jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonas Grandel | Philipp Benner | Janine George
Source: arXiv (Cornell University) (preprint, 2026-04-01)
Field: Materials Science — Materials Chemistry

Parameter-Efficient Fine-Tuning of Machine-Learning Interatomic Potentials for Phonon and Thermal Properties Machine-learning interatomic potentials are widely used as computationally efficient surrogates for density functional theory in atomistic simulations, enabling large-scale, long-time modeling of materials systems. We investigate how different fine-tuning strategies influence the prediction of harmonic phonon band structures, thermal properties, and the potential energy surface along imaginary phonon modes. We achieve substantial accuracy improvements with minimal additional data, with as few as 10 additional training structures already yielding significant gains. In addition to existing approaches, we introduce Equitrain, a finetuning framework that implements LoRA-based adaptation. Across 53 materials systems, we show that fine-tuned models consistently outperform both the underlying pretrained model and models trained from scratch. Equitrain achieves the best overall performance, and our results demonstrate that fine-tuning enables accurate phonon predictions. Phonon ; Thermal ; Interatomic potential ; Energy (signal processing) ; Density functional theory Machine Learning in Materials Science ; Thermal properties of materials ; Quantum many-body systems
Authors: Longsheng Feng | Bo Wang | Sourav Chatterjee | Tae Wook Heo | Juergen Biener
Source: Acta Materialia (article, 2026-03-08)
Field: Materials Science — Materials Chemistry

Machine learning insights into microstructural origins of transport and mechanical properties in porous microstructures Microstructure ; Porosity ; Porous medium ; Deformation (meteorology) Machine Learning in Materials Science ; Nanoporous metals and alloys ; Theoretical and Computational Physics
Authors: Yunyang Li | Lin Huang | Luojia Xia | Wenhe Zhang | Mark Gerstein
Source: Open MIND (preprint, 2026-01-29)
Field: Materials Science — Materials Chemistry

Elign: Equivariant Diffusion Model Alignment from Foundational Machine Learning Force Fields Generative models for 3D molecular conformations must respect Euclidean symmetries and concentrate probability mass on thermodynamically favorable, mechanically stable structures. However, E(3)-equivariant diffusion models often reproduce biases from semi-empirical training data rather than capturing the equilibrium distribution of a high-fidelity Hamiltonian. While physics-based guidance can correct this, it faces two computational bottlenecks: expensive quantum-chemical evaluations (e.g., DFT) and the need to repeat such queries at every sampling step. We present Elign, a post-training framework that amortizes both costs. First, we replace expensive DFT evaluations with a faster, pretrained foundational machine-learning force field (MLFF) to provide physical signals. Second, we eliminate repeated run-time queries by shifting physical steering to the training phase. To achieve the second amortization, we formulate reverse diffusion as a reinforcement learning problem and introduce Force--Energy Disentangled Group Relative Policy Optimization (FED-GRPO) to fine-tune the denoising policy. FED-GRPO includes a potential-based energy reward and a force-based stability reward, which are optimized and group-normalized independently. Experiments show that Elign generates conformations with lower gold-standard DFT energies and forces, while improving stability. Crucially, inference remains as fast as unguided sampling, since no energy evaluations are required during generation. Stability (learning theory) ; Inference ; Force field (fiction) ; Field (mathematics) ; Equivariant map ; Reinforcement learning ; Probability distribution ; Generative model ; Free energy principle ; Generative grammar Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Pranav Kumar | Fritz Körmann | Kaveh Edalati | Blazej Grabowski | Yuji Ikeda
Source: Acta Materialia (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Hydrogen diffusion in TiCr2H Laves phases: A combined ab initio and machine-learning-potential study The kinetics of hydrogen diffusion in C15 cubic and C14 hexagonal TiCr 2 H x (0 < x ≤ 4) Laves-phase hydrogen storage alloys is investigated with density functional theory (DFT) and machine learning interatomic potentials (MLIPs). Generalized solid-state nudged elastic band calculations are conducted based on DFT for all symmetrically inequivalent paths between the first-nearest-neighbor face-sharing interstitial sites. The hydrogen migration barriers are substantially higher for the paths that require breaking a Ti–H bond than for those that require breaking a Cr–H bond. Molecular dynamics (MD) simulations with the MLIPs also demonstrate that hydrogen migration occurs more frequently within the hexagonal rings made of the A 2 B 2 interstitial paths, each requiring the breaking of Cr–H bonds, than along the inter-ring paths. The diffusion coefficients of hydrogen obtained from the MD simulations reveal a non-monotonic dependence on hydrogen concentration, which is more pronounced at lower temperatures. Time-averaged radial distribution functions of hydrogen further show that hydrogen avoids face-sharing positions during diffusion and that the hydrogen occupancy at the second-nearest-neighbor edge-sharing positions increases with increasing hydrogen concentration. The diffusion coefficients of hydrogen within 400–1000 K follow an Arrhenius relationship, with activation barriers consistent with most experimental values. One-order of magnitude overestimation of diffusion coefficients compared with some experiments suggests a substantial impact of hydrogen trapping by defects such as Cr vacancies and Ti anti-sites in non-stoichiometric TiCr 2 in experiments. Hydrogen ; Diffusion ; Interstitial defect ; Ab initio ; Density functional theory ; Arrhenius equation ; Hydrogen atom ; Molecular dynamics Hydrogen Storage and Materials ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties
Authors: Huake Yao | Yong Cao | Chuanhao Lu | Jingyu Liu | Wenzhi Wang | Zhihua Wang
Source: Composites Communications (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

A data-driven framework for predicting residual strength of composite panels with cutouts under high-velocity multi-fragment impact Residual strength ; Residual ; Parametric statistics ; Composite number ; Randomness ; Partition (number theory) ; Finite element method ; Stiffness ; Damage mechanics High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Mechanical Behavior of Composites
Authors: Yunyang Li | Lin Huang | Luojia Xia | Wenhe Zhang | Mark Gerstein
Source: ArXiv.org (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Elign: Equivariant Diffusion Model Alignment from Foundational Machine Learning Force Fields Generative models for 3D molecular conformations must respect Euclidean symmetries and concentrate probability mass on thermodynamically favorable, mechanically stable structures. However, E(3)-equivariant diffusion models often reproduce biases from semi-empirical training data rather than capturing the equilibrium distribution of a high-fidelity Hamiltonian. While physics-based guidance can correct this, it faces two computational bottlenecks: expensive quantum-chemical evaluations (e.g., DFT) and the need to repeat such queries at every sampling step. We present Elign, a post-training framework that amortizes both costs. First, we replace expensive DFT evaluations with a faster, pretrained foundational machine-learning force field (MLFF) to provide physical signals. Second, we eliminate repeated run-time queries by shifting physical steering to the training phase. To achieve the second amortization, we formulate reverse diffusion as a reinforcement learning problem and introduce Force--Energy Disentangled Group Relative Policy Optimization (FED-GRPO) to fine-tune the denoising policy. FED-GRPO includes a potential-based energy reward and a force-based stability reward, which are optimized and group-normalized independently. Experiments show that Elign generates conformations with lower gold-standard DFT energies and forces, while improving stability. Crucially, inference remains as fast as unguided sampling, since no energy evaluations are required during generation. Stability (learning theory) ; Inference ; Force field (fiction) ; Field (mathematics) ; Equivariant map ; Reinforcement learning ; Probability distribution ; Generative model ; Free energy principle ; Generative grammar Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Back cover Data-driven approach to elucidate the correlation between photocatalytic activity and rate constants from excited states This work presents a data-driven framework that uncovers how excited-state kinetics govern the catalytic activity of organic photosensitizers by integrating machine learning with quantum chemical calculations.Theoretically simulated rate constants from excited states were shown to outperform experimentally measured excited-state lifetimes as descriptors.SHAP-based visual analyses revealed that transitions from the T1 state, together with the non-radiative decay process between the S1 and S0 states, play key roles in determining photocatalytic activity.This computational approach provides insights that are not readily accessible through conventional experimental methods. Nucleofection ; Gestational period ; TSG101 ; Liquation ; Dysgeusia ; Diafiltration ; Emperipolesis ; Triacetin ; Demotion Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Radical Photochemical Reactions
Authors: Zhengwen Wei | Sicheng Jing | Xiang‐fei Lü | Wei Wang | Giuseppe Mele | Kangze Yuan | Wankui Ni | Zhenyi Jiang | Yajie Guo
Source: Journal of Colloid and Interface Science (article, 2026-03-02)
Field: Chemistry — Inorganic Chemistry

Advancing metal organic frameworks screening and remediation condition optimization for removing persistent organic pollutant via integrated machine learning and theoretical calculation insights Metal-organic framework ; Stacking ; Environmental remediation ; Groundwater remediation ; Pollutant ; CMA-ES Metal-Organic Frameworks: Synthesis and Applications ; Environmental remediation with nanomaterials ; Zeolite Catalysis and Synthesis
Authors: Bin Gao | Xinyu Jiang | Lusheng Wang | Jun Ding | Yanhong Peng | Xin Yang | Hongzhou Yan | Shaojie Gu
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Multiscale Numerical Simulation of Dynamic Damage and Fracture in Metallic Materials: A Review This paper provides a comprehensive review of recent advances in multi-scale modeling for simulating dynamic damage and fracture in metallic materials, a critical area due to the widespread application of metals and their susceptibility to complex failure in engineering practice. The paper first outlines the mechanisms of damage evolution and crack propagation across different spatial and temporal scales. It then introduces commonly used simulation approaches spanning micro- to macro-scales for studying damage and fracture in metals, analyzing the evolution of mechanical properties from defect initiation to ultimate failure, and elucidating the underlying damage mechanisms at different scales. Finally, the review summarizes multi-scale coupling strategies and mechanisms, as well as the integration of machine learning (ML) into multi-scale frameworks. These advanced approaches are recognized as key tools for improving predictive accuracy and computational efficiency, facilitating the scalability of multi-scale damage modeling for metallic materials in large-scale engineering applications and digital twin platforms. This review aims to provide a theoretical foundation for future research toward more reliable, efficient, and predictive multi-scale modeling of metallic materials. Fracture (geology) ; Computer simulation ; Finite element method ; Numerical models ; Deformation (meteorology) High-Velocity Impact and Material Behavior ; Fatigue and fracture mechanics ; Microstructure and mechanical properties
Authors: Qiang Zhou | Yilun Dong | Mingyu Xia | Wei Wu | Menghan Zhang | Wenhao Niu | Zhenyu Wang | Wanqun Hu | Panpan Zhu
Source: Journal of Chemical Education (article, 2026-03-23)
Field: Chemistry — Physical and Theoretical Chemistry

Machine Learning-Enabled Quantification of Characteristic Carbonyl Peak Shifts in Infrared Spectra: An Instrumental Analysis Laboratory Experiment for Upper-Division Undergraduates Infrared (IR) spectroscopy is one of the most important methods to characterize molecular structure, and it is also an important part of the instrumental analysis experiment course for undergraduate students. In current teaching of IR spectroscopy, descriptions of how functional groups affect characteristic peak shifts are mostly qualitative, and the specific influences of different functional groups on characteristic peak shifts are rarely quantified. Machine learning (ML), an important subset of artificial intelligence (AI), enables systematic extraction of actionable insights from complex data sets. Using ML techniques to quantify the shift behaviors of characteristic peaks is a beneficial supplement to theoretical and practical teaching of IR spectroscopy. This educational study combines ML methods with IR spectroscopy and uses this integration as the teaching content of the instrumental analysis course for third-year undergraduate students majoring in chemistry. The study focuses on the characteristic peak of carbonyl (C═O) stretching vibrations. Based on IR spectroscopy teaching experiments, it guides students to construct a spectral database, develop ML models, experimentally validate the model’s prediction results, and perform SHAP (SHapley Additive exPlanations) interpretability analysis. Through this process, students successfully quantified the influences of different functional groups on carbonyl characteristic peak shifts in IR spectra. This ML-integrated teaching method can not only deepen students’ understanding of spectra-structure correlations, but also cultivate their ability to use ML techniques to solve spectrochemical problems. This work provides a foundational and replicable pedagogical framework. We encourage educators to implement or modify the provided workflow and data with proper citation and attribution, and to extend the approach to other functional groups. Infrared ; Analytical Chemistry (journal) ; Infrared spectroscopy ; Chemometrics ; Instrumentation (computer programming) Various Chemistry Research Topics ; Science Education and Pedagogy ; Machine Learning in Materials Science
Authors: Aaron Flötotto | Benjamin Spetzler | Rose von Stackelberg | Martin Ziegler | Erich Runge | Christian Dreßler
Source: Small (article, 2026-02-15)
Field: Materials Science — Materials Chemistry

Large‐Scale Cooperative Sulfur Vacancy Dynamics in Two‐Dimensional MoS <sub>2</sub> From Machine Learning Interatomic Potentials The formation of extended sulfur vacancies in MoS<sub>2</sub> monolayers is closely associated with catalytic activity and may also be the basis for its memristive behavior. Nanosecond-scale molecular dynamics simulations using machine learning interatomic potentials (MLIPs) reveal key mechanisms of cooperative vacancy transport, including incorporation of vacancies into clusters of arbitrary size. The simulations provide a coherent atomistic explanation for irradiation-induced vacancy patterns observed experimentally, especially the formation of line defects spanning tens of nanometers. Results and performance are compared of two MLIP frameworks: (i) on-the-fly learning with Gaussian approximation potential, and (ii) fine-tuning of an equivariant foundation model. Vacancy defect ; Molecular dynamics ; Sulfur ; Interatomic potential ; Gaussian ; Line (geometry) 2D Materials and Applications ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Anguo Xie | Bingqian Liu | Han Wu | Yunhong Liu
Source: Journal of Food Measurement & Characterization (article, 2026-03-16)
Field: Chemistry — Analytical Chemistry

A dual-branch encoder model for nondestructive detection and end-point discrimination of active components in nine-steamed and nine-dried Polygonatum sibiricum using hyperspectral technology Hyperspectral imaging ; Convolutional neural network ; Encoder ; Pattern recognition (psychology) ; Artificial neural network ; Process (computing) ; Partial least squares regression ; Steaming ; Deep learning Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Remote-Sensing Image Classification
Authors: Junjie Zeng | Yongchao Liang | Gongmin Wei | Shengli He | Can Wu | Jia-mu Zhao | Qian Chen
Source: Physica Scripta (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

An end-to-end glass-forming ability prediction model based on the Mamba architecture Abstract In recent years, machine learning (ML) approaches that predict glass-forming ability (GFA) from characteristic temperatures have opened new avenues for the development of metallic glasses (MGs). However, these temperatures are available only after sample preparation, which is time-consuming and resource-intensive. To overcome this limitation, we encode alloy compositions to jointly represent element identity and fraction and, on this representation, adopt a sequence-modeling paradigm to capture long-range dependencies and global inter-element relations. Nevertheless, conventional sequence models lack input-conditioned adaptivity and struggle with the complexity and heterogeneity of multicomponent alloys. Accordingly, we propose the Gated-channel Attention Mamba (GA-Mamba), a hybrid architecture that embeds a MambaBlock to adapt parameters to the input and integrates a composition-oriented Gated-channel Attention (GA) module to align and reweight elemental embeddings, thereby strengthening representation and prediction for complex alloy systems. Compared with baselines, GA-Mamba more effectively captures nonlinear inter-element interactions and improves prediction accuracy and generalization. To mitigate data scarcity and distribution shift, we employ three oversampling strategies, including Gaussian noise (GN), Synthetic Minority Over-sampling Technique (SMOTE), and Mixup, to better balance the training data; Mixup yields the largest gain, and the model attains <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:math> = 0.8722 on the test set. Predictions for the Cu-Ca-Mg and Cu-Al-Zr ternary systems further demonstrate strong generalization. Representation (politics) ; Oversampling ; Ternary operation ; Nonlinear system ; Gaussian ; Noise (video) ; Distribution (mathematics) ; Identity (music) Machine Learning in Materials Science ; Metallic Glasses and Amorphous Alloys ; Material Dynamics and Properties
Authors: Khalid El-Awady
Source: ArXiv.org (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Data-Driven Plasticity Modeling via Acoustic Profiling This paper presents a data-driven framework for modeling plastic deformation in crystalline metals through acoustic emission (AE) analysis. Building on experimental data from compressive loading of nickel micropillars, the study introduces a wavelet-based method using Morlet transforms to detect AE events across distinct frequency bands, enabling identification of both large and previously overlooked small-scale events. The detected events are validated against stress-drop dynamics, demonstrating strong physical consistency and revealing a relationship between AE energy release and strain evolution, including the onset of increased strain rate following major events. Leveraging labeled datasets of events and non-events, the work applies machine learning techniques, showing that engineered time and frequency domain features significantly outperform raw signal classifiers, and identifies key discriminative features such as RMS amplitude, zero crossing rate, and spectral centroid. Finally, clustering analysis uncovers four distinct AE event archetypes corresponding to different deformation mechanisms, highlighting the potential for transitioning from retrospective analysis to predictive modeling of material behavior using acoustic signals. Acoustic emission ; Discriminative model ; Cluster analysis ; Plasticity ; Pattern recognition (psychology) ; Profiling (computer programming) ; Event (particle physics) ; Frequency domain ; Consistency (knowledge bases) Machine Learning in Materials Science ; Ultrasonics and Acoustic Wave Propagation ; High-Velocity Impact and Material Behavior
Authors: Kham Lek Chaton | Eric D. Boittier | M. Devereux | Markus Meuwly
Source: arXiv (Cornell University) (preprint, 2026-03-15)
Field: Materials Science — Materials Chemistry

Explicit, Machine-Learned Two-Body Potentials for Molecular Simulations A new pairwise hybrid machine-learning/molecular mechanics (ML/MM) potential is introduced that is conceived for application to large, heterogeneous condensed-phase systems. The PhysNet ML method describes monomers and short-range dimer interactions, while a classical MM force field describes pairwise interactions beyond a defined switching distance. Models are fitted to MP2 dimer and pairwise cluster energies, and the quality of each model is assessed at different switching distances and using MM approaches with and without detailed distributed charge electrostatics. The applicability of the approach to molecular dynamics simulations is demonstrated for a basic implementation applied to a small model system. Dichloromethane and acetone are used as test systems to demonstrate the accuracy of the approach in describing pairwise reference data, and also to highlight the limitations of the pairwise approach for systems that exhibit significant many-body effects in condensed phase, paving the way for the addition of a general many-body correction in future work. Pairwise comparison ; Molecular dynamics ; Field (mathematics) ; Set (abstract data type) ; Force field (fiction) ; Dimer ; Cluster (spacecraft) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Block Copolymer Self-Assembly