jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yiding Wang | Fengpei Zhang | Tianqing Li | Xiangdong Ding | Graeme J. Ackland | Hongxiang Zong | Turab Lookman | Jun Sun
Source: Acta Materialia (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Corrigendum to “Nested crystal graph neural networks for modeling chemically complex materials” [Acta Materialia 303 (2026) 121725] Artificial neural network ; Graph ; Deep neural networks ; Crystal (programming language) ; Crystal structure ; Molecular graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Metallurgical and Alloy Processes
Authors: Kan Liu | Chenhao Ying | Bangban Zhu | Gui-Fa Xu | Minghao Sun | Hao Wang | Liqiong Luo | Khak  Ho Lim | Jieyuan Zheng | Pingwei Liu | Jiangang Lu | Wei Wang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Modeling of Tandem Catalytic Polymerization for Tailoring Comb-branched Polyolefin Elastomers Polyolefin ; Elastomer ; Polymerization ; Solver ; Ode ; Polymer Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Polymer crystallization and properties
Authors: Hongcai Liang | Yi Wang | Wensheng Lai
Source: Journal of Nuclear Materials (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Elucidating plastic deformation mechanisms in γ-phase U-Mo-Zr ternary alloys using a machine-learning moment tensor potential Slip (aerodynamics) ; Interatomic potential ; Stacking fault ; Molecular dynamics ; Ternary operation ; Tensor (intrinsic definition) ; Deformation (meteorology) ; Density functional theory Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Fusion materials and technologies
Authors: Udit Mamodiya | Indra Kishor | P. Satish Reddy | K. Lakshmi Kalpana | Radha Seelaboyina | Harish Reddy Gantla
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Enhanced Multiscale Computational Framework for Optimizing Thermoelectric Performance in Nanostructured Materials The direct conversion of solid-state heat to electricity using thermoelectric materials has attracted attention; however, their effective application is limited because of the challenge of ensuring a balance between the microstructural features at the quantum, mesoscale, and continuum scales. Current computational and machine-learning methods have a small design space, wherein few to no interactions between the electronic structure, phonon transport, and device-level are considered. This makes it difficult to discover stable high-figure of merit (ZT) settings that are manufacturable and strong in the actual working environment. This study presents a multiscale hybrid optimization framework that combines first-principles descriptors, synthetic microstructure optimization, machine-learning surrogate modeling, Finite Element Method (FEM)-based transport modeling and optimization, and an uncertainty-sensitive reinforcement-learning optimization framework. The results of the performance improvements are compared with those of physics-only, ML-only, and recent optimization baselines using hybrid thermoelectric. The integrated framework offers an accuracy of 97.2%–95.8% in predicting the transport coefficients and offering 18%–32% ZT improvements from the baselines. The optimized configurations remained stable under ±10% fabrication-style perturbations, confirming that the discovered designs were not fragile numerical artifacts. The proposed approach provides a valuable solution for finding a reliable way to obtain high-ZT, fabrication-tolerant thermoelectric designs, which opens the way to accelerated material discovery and the design of next-generation thermoelectric (TE) devices. Thermoelectric effect ; Thermoelectric materials ; Finite element method ; Thermoelectric generator ; Multiscale modeling ; Electricity ; Figure of merit Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Yanjin Xiang | Yihan Nie | Yunzhi Gao | Haidi Wang | Wei Hu
Source: arXiv (Cornell University) (preprint, 2026-03-11)
Field: Materials Science — Materials Chemistry

UniMatSim: A High-Throughput Materials Simulation Automation Framework Based on Universal Machine Learning Potentials Universal machine learning interatomic potentials (UMLIPs) offer accuracy close to first-principles calculations at a fraction of the cost, showing significant potential for large-scale material simulations. However, the fragmented UMLIPs ecosystem lacks unified interface standards and integration frameworks, hindering their automated deployment in high-throughput workflows. To address this, we present UniMatSim, a modular Python framework. It systematically integrates various UMLIPs (e.g., CHGNet, M3GNet, MACE) and automates workflows from structural optimization to stability verification. The framework enables seamless model switching via abstracted interfaces, incorporates task orchestration, and provides standardized modules for key properties (elasticity, phonons, molecular dynamics), including automated handling for low-dimensional materials. As a test case, using the 2D Lieb lattice system, we constructed a multi-stage high-throughput screening workflow covering structural optimization, elastic stability, and phonon spectrum calculations. Starting from 1,176 candidate compositions, a four-model consensus pipeline yields 393 stable structures. These are refined by magnetic-state screening and DFT band-structure calculations to 59 Lieb-lattice candidates with staggered-magnetic-band characteristics. Results show UniMatSim significantly improves computational efficiency and reproducibility, providing a reliable infrastructure for data-driven materials discovery and design. Workflow ; Python (programming language) ; Automation ; Modular design ; Interface (matter) ; Key (lock) ; Software ; Software deployment Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Ram Sewak | Anirban Mondal
Source: ChemRxiv (article, 2026-03-10)
Field: Chemistry — Inorganic Chemistry

Composition-Dependent Li-Ion Transport in Mixed-Metal ZIF-62 Glasses Revealed by Machine-Learning Molecular Dynamics Understanding ion transport in metal-organic frameworks requires resolving the interplay between framework dynamics, local disorder, and thermally activated hopping on extended time and length scales. Here, we develop a robust deep neural network (DNN) interatomic potential, trained and validated against density functional theory data using the DP-GEN framework, to investigate Li-ion transport in pure and mixed-metal ZIF-62 across a wide range of Co/(Co+Zn) ratios. Large-scale deep potential molecular dynamics simulations at 450 K reveal that Li-ion motion is governed by localized vibrational dynamics within metastable cages, punctuated by activated hopping events through the interconnected pore network. Van Hove correlation analysis reveals that intermediate metal substitution [Co/(Co+Zn) ≈0.25-0.50] optimizes the spatial extent of Li displacements while maintaining a stable local coordination environment. Jump-resolved analysis further demonstrates that the 0.50-ZIF-62 composition exhibits the lowest effective activation energy (∼0.093 eV) and the highest ionic conductivity (∼6.0 ×10 −3 S cm −1), arising from a dynamically evolving, composition-dependent energy landscape. Correlated-motion analysis indicates that long-range diffusion proceeds predominantly via independent hopping rather than sustained collective migration. Together, these results establish a unified and dynamic picture of Li-ion transport in mixed-metal MOF electrolytes, demonstrating the predictive capability of machine-learning interatomic potentials for the rational design of highconductivity solid-state ion conductors. Metastability ; Molecular dynamics ; Ionic bonding ; Ionic conductivity ; Diffusion ; Interatomic potential ; Ion ; Range (aeronautics) Metal-Organic Frameworks: Synthesis and Applications ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Michele Assante | Sara Pahlén | Brenda Pijper | Lili Cao | Ole Specht | Nadja A. Simeth | Alexei A. Lapkin | Clive Green | Ola Engkvist | Giulia Bergonzini | Mikhail Kabeshov
Source: ACS Catalysis (article, 2026-02-10)
Field: Chemistry — Organic Chemistry

Automated DFT-Machine Learning Integration Enables Data-Efficient and Generalizable Feasibility Predictions in Metallaphotoredox sp <sup>2</sup> –sp <sup>3</sup> Cross-Coupling Reactions Nickel/photoredox catalysis in cross-coupling reactions offers mild operating conditions for efficient C–C bond formation, expanding synthetic access to pharmaceutically relevant molecules. However, routine implementation of such reactions remains constrained by the intricate reaction mechanism and limited availability of experimental data, which complicate optimization tasks and the development of predictive models. The integration of quantum-mechanical (QM) calculations with machine learning (ML) has proven to be effective for developing predictive models of complex reactions with sparse experimental data. Here, we present a combined approach that integrates automated density functional theory calculations, ML, and parallel synthesis to develop quantum mechanics-machine learning (QM-ML) models for the nickel metallophotoredox cross-coupling reaction feasibility prediction. Random-Forest classification models are trained to predict the outcome of a given reaction using DFT-computed descriptors from automatically generated 3D structures of catalytic cycle intermediates. We demonstrate the broad applicability of this approach, applying it to a diverse data set encompassing four reaction subtypes, namely, bromide cross-electrophile couplings, chloride cross-electrophile couplings, deoxygenative couplings, and amino radical transfer (ART) couplings, augmented with additional experiments curated by a systematic cheminformatics method to broaden the alkyl halides scope. We show on a blind literature data set that such a QM-ML approach can successfully predict the feasibility of complex reactions from heterogeneous data sets with minimal data requirements and can generalize it to unseen reaction subtypes with a few-shot learning approach, affording a computational model for ART coupling. Together, these capabilities provide a data-efficient solution for rapidly predicting the outcome of cross-coupling reactions and facilitate the adoption of nickel photocatalysis in the MAKE stage of the DMTA cycle. Cheminformatics ; Set (abstract data type) ; Outcome (game theory) ; Reaction conditions ; Data integration ; Training set ; Halide Radical Photochemical Reactions ; Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Jian Chen | Fei Zhou | Shumin Han | Xin Chen | Mingqing Liao | Hongli Wang | Yong Cao
Source: Materials Characterization (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Machine learning accelerate the design of low cost and high strength Cu Zn alloys based on the small sample dataset Sample (material) ; Sample size determination ; Pattern recognition (psychology) ; Training set Machine Learning in Materials Science ; Metallurgy and Material Science ; Microstructure and mechanical properties
Authors: Ran Tian | Yan An | Ze-Yuan Yang | Xing Yang | Zhen-Hua Ge
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Controllable synthesis and enhanced photochromic performance of Er-doped WO <sub>3</sub> nano neural network-like structures Tungsten trioxide (WO<sub>3</sub>) is a well-known photochromic material that undergoes a reversible color change under light irradiation, making it promising for applications in smart windows, optical storage, and sensing devices. This study presents the successful synthesis of a uniform and morphology-controlled WO<sub>3</sub> neural network-like structure by tuning the hydrothermal reaction temperature and hydrochloric acid concentration. Furthermore, the photochromic response time of the as-prepared WO<sub>3</sub> particles was systematically investigated, and the optimal sample exhibiting the fastest response was identified. To significantly enhance the photochromic performance, erbium (Er), a rare earth element, was introduced as a dopant. According to the results, Er doping significantly prolonged the photochromic response time of WO<sub>3</sub>. The incorporation of Er led to a noticeable narrowing of the optical bandgap, as confirmed by UV-vis absorption and Tauc analysis. The Er<sub>0.03</sub>W<sub>0.97</sub>O<sub>3</sub> composition demonstrated superior performance compared to the other analyzed samples, indicating that the incorporation of rare earth elements can effectively regulate the photochromic behavior of WO<sub>3</sub> and provide a new pathway for tailoring its optical properties. Photochromism ; Tungsten trioxide ; Hydrothermal circulation ; Nano- ; Doping ; Absorption (acoustics) ; Erbium ; Response time Transition Metal Oxide Nanomaterials ; Photochromic and Fluorescence Chemistry ; Advanced Photocatalysis Techniques
Authors: Haoyu Zhou | Ping Xue | Zhang, Hao | Fu, Tianfan
Source: arXiv (Cornell University) (preprint, 2026-01-05)
Field: Materials Science — Materials Chemistry

Quantized SO(3)-Equivariant Graph Neural Networks for Efficient Molecular Property Prediction Deploying 3D graph neural networks (GNNs) that are equivariant to 3D rotations (the group SO(3)) on edge devices is challenging due to their high computational cost. This paper addresses the problem by compressing and accelerating an SO(3)-equivariant GNN using low-bit quantization techniques. Specifically, we introduce three innovations for quantized equivariant transformers: (1) a magnitude-direction decoupled quantization scheme that separately quantizes the norm and orientation of equivariant (vector) features, (2) a branch-separated quantization-aware training strategy that treats invariant and equivariant feature channels differently in an attention-based $SO(3)$-GNN, and (3) a robustness-enhancing attention normalization mechanism that stabilizes low-precision attention computations. Experiments on the QM9 and rMD17 molecular benchmarks demonstrate that our 8-bit models achieve accuracy on energy and force predictions comparable to full-precision baselines with markedly improved efficiency. We also conduct ablation studies to quantify the contribution of each component to maintain accuracy and equivariance under quantization, using the Local error of equivariance (LEE) metric. The proposed techniques enable the deployment of symmetry-aware GNNs in practical chemistry applications with 2.37--2.73x faster inference and 4x smaller model size, without sacrificing accuracy or physical symmetry. Equivariant map ; Quantization (signal processing) ; Normalization (sociology) ; Graph ; Invariant (physics) ; Inference ; Artificial neural network ; Norm (philosophy) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Stephen E. Farr | Stefan Doerr | Antonio Mirarchi | Francesc Sabanés Zariquiey | Gianni De Fabritiis
Source: arXiv (Cornell University) (preprint, 2026-01-02)
Field: Materials Science — Materials Chemistry

AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules We introduce AceFF, a pre-trained machine learning interatomic potential (MLIP) optimized for small molecule drug discovery. While MLIPs have emerged as efficient alternatives to Density Functional Theory (DFT), generalizability across diverse chemical spaces remains difficult. AceFF addresses this via a refined TensorNet2 architecture trained on a comprehensive dataset of drug-like compounds. This approach yields a force field that balances high-throughput inference speed with DFT-level accuracy. \mbox{AceFF} fully supports the essential medicinal chemistry elements (H, B, C, N, O, F, Si, P, S, Cl, Br, I) and is explicitly trained to handle charged states. Validation against rigorous benchmarks, including complex torsional energy scans, molecular dynamics trajectories, batched minimizations, and tests of force and energy accuracy, demonstrates that AceFF is state-of-the-art for organic molecules in the accuracy and speed regime important for drug discovery. The AceFF-2 model weights and inference code are available at https://huggingface.co/Acellera/AceFF-2.0. Generalizability theory ; Inference ; Field (mathematics) ; Force field (fiction) ; Energy (signal processing) ; Density functional theory ; Small molecule ; Code (set theory) ; Statistical inference Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Changhong Zhang | Jie Liang | Yi Ming Li | Z. Wang | Haoyuan Li | Zhen Yuan | Jiuren Zhou | Rongmei Chen | Wensheng Zhao | Lei Zhang | Hanshen Xin | Jianhua Zhang
Source: Energy & environment materials (article, 2026-02-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computationally Driven Advances in Cu‐ <scp>CNT</scp> On‐Chip Interconnect Materials: From First Principles to Machine Learning As the component dimensions in integrated circuits shrink to extreme scales, the complexity of interconnect systems is increasing significantly, necessitating an urgent and comprehensive upgrade of interconnect materials and manufacturing processes. As the “bridge” linking various on‐chip components, the performance of interconnect materials directly influences the overall chip performance, and their evolution has long been a critical driver of advances in chip technology. In recent years, copper‐carbon nanotube (Cu‐CNT) interconnects have garnered significant attention because they offer electrical conductivity that surpasses that of pure copper, along with a more straightforward fabrication process. Despite the exceptional overall performance of Cu‐CNT composites, systematically elucidating their bulk behavior and interfacial bonding mechanisms remains a formidable challenge. Overcoming this bottleneck requires an in‐depth investigation of the complex interactions at the Cu‐CNT interface and assessment of their effects on the material's mechanical stability and thermal management performance. This review summarizes the applications of atomic‐scale first‐principles calculations, molecular dynamics (MD) simulations, multiphysics modeling, and machine learning methods to Cu‐CNT materials and illustrates their use with examples from recent representative studies. Specifically, it emphasizes the pivotal role of machine learning in deciphering the mechanisms of Cu‐CNT composites over multiple spatial and temporal scales. This review provides a systematic reference for academic research and engineering applications of Cu‐CNT‐based chip interconnect materials and offers perspectives for the development of next‐generation high‐performance interconnects. Multiphysics ; Interconnection ; Bottleneck ; Interface (matter) ; Integrated circuit ; Chip Copper Interconnects and Reliability ; Machine Learning in Materials Science ; Electronic Packaging and Soldering Technologies
Authors: Azizeh Hosseinjany | Tatiana Blank | D. Canadinç | M. Baris Yagci | Hans Jürgen Maier
Source: Electrochimica Acta (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Machine learning-aided design of two novel high entropy alloys with enhanced corrosion resistance for orthopedic implants Corrosion ; High entropy alloys ; Polarization (electrochemistry) ; X-ray photoelectron spectroscopy ; Alloy ; Biocompatibility Titanium Alloys Microstructure and Properties ; High Entropy Alloys Studies ; Nuclear Materials and Properties
Authors: M. Erdem Günay | Ramazan Yıldırım
Source: ACS Engineering Au (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Machine Learning for Catalytic Reaction Systems: A Framework for Complex Chemical Processes Process (computing) ; Chemical process ; Catalysis ; Chemical reaction ; Process dynamics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Jessica A. Martinez B. | Bhaskar Rana | Xuecheng Shao | Katarzyna Pernal | Michele Pavanello
Source: arXiv (Cornell University) (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Machine learning the two-electron reduced density matrix in molecules and condensed phases Machine learning is rapidly accelerating materials and chemical discovery, but most current models target energies, forces, or selected molecular properties rather than the underlying many-body electronic structure. Learning electronic-structure proxies, such as reduced density matrices, offers a path to surrogates that can predict a broad range of observables from a single ML model. Short of learning the full wavefunction, the two-electron reduced density matrix (2-RDM) is among the most information-rich, minimally lossy targets, providing direct access to expectation values of arbitrary one- and two-electron operators regardless of the strength of the underlying electron correlation. Here we show that learning the 2-RDM is a feasible goal, yielding exceptionally accurate models. We develop surrogates for correlated wavefunction methods (including configuration interaction and coupled cluster) that yield 2-RDMs with sufficient fidelity to provide direct, training-free access to energies and forces for driving energy-conserving molecular dynamics. To tackle realistic molecular condensed phases, we leverage a many-body expansion of the 2-RDM, using our ML models to supply the expansion terms and enabling ML-powered, coupled-cluster-quality electronic structure and energetics for large solvated systems. As a demonstration, we showcase a coupled-cluster-level electronic-structure calculation of glucose solvated by 500 water molecules achieved at Hartree-Fock cost. This work establishes a general framework for learning correlated electronic structure with high fidelity and deploying it to systems beyond the reach of conventional ab initio methods. Leverage (statistics) ; Density functional theory ; Ab initio ; Electronic structure ; Observable ; Wave function ; Molecule ; Range (aeronautics) ; Ab initio quantum chemistry methods Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems