jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ayoub Shahnazari | Zeliang Zhang | Sachith Dissanayake | Chenliang Xu | Niaz Abdolrahim
Source: Journal of Applied Crystallography (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Machine learning approaches for crystallographic classification from synthetic 2D X-ray diffraction data Crystallographic structure identification is crucial for understanding material properties; however, current methodologies often depend on labor-intensive and time-consuming analyses of 2D X-ray diffraction (XRD) patterns. To address these limitations, this study employs synthetic 2D XRD patterns combined with deep learning (DL) techniques to enable automated and high-throughput classification of the seven crystal systems and 230 space groups. We introduce the novel Auto Diffraction Pipeline, designed to generate synthetic 2D XRD spot patterns from crystallographic information files under diverse conditions, including varying zone axes, atomic substitution, atomic depletion and mechanical loading. These conditions enhance the realism of synthetic data, mitigating the scarcity of experimental datasets and enabling the creation of large representative training sets. Convolutional neural networks were trained and validated on these synthetic datasets to classify crystallographic structures across multiple scenarios. Our results demonstrate that integrating synthetic 2D XRD patterns with DL facilitates rapid, accurate and automated crystallographic classification, promoting the wider adoption of data-driven approaches in materials science. Diffraction ; Convolutional neural network ; X-ray crystallography ; Identification (biology) ; Synthetic data ; Crystal structure Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Nguyen T. T. Duyen | Ngo T. Que | Anh D. Phan
Source: arXiv (Cornell University) (preprint, 2026-01-19)
Field: Materials Science — Materials Chemistry

Multiscale Prediction of Polymer Relaxation Dynamics via Computational and Data-Driven Methods We present a multiscale modeling approach that integrates molecular dynamics simulations, machine learning, and the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory to investigate the glass transition dynamics of polymer systems. The glass transition temperatures (Tg) of four representative polymers are estimated using simulations and machine learning model trained on experimental datasets. These predicted Tg values are used as inputs to the ECNLE theory to compute the temperature dependence of structural relaxation times and diffusion coefficients, and the dynamic fragility. The Tg values predicted from simulations show good quantitative agreement with experimental data. While machine learning tends to slightly overestimate Tg, the resulting dynamic fragility values remain close to experimental fragilities. Overall, ECNLE calculations using these inputs agree well with broadband dielectric spectroscopy results. Our integrated approach provides a practical and scalable tool for predicting the dynamic behavior of polymers, particularly in systems where experimental data are limited. Fragility ; Relaxation (psychology) ; Glass transition ; Molecular dynamics ; Dynamics (music) ; Diffusion ; Nonlinear system ; Dielectric Material Dynamics and Properties ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Taiqiao Liu | Tongtong Wang | Zeyuan Li | E Zhou | Junlei Zhao | Jiaren Feng | Xiaoyu Fei | Yu Guo | Flyura Djurabekova | Sheng Liu | Zhaofu Zhang
Source: ArXiv.org (article, 2026-01-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Anisotropic Kinetics of Ion-Irradiation-Induced Phase Transition in Gallium Oxide Radiation-tolerant semiconductors have traditionally been engineered by the principle of suppressing defect accumulation and amorphization, based on the assumption that radiation damage is inherently stochastic. Here we show that, in monoclinic $β$-\ce{Ga2O3}, a promising ultrawide-bandgap semiconductor, surface crystallographic orientation deterministically governs radiation tolerance through highly anisotropic kinetics of the $β$-to-$γ$ phase transition. Using machine-learning molecular dynamics coupled with a local configurational-entropy descriptor, we quantitatively map anisotropic $β$-to-$γ$ transition kinetics, showing that the critical dose, transition-layer depth, and kinetic stability of the $γ$-phase are fundamentally governed by surface orientation. Under ion irradiation, non-channeling surfaces such as (100), (001), and (-201) undergo severe surface amorphization, whereas the strongly channeling (010) surface resists damage accumulation and promotes subsurface $γ$-phase nucleation. During thermal annealing recovery process, these initial states follow two distinct recovery pathways: the channeling (010) surface reverts directly from $γ$-to-$β$, whereas non-channeling surfaces follow a sequential amorphous-to-$γ$-to-$β$ transition pathway. This work establishes surface orientation as a fundamental design principle for achieving radiation tolerance through controlled polymorphic transitions, providing a universal framework for engineering functional materials capable of withstanding extreme irradiation environments. Anisotropy ; Monoclinic crystal system ; Phase transition ; Irradiation ; Annealing (glass) ; Kinetics ; Radiation damage ; Oxide ; Surface engineering ; Work (physics) Ga2O3 and related materials ; Machine Learning in Materials Science ; Ion-surface interactions and analysis
Authors: Mingxuan Jiang | Biao Xu | Yixin Deng | Shihua Ma | Ji-Jung Kai | Fei Gao | Huiqiu Deng
Source: npj Computational Materials (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Scaling reliable interatomic potentials to complex nuclear alloys via pretrained atomic models Abstract Next-generation fission and fusion reactors impose unprecedented demands on structural materials, requiring simultaneous resistance to high temperatures, high-dose irradiation, and aggressive corrosion. Designing materials that harness the intrinsic properties of multiple elements and their synergistic interactions has emerged as a key strategy to achieve such integrated performance. To guide this design paradigm, a mechanistic understanding of chemically and structurally complex systems is essential. However, such understanding is currently constrained by the lack of high-fidelity interatomic potentials (IAPs) that enable predictive, large-scale atomistic simulations. Here, we employ, for the first time, a multi-task, physics-informed pretraining strategy with the large atomic model (LAM) to systematically evaluate the construction and predictive capability of IAPs for complex nuclear alloy systems. Using Ta-Nb-W-Mo-V as a representative case, the resulting DPA2-5E model—trained solely on the quinary dataset—significantly outperforms conventional machine learning IAPs, demonstrates superior transferability to lower-order subsystems, and accurately reproduces cascade damage and stress-strain behavior. Furthermore, this approach extends to nuclear-relevant structures and corrosive/oxide environments, enabling high-fidelity IAPs and large-scale simulations at reactor extremes. Scaling ; Quinary ; Transferability ; Key (lock) ; Cascade ; Interatomic potential ; Multiscale modeling Machine Learning in Materials Science ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Hao Li | Andrey Bogdanov
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MetaDiT: Enabling Fine-grained Constraints in High-degree-of Freedom Metasurface Design Metasurfaces are ultrathin, engineered materials composed of nanostructures that manipulate light in ways unattainable by natural materials. Recent advances have leveraged computational optimization, machine learning, and deep learning to automate their design. However, existing approaches exhibit two fundamental limitations: (1) they often restrict the model to generating only a subset of design parameters, and (2) they rely on heavily downsampled spectral targets, which compromises both the novelty and accuracy of the resulting structures. The core challenge lies in developing a generative model capable of exploring a large, unconstrained design space while precisely capturing the intricate physical relationships between material parameters and their high-resolution spectral responses. In this paper, we introduce MetaDiT, a novel framework for high-fidelity metasurface design that addresses these limitations. Our approach leverages a robust spectrum encoder pretrained with contrastive learning, providing strong conditional guidance to a Diffusion Transformer-based backbone. Experiments demonstrate that MetaDiT outperforms existing baselines in spectral accuracy, we further validate our method through extensive ablation studies. Encoder ; Degrees of freedom (physics and chemistry) ; Novelty ; Key (lock) ; Property (philosophy) ; Encoding (memory) ; Space (punctuation) Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Advanced Wireless Communication Technologies
Authors: Neerav Kaushal | Ajay MNV Penmatsa
Source: ChemRxiv (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

FragBERTa: Exploring Fragment-based Molecular Representation Learning with SAFE Automated exploration of chemical space through machine learning has become a central strategy in modern drug discovery, where the ability to learn transferable molecular representations directly impacts predictive modeling performance. Recent advances in chemical language models have primarily relied on atom-level string encodings such as SMILES or SELFIES, treating molecules as sequences of individual atoms or characters. However, medicinal chemistry reasoning is often fragment-centric, emphasizing scaffolds, functional groups, and modular substructures rather than isolated atoms. In this study, we introduce FragBERTa, a molecular fragment-aware transformer-based representation language model pretrained using masked language modeling on Sequential Attachment-based Fragment Embedding (SAFE) representations. SAFE decomposes molecules into chemically meaningful fragments while preserving attachment information, enabling the model to learn fragment-level structural patterns within a sequence framework. FragBERTa is pretrained on 11 million unique SAFE sequences extracted from multiple public chemical databases and subsequently finetuned on diverse MoleculeNet benchmarks covering both regression (ESOL, FreeSolv, Lipophilicity, PDBBind) and classification (BACE, BBBP, HIV, Tox21, SIDER) tasks under random and scaffold splits. Our evaluation shows that FragBERTa achieves competitive performance across tasks, often matching or surpassing prior transformer-based models. In regression, it demonstrates strong gains in lipophilicity and protein-ligand binding affinity, including the best scaffold-split PDBBind performance within our comparison. In classification, FragBERTa attains the strongest reported random split ROC-AUC results on HIV and Tox21 and delivers robust performance across bioactivity, permeability, and toxicity benchmarks, indicating that fragment-based representations effectively capture biologically driven and interaction-sensitive endpoints. Collectively, these findings suggest that SAFE representations offer advantages over atom-level representations such as SMILES and SELFIES for scaffold-sensitive and interaction-driven tasks, highlighting the value of fragment-level inductive bias for future molecular foundation models in drug discovery. Chemical space ; Representation (politics) ; Embedding ; Modular design ; Matching (statistics) ; String (physics) ; Sequence (biology) ; Language model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Dongfang Yu | Zhewen Zhu | Fucheng Leng | Yizhou Zhu
Source: Nature Communications (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Equivariant diffusion solution for inorganic crystal structure determination from powder X-ray diffraction data Determining the crystal structures of inorganic materials is critical because atomic arrangement governs their physical, chemical, and mechanical properties. Powder X-ray diffraction (PXRD) remains a cornerstone characterization technique, yet solving structures directly from experimental PXRD patterns is challenging and traditionally depends heavily on expert manual interpretation. Even leading databases contain thousands of entries with incomplete or implausible structural models. In this work, we develop an equivariant graph neural network-based diffusion model that directly infers atomic coordinates from PXRD patterns. Starting from random noise, the model iteratively refines coordinates to produce chemically valid structures that match the target pattern. It solves crystal structure in an average of 0.6 second on a single GPU, several orders of magnitude faster than previous methods, and achieves success rates of 82.3% and 81.6% on simulated and experimental datasets, respectively. We use the model to correct 39 energetically unfavorable database entries and to complete high quality structural models for 912 entries lacking atomic positions, including difficult cases with light elements, natural minerals, and chemical disorder. This conditional equivariant generative approach enables robust automated crystal structure solution from diffraction data and supports future developments in closed loop autonomous materials discovery. Powder diffraction ; Crystal structure ; Diffraction ; Equivariant map ; Diffusion ; Graph X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advanced NMR Techniques and Applications
Authors: Chan Young Joe | Kyungwoo Song | Rakwoo Chang
Source: Journal of Computational Chemistry (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Evaluating In‐Context Learning in Large Language Models for Molecular Property Regression Large language models (LLMs) demonstrate strong performance in natural language tasks, but their capacity for genuine in-context learning (ICL) in scientific regression remains unclear. We systematically assessed seven LLMs on molecular property prediction using a controlled framework of 56 transformed tasks that isolate shortcut learning and are designed to induce functional out-of-distribution (OOD) behavior. LLMs performed nearly perfectly on raw molecular weight prediction via shortcut cues but deteriorated under nonlinear transformations, whereas machine learning (ML) baselines showed greater robustness, yielding a performance crossover. Meta-analysis revealed that distributional descriptors and structure-activity landscape indices (SALI) predict task favorability, providing a framework for selecting between LLM- and ML-based approaches in chemistry. Property (philosophy) ; Task (project management) ; Regression ; Raw data ; Natural language ; Regression analysis ; Linear regression Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Xian-Ze Meng | Xin-Ran Li | Yang Li | Hong-Ji Wan | Hao-Jie Yan | Lian-Kui Wu | Fa-He Cao
Source: ACS Applied Materials & Interfaces (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Rational Construction of a Cu–Cu <sub>2</sub> O–CuHCF Heterojunction Interface for High-Performance Potassium-Ion Sensing The deployment of potassium ion (K<sup>+</sup>) sensors in demanding environments, such as marine resource utilization, energy storage systems, and industrial processes, is severely limited by the thermal instability of state-of-the-art sensing materials. Traditional ionophores like valinomycin and crown ethers irreversibly lose activity above 50 °C, rendering them unsuitable for applications involving significant temperature fluctuations. Herein, we report a breakthrough all-solid-state K<sup>+</sup> sensor engineered to overcome this fundamental limitation. The sensor architecture integrates a Cu<sub>2</sub>O semiconductor as an ion-electron transduction layer and a potassium-copper hexacyanoferrate (CuHCF(K)) active layer, forming a robust Cu-Cu<sub>2</sub>O-CuHCF heterojunction. First-principles density functional theory (DFT) calculations guided the rational design, predicting superior K<sup>+</sup> selectivity over Na<sup>+</sup> based on adsorption energetics and d-band center analysis. A one-step electrochemical synthesis protocol was developed to fabricate this heterostructure with comprehensive characterization (EIS, XPS, XRD, SEM) confirming its successful formation and interfacial properties. The sensor demonstrates exceptional analytical performance, operating reliably within a wide temperature range of 25-100 °C and maintaining high selectivity for K<sup>+</sup> in high-salinity seawater matrices. Furthermore, to bridge the gap between laboratory data and field deployment, we developed and open-sourced a Potential-Temperature-Concentration (PTC) machine learning model based on a back-propagation neural network. This work provides a new paradigm for designing robust electrochemical sensors for harsh environments, combining theoretical material design with intelligent data processing for practical industrial applications. Heterojunction ; Rational design ; Density functional theory ; Electronics ; Semiconductor ; Energy storage ; Electrochemical energy storage ; Rendering (computer graphics) Copper-based nanomaterials and applications ; Metal-Organic Frameworks: Synthesis and Applications ; Analytical Chemistry and Sensors
Authors: Diego Alexander Garzón | Lauri Himanen | Luisa Andrade | Sascha Sadewasser | José A. Márquez
Source: arXiv (Cornell University) (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

ML-guided screening of chalcogenide perovskites as solar energy materials Chalcogenide perovskites have emerged as promising absorber materials for next-generation photovoltaic devices, yet their experimental realization remains limited by competing phases, structural polymorphism, and synthetic challenges. Here, we present a fully data-driven and experimentally grounded screening and ranking framework to assess the stability and experimental feasibility of chalcogenide perovskites, integrating interpretable analytical descriptors, machine-learning models, and sustainability metrics. Using a curated experimental dataset of halide and chalcogenide compounds, we derive a new tolerance factor via the SISSO (sure independence screening and sparsifying operator) algorithm that more accurately distinguishes perovskite-forming compositions than established tolerance-factor-based screening criteria. This descriptor is combined with generative crystal structure prediction, composition-based bandgap estimation, and machine-learning-based feasibility assessment to systematically explore a wide chemical space of hypothetical chalcogenide perovskites. The resulting candidates are further evaluated using sustainability indicators, enabling multi-objective ranking tailored to both single-junction and tandem photovoltaic architectures. Beyond identifying several promising and previously unexplored chalcogenide perovskites, this work demonstrates a transferable screening strategy for chemically constrained materials spaces that balances optoelectronic performance, experimental viability, and long-term sustainability. Chalcogenide ; Photovoltaic system ; Ranking (information retrieval) ; Band gap ; Photovoltaics ; Realization (probability) ; Tandem ; Perovskite (structure) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: O. DAN’KIV | O. KUZYK | A. STOLYARCHUK | I. STOLYARCHUK
Source: Romanian Journal of Physics (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Research of Deformation Effects in A2B6 Quantum Dots with Multilayer Shells and Bionanocomplexes Based on Them Using Deep Machine Learning The architecture of artificial neural networks that are capable of predicting the deformation characteristics of quantum dots based on their geometric sizes, elastic constants of the quantum dot materials and the surrounding medium, and the surface concentration of human serum albumin has been developed. This approach allows us to quickly obtain accurate estimates of the strain-deformation state and the associated changes in the potential profile and energy structure of quantum dots without the need to numerically solve a complex system of equations for each new set of parameters. Within the framework of neural network and mathematical models, calculations of the deformation and energy shift of the bottom of the conduction band of CdSe-core / ZnS/CdS/ZnS-shell quantum dots and bionanocomplexes based on them were carried out at different values of the geometric sizes of the core and shell, HSA concentration, and the elasticity of the surrounding medium. A comparison of the results obtained within these models was conducted. It was shown that these results agree well even for parameters that fall outside the intervals used to train the neural network. Within the framework of the developed model, it was established that the quantum dot–human serum albumin bionanocomplex is more sensitive to deformation than the quantum dot. The model showed high generalizability, allowing it to be applied to a wide range of A²B⁶ semiconductors with a multilayer shell and in various biological conditions. Quantum dot ; Deformation (meteorology) ; Quantum ; Artificial neural network ; Elasticity (physics) ; Quantum system ; Range (aeronautics) Quantum Dots Synthesis And Properties ; Advanced Scientific Research Methods ; Machine Learning in Materials Science
Authors: Yang Li | Yanzhen Wang | Boheng Zhao | Xiaoxun Gong | Yuxiang Wang | Zechen Tang | Zixu Wang | Zilong Yuan | Jialin Li | Minghui Sun | Zezhou Chen | Honggeng Tao | Baochun Wu | Yuhang Yu | He Li | Felipe H. da Jornada | WenHui DUAN | Yong Xu
Source: arXiv (Cornell University) (preprint, 2026-01-06)
Field: Materials Science — Materials Chemistry

DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and materials design. Recently, rapid advances in artificial intelligence (AI) have begun to reshape the research landscape, giving rise to the emerging field of deep-learning electronic structure calculations. Despite numerous pioneering studies, the field remains in its early stages; existing software implementations are often fragmented, lacking unified frameworks and standardized interfaces required for broad community adoption. Here we present DeepH-pack, a comprehensive and unified software package that integrates first-principles calculations with deep learning. By incorporating fundamental physical principles into neural-network design, such as the nearsightedness principle and the equivariance principle, DeepH-pack achieves robust cross-scale and cross-material generalizability. This allows models trained on small-scale structures to generalize to large-scale and previously unseen materials. The toolkit preserves first-principles accuracy while accelerating electronic structure calculations by several orders of magnitude, establishing an efficient and intelligent computational paradigm for large-scale materials simulation, high-throughput materials database construction, and AI-driven materials discovery. Field (mathematics) ; Artificial neural network ; Software ; Electronic structure ; Implementation ; Electronic materials ; Deep learning Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly