jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Feng Zhang | Zhuo Ye | Maxim Moraru | Ying Wai Li | Weiyi Xia | Yan Yao | Cai‐Zhuang Wang
Source: ArXiv.org (preprint, 2025-10-01)
Field: Materials Science — Materials Chemistry

exaPD: A highly parallelizable workflow for multi-element phase diagram (PD) construction Phase diagrams (PDs) illustrate the relative stability of competing phases under varying conditions, serving as critical tools for synthesizing complex materials. Reliable phase diagrams rely on precise free energy calculations, which are computationally intensive. We introduce exaPD, a user-friendly workflow that enables simultaneous sampling of multiple phases across a fine mesh of temperature and composition for free energy calculations. The package employs standard molecular dynamics (MD) and Monte Carlo (MC) sampling techniques, as implemented in the LAMMPS package. Various interatomic potentials are supported, including the neural network potentials with near {\it ab initio} accuracy. A global controller, built with Parsl, manages the MD/MC jobs to achieve massive parallelization with near ideal scalability. The resulting free energies of both liquid and solid phases, including solid solutions, are integrated into CALPHAD modeling using the PYCALPHAD package for constructing the phase diagram. Workflow ; Thermodynamic integration ; CALPHAD ; Sampling (signal processing) ; Phase diagram ; Stability (learning theory) ; Monte Carlo method Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Pedro Ojeda-May
Source: Preprints.org (preprint, 2025-01-23)
Field: Materials Science — Materials Chemistry

Comparison of Three Autoencoder-Based Models Using Molecular Dynamics (MD) Simulations Data Molecular dynamics Molecular dynamics (MD) simulations have proven useful in studying the dynamics of Biomolecules, for instance proteins. However, the computational cost for conducting such simulations is high as reflected in the number of core-hours consumed in high performance computing (HPC) clusters. Although some techniques are available for enhancing the sampling of the conformational space, they usually make assumptions about the system by introducing empirical parameters. Machine learning (ML) models can overcome this issue because here, important features of the landscape can be inferred from the data themselves. In this work, we use an autoencoder ML model with three different flavors: Variational, Wasserstein, and Denoising to generate new protein conformations using MD trajectories as the training data. These generated structures can potentially enhance the ensemble of the original MD data. Autoencoder ; Molecular dynamics ; Computer science ; Sampling (signal processing) ; Space (punctuation) ; Artificial intelligence ; Statistical physics ; Algorithm ; Machine learning ; Computational chemistry ; Deep learning ; Chemistry ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Parallel Computing and Optimization Techniques
Authors: Zareen Akhter | Sujay Prabakar
Source: ChemistrySelect (article, 2025-10-28)
Field: Materials Science — Biomaterials

Artificial Intelligence in Collagen‐based Biomaterials Abstract Collagen, the most abundant structural protein in animals, is central to biomedical devices, cosmetics, nutraceuticals and regenerative materials. However, traditional approaches to collagen sourcing, extraction and functionalisation are time‐consuming, empirical and increasingly inadequate in meeting the growing demand for sustainable and high‐performance collagen‐based products. This review explores how artificial intelligence (AI) is transforming collagen research and development (R&amp;D) by enabling predictive, adaptive and data‐driven decision‐making across the full value chain, from raw material selection and extraction optimisation to characterisation, peptide discovery, imaging and product design. The review synthesises over 160 studies and technical reports, offering an application focused overview of how machine learning, deep learning and data modelling are being used to: optimise enzymatic hydrolysis protocols; predict peptide functionality for food, cosmetic and pharmaceutical applications; classify collagen materials through spectroscopic and image‐based data; and design customised formulations using in silico tools. It highlights emerging uses of AI in second harmonic generation imaging for tissue diagnostics, patient‐specific scaffold modelling in biomedicine and AI‐integrated traceability systems that support regulatory compliance and ethical sourcing. In addition to outlining successful implementations, the review addresses current challenges including data scarcity, model interpretability, computational access and ethical concerns around AI‐generated bioactive. It further discusses future directions such as digital twins, multimodal learning and federated data frameworks that could enable real‐time simulation and personalised biomaterial design. Altogether, these developments mark a major change in how collagen is studied and used, highlighting the growing role of smart technologies, clear traceability and environmentally responsible approaches. They also show how essential it is for experts from different fields to work together and for strong digital systems to support progress in collagen research. Traceability ; Biomedicine ; Applications of artificial intelligence ; Data extraction ; Biofabrication ; Sustainability Collagen: Extraction and Characterization ; Nasal Surgery and Airway Studies ; Protein Hydrolysis and Bioactive Peptides
Authors: Moon, Minseok | Hwang, Seungwoo | Kim, Jaesun | Park, Yutack | Hong, Changho | Han, Seungwu
Source: ArXiv.org (preprint, 2025-06-19)
Field: Materials Science — Materials Chemistry

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Ovonic threshold switching (OTS) selectors play a critical role in non-volatile memory devices because of their nonlinear electrical behavior and polarity-dependent threshold voltages. However, the atomic-scale origins of the defect states responsible for these properties are not yet fully understood. In this study, we use molecular dynamics simulations accelerated by machine-learning interatomic potentials to investigate defects in amorphous GeSe. We begin by benchmarking several potential architectures-including descriptor-based models and graph neural network (GNN) models-and show that faithfully representing amorphous GeSe requires capturing higher-order interactions (at least four-body correlations) and medium-range structural order. We find that GNN architectures with multiple interaction layers successfully capture these correlations and structural motifs, preventing the spurious defects that less expressive models introduce. With our optimized GNN potential, we examine twenty independent 960-atom amorphous GeSe structures and identify two distinct defect motifs: aligned Ge chains, which give rise to defect states near the conduction band, and overcoordinated Ge chains, which produce defect states near the valence band. We further correlate these electronic defect levels with specific structural features-namely, the average alignment of bond angles in the aligned chains and the degree of local Peierls distortion around overcoordinated Ge atoms. These findings provide a theoretical framework for interpreting experimental observations and deepen our understanding of defect-driven OTS phenomena in amorphous GeSe. Amorphous solid ; Spurious relationship ; Valence (chemistry) ; Interatomic potential ; Molecular dynamics ; Artificial neural network ; Crystallographic defect Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Machine Learning in Materials Science
Authors: Feng Zhang | Zhuo Ye | Maxim Moraru | Ying Wai Li | Weiyi Xia | Yan Yao | Ryan M. Richard | Cai‐Zhuang Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

exaPD: A highly parallelizable workflow for multi-element phase diagram (PD) construction Workflow ; Thermodynamic integration ; CALPHAD ; Phase diagram ; Sampling (signal processing) ; Stability (learning theory) ; Monte Carlo method Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; X-ray Diffraction in Crystallography
Authors: Shan Gao (46743) | David Julian McClements (2624341) | Bing Wang (126120) | Arif Rashid (7306046) | Xiaolu Geng (22486900) | Shenglin Sun (22486903) | Wentao Wang (120529) | Hanxue Hou (18466599)
Source: Figshare (article, 2025-10-23)
Field: Materials Science — Biomaterials

Regulation of Mechanical and Barrier Properties ofStarch/PBAT Films from Extrusion Perspective: Structural Evolution,Correlation Insights, and Predictive Model Development Starch/poly(butylene adipate-<i>co</i>-terephthalate) (PBAT) composites are promising biodegradable materials that may be used as sustainable packaging materials in the food and other industries. These composite materials are usually fabricated by using blown-extrusion methods. In this study, the structural evolution of starch/PBAT composites during extrusion was elucidated. Starch underwent a transition from its typical A-type crystalline structure to a predominantly amorphous structure, and then recrystallized into a V<sub>6</sub>-type crystalline structure in the composite pellets and films. These structural changes were accompanied by hydrogen-bond rearrangement (starch–starch to starch–glycerol), double-helix disintegration, and molecular-weight reduction. Phase separation during film blowing contributed to molecular rearrangements and changes in the crystallization state within the starch and PBAT phases. The relationships between the composition and processing parameters used to form the blown-extrusion films and their final physicochemical properties was established. Correlation analysis revealed the maximum effect size (partial eta-square ≥ 0.9) and pronounced correlation (Mantel’s <i>p</i> ≤ 0.001, <i>r</i> ≥ 0.25) of “starch proportion” with the films’ mechanical and barrier properties, alongside significant interactions among the film-forming parameters (<i>P</i> < 0.05). On this basis, an artificial neural network (3 × 88 × 126 × 15) was constructed to predict the mechanical and barrier properties from these film-forming parameters, with high prediction accuracy (<i>R</i> ≥ 0.9) and generalization ability. These findings enhance the understanding of how extrusion regulates the mechanical and barrier properties of starch/PBAT films, which may help in the industrial production of these sustainable materials. Extrusion ; Composite number ; Amorphous solid ; Crystallization ; Fused deposition modeling ; Starch ; Phase (matter) Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Polymer Foaming and Composites
Authors: Jovan Badzoka | Christoph Kappacher | Jakob Lauß | Christian W. Huck
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Accelerating Microplastic Analysis with Machine Learning: Utilizing Multi-Layer Perceptron (Mlp) for Classification of Hyperspectral Data Hyperspectral imaging ; Perceptron ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Multilayer perceptron ; Layer (electronics) ; Machine learning ; Artificial neural network ; Materials science ; Nanotechnology Spectroscopy and Chemometric Analyses
Authors: Hassan Gashmard | H. Shakeripour | Mojtaba Alaei
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-Driven Discovery of High-Temperature Superconductors via Materials Genome Initiative and High-Throughput Screening Pipeline (software) ; Convolutional neural network ; Extrapolation ; Superconductivity ; Transformative learning ; Key (lock) Machine Learning in Materials Science ; Superconductivity in MgB2 and Alloys ; Physics of Superconductivity and Magnetism
Authors: Ryan Stocks (16867476) | Giuseppe M. J. Barca (4922122)
Source: Figshare (article, 2025-10-08)
Field: Materials Science — Materials Chemistry

Efficient Algorithmsfor GPU Accelerated Evaluationof the DFT Exchange-Correlation Functional Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. Improving algorithmic efficiency through hardware-aware implementations enables application to larger systems and more efficient generation of larger training data sets for machine-learning. In this work, we present a comparative study of four GPU-accelerated algorithms for evaluating the KS-DFT exchange–correlation (XC) potential with an atom-centered Gaussian basis. Two approaches, both leveraging batched dense linear algebra, are found to outperform the others across a suite of molecular benchmarks. We show that batched formation of the XC matrix from the density matrix yields the best performance for large (>O(103) basis functions), sparse systems such as glycine chains and water clusters. In contrast, for smaller and denser systems such as diamond nanoparticles, especially if employing large basis sets, algorithms that use the underlying molecular orbital coefficients offer superior performance, despite their higher formal scaling. Our implementations deliver speedups of 1.4–5.2× for XC potential evaluation relative to leading GPU-accelerated KS-DFT codes, significantly lowering the computational cost and enabling the routine use of larger integration grids. Finally, we outline directions for continued performance improvements in light of emerging GPU architectures with emphasis on utilizing mixed-precision capabilities. Implementation ; Suite ; Basis (linear algebra) ; Gaussian ; Matrix (chemical analysis) ; Gaussian elimination ; Sparse matrix ; Emphasis (telecommunications) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced NMR Techniques and Applications
Authors: Hao Gao | Yue-Wen Fang | Ion Errea
Source: arXiv (Cornell University) (preprint, 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: Chuang Lin (4615603) | Lin Zhang (8926) | Yi Dong (440404)
Source: Figshare (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

Unlocking EnhancedIonic Transport: A Machine Learning-DrivenAIMD Study on Doping, Defects, and Strain in Antiperovskite Solid-StateElectrolytes Ab initio molecular dynamics (AIMD) simulations have been employed to investigate doped antiperovskite solid-state electrolyte (AP SSE) structures, specifically formulated as Li<sub>3</sub>OCl<sub><i>x</i></sub>Br<sub>1–<i>x</i></sub> (<i>x</i> = 0, 0.25, 0.50, 0.75, 1). Various defects, including lithium vacancies, interstitials, as well as Schottky and Frenkel defects, were analyzed under elastic biaxial strain to simulate practical conditions. Machine learning (ML) was then applied to the data from AIMD simulations, targeting lithium diffusivity and ionic conductivity. Our results show that the most significant enhancement in lithium diffusivity occurs when the Cl/Br ratio is 0.5/0.5, and the defect type is the double lithium ion interstitial. Lithium diffusivity and conductivity are mainly governed by the vibration amplitude and number of lithium ions. Although biaxial strain offers a slight promotion effect, it is less influential compared to doping and defect structures. Additionally, SHAP analysis was conducted to assess the relative importance and interactions of each feature descriptor, offering novel insights into the design strategies for enhanced AP SSEs. Antiperovskite ; Lithium (medication) ; Ionic conductivity ; Thermal diffusivity ; Electrolyte ; Ionic bonding ; Doping ; Strain (injury) Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies ; Machine Learning in Materials Science
Authors: Scott M. Reed (1271328)
Source: Figshare (article, 2025-04-22)
Field: Materials Science — Materials Chemistry

Augmented and ProgrammaticallyOptimized LLM PromptsReduce Chemical Hallucinations Utilizing large Language models (LLMs) for handling scientific information comes with risk of the outputs not matching expectations, commonly called hallucinations. To fully utilize LLMs in research requires improving their accuracy, avoiding hallucinations, and extending their scope to research topics outside their direct training. There is also a benefit to getting the most accurate information from an LLM at the time of inference without having to create and train custom new models for each application. Here, augmented generation and machine learning-driven prompt optimization are combined to extract performance improvements over base LLM function on a common chemical research task. Specifically, an LLM was used to predict the topological polar surface area (TPSA) of molecules. By using augmented generation and machine learning-optimized prompts, the error in the prediction was reduced to 11.76 root-mean-squared error (RMSE) from 62.34 RMSE with direct calls to the same LLM. Scope (computer science) ; Matching (statistics) ; Inference ; Function (biology) ; Base (topology) ; Mean squared error Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Eric D. Boittier (6329909) | Silvan Käser (9261566) | Markus Meuwly (1433455)
Source: Figshare (article, 2025-09-12)
Field: Materials Science — Materials Chemistry

Roadmap to CCSD(T)-QualityMachine-Learned Potentialsfor Condensed Phase Simulations Accurate, yet computationally efficient energy functions are essential for state-of-the-art molecular dynamics (MD) studies of condensed phase systems. Here, a generic workflow based on a combination of machine-learning-based and empirical representations of intra- and intermolecular interactions is presented. The total energy is decomposed into internal contributions and electrostatic and van der Waals interactions between monomers. The monomer potential energy surface is described using a neural network, whereas for the electrostatics the flexible minimally distributed charge model is employed. Remaining contributions between reference energies from electronic structure calculations and the model are fitted to standard Lennard-Jones (12-6) terms. For water as a topical example, reference energies for the monomers are determined from CCSD(T)-F12 calculations, whereas for an ensemble of cluster structures containing [2, 60] and [2, 4] monomers DFT and CCSD(T) energies, respectively, were used to adjust the van der Waals contributions. Based on the bulk liquid density and heat of vaporization, the best-performing set of LJ(12-6) parameters was selected and a wide range of condensed phase properties were determined and compared with experiment. MD simulations on the multiple-nanosecond time scale were carried out for water boxes containing 2000 to 8000 monomers, depending on the property considered. The performance of such a generic ML-inspired parametrization scheme is very promising and future improvements and extensions are discussed, also in view of recent advances for water in particular in the literature. van der Waals force ; Parametrization (atmospheric modeling) ; Intermolecular force ; Electrostatics ; Molecular dynamics ; Phase (matter) ; Range (aeronautics) ; Monomer ; Water model Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Eric D. Boittier | Silvan Käser | Markus Meuwly
Source: ArXiv.org (preprint, 2025-06-29)
Field: Materials Science — Materials Chemistry

Towards Large-Scale Condensed Phase Simulations using Machine Learned Energy Functions Accurate, yet computationally efficient energy functions are essential for state-of-the art molecular dynamics (MD) studies of condensed phase systems. Here, a generic workflow based on a combination of machine learning-based and empirical representations of intra- and intermolecular interactions is presented. The total energy is decomposed into internal contributions, and electrostatic and van der Waals interactions between monomers. The monomer potential energy surface is described using a neural network, whereas for the electrostatics the flexible minimally distributed charge model is employed. Remaining contributions between reference energies from electronic structure calculations and the model are fitted to standard Lennard-Jones (12-6) terms. For water as a topical example, reference energies for the monomers are determined from CCSD(T)-F12 calculations whereas for an ensemble of cluster structures containing $[2,60]$ and $[2,4]$ monomers DFT and CCSD(T) energies, respectively, were used to best match the van der Waals contributions. Based on the bulk liquid density and heat of vaporization, the best-performing set of LJ(12-6) parameters was selected and a wide range of condensed phase properties were determined and compared with experiment. MD Simulations on the multiple-nanosecond time scale were carried out for water boxes containing 2000 to 8000 monomers, depending on the property considered. The performance of such a generic ML-inspired parametrization scheme is very promising and future improvements and extensions are discussed, also in view of recent advances for water in particular in the literature. van der Waals force ; Parametrization (atmospheric modeling) ; Intermolecular force ; Electrostatics ; Molecular dynamics ; Range (aeronautics) ; Phase (matter) ; Energy minimization ; Energy (signal processing) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Phase Equilibria and Thermodynamics