino | R. Machado | M. G. Stachiotti
Source: ArXiv.org (preprint, 2025-09-10)
Field: Materials Science — Materials Chemistry

A Local-Phase Framework for the BaTi_{1-x}Zr_xO_3$ Phase Diagram: From Ferroelectricity to Dipolar Glass We apply a first-principles-based atomistic model to investigate the BaTi(1-x)Zr(x)O3 phase diagram, focusing on both macroscopic and local structural changes. Our approach, which combines molecular dynamics with machine learning techniques, accurately captures the influence of Ti and Zr cations on their local environment and its evolution with composition and temperature. The computed phase diagram shows excellent agreement with existing experimental and theoretical data. Beyond reproducing known results, our analysis reveals that the behavior of the solid solution across different compositions and temperatures can be understood in terms of coexisting Ti cells with different symmetries, whose stability depends on the local B-site configuration. This local-phase-based approach provides a unified description of the distinct regions of the solid solution, including ferroelectric, relaxor, and dipolar glass phases, and captures the continuous evolution from one regime to another. Our findings demonstrate how atomic-level distortions drive the complex macroscopic behavior of the material. Phase diagram ; Ferroelectricity ; Stability (learning theory) ; Phase (matter) ; Dipole ; Solid solution ; Phase transition ; Molecular dynamics Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; X-ray Diffraction in Crystallography
Authors: Kian P. Abdolazizi | Roland C. Aydin | Christian J. Cyron | Kevin Linka
Source: ArXiv.org (preprint, 2025-02-08)
Field: Materials Science — Materials Chemistry

Constitutive Kolmogorov-Arnold Networks (CKANs): Combining Accuracy and Interpretability in Data-Driven Material Modeling Hybrid constitutive modeling integrates two complementary approaches for describing and predicting a material's mechanical behavior: purely data-driven black-box methods and physically constrained, theory-based models. While black-box methods offer high accuracy, they often lack interpretability and extrapolability. Conversely, physics-based models provide theoretical insight and generalizability but may not capture complex behaviors with the same accuracy. Traditionally, hybrid modeling has required a trade-off between these aspects. In this paper, we show how recent advances in symbolic machine learning, specifically Kolmogorov-Arnold Networks (KANs), help to overcome this limitation. We introduce Constitutive Kolmogorov-Arnold Networks (CKANs) as a new class of hybrid constitutive models. By incorporating a post-processing symbolification step, CKANs combine the predictive accuracy of data-driven models with the interpretability and extrapolation capabilities of symbolic expressions, bridging the gap between machine learning and physical modeling. Interpretability ; Computer science ; Artificial intelligence ; Data mining Machine Learning in Materials Science
Authors: Liwen F. Wan | Nikhil Rampal | Stephen E. Weitzner
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> Machine-Learning Assisted Atomistic Modeling of Na Storage in Hard Carbon Anode Na-ion batteries offer promising energy storage solution for large-scale deployment given its earth-abundant nature, low cost, improved safety feature, and resilience to domestic supply chain issues. To date, hard carbon (HC) remains the optimal choice as the anode material due to its balanced performance-cost tradeoff for commercialization. In addition, HC can be sourced from a wide variety of biowastes and are versatile to both conventional and advanced processing and fabrication techniques, making their microstructure and storage capacity, reversibly highly tunable. However, for the same reason, their performance metrics such as initial coulombic efficiency, specific and rate capacities can be strongly dictated by their microstructures resulting from different preparation and pre-treatment processes. Without a comprehensive understanding of the processing-structure-property relationship, tremendous trial-and-error efforts and laboratory exploration are needed, which significantly impede the advancement, quality control and maturity of Na-ion battery technology for large-scale implementation. In this talk, I will demonstrate how machine-learning assisted atomistic modeling can help to elucidate various competing Na storage mechanisms in hard carbon and their correlation to common microstructural features, such as randomly oriented graphitic and amorphous nano-domains, defective regions of different sizes, nanovoids/pores. Local Na diffusivities will be extracted to provide guidance of transport and storage optimization via microstructure tuning. Ultimately, insights obtained in these atomistic simulations will allow rational design of low-cost hard carbon anode with high Na storage capacity, enhanced rate performance and prolonged cycling life. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Anode ; Microstructure ; Energy storage ; Carbon fibers ; Faraday efficiency ; Fabrication ; Battery (electricity) ; Amorphous carbon Machine Learning in Materials Science ; Advancements in Battery Materials ; Fiber-reinforced polymer composites
Authors: Yemiao Xing | Yuan Gao | Zhang De | Jie Huang | Qianmin Dong | Qiao Tang | Yufeng Wang | Zihong Ye | Pei Liang
Source: Journal of Advanced Research (article, 2025-11-01)
Field: Chemistry — Analytical Chemistry

From theory to practice: DFT-guided Raman spectral analysis and machine learning for high-accuracy pesticide identification Pesticide residues pose threats to the environment and food safety, and accurate and efficient detection technologies are key to addressing the challenges of pesticide residues. Theoretical calculations of Raman spectroscopy can guide molecular structure analysis, signal analysis, and optimization of detection methods, significantly improving detection accuracy. This study aims to verify the reliability of Raman spectroscopy in identifying pesticides and their isomers, and to provide theoretical and technical support for pesticide residue detection. Based on density functional theory (DFT), we calculated the Raman spectra of 166 pesticides, focused on analyzing the Raman peaks and vibrational modes of 22 heterocyclic pesticides, and explored the effects of functional group isomers and chain isomers on the spectra; additionally, we used PCA and t-SNE machine learning algorithms for the identification of these 22 pesticides. The results clarified the spectral characteristics of different pesticides and the regularity of isomers' influence on spectra, and the machine learning algorithms achieved accurate identification of the 22 pesticides. Raman spectroscopy combined with theoretical calculations and machine learning exhibits high reliability and application potential in the identification of pesticides and their isomers, providing strong technical support for environmental and food safety supervision. Raman spectroscopy ; Reliability (semiconductor) ; Identification (biology) ; Pesticide residue ; Pesticide ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Pesticide Residue Analysis and Safety
Authors: Tadahaya Mizuno | S Nemoto | Yasunobu Yoshikai | Yasunori Kikuchi | Hiroyuki Kusuhara
Source: Toxicology Letters (article, 2025-09-01)
Field: Chemistry — Spectroscopy

P30-25 Investigation of Chirality Recognition by Encoder-Decoder Chemical Language Models Transformer ; ENCODE ; Chirality (physics) ; Language model ; Deep learning ; Key (lock) ; Process (computing) ; Artificial neural network Analytical Chemistry and Chromatography ; Chemical Synthesis and Analysis ; Molecular spectroscopy and chirality