jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dulexy Solano-Orrala | Eliana Díaz-Cruces | Jorge Troconis | Ezequiel Zamora-Ledezma | Joan Manuel Rodriguez-Diaz | Mădălina Simona Bălțatu | Andrei Victor Sandu | Javier Hermoso-Gil | Frank Alexis | Petrică Vizureanu | Camilo Zamora-Ledezma
Source: Biomaterials Advances (article, 2026-02-07)
Field: Materials Science — Materials Chemistry

Advances in β-titanium alloys for safer and greener biomedical implants In the biomedical field, titanium alloys have long been preferred for orthopedic and dental devices due to their excellent biocompatibility and mechanical strength, making them suitable for long-term implantation. However, recent findings indicate that certain alloying elements, such as vanadium, cobalt, and copper, may pose cytotoxic risks when present at higher concentrations or under specific conditions. As a response to these concerns, current research is focused on developing titanium alloys that feature a lower elastic modulus and improved compatibility with bone elasticity. It also aims to exclude potentially cytotoxic elements and incorporate advanced surface modifications, thereby providing effective solutions to these challenges. Based on these identified needs this review highlights the latest advancements in the design of β-Ti alloys through safer and greener methods. It places particular emphasis on pre-clinical in vitro and in vivo studies that evaluate the safety and performance of implants. Additionally, discusses the potential of artificial intelligence and computational methods for predicting and optimizing alloy properties. Unlike previous reviews that focus mainly on microstructure, mechanical behavior or specific clinical niches, this review includes alloy design and processing with pre-clinical evidence, regulatory and intellectual-property dimensions, and life-cycle and sustainability assessments. By linking β-Ti alloy development to circular-economy strategies, biodegradable metallic alternatives and emerging machine-learning tools for alloy prediction, the review provides a framework for the clinical translation of safer and greener titanium implants, offering a complete overview of the critical factors influencing the future development of titanium alloy implants for biomedical applications. SAFER ; Titanium alloy ; Context (archaeology) ; Biocompatible material ; Alloy ; Biological safety ; Biocompatibility Titanium Alloys Microstructure and Properties ; Magnesium Alloys: Properties and Applications ; Bone Tissue Engineering Materials
Authors: Andong Hu | Luca Pennati | Stefano Markidis | Ivy Peng
Source: Open MIND (preprint, 2026-02-02)
Field: Materials Science — Materials Chemistry

Enabling AI Deep Potentials for Ab Initio-quality Molecular Dynamics Simulations in GROMACS State-of-the-art AI deep potentials provide ab initio-quality results, but at a fraction of the computational cost of first-principles quantum mechanical calculations, such as density functional theory. In this work, we bring AI deep potentials into GROMACS, a production-level Molecular Dynamics (MD) code, by integrating with DeePMD-kit that provides domain-specific deep learning (DL) models of interatomic potential energy and force fields. In particular, we enable AI deep potentials inference across multiple DP model families and DL backends by coupling GROMACS Neural Network Potentials with the C++/CUDA backend in DeePMD-kit. We evaluate two recent large-atom-model architectures, DPA2 that is based on the attention mechanism and DPA3 that is based on GNN, in GROMACS using four ab initio-quality protein-in-water benchmarks (1YRF, 1UBQ, 3LZM, 2PTC) on NVIDIA A100 and GH200 GPUs. Our results show that DPA2 delivers up to 4.23x and 3.18x higher throughput than DPA3 on A100 and GH200 GPUs, respectively. We also provide a characterization study to further contrast DPA2 and DPA3 in throughput, memory usage, and kernel-level execution on GPUs. Our findings identify kernel-launch overhead and domain-decomposed inference as the main optimization priorities for AI deep potentials in production MD simulations. Deep learning ; Molecular dynamics ; Inference ; Artificial neural network ; Throughput ; Overhead (engineering) ; Interatomic potential ; Energy minimization Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Ningyu Yan | Zhuocheng Xie | Kai Guo | Yejun Gu | Huajian Gao | Yang Xiang
Source: arXiv (Cornell University) (preprint, 2026-04-02)
Field: Materials Science — Materials Chemistry

AlloyVAE: A generative model for complex probabilistic field-to-field relationships in alloys The inherent compositional heterogeneity of multi-principal element alloys (MPEAs) gives rise to complex, spatially varying mechanical fields that cannot be uniquely determined from coarse-grained composition descriptors. This non-uniqueness introduces intrinsically probabilistic structure-property relationships, posing a fundamental challenge to conventional deterministic modeling and machine learning approaches that collapse such mappings into average predictions. Here, we present AlloyVAE, a physics-informed generative framework that learns the full conditional distribution of mechanical fields from microstructural inputs. Built upon a conditional variational autoencoder architecture, the model incorporates learned smoothing operators to enhance functional regularity and a self-consistency mechanism to enforce physical plausibility. Trained on atomistic simulation data, AlloyVAE accurately predicts distributions of residual stress fields from composition and short-range order, and enables the generation of multiple physically consistent realizations under identical input conditions. Beyond forward prediction, the framework supports inverse design by optimizing composition fields to achieve targeted mechanical responses, and is extensible to coupled mappings involving eigenstrain. By capturing one-to-many structure-property relationships in heterogeneous materials, this work establishes a probabilistic paradigm for materials modeling and design, providing a scalable alternative to conventional simulations for navigating high-dimensional compositional spaces. Probabilistic logic ; Generative grammar ; Smoothing ; Scalability ; Autoencoder ; Inverse ; Statistical model ; Probability distribution ; Generative model Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis
Authors: Ningyu Yan | Zhuocheng Xie | Kai Guo | Yejun Gu | Huajian Gao | Yang Xiang
Source: ArXiv.org (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

AlloyVAE: A generative model for complex probabilistic field-to-field relationships in alloys The inherent compositional heterogeneity of multi-principal element alloys (MPEAs) gives rise to complex, spatially varying mechanical fields that cannot be uniquely determined from coarse-grained composition descriptors. This non-uniqueness introduces intrinsically probabilistic structure-property relationships, posing a fundamental challenge to conventional deterministic modeling and machine learning approaches that collapse such mappings into average predictions. Here, we present AlloyVAE, a physics-informed generative framework that learns the full conditional distribution of mechanical fields from microstructural inputs. Built upon a conditional variational autoencoder architecture, the model incorporates learned smoothing operators to enhance functional regularity and a self-consistency mechanism to enforce physical plausibility. Trained on atomistic simulation data, AlloyVAE accurately predicts distributions of residual stress fields from composition and short-range order, and enables the generation of multiple physically consistent realizations under identical input conditions. Beyond forward prediction, the framework supports inverse design by optimizing composition fields to achieve targeted mechanical responses, and is extensible to coupled mappings involving eigenstrain. By capturing one-to-many structure-property relationships in heterogeneous materials, this work establishes a probabilistic paradigm for materials modeling and design, providing a scalable alternative to conventional simulations for navigating high-dimensional compositional spaces. Probabilistic logic ; Generative grammar ; Smoothing ; Scalability ; Autoencoder ; Inverse ; Statistical model ; Probability distribution ; Generative model Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis
Authors: Yuan Fang | Ipen Demirel | Xiaopu Zhang | Yuchuan Shao | Jianda Shao | John J. Boland
Source: ArXiv.org (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Pathway to lowest-energy structures for the surface triple junction verified by machine learning The behavior of surface triple junctions (STJ) at emergent grain boundaries on free surfaces are critical contributors to material properties, particularly at the nanoscale. Interest has been stimulated by recent experimental observations of local grain rotation, the restructuring of emergent grain boundaries and the resultant levels of stress generation, and computationally by the introduction of the core-shift method for building wedge disclination structures at STJ and the establishment of relationships between atomic calculation and the continuum elastic theory. Despite this progress, the lowest-energy structure for the surface triple junction is still unknown. Here, we carried out the experimental observation of the local surface deformation at STJ, the local stress analysis and ergodic searches for metastable structures. We identified the zipped Y-shaped notch, rather than the zipped V-shaped notch or wedge disclination, as the most stable structures, which was subsequently verified with machine learning methods for a wide range of boundaries. Local stress analysis explains this energetic preference. Our findings provide fundamental insight into STJ energetics and offer a possible framework for understanding kinetically diffusive deformation and the pathway for engineering related material properties, in particular thin-film properties. Triple junction ; Wedge (geometry) ; Metastability ; Grain boundary ; Disclination ; Deformation (meteorology) Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Microstructure and mechanical properties
Authors: Abdulilah Mohammad Mayet | Salman Arafath Mohammed | Shamimul Qamar | Hassen Loukil | Neeraj Kumar Shukla
Source: Review of Scientific Instruments (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Accurate X-ray-based thickness determination of aluminum sheets using ACO-optimized MLP neural networks Accurate thickness measurement of aluminum sheets is critical for industries such as aerospace and automotive but is challenged by traditional methods' dependency on known alloy compositions. This study proposes a novel x-ray-based system to determine thickness across four aluminum alloys (1050, 3105, 5052, and 6061) with thicknesses ranging from 1 to 45 mm, independent of composition. Using Monte Carlo N-particle simulations, an optimized multi-layer perceptron (MLP) neural network, and ant colony optimization (ACO) for feature selection, the approach achieves precise predictions with reduced computational complexity. The model demonstrated high accuracy, with a mean relative error (MRE) of 1.06% on test data, outperforming conventional methods. This scalable, calibration-free system offers a robust solution for real-time thickness measurement in diverse industrial applications. Artificial neural network ; Ranging ; Aerospace ; Multilayer perceptron ; Monte Carlo method ; Automotive industry ; Aluminium ; Metrology ; Alloy Machine Learning in Materials Science ; Neural Networks and Applications ; Welding Techniques and Residual Stresses
Authors: Nicholas Gao | Till Grutschus | Frank Noé | Stephan Günnemann
Source: arXiv (Cornell University) (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State Neural-network wave functions in Variational Monte Carlo (VMC) have achieved great success in accurately representing both ground and excited states. However, achieving sufficient numerical accuracy in state overlaps requires increasing the number of Monte Carlo samples, and consequently the computational cost, with the number of states. We present a nearly constant sample-size approach, Multi-State Importance Sampling (MSIS), that leverages samples from all states to estimate pairwise overlap. To efficiently evaluate all states for all samples, we introduce Excited Pfaffians. Inspired by Hartree-Fock, this architecture represents many states within a single neural network. Excited Pfaffians also serve as generalized wave functions, allowing a single model to represent multi-state potential energy surfaces. On the carbon dimer, we match the $O(N_s^4)$-scaling natural excited states while training $>200\times$ faster and modeling 50\% more states. Our favorable scaling enables us to be the first to use neural networks to find all distinct energy levels of the beryllium atom. Finally, we demonstrate that a single wave function can represent excited states across various molecules. Excited state ; Wave function ; Scaling ; Monte Carlo method ; Energy (signal processing) ; Artificial neural network ; Pairwise comparison ; Variational Monte Carlo Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Electron Microscopy Techniques and Applications
Authors: Chu Wang | Lin Huang | Xinran Wei | Tao Qin | Arthur Jiang | Lixue Cheng | Jia Zhang
Source: arXiv (Cornell University) (preprint, 2026-01-07)
Field: Materials Science — Materials Chemistry

Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing Machine learning force fields (MLFFs) have revolutionized molecular simulations by providing quantum mechanical accuracy at the speed of molecular mechanical computations. However, a fundamental reliance of these models on fixed-cutoff architectures limits their applicability to macromolecular systems where long-range interactions dominate. We demonstrate that this locality constraint causes force prediction errors to scale monotonically with system size, revealing a critical architectural bottleneck. To overcome this, we establish the systematically designed MolLR25 ({Mol}ecules with {L}ong-{R}ange effect) benchmark up to 1200 atoms, generated using high-fidelity DFT, and introduce E2Former-LSR, an equivariant transformer that explicitly integrates long-range attention blocks. E2Former-LSR exhibits stable error scaling, achieves superior fidelity in capturing non-covalent decay, and maintains precision on complex protein conformations. Crucially, its efficient design provides up to 30% speedup compared to purely local models. This work validates the necessity of non-local architectures for generalizable MLFFs, enabling high-fidelity molecular dynamics for large-scale chemical and biological systems. Scalability ; Speedup ; Locality ; Fidelity ; Benchmark (surveying) ; Robustness (evolution) ; Force field (fiction) ; High fidelity Machine Learning in Materials Science ; Protein Structure and Dynamics ; Nanopore and Nanochannel Transport Studies
Authors: Zhenzhong Wang | Haowei Hua | Wanyu Lin | Ming Yang | Kay Chen Tan
Source: ACM Computing Surveys (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Crystalline Material Discovery in the Era of Artificial Intelligence Crystalline materials, with symmetrical and periodic structures, exhibit a wide spectrum of properties and have been widely used in numerous applications across electronics, energy, and beyond. For crystalline materials discovery, traditional experimental and computational approaches are time-consuming and expensive. In these years, thanks to the explosive amount of crystalline materials data, great interest has been given to data-driven materials discovery. Particularly, recent advancements have exploited the expressive representation ability of deep learning to model the highly complex atomic systems within crystalline materials, opening up new avenues for efficient and accurate materials discovery. These works main focus on four types of tasks, including physicochemical property prediction, generative design of crystalline materials, aiding characterization, and accelerating theoretical computations. Despite the remarkable progress, there is still a lack of systematic investigation to summarize their distinctions and limitations. To fill this gap, we systematically investigated the progress of crystalline materials discovery using artificial intelligence made in recent years. We first introduce several data representations of the crystalline materials. Based on the representations, we summarize various fundamental deep learning models and their tailored usages in various material discovery tasks. Finally, we highlight the remaining challenges and propose future directions. Representation (politics) ; Deep learning ; Focus (optics) ; Generative grammar ; Property (philosophy) ; Explosive material Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Sam Giannakoulias | Yanan Chang | Priyanda Giri | Andrew Apicello | E. James Petersson | John J. Ferrie
Source: ChemRxiv (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

FEP Ω: The End of Parameter Tuning Free energy perturbation (FEP) is the gold standard in structure-based drug design, but its lack of out-of-the-box accuracy requires extensive parameter tuning, often delaying its use in compound design. To address this, we present FEP Ω, a novel machine learning native platform for FEP that directly resolves these limitations and accelerates deployment. FEP Ω flips the traditional paradigm of free energy perturbation on its head by eliminating a priori force field adjustments, alchemical intermediates, and network corrections. Instead, FEP Ω uses a standardized and automated setup protocol while leveraging machine learning post-simulation to achieve unprecedented, data-driven accuracy in a streamlined workflow. Together, these innovations make FEP lighter, faster, more accurate, and immediately practical for hit-to-lead and lead optimization. Finally, benchmarking against Schrödinger’s FEP-PB demonstrates superior accuracy in a fraction of the time, removing longstanding barriers to FEP adoption in drug discovery. Prospective virtual screening with FEP Ω identified novel dipeptidyl peptidase-4 chemical matter with experimental potencies exceeding that of sitagliptin (Januvia), achieving a 100% hit rate across all ten compounds tested. Benchmarking ; Pairwise comparison ; A priori and a posteriori ; Field (mathematics) ; Efficient energy use ; Potential field ; Protocol (science) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Xinyu Liu
Source: Mozi (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Comment on "Proteoliposomes on 2D-MoS₂ Plasmonic Nanocavities for Enhanced Raman Spectroscopy with Machine Learning-based Identification and Classification" The integration of engineered lipid vesicles with surface-enhanced Raman spectroscopy (SERS) and supervised machine learning represents a productive strategy for label-free molecular profiling of nanoparticle biomarkers. The recent paper by Shiekh et al.1 describes a library of synthetic proteoliposomes functionalized with cancer-associated surface markers and a 2D molybdenum disulfide (MoS₂) plasmonic nanocavity microchip (MoSERS) for SERS classification. The authors report Random Forest Classifier (RFC) and Support Vector Machine (SVM) test accuracies of 82% and 76%, respectively, with area under the ROC curve (AUC) values of approximately 0.97–0.98. While the experimental platform and its concept are noteworthy, this comment identifies several methodological concerns—encompassing biomarker identification, machine learning evaluation practice, enhancement factor definition, and the impact of partial protein incorporation—whose resolution would substantially strengthen the reproducibility and comparability of the reported findings. Plasmon ; Raman spectroscopy ; Identification (biology) ; Spectroscopy ; Surface-enhanced Raman spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; 2D Materials and Applications
Authors: Chu Wang | Lin Huang | Xinran Wei | Tao Qin | Arthur Jiang | Lixue Cheng | Jia Zhang
Source: ArXiv.org (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing Machine learning force fields (MLFFs) have revolutionized molecular simulations by providing quantum mechanical accuracy at the speed of molecular mechanical computations. However, a fundamental reliance of these models on fixed-cutoff architectures limits their applicability to macromolecular systems where long-range interactions dominate. We demonstrate that this locality constraint causes force prediction errors to scale monotonically with system size, revealing a critical architectural bottleneck. To overcome this, we establish the systematically designed MolLR25 ({Mol}ecules with {L}ong-{R}ange effect) benchmark up to 1200 atoms, generated using high-fidelity DFT, and introduce E2Former-LSR, an equivariant transformer that explicitly integrates long-range attention blocks. E2Former-LSR exhibits stable error scaling, achieves superior fidelity in capturing non-covalent decay, and maintains precision on complex protein conformations. Crucially, its efficient design provides up to 30% speedup compared to purely local models. This work validates the necessity of non-local architectures for generalizable MLFFs, enabling high-fidelity molecular dynamics for large-scale chemical and biological systems. Scalability ; Speedup ; Locality ; Fidelity ; Benchmark (surveying) ; Robustness (evolution) ; Force field (fiction) ; High fidelity Machine Learning in Materials Science ; Protein Structure and Dynamics ; Nanopore and Nanochannel Transport Studies
Authors: Na-Im Kim | Min-Jin Choi | Choon-Ho Lee | Song-Joo Choi | Jung-Wook Wee
Source: Engineering Failure Analysis (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Hygrothermal degradation failure of poly(methyl methacrylate): chemo-mechanical correlations and image-based deep learning prediction Ultimate tensile strength ; Degradation (telecommunications) ; Convolutional neural network ; Toughness ; Relative humidity ; Deep learning ; Artificial neural network ; Service life ; Polymer Machine Learning in Materials Science ; Polymer crystallization and properties ; Fiber-reinforced polymer composites