jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Reilly Osadchey (22312763) | Kwangho Nam (1318347) | Qiang Cui (214006)
Source: Figshare (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Toward Improving Multiple Time Step QM/MM Simulationswith Δ‑Machine Learning Semiempirical methods are popular for QM/MM simulations of chemical reactions in the condensed phase due to their immense speedup compared to higher-level <i>ab initio</i> or density functional theory (DFT) methods but can be much less accurate depending on the system of interest. Multiple time step (MTS) methods can improve the accuracy of semiempirical QM/MM simulations by using higher-level calculations at less frequent outer time steps, yet accurate results and efficient sampling require the low- and high-level methods to be sufficiently similar. In this work, we show the limitations of standard semiempirical methods (e.g., AM1) for MTS applications. In a condensed-phase reaction of proton transfer from water to methyl phosphate, for which DFT (B3LYP) is used as the high-level method, we can only reach an outer time step of 4, even with the stochastic isokinetic thermostat. We then explore the value of employing Δ-machine learning to enhance the efficiency of MTS QM/MM simulations. As an initial step, we train neural network potentials and Δ-learning corrections to the AM1 method for the corresponding gas-phase reaction. We show that Δ-corrections outperform ML potentials and that the amount of training data has a major impact on the accuracy and transferability of the learned correction. In a gas-phase MTS simulation with appropriate machine-learned Δ-corrections, we can reach an outer integration frequency of 25 for a nearly exact result compared to B3LYP and 30 if we accept an error of 0.3 kcal·mol<sup>–1</sup> for the free energy profile. The study validates and provides guidance to Δ-learning based MTS simulations, setting the stage for future development and realistic applications in the condensed phase. Speedup ; Transferability ; Sampling (signal processing) ; Artificial neural network ; Phase (matter) ; Energy (signal processing) ; Importance sampling ; Experimental data Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Peter Hedström | Victor Lamelas Cubero | Jón Sigurdsson | Viktor Österberg | Satish Kolli | Joakim Odqvist | Zhiyuan Hou | Wangzhong Mu | Viswanadh Gowtham Arigela
Source: ArXiv.org (preprint, 2025-11-21)
Field: Materials Science — Materials Chemistry

Physics-Informed Machine Learning for Steel Development: A Computational Framework and CCT Diagram Modelling Machine learning (ML) has emerged as a powerful tool for accelerating the computational design and production of materials. In materials science, ML has primarily supported large-scale discovery of novel compounds using first-principles data and digital twin applications for optimizing manufacturing processes. However, applying general-purpose ML frameworks to complex industrial materials such as steel remains a challenge. A key obstacle is accurately capturing the intricate relationship between chemical composition, processing parameters, and the resulting microstructure and properties. To address this, we introduce a computational framework that combines physical insights with ML to develop a physics-informed continuous cooling transformation (CCT) model for steels. Our model, trained on a dataset of 4,100 diagrams, is validated against literature and experimental data. It demonstrates high computational efficiency, generating complete CCT diagrams with 100 cooling curves in under 5 seconds. It also shows strong generalizability across alloy steels, achieving phase classification F1 scores above 88% for all phases. For phase transition temperature regression, it attains mean absolute errors (MAE) below 20 °C across all phases except bainite, which shows a slightly higher MAE of 27 °C. This framework can be extended with additional generic and customized ML models to establish a universal digital twin platform for heat treatment. Integration with complementary simulation tools and targeted experiments will further support accelerated materials design workflows. Generalizability theory ; Continuous cooling transformation ; Key (lock) ; Transformation (genetics) ; Machine tool ; Obstacle ; Diagram Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metallurgical Processes and Thermodynamics
Authors: Yushun Chen | Zhiyong Xu | Tan Li | Wenbo Zhao
Source: The Journal of Physical Chemistry A (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

AILD: An Interpretable AI Framework for Designing Logical Descriptors in Predicting CO Adsorption on Catalysts A rigorous understanding of structure-property relationships is pivotal for the rational design of high-performance catalysts, which requires descriptors that are both predictive and physically interpretable. To overcome the limitations of empirical metrics and the opacity of machine-learning "black boxes", we propose a 4-stage Artificial Intelligence-guided Logical Descriptors (AILD) framework: (1) Knowledge-driven feature generation, (2) Computational data and preprocessing, (3) Feature engineering and predictive modeling, and (4) Mechanistic interpretation and experimental guidance. By uniting domain knowledge, first-principles simulation, and interpretable machine learning, this framework links mechanism to design and advances catalyst development beyond empirical trial-and-error toward a knowledge-driven, science-based paradigm. To validate these logical descriptors, we have implemented them in the open-source AILD software, enabling reproducible research and accelerated rational catalyst design. Feature (linguistics) ; Domain (mathematical analysis) ; Interpretation (philosophy) ; Mechanism (biology) ; Applicability domain ; Relation (database) ; Rational design ; Domain knowledge Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Alfredo Pasquarello
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Chemistry — Electrochemistry

(<i>Invited</i>) Electron Transfer at Transition-Metal-Oxide/Water Interfaces In this presentation, we focus on charge hopping processes occurring at transition-metal oxide/water interfaces, such as they might be relevant in describing the water splitting mechanism. In the first part of the presentation, we focus on charge migration across the bulk of the transition-metal oxide. In these materials, the charge is often found in localized polaronic states, which are particularly challenging to describe computationally because of the necessity of properly accounting for self-interaction effects. Focusing on hole migration in TiO 2 polymorphs (anatase and rutile), we turn to piecewise linear functionals to determine binding energies and hopping barriers [1]. In anatase, the charge is found to hop from one localized polaron state to a neighboring state of the same kind. At variance, in rutile, the charge diffusion occurs through intermediate states which are fully delocalized. In the second part of the presentation, we specifically focus on electron transfer across the semiconductor-water interface. One specific interest of our investigation is to determine to what extent the thermal motion of the water component affects the electron transfer mechanism and the involved kinetic barriers. In this case, we focus on the first proton coupled electron transfer across the BiVO 4 (010)-water interface [2], which appears to be the rate-determinant step of the oxygen evolution reaction (OER) at this interface. To determine the free-energy surface while keeping a hybrid-functional level of accuracy, we utilize machine learning potentials. In this way, we are able not only to determine the kinetic barrier of the OER process but also to provide insight into the underlying atomistic mechanism. One of the issues addressed is the effect of static [3] vs dynamic water [2] on the hopping barrier of the electron transfer across the interface. It is found that the impact of the water dynamics is essential, leading not only to a significantly lowering of the barrier, but also to a qualitatively different reaction path [2]. [1] G. Palermo, S. Falletta, and A. Pasquarello, Migration of hole polarons in anatase and rutile TiO 2 through piecewise-linear functionals, Phys. Rev. B, 2024 , 110 , 235205. [2] Y. Zhuang and A. Pasquarello, unpublished. [3] S. Liu, J. Wiktor, and A. Pasquarello, Oxygen evolution at the BiVO 4 -water interface: Mechanism of the water dehydrogenation reaction, ACS Catalysis , 2022 , 12 , 11734-11742. Oxide ; Electron transfer ; Transition metal ; Electron ; Materials science ; Chemical physics ; Metal ; Nanotechnology ; Chemistry ; Metallurgy ; Physics ; Photochemistry ; Nuclear physics Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies ; Analytical Chemistry and Sensors
Authors: Tom W. A. Montgomery | Adrian Varela-Alvarez | Sam Genway | Philip L. Llewellyn | Phalgun Lolur
Source: ArXiv.org (preprint, 2025-05-07)
Field: Chemistry — Inorganic Chemistry

A Multi-Scale Quantum Framework for Evaluating Metal-Organic Frameworks in Carbon Capture Metal Organic Frameworks (MOFs) are promising materials to help mitigate the effects of global warming by selectively absorbing $\text{CO}_{2}$ for direct capture. Accurate quantum chemistry simulations are a useful tool to help select and design optimal MOF structures, replacing costly or impractical experiments or providing chemically inspired features for data-driven approaches such as machine learning. However, applying simulations over large datasets requires efficient simulation methods such as Density Functional Theory (DFT) which, despite often being accurate, introduces uncontrolled approximations and a lack of systematic improvability. In this work we outline a hierarchical cluster model that includes a recently developed quantum embedding that provides a more systematic approach to efficiently tune accuracy. We apply this workflow to calculate the binding affinity for a small set of MOF structures and $\text{CO}_{2}$ using experimentally measured heat of adsorption as a reference. Since quantum embeddings have also been proposed as a framework to accelerate the utility of quantum hardware, we discuss some of the benefits and challenges of integrating quantum solvers into the workflow outlined in this work. Workflow ; Quantum ; Set (abstract data type) ; Embedding ; Density functional theory ; Metal-organic framework ; Work (physics) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Yair Davidson | Aviad Philipp | Sabyasachi Chakraborty | Alex Bronstein | Renana Gershoni‐Poranne
Source: ChemRxiv (preprint, 2025-01-14)
Field: Materials Science — Materials Chemistry

How Local is `Local'? Deep Learning Reveals Locality of the Induced Magnetic Field of Polycyclic Aromatic Hydrocarbons We investigate the locality of magnetic response in polycyclic aromatic molecules using a novel deep-learning approach. Our method employs graph neural networks (GNNs) with a graph-of-rings representation to predict Nucleus-Independent Chemical Shifts in the space around the molecule. We train a series of models, each time reducing the size of the largest molecules used in training. The accuracy of prediction remains high (MAE < 0.5 ppm), even when training the model only on molecules with up to 4 rings, thus providing strong evidence for the locality of magnetic response. To overcome the known problem of generalization of GNNs, we implement a k-hop expansion strategy and succeed in achieving accurate predictions for molecules with up to 15 rings (almost 4 times the size of the largest training example). Our findings have implications for understanding the magnetic response in complex molecules and demonstrate a promising approach to overcoming GNN scalability limitations. Furthermore, the trained models enable rapid characterization, without the need for more expensive DFT calculations. Locality ; Scalability ; Chemical space ; Molecule ; Computer science ; Graph ; Generalization ; Aromaticity ; Representation (politics) ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Chemistry ; Mathematics ; Drug discovery ; Organic chemistry Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; CO2 Reduction Techniques and Catalysts
Authors: Shijia Xiang | Yijun Sheng
Source:  (article, 2025-08-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of Space-Time-Coding Metasurface Phase Sequences via Physics-Constrained Inverse Neural Networks A new physics-constrained inverse neural networks is proposed for multi-frequency synthesis of space-time-coding digital metasurfaces (STC-DMs). For STC-DM, knowing the time-coded sequence for each cell, the equivalent current associated with each harmonic for that cell can be obtained. These equivalent currents are used as inputs for a deep neural network, the corresponding coding sequence can be reconstructed through the physics-constrained inverse DNN. This approach enables the independent and simultaneous synthesis of multiple harmonics. Artificial neural network ; Inverse ; Sequence (biology) ; Inverse problem ; Phase (matter) ; Harmonic ; Waveform Metamaterials and Metasurfaces Applications ; Antenna Design and Optimization ; Advanced Wireless Communication Technologies
Authors: Murilo Neco Saraiva | Cléber Renato Mendonça
Source: Journal of Applied Physics (article, 2025-10-08)
Field: Materials Science — Ceramics and Composites

Assessing the potential of machine learning for predicting the nonlinear refractive index of glasses Glass is a fundamental material in various applications, from telecommunication to healthcare and energy. Understanding the relationship between the glass matrix composition and the optical properties is crucial for developing advanced photonic materials. Machine learning (ML) provides a powerful tool to model complex, nonlinear relationships in materials science, enabling predictive insights that surpass conventional approaches. In this work, we explore the feasibility of five ML algorithms to predict the nonlinear refractive index (n2) of glasses based on their chemical composition. Data were compiled from the literature and analyzed separately as oxide and chalcogenide datasets. The predictor variables were preprocessed, and the ML models were tuned and evaluated using stratified train-test splits with leave-one-out cross-validation and nested cross-validation. Ensemble-based ML models, such as Random Forest Regressor (RF) and Gradient Boosting Regressor (GB), consistently outperformed others, with GB achieving the lowest Mean Absolute Percentage Error (MAPE) on oxides (28% ± 8%) and RF showing stable performance on chalcogenides (13% ± 3%). MLPRegressor and Support Vector Regression showed high variability, while Linear Regression performed poorly. These findings highlight the potential of ML-driven approaches for accelerating the discovery and optimization of nonlinear optical materials. However, the small dataset used in this study reflects a common limitation in the field, which restricts the models’ reliability and generalizability for predicting new data, potentially leading to overly optimistic performance metrics. The results should be considered preliminary, serving as proof-of-concept for the application of ML to nonlinear optical materials. Random forest ; Generalizability theory ; Nonlinear system ; Support vector machine ; Regression ; Gradient boosting ; Nonlinear regression ; Mean squared error ; Refractive index ; Boosting (machine learning) Glass properties and applications ; Laser Material Processing Techniques ; Phase-change materials and chalcogenides
Authors: Hao Gao | Yue-Wen Fang | Ion Errea
Source: ArXiv.org (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Iterative learning scheme for crystal structure prediction with anharmonic lattice dynamics First-principles based crystal structure prediction (CSP) methods have revealed an essential tool for the discovery of new materials. However, in solids close to displacive phase transitions, which are common in ferroelectrics, thermoelectrics, charge-density wave systems, or superconducting hydrides, the ionic contribution to the free energy and lattice anharmonicity become essential, limiting the capacity of CSP techniques to determine the thermodynamical stability of competing phases. While variational methods like the stochastic self-consistent harmonic approximation (SSCHA) accurately account for anharmonic lattice dynamics \emph{ab initio}, their high computational cost makes them impractical for CSP. Machine-learning interatomic potentials offer accelerated sampling of the energy landscape compared to purely first-principles approaches, but their reliance on extensive training data and limited generalization restricts practical applications. Here, we propose an iterative learning framework combining evolutionary algorithms, atomic foundation models, and SSCHA to enable CSP with anharmonic lattice dynamics. Foundation models enable robust relaxations of random structures, drastically reducing required training data. Applied to the highly anharmonic H$_3$S system, our framework achieves good agreement with the benchmarks based on density functional theory, accurately predicting phase stability and vibrational properties from 50 to 200 GPa. Importantly, we find that the statistical averaging in the SSCHA reduces the error in the free energy evaluation, avoiding the need for extremely high accuracy of machine-learning potentials. This approach bridges the gap between data efficiency and predictive power, establishing a practical pathway for CSP with anharmonic lattice dynamics. Anharmonicity ; Lattice (music) ; Stability (learning theory) ; Crystal structure ; Energy landscape ; Statistical mechanics ; Molecular dynamics ; Crystal structure prediction ; Limiting Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Physical and Chemical Molecular Interactions
Authors: Jiafu Yang | Dayu Hu | Pengcheng Xing | Yikai Zhang | Zongjian Ye | Kehan Liu | Jieyi Xia | Jing He | Yijing Qian | Tianshu Wu
Source: Toxics (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Machine Learning Framework for Multi-Endpoint Quantum Dot Toxicity Prediction with Organoid Validation and Drug Target Discovery Quantum dots (QDs) possess unique optical and electronic properties, enabling wide applications in biomedicine and optoelectronics, but their nanoscale size and surface chemistry could pose potential toxicity risks. This study established a systematic, multi-endpoint framework for QD toxicity assessment. Physicochemical properties of various QDs and their multiple toxicity endpoints, including cell death, inflammation, and oxidative stress, were collected to build machine learning models (RF, XGBoost, KNN, SVM). The predictive toxic effects were then validated based on the brain organoid. Shapley Additive exPlanations (SHAP) analysis revealed that exposure dose and particle size were key cross-model drivers, while zeta potential and optical properties differentially affected specific toxicity endpoints. Integration of GEO-derived differentially expressed genes with protein-protein interaction networks and molecular docking showed that the proteasome inhibitor Carfilzomib is an efficient interventive drug because of its strongest binding to core targets. In this study, the framework of prediction, validation and intervention effectively evaluated multi-endpoint QD toxicity and provided a systematic approach for safety assessments and strategy developments of nanomaterials. Toxicity ; Quantum dot ; Deep learning ; Nanomedicine ; Drug ; Drug discovery ; Cheminformatics ; Nanoparticle Quantum Dots Synthesis And Properties ; Cancer Research and Treatment ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Yuzhe Zhang (696653) | Zhe Liu (77240) | Yang Yang (45629)
Source: Figshare (article, 2025-05-23)
Field: Materials Science — Materials Chemistry

Finite-Temperature Double Proton Transfer in FormicAcid Dimer via Constrained Nuclear-Electronic Orbital Molecular Dynamics:Lower Barriers and Enhanced Rates from Nuclear Quantum Delocalization Proton transfer plays a crucial role in various chemical and biological processes, yet accurately and efficiently describing such reactions remains challenging due to nuclear quantum effects (NQEs). In this work, we employ constrained nuclear-electronic orbital molecular dynamics (CNEO–MD), a method that inherently incorporates NQEs into classical dynamics to investigate double proton transfer in the formic acid dimer (FAD). Leveraging machine learning techniques, we efficiently construct free energy landscapes with NQEs at finite temperatures and obtain transition state theory (TST) rates. Transmission coefficients are further evaluated using flux-side correlation functions to obtain corrected rates that account for recrossing effects. CNEO–MD predicts significantly lowered free energy barriers and enhanced rates compared to conventional DFT-based <i>ab initio</i> molecular dynamics (AIMD), agreeing qualitatively with previous path-integral simulations. Additionally, we explore solvent effects using CNEO–MD with a dielectric continuum solvent model and find the change of the solvent model makes negligible effects. Our findings demonstrate the effectiveness of CNEO–MD in describing proton transfer reactions at finite temperatures and highlight its potential for applications in more complex systems. Delocalized electron ; Proton ; Formic acid ; Dimer ; Molecular dynamics ; Molecular orbital ; Solvent effects ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Saaketh Desai | Prasad P. Iyer | Rémi Dingreville
Source: ArXiv.org (preprint, 2025-12-02)
Field: Materials Science — Materials Chemistry

Learning interpretable surface elasticity properties from bulk properties Surface elasticity is central to understanding the mechanics and stability of surfaces and interfaces. It is characterized by quantities such as surface tension, residual surface stress, and surface stiffness, however their analytical expressions are typically difficult to derive from atomistic data, and depend strongly on modeling choices. This work presents a neural network-based equation learner which combines customized activation functions and connection-based pruning to discover parsimonious, closed-form equations for surface elasticity from atomistic simulations. Applying the method to seven face centered cubic (FCC) metals, our equation learner uncovers interpretable equations that describe both low-Miller index and high-Miller index surface properties, capturing long-tail property distributions accurately. The discovered expressions are decoupled into two components: a universal, geometry-driven orientation function, and material-specific baseline coefficients. We find that lower-order properties such as surface tension are fundamentally geometry dependent, while higher-order properties such as surface stress and elasticity show more complex geometry and material dependence. We also relate material dependent coefficients to bulk properties, forming a clear map from bulk material properties to surface elasticity. Overall, this approach demonstrates that interpretable neurosymbolic machine learning can bridge the gap between atomistic simulations and physical laws, enabling the discovery of generalizable structure-property relationships for materials science phenomena such as surface elasticity. Elasticity (physics) ; Surface stress ; Surface (topology) ; Surface tension ; Material properties ; Residual stress Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems