jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ole‐Christian Galbo Engstrøm | Michela Albano‐Gaglio | Erik Schou Dreier | Yamine Bouzembrak | Maria Font‐i‐Furnols | Puneet Mishra | Kim Steenstrup Pedersen
Source: Journal of Chemometrics (article, 2025-07-16)
Field: Chemistry — Analytical Chemistry

Transforming Hyperspectral Images Into Chemical Maps: A Novel End‐to‐End Deep Learning Approach ABSTRACT Current approaches to chemical map generation from hyperspectral images are based on models such as partial least squares (PLS) regression, generating pixel‐wise predictions that do not consider spatial context and suffer from a high degree of noise. This study proposes an end‐to‐end deep learning approach using a modified version of U‐Net and a custom loss function to directly obtain chemical maps from hyperspectral images, skipping all intermediate steps required for traditional pixel‐wise analysis. The U‐Net is compared with the traditional PLS regression on a real dataset of pork belly samples with associated mean fat reference values. The U‐Net obtains a test set root mean squared error that is 7% lower than that of PLS regression on the task of mean fat prediction. At the same time, U‐Net generates fine detail chemical maps where 99.91% of the variance is spatially correlated. Conversely, only 2.37% of the variance in the PLS‐generated chemical maps is spatially correlated, indicating that each pixel‐wise prediction is largely independent of neighboring pixels. Additionally, while the PLS‐generated chemical maps contain predictions far beyond the physically possible range of 0%–100%, U‐Net learns to stay inside this range. Thus, the findings of this study indicate that U‐Net is superior to PLS for chemical map generation. Hyperspectral imaging ; End-to-end principle ; Artificial intelligence ; Computer science ; Deep learning ; Remote sensing ; Pattern recognition (psychology) ; Computer vision ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote-Sensing Image Classification
Authors: Matthew Witman | Brian Davis | Vitalie Stavila | Terry A. Johnson
Source: ChemRxiv (preprint, 2025-02-19)
Field: Materials Science — Materials Chemistry

Machine learning-guided materials and system co-design for high-pressure hydrogen compression Cost effective and reliable hydrogen compression remains a challenging barrier in the wide-spread adoption of hydrogen as an energy carrier. The prevailing technology of mechanical compression suffers from several drawbacks, some of which can be addressed by non-mechanical compression strategies (e.g., electrochemical or metal hydride-based thermal compression). Thermally driven metal hydride compression strategies typically rely on multi-stage metal hydride-based compressors; however, discovering or optimizing low-stability metal hydrides that can pressurize hydrogen upwards of 1000 bar is difficult, both with respect to computational predictions and experimental validation. Here we (1) demonstrate that simple machine learning-derived design rules can inform rational design of alloying strategies yielding low-stability hydrides, (2) validate their experimental pressure-composition-temperature (PCT) isotherms up to 875 bar, and (3) utilize a dynamic systems-level model of a metal hydride compressor design to evaluate their performance under realistic operating conditions. Importantly, this analysis yields predicted operational efficiencies of both 2-stage (90-875 bar) and 3-stage (20-875 bar) metal hydride compressors to enable further evaluation of this technology and its techno-economic outlook. Compression (physics) ; Hydrogen ; High pressure ; Materials science ; Computer science ; Process engineering ; Chemistry ; Composite material ; Engineering ; Engineering physics ; Organic chemistry Machine Learning in Materials Science ; Mechanical Failure Analysis and Simulation ; Advanced ceramic materials synthesis
Authors: Hongtao Xu | Zibo Wu | Mingzhen Li | Weile Jia
Source:  (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Large-Scale Neural Network Quantum States for ab initio Quantum Chemistry Simulations on Fugaku Solving quantum many-body problems is one of the fundamental challenges in quantum chemistry. While neural network quantum states (NQS) have emerged as a promising computational tool, its training process incurs exponentially growing computational demands, becoming prohibitively expensive for large-scale molecular systems and creating fundamental scalability barriers for real-world applications. To address the above challenges, we present QChem-Trainer, a highperformance NQS training framework for ab initio electronic structure calculations. First, we propose a scalable sampling parallelism strategy with multi-layer workload division and hybrid sampling scheme, which breaks the scalability barriers in large-scale NQS training. Second, we introduce multi-level energy calculation parallelism, enabling more efficient local energy computation in NQS training. Last, we employ cachecentric optimization for transformer-based ansatz and incorporate it with sampling parallelism strategy, which further accelerates the NQS training and achieves stable memory footprints at scale. Experiments demonstrate that QChem-Trainer provides up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8.41 \times$</tex> speedup in NQS training and attains a parallel efficiency of up to 95.8% when scaling to 1,536 nodes. Scalability ; Speedup ; Quantum ; Quantum computer ; Ansatz ; Ab initio ; Sampling (signal processing) ; Artificial neural network Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Teh Yit Hou | Min Yuan
Source: Integrative Science Advances (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

When Chemistry Meets Physics: Synergistic Effects in Materials Science The historical demarcation between chemistry and physics has increasingly blurred within the modern paradigm of materials science. This discipline thrives on the synergistic integration of chemical principles, which govern atomic and molecular interactions and synthesis, with physical laws, which dictate macroscopic properties and phenomena. This review article comprehensively examines this synergy, arguing that the most profound advancements in contemporary materials research occur at the intricate interface of these two foundational sciences. We begin by exploring the theoretical and computational frameworks, such as density functional theory (DFT) and molecular dynamics (MD), which provide a physical basis for understanding chemical bonding and reactions. Subsequently, we analyze how advanced physical characterization techniques-including scanning probe microscopies, synchrotron-based X-ray methods, and ultrafast spectroscopy-elucidate atomic-scale chemical structures and dynamics. The core of this review presents case studies where this chemistry-physics synergy has been pivotal. These include the development of high-performance organic-inorganic perovskite photovoltaics, the design of multifunctional metal-organic frameworks (MOFs), the engineering of 2D materials beyond graphene, and the creation of bio-inspired self-healing polymers. In each case, we demonstrate how chemical synthesis and manipulation are guided by physical insights to achieve targeted electronic, optical, mechanical, and catalytic properties. Finally, we discuss emerging frontiers and future directions, emphasizing the role of machine learning in accelerating materials discovery and the growing importance of controlling non-equilibrium states. This review underscores that the continued convergence of chemical and physical perspectives is not merely beneficial but essential for addressing the grand challenges in energy, sustainability, and information technology. Characterization (materials science) ; Interface (matter) ; Paradigm shift ; Physical science ; Grand Challenges ; Chemical process Machine Learning in Materials Science ; 2D Materials and Applications ; Chemical and Physical Properties of Materials
Authors: Jueyi Ye | Zhijie He | Li Ma | Keyuan Chen | Ju Rong | Yudong Sui | Xiangjie Fu | Xiaohua Yu | Jing Feng
Source: Journal of Electronic Materials (article, 2025-09-15)
Field: Materials Science — Materials Chemistry

Efficient Deep Learning Rhodium Potential and Feasibility Validation in Large-Scale Molecular Dynamics Simulations Molecular dynamics ; Density functional theory ; Rhodium ; Interatomic potential ; Phonon ; Thermal conductivity ; Deep learning ; Potential of mean force ; Embedded atom model ; Melting point Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies
Authors: Takeyuki Kato | Ryuki Kayano | Takahiro Ohkubo
Source: The Journal of Physical Chemistry B (article, 2025-08-08)
Field: Materials Science — Ceramics and Composites

Machine-Learning Molecular Dynamics Study on the Structure and Glass Transition of Calcium Aluminosilicate Glasses Calcium aluminosilicate (CAS) glass systems represent an important class of materials for industrial applications due to their superior thermal and mechanical properties. Although CAS glasses have been extensively studied, the structure-property relationships, particularly in the peraluminous region (Al<sub>2</sub>O<sub>3</sub>/CaO > 1), remain insufficiently understood. Experimental studies have identified the presence of five-coordinated aluminum (Al<sup>5</sup>) depending on the Al<sub>2</sub>O<sub>3</sub> content; however, classical molecular dynamics simulations have struggled to accurately reproduce the aluminum coordination environment. To address this limitation, we developed a machine-learning potential tailored for CAS systems, trained on a comprehensive molecular dynamics simulation based on density functional theory data set and refined using a fine-tuning approach. Melt-quench simulations were then carried out to model CAS glass structures. The resulting structures from machine learning-based molecular dynamics (MLMD) accurately reproduced both experimental glass densities and the fractions of Al<sup>5</sup>, including the observed increase in Al<sup>5</sup> and oxygen triclusters (TBOs) in the peraluminous region. In addition, we performed heating simulations to evaluate enthalpy changes and structural evolution as a function of temperature. Analogous to differential scanning calorimetry experiments, the glass transition temperature (<i>T</i><sub>g</sub>) was determined from the MLMD data. The compositional dependence of Al<sup>5</sup> and TBO near the <i>T</i><sub>g</sub> was quantitatively analyzed, providing insights into the role of aluminum in structural rearrangements. These findings demonstrate that MLMD enables the accurate modeling of CAS glass structures and yields valuable insights into their thermal behavior. This approach offers a robust framework for understanding structure-property relationships in complex glass systems. Aluminosilicate ; Glass transition ; Molecular dynamics ; Differential scanning calorimetry ; Calcium aluminosilicate ; Thermal ; Enthalpy ; Chemical physics ; Aluminium ; Materials science ; Chemistry ; Thermodynamics ; Mineralogy ; Computational chemistry ; Physics ; Composite material ; Polymer Glass properties and applications ; Pigment Synthesis and Properties ; Material Dynamics and Properties
Authors: Lino Garda Denaro | Shu-Yen Lin | Cho‐ying Huang
Source: Applied Spectroscopy Practica (article, 2025-06-01)
Field: Chemistry — Analytical Chemistry

Spectroscopic Assessment of Flavor-Related Chemical Compounds in Fresh Tea Shoots Using Deep Learning This study employs a deep-learning method (Y-Net) to estimate 10 tea flavor-related chemical compounds (TFCCs) using fresh tea shoot reflectance and transmittance. The unique aspect of Y-Net lies in its utilization of dual inputs, reflectance, and transmittance, which are seamlessly integrated within the Y-Net architecture. This architecture harnesses the power of a convolutional neural network-based residual network to fuse tea shoot spectra effectively. This strategic combination enhances the capacity of the model to discern intricate patterns in the optical characteristics of fresh tea shoots, providing a comprehensive framework for TFCC estimation. In this study, we sampled tea shoots in Alishan, Central Taiwan, and measured their reflectance and transmittance ( n = 2032) within the optical region (400–2500 nm) using a portable spectroradiometer. Corresponding TFCCs were quantified using the high-performance liquid chromatography analysis. To enhance Y-Net, we employed data augmentation techniques for model training. We compared the performances of Y-Net with seven other commonly utilized statistical, machine-/deep-learning models. Furthermore, we assessed the prediction accuracies of Y-Net, and Y-Net using spectra within the visible–near-infrared (Vis-NIR) regions (for higher energy throughput and low-cost instruments) and reflectance only (for airborne/spaceborne remote sensing applications). The results showed that overall Y-Net, i.e., mean root mean square error (RMSE) ± standard deviation (SD) = 0.65 ± 0.58 mg g –1 , outperformed the other statistical, machine and deep learning models (≥2.33 ± 2.14 mg g –1 ), demonstrating its superiority in predicting TFCCs. In addition, this original Y-Net also yielded significantly lower mean RMSE ± SD compared with Vis-NIR (3.15 ± 3.35 mg g –1 ) and reflectance-only (2.23 ± 2.25 mg g –1 ) Y-Nets using validation data. This study highlights the feasibility of using spectroscopy and Y-Net to assess minor biochemical components in fresh tea shoots and sheds light on the potential of the proposed approach for effective regional monitoring of tea shoot quality. Shoot ; Mean squared error ; Spectroradiometer ; Remote sensing ; Artificial intelligence ; Transmittance ; Convolutional neural network ; Reflectivity ; Environmental science ; Computer science ; Chemistry ; Mathematics ; Horticulture ; Materials science ; Optics ; Statistics ; Biology ; Geography ; Physics ; Optoelectronics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Tea Polyphenols and Effects
Authors: Kunwoo Kim | Jejoong Yoo
Source: Biophysical Journal (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

BPS2025 – Deep-learning-based force field for coarse-grained molecular dynamics Force field (fiction) ; Field (mathematics) ; Molecular dynamics ; Dynamics (music) ; Computer science ; Statistical physics ; Artificial intelligence ; Physics ; Chemistry ; Mathematics ; Computational chemistry ; Acoustics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Nuclear Physics and Applications
Authors: Zhen Ren | Yu Liu | Sen Zhang
Source: PLoS ONE (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

PairReg: A method for enhancing the learning of molecular structure representation in equivariant graph neural networks The 3D structure of molecules contains a wealth of important information, but traditional 3DCNN-based methods fail to adequately address the transformations of rigid motions (rotation, translation, and mapping). Equivariant graph neural networks (EGNNs) emerge as efficient models to handle molecular 3D structures due to their unique mechanisms for capturing topological properties and equivariance to rigid motions. Historically, the optimization of EGNN models has been achieved by incorporating higher-order features to capture more complex topological properties. However, adding higher-order features incurs high computational costs. To address this issue, we explore the mechanism to mitigate the oversmoothing of equivariant graph neural networks and propose a new method (PairReg) to mitigate oversmoothing by utilizing equivariant information, such as coordinates, to enhance the model's performance. We validate the performance of the model using the QM9 dataset and conduct ablation experiments on the rMD17 dataset. The results show that our approach enhances the model's ability to characterize the 3D structure of molecules and offers new insights for enhancing the performance of EGNNs. Equivariant map ; Computer science ; Translation (biology) ; Artificial neural network ; Representation (politics) ; Graph ; Topology (electrical circuits) ; Theoretical computer science ; Artificial intelligence ; Algorithm ; Biological system ; Mathematics ; Biology ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Jooyoung Roh (21989941) | Kang-Ting Huang (1521796) | Anmol Choudhury (21989944) | Tairan Quan (21989947) | Chun-Jen Huang (436555) | Priyank Kumar (285156) | Cyrille Boyer (1352049)
Source: Figshare (article, 2025-08-06)
Field: Chemistry — Organic Chemistry

Machine Learning-GuidedDesign of High-PerformanceAntifungal Polymers against <i>Candida albicans</i> This study leverages machine learning (ML) to accelerate the development of antifungal synthetic polymers. An in-house curated data set was used to train a Random Forest binary classification model, which identified five critical features for predicting antifungal activity against the fungal pathogen Candida albicans. This model shows that antifungal polymers should contain a hydrophilic composition of at least 30%, a calculated partition coefficient (cLogP) of 0 to +0.5, hydrophobic composition limited to 20%, degree of polymerization (DP) of 18 or less, and cationic composition limited to 50% for the polymer to have an MIC<sub>90</sub> of 64 μg/mL or less. Based on these insights, a library of polymers was synthesized via reversible addition–fragmentation chain transfer (RAFT) polymerization using 2-(butylthiocarbonothioylthio)propanoic acid (BTPA) as the RAFT agent, exploring combinations of these features. A polymer exhibiting all five feature characteristics for an effective antifungal polymer presented an MIC<sub>90</sub> of 32 μg/mLlower than the 64 μg/mL Class 1 “good” polymer threshold. After optimizing the polymer for significant antifungal activity by fulfilling the five features of the antifungal impact, we improved its biocompatibility. We did this by replacing the BTPA RAFT agent with 2-dodecyl thiocarbonothioylthio-propanoic acid (DTPA), which has a longer, more hydrophobic end-group. This modification resulted in a polymer with an MIC90 of 16 μg/mL and a hemolysis concentration (HC50 exceeding 2000 μg/mL, yielding a selectivity index greater than 125aligning with the most competent polymer involved in the ML data set. This work validates the efficiency of ML-guided design for developing advanced antifungal polymers with enhanced potency and biocompatibility. Polymer ; Antifungal ; Cationic polymerization ; Raft ; Polymerization ; Monomer ; Polymer architecture ; Polyethylene Antimicrobial agents and applications ; biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization
Authors: Anju Chandran | Archa Santhosh | Claudio Pistidda | Paul Jerabek | Roland C. Aydin | Christian J. Cyron
Source: Frontiers in Materials (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

TiAlNb alloy interatomic potentials: comparing passive and active machine learning techniques with MTP and DeePMD Intermetallic titanium aluminides are interesting for aerospace and automotive applications due to their superior high-temperature mechanical properties. In particular, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi>γ</mml:mi></mml:mrow></mml:math> -TiAl-based alloys containing 5–10 at.% Niobium (Nb) have attracted significant attention. Molecular dynamics (MD) simulations can elucidate and optimize these materials, provided that accurate interatomic potentials are available. In this work, we compare active and passive machine learning approaches for developing TiAlNb interatomic potentials using both deep potential molecular dynamics (DeePMD) and the moment tensor potential (MTP) methods. Our comprehensive evaluation encompasses elastic constants, equilibrium volume, lattice parameters, and finite-temperature behavior, as well as simulated tension tests and generalized stacking fault energy calculations to assess the impact of Nb on the thermo-mechanical properties of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mrow><mml:mi>γ</mml:mi></mml:mrow></mml:math> -TiAl and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m3"><mml:mrow><mml:msub><mml:mrow><mml:mi>α</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> -Ti 3 Al phases. Active learning consistently outperformed passive learning for both methods while requiring only a fraction of the training samples. Notably, active learning with DeePMD yielded a single potential capable of predicting the properties of both phases, whereas MTP exhibited limitations that necessitated separate training for each phase. Although active learning potentials excelled in predicting high-temperature behavior, their room-temperature property predictions were less accurate due to a sample selection bias toward higher temperatures. Overall, our thermomechanical analysis demonstrates that Nb incorporation enhances ductility while simultaneously reducing strength. Alloy ; Interatomic potential ; Materials science ; Chemical physics ; Computer science ; Nanotechnology ; Chemistry ; Molecular dynamics ; Composite material ; Computational chemistry Machine Learning in Materials Science ; Intermetallics and Advanced Alloy Properties ; MXene and MAX Phase Materials
Authors: Suman Hazra | Urvesh Patil | Stefano Sanvito
Source: The Journal of Chemical Physics (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

A charge-density machine-learning workflow for computing the infrared spectrum of molecules We present a machine-learning workflow for the calculation of the infrared spectrum of molecules and more generally of other temperature-dependent electronic observables. The main idea is to use the Jacobi-Legendre cluster expansion to predict the real-space charge density of a converged density-functional-theory calculation. This gives us access to both energy and forces and to electronic observables such as the dipole moment or the electronic gap. Thus, the same model can simultaneously drive a molecular dynamics simulation and evaluate electronic quantities along the trajectory, namely, it has access to the same information of ab initio molecular dynamics. A similar approach within the framework of machine-learning force fields would require the training of multiple models, one for the molecular dynamics and others for predicting the electronic quantities. The scheme is implemented here within the numerical framework of the PySCF code and applied to the infrared spectrum of the uracil molecule in the gas phase. Infrared ; Workflow ; Dipole ; Observable ; Electronic structure ; Molecular dynamics ; Charge (physics) ; Moment (physics) ; Molecule ; Ab initio Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Sagar Ghorai | Ruben Staub | Yu Harabuchi | Takashi Nakano | Alexandre Varnek | Satoshi Maeda
Source: ChemRxiv (preprint, 2025-10-20)
Field: Materials Science — Materials Chemistry

Towards reaction vessel mimicry: Machine learning assisted automated exploration of alkene and alkyne polymerization International audience Polymerization ; Alkene ; Path (computing) ; Artificial neural network ; Catalysis ; Ethylene ; Alkyne Machine Learning in Materials Science ; Catalysis and Oxidation Reactions