jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qiao Yin | Yuwei Liu | Hongliang Zhao | Jincheng Wang | Shuya Zhang | Yuheng Fan | Xianglei Dong | Yang Song | Chunwen Guo
Source: Materials & Design (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Accelerated Cu-Ti alloy design under data scarcity through fine-tuned transfer learning • Layer-wise unfreezing transfer learning models achieve R 2 > 0.93 for Cu-Ti properties with only 40 samples. • Bayesian optimization identifies optimal CTFC alloy (Cu-4Ti-0.3Fe-0.2Cr) within three active learning iterations. • The CTFC alloy exhibits 1107 MPa UTS, 20.8 % IACS EC by dual dislocation/precipitate strengthening. This study presents a novel machine learning framework using transfer learning (TL) to overcome data scarcity in designing high-performance Cu-Ti alloys with balanced high ultimate tensile strength (UTS) and electrical conductivity (EC). We leveraged a pre-trained model built on a large dataset of multi-system copper alloys and transferred this knowledge to the target Cu-Ti system with only 40 samples. Two TL strategies here were developed: first-layer freezing (TL1) and layer-wise unfreezing (TL2). For the Cu-Ti dataset, TL1 froze only the first hidden layer during training, while TL2 employed a gradual layer-wise unfreezing method. The TL2 models for Cu-Ti alloys achieve superior prediction accuracy (UTS model: R 2 = 0.96, EC model: R 2 = 0.93). Such TL models significantly outperform conventional machine learning models with R 2 < 0.77. The optimized TL2 model guides a Bayesian optimization-driven inverse design with active learning, rapidly identifying an optimal alloy—Cu-4Ti-0.3Fe-0.2Cr (CTFC)—within three iterations. Experimental validation confirms CTFC’s exceptional properties: UTS = 1107 MPa (1.8 % prediction error) and EC = 20.8 % IACS (2.8 % prediction error). Microstructural analysis reveals that the synergistic precipitation of nanoprecipitates β′-Cu 4 Ti, Fe 2 Ti, and Cr enhances both strength and electrical conductivity. This machine learning framework suggests an efficient pathway for designing high-performance materials under constrained data conditions. Alloy ; Precipitation ; Ultimate tensile strength ; Transfer of learning ; Bayesian optimization ; Bayesian probability ; Bayesian inference Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Microstructure and mechanical properties
Authors: Vassilis Gkatsis | Petros Maratos | Christoforos Rekatsinas | George Giannakopoulos | Panagiotis Krokidas
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Density-aware active learning for materials discovery: a case study on functionalized nanoporous materials Machine learning algorithms often rely on large training datasets to achieve high performance. However, in domains like chemistry and materials science, acquiring such data is an expensive and laborious process, involving highly trained human experts and material costs. Therefore, it is crucial to develop strategies that minimize the size of training sets while preserving predictive accuracy. The objective is to select an optimal subset of data points from a larger pool of possible samples, one that is sufficiently informative to train an effective machine learning model. Active learning (AL) methods, which iteratively annotate data points by querying an oracle (<i>e.g.</i>, a scientist conducting experiments), have proven highly effective for such tasks. However, challenges remain, particularly for regression tasks, which are generally considered more complex in the AL framework. This complexity stems from the need for uncertainty estimation and the continuous nature of the output space. In this work, we introduce density-aware greedy sampling (DAGS), an active learning method for regression that integrates uncertainty estimation with data density, specifically designed for large design spaces (DS). We evaluate DAGS in both synthetic data and multiple real-world datasets of functionalized nanoporous materials, such as metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs), for separation applications. Our results demonstrate that DAGS consistently outperforms both random sampling and state-of-the-art AL techniques in training regression models effectively with a limited number of data points, even in datasets with a high number of features. Nanoporous ; Oracle ; Active learning (machine learning) ; Regression ; Bayesian optimization ; Sampling (signal processing) ; Synthetic data ; Cheminformatics ; Supervised learning Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Ruoyu Wang (544196) | Shaoheng Fang (21796263) | Qixing Huang (819395) | Yuanyue Liu (1295970)
Source: Figshare (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Constant-Potential Machine Learning Force Field forthe Electrochemical Interface Better understanding and prediction of the electrochemical interface require large-scale atomistic simulations. Machine learning force fields (MLFFs) have proven to be an effective approach. However, current MLFFs typically do not account for the effect of electrode potential, which requires treating interface electrons with a grand canonical ensemble. Here, we develop a constant potential MLFF (CP-MLFF) based on an equivariant graph neural network and implement it into MACE. Specifically, we design an architecture that can take the number of electrons as the input and accurately predict the Fermi level. The CP-MLFF allows us to examine the convergency of the electrochemical barrier with respect to sampling, which we demonstrate through the example of CO<sub>2</sub> reduction on the Ni–N–C catalyst. Our work provides a useful method and tool enabling accurate and efficient large-scale simulation of the electrochemical interface. Interface (matter) ; Artificial neural network ; Work (physics) ; Field (mathematics) ; Electron ; Electrode ; Reduction (mathematics) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Manish Sahoo | Akash A. Deshmukh | Yash Kokane | H M Jayaprakash | Raghavan Ranganathan
Source: ArXiv.org (preprint, 2025-11-16)
Field: Materials Science — Materials Chemistry

An Active Learning Interatomic Potential For Defect-Engineered CoCrFeMnNi High-Entropy Alloy High-entropy alloys (HEAs) exhibit exceptional properties arising from a combination of thermodynamic, kinetic and structural factors and have found applications in numerous fields such as aerospace, energy, chemical industries, hydrogen storage, and ocean engineering. However, a large compositional space remains to be explored. Unlike conventional approaches, computational methods have shown accelerated discovery of novel alloys in a short time. However, the lack of interatomic potentials have posed a challenge in discovering new alloy compositions and property measurements. In the present work, we have developed a Moment Tensor Potential (MTP) trained by Machine Learning based approach using the BFGS unconstrained optimization algorithm for the CoCrFeMnNi High-entropy alloy. Our training set consists of various defects induced configurations such as vacancies, dislocations and stacking-faults. An active learning scheme to re-train the potential was undertaken to dynamically to add training data upon encountering extrapolative configurations during non-equilibrium simulations. A thorough investigation of the error metrics, equation of state, uniaxial tensile deformation, nano-indentation and solid-liquid interface stability for this alloy was carried out, and it is seen that the MTP potential outperforms the popular Modified Embedded Atom Method (MEAM) potential on physical properties prediction. The accuracy and high computational speed are discussed using scaling performance. The potential is prepared for public use by embedding it into the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code. Broyden–Fletcher–Goldfarb–Shanno algorithm ; Interatomic potential ; Alloy ; Embedding ; Tensor (intrinsic definition) ; Property (philosophy) ; Embedded atom model ; Stability (learning theory) ; Position (finance) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: Ran Mo | Zhishuo Huang | Liviu Ungur
Source: Chemical Physics Impact (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Efficient modeling of dynamic properties in K <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si164.svg" display="inline" id="d1e2624"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msub> </mml:math> C60 using machine learning force fields Dynamic properties of the alkali-doped molecular crystal K 3 C 60 are investigated using machine learning force field (MLFF). Our trained force fields successfully reproduce phonons comparable to those from density functional theory 10-times faster in computational efficiency. Specific heat with MLFF also show good agreement with experimental data, demonstrating its reliability for thermodynamic analysis. Two descriptor schemes, Smooth Overlap of Atomic Positions and Atomic Cluster Expansion, are employed and systematically compared. This study represents the first detailed exploration of dynamic properties in molecular crystals using MLFF, highlighting MLFF’s potential in more complex molecular crystals, such as C 60 disorder in fullerides, molecular melting. • Present the first rigorous benchmark of MLFF for molecular crystal dynamics. • MLFF-predicted dynamics properties with good accuracy against DFT results. • ≈ Significant speedup for dynamics properties simulations with MLFF over DFT Computer science ; Algorithm ; Computer graphics (images) Machine Learning in Materials Science ; Fullerene Chemistry and Applications ; Diamond and Carbon-based Materials Research
Authors: Nils Gönnheimer (20420743) | Karsten Reuter (1342152) | Johannes T. Margraf (1707877)
Source: Figshare (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Beyond NumericalHessians: Higher-Order Derivativesfor Machine Learning Interatomic Potentials via Automatic Differentiation The development of machine learning interatomic potentials (MLIPs) has revolutionized computational chemistry by enhancing the accuracy of empirical force fields while retaining a large computational speed-up compared to first-principles calculations. Despite these advancements, the calculation of Hessian matrices for large systems remains challenging, in particular because analytical second-order derivatives are often not implemented. This necessitates the use of computationally expensive finite-difference methods, which can furthermore display low precision in some cases. Automatic differentiation (AD) offers a promising alternative to reduce this computational effort and makes the calculation of Hessian matrices more efficient and accurate. Here, we present the implementation of AD-based second-order derivatives for the popular MACE equivariant graph neural network architecture. The benefits of this method are showcased via a high-throughput prediction of heat capacities of porous materials with the MACE-MP-0 foundation model. This is essential for precisely describing gas adsorption in these systems and was previously possible only with bespoke ML models or expensive first-principles calculations. We find that the availability of foundation models and accurate analytical Hessian matrices offers comparable accuracy to bespoke ML models in a zero-shot manner and additionally allows for the investigation of finite-size and rounding errors in the first-principles data. Bespoke ; Hessian matrix ; Artificial neural network ; Scalability ; Graph ; Deep learning ; Computation Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Graph Neural Networks
Authors: David A. King | Daniel Grzenda | Ruijie Zhu | Nathaniel Hudson | Ian Foster | Bingqing Cheng | Laura Gagliardi
Source: Proceedings of the National Academy of Sciences (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Cartesian equivariant representations for learning and understanding molecular orbitals Qualitative and quantitative orbital properties such as bonding/antibonding character, localization, and orbital energies are critical to how chemists understand reactivity, catalysis, and excited-state behavior. Despite this, representations of orbitals in deep learning models have been very underdeveloped relative to representations of molecular geometries and Hamiltonians. Here, we apply state-of-the-art equivariant deep learning architectures to the task of assigning global labels to orbitals, namely energies characterizations, given the molecular coefficients from Hartree-Fock or density functional theory. The architecture we have developed, the Cartesian Equivariant Orbital Network (CEONET), shows how molecular orbital coefficients are readily featurized as equivariant node features common to all graph-based machine-learned potentials. We find that CEONET performs well at predicting difficult quantitative labels such as the orbital energy and orbital entropy. Furthermore, we find that the CEONET representation provides an intuitive latent space for differentiating orbital character for the qualitative assignment of e.g. bonding or antibonding character. In addition to providing a useful representation for further integrating deep learning with electronic structure theory, we expect CEONET to be useful for automatizing and interpreting the results of advanced electronic structure methods such as complete active space self-consistent field theory. In particular, the ability of CEONET to infer multireference character via the orbital entropy paves the way toward the machine-learned selection of active spaces. Equivariant map ; Antibonding molecular orbital ; STO-nG basis sets ; Molecular orbital ; Fragment molecular orbital ; Representation (politics) ; Atomic orbital ; Non-bonding orbital Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Mingrou Xie | Daniel Schwalbe‐Koda | Yolanda M. Semanate-Esquivel | Estafanía Bello-Jurado | Alexander J. Hoffman | Omar A. Santiago-Reyes | Cecilia Paris | Manuel Moliner | Rafael Gómez‐Bombarelli
Source: ChemRxiv (preprint, 2025-02-27)
Field: Chemistry — Inorganic Chemistry

An exhaustive mapping of zeolite-template chemical space Zeolites are industrial catalysts and adsorbents whose synthesis usually employs specific molecules known as organic structure-directing agents (OSDAs). The OSDA’s templating effect is pivotal in determining the zeolite polymorph formed and its physicochemical properties. However, de novo design of selective OSDAs is challenging because of the diversity and size of the zeolite-OSDA chemical space. Here, a computational workflow powered by machine learning enables an exhaustive exploration of the OSDA space for known zeolites. Models were developed to predict molecule-zeolite binding energies and trained on hundreds of thousands of datapoints, the largest ever library of synthetically accessible, hypothetical OSDA-like molecules was enumerated from commercially available precursors, and nearly 500 million zeolite-molecule pairs were screened. From these, two new OSDAs were identified and validated experimentally to template zeolites with unique compositions. The nearly exhaustive scale of the OSDA library and open-access data are expected to accelerate OSDA design for the entire field. Zeolite ; Space (punctuation) ; Chemical space ; Computer science ; Chemistry ; Organic chemistry ; Catalysis Zeolite Catalysis and Synthesis ; Inorganic and Organometallic Chemistry ; Molecular spectroscopy and chirality
Authors: Khang Huynh | Rajesh V. Shende
Source: Sustainable Chemistry for Energy Materials (article, 2025-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Porous carbon from lignocellulosic biomass with emphasis on corn plant waste residue for energy storage The rising global demand for sustainable energy storage materials has driven the search for environmentally friendly and cost-effective electrode options. Hydrothermal conversion of lignocellulosic biomass has gained attention due to its low energy requirements and operation at relatively low temperatures, presenting a green alternative to traditional thermochemical methods. The resulting solid product, hydrochar, has been used as an adsorbent and soil amendment; however, chemical/thermal treatment significantly enhances its physical properties. These structural modifications transform hydrochar into an effective porous carbon electrode, offering abundant sites for electrolyte ion transport, critical for high-performance devices like supercapacitors and batteries. This review first discusses various waste biomass and sustainable feedstocks available globally. It compares two primary thermochemical conversion techniques, pyrolysis and hydrothermal carbonization/liquefaction, and examines their respective solid products, biochar and hydrochar, analyzing differences in their physical and chemical characteristics. The focus is placed on hydrochar, summarizing activation methods to produce porous carbon suitable for energy storage applications. Additionally, this review will include a dedicated section on the application of porous carbon derived from corn plant waste residue, considering that corn is one of the most abundant crops grown worldwide, which makes it an important and promising source for sustainable porous carbon production. The role of machine learning models in optimizing hydrothermal processes to produce high-quality hydrochar is also discussed, emphasizing how data-driven approaches can streamline process development. Finally, the review identifies the current challenges and prospects for lignocellulosic biomass-derived porous carbon as a sustainable electrode material in next-generation energy storage technologies. • Pyrolysis and HTC/HTL generate sustainable biochar and hydrochar from biomass. • Activation of biochar and hydrochar results in porous carbon (POC). • Corn stover-derived POC is found to be suitable for energy storage. • Challenges include feedstock variability, supply chain, and HTC/HTL scale-up. Biochar ; Pyrolysis ; Biomass (ecology) ; Lignocellulosic biomass ; Hydrothermal carbonization ; Carbon fibers ; Carbon sequestration ; Supercapacitor ; Environmentally friendly Supercapacitor Materials and Fabrication ; Thermochemical Biomass Conversion Processes ; Adsorption and Cooling Systems
Authors: Mohamed  Khalil Elhachimi | Guangfa Li | Mir  Jalil Razavi | Dehao Liu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Inverse Design for Tailoring the Multidirectional Mechanical Properties of Voxel-based Composite Structures Inverse ; Inverse problem ; Anisotropy ; Pipeline (software) ; Finite element method ; Artificial neural network ; Composite number ; Convolutional neural network Machine Learning in Materials Science ; Cellular and Composite Structures ; Topology Optimization in Engineering
Authors: Haizhou Yang | Adam Wold | Junlin Ou | Jeromy James Rech | Wei You | Yi Wang
Source: Journal of Materials Chemistry A (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

OSC-Net: a multi-fidelity machine learning model for organic solar cells OSC-Net, a novel multi-fidelity machine learning framework, enables accurate prediction of organic solar cell (OSC) performance and accelerates OSC materials discovery. Organic solar cell ; Solar energy ; Deep learning ; Photovoltaic system ; Artificial neural network Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: Jun Zhang | Weifeng Su | Yingying Li | Shuaishuai Man | Lifeng Liu | Shijun Zhao | Guang‐Jie Xia
Source: ACS Catalysis (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Boosting Screening of Nonequiatomic High-Entropy Electrocatalysts by Inverse Design via Active Graph Learning High-entropy alloys are promising for enhancing the performance of electrocatalysts. However, the screening and rational design of such catalysts face formidable challenges due to their vast compositional space and diverse local atomic arrangements. Traditional bottom-up approaches of deep-learning algorithms remain limited by their heavy reliance on data sets prepared by density functional theory (DFT), bottlenecking the rapid screening of high-entropy catalysts. To address these limitations, we present an inverse-design strategy based on an active-learning (AL) framework that integrates conditional generative adversarial networks, atomic graph attention networks, k-nearest neighbors, and high-throughput DFT calculations. This top-down framework reduces the required training data set size for high-performance, high-entropy electrocatalyst (HEEC) design. Using the established AL workflow, we systematically explore the compositional space of HEECs composed of Ni, Co, Fe, Pd, and Pt and identify optimized nonequiatomic compositions with high hydrogen evolution activity. Moreover, electronic structure analyses reveal that Pd and Pt are active species, while Ni, Co, and Fe contribute to triggering the “cocktail effect”, which distinguishes HEECs from ordered metals or alloys. Based on these findings, we propose two design principles to guide the discovery of high-performance HEECs: (i) retaining Pd/Pt as essential reaction centers and (ii) utilizing Fe, Co, and Ni to boost entropy for cocktail synergy. This AL-driven approach offers a powerful platform for the accelerated discovery and design of next-generation electrocatalysts. Chemical space ; Boosting (machine learning) ; Generative grammar ; Graph ; Rational design ; Electrocatalyst ; USable ; Adversarial system ; Entropy (arrow of time) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; High Entropy Alloys Studies
Authors: Shinya Sakuma | Shunsaku Yasumura | Kenichiro Saita | Tetsuya Taketsugu | Masaru Ogura
Source: ChemRxiv (preprint, 2025-03-26)
Field: Materials Science — Materials Chemistry

Predicting Catalytic Activity for CH4 Combustion on Pd-exchanged Zeolite Catalysts Using Automated Reaction Route Mapping To address CH4 emissions, one of the major contributors to global warming, developing efficient CH4 combustion catalysts is crucial. While computational predictions of catalytic activity are highly desired, conventional methods struggle with the complexity of reactions on solid catalyst surfaces. We explored reaction mechanisms of CH4 combustion on Pd-exchanged zeolites (Pd-CHA, Pd-beta, and Pd-MOR) by combining neural network potential (NNP) with automated reaction route mapping. The predicted reaction map of CH4 combustion over Pd2+ site revealed partially oxidized species such as CH2O, HCOOH, and bicarbonate as potential intermediates toward CO2 + 2H2O. Activation energies (Ea) of the rate-determining steps (RDS) were evaluated, revealing the order of Ea as Pd-MOR < Pd-beta < Pd-CHA. A kinetic analysis using rate constant matrix contraction (RCMC) method estimated the catalytic activity of these catalysts. Pd-beta and Pd-MOR exhibited no intermediates with significant lifetimes, whereas Pd-CHA showed stable bicarbonate intermediates, decreasing the formation rate of CO2 + 2H2O. Kinetic analysis further predicted the pseudo CO2 formation rate with activity order Pd-MOR > Pd-beta > Pd-CHA, aligning with experimental results. These findings demonstrate the potential of the automated reaction route mapping with NNP for predicting zeolite catalyst activity, enabling pre-screening and rational design of solid catalysts. Catalysis ; Zeolite ; Combustion ; Catalytic combustion ; Chemical engineering ; Chemistry ; Materials science ; Physical chemistry ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Zeolite Catalysis and Synthesis