jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Udari De Alwis | Benjamin E. Mayer | Tom Ashby | Maria Cecilia Barrera | Timon Evenblij | Joyjit Kundu
Source: ArXiv.org (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Characterizing Machine Learning Force Fields as Emerging Molecular Dynamics Workloads on Graphics Processing Units Molecular dynamics (MD) simulates the time evolution of atomic systems governed by interatomic forces, and the fidelity of these simulations depends critically on the underlying force model. Classical force fields (CFFs) rely on fixed functional forms fitted to experimental or theoretical data, offering computational efficiency and broad applicability but limited accuracy in chemically diverse or reactive environments. In contrast, machine learning force fields (MLFFs) deliver near quantum chemical accuracy at molecular-mechanics cost by learning interatomic interactions directly from high level electronic structure data. While MLFFs offer improved accuracy at a fraction of the cost of quantum methods, they introduce significant computational overhead, particularly in descriptor evaluation and neural network inference. These operations pose challenges for parallel hardware due to irregular memory access, minimum data reuse and inefficient kernel execution. This work investigates the hardware performance of such models using poly alanine chains, a novel benchmark molecule system(s) with controllable input size, which used as performance evaluation test cases highlighting the computational bottlenecks of the graphical processor units when scaling out MLFF simulations. The analysis identifies key bottlenecks in descriptor and force computation, memory handling, highlighting the opportunities for improvements in the emerging area of MLFF based MD in drug discovery, that has received limited attention from a computer architecture perspective. Kernel (algebra) ; Benchmark (surveying) ; Graphics ; Force field (fiction) ; Reuse ; Key (lock) ; Molecular dynamics ; Field (mathematics) ; Computational resource ; Computer graphics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Physical and Chemical Molecular Interactions
Authors: Paolo Lazzaroni | Shubham Sharma | Mariana Rossi
Source: Physical review. B./Physical review. B (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Investigating anharmonicities in polarization-orientation Raman spectra of acene crystals with machine learning We present a first-principles machine-learning computational framework to investigate anharmonic effects in polarization-orientation (PO) Raman spectra of molecular crystals, focusing on anthracene and naphthalene. By combining machine learning models for interatomic potentials and polarizability tensors, we enable efficient, large-scale simulations that capture temperature-dependent vibrational dynamics beyond the harmonic approximation. Our approach reproduces key qualitative features observed experimentally. We show, systematically, what are the fingerprints of anharmonic lattice dynamics, thermal expansion, and Raman tensor symmetries on PO-Raman intensities. However, we find that the simulated polarization dependence of Raman intensities shows only subtle deviations from quasiharmonic predictions, failing to capture the pronounced temperature-dependent changes that have been reported experimentally in anthracene. We propose that part of these inconsistencies stem from the impossibility to deconvolute certain vibrational peaks when only experimental data is available. This work therefore provides a foundation to improve the interpretation of PO-Raman experiments in complex molecular crystals with the aid of theoretical simulations. Anharmonicity ; Raman spectroscopy ; Polarizability ; Raman scattering ; Spectral line ; Molecular dynamics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Yusuke TATEISHI | L Botha | A.E. Kozhukhova | Manabu Sugimoto | K. Aika | Dmitri Bessarabov
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Molecular dynamics study of catalytic H <sub>2</sub> /O <sub>2</sub> recombination on Pd, Pt, Cu, Ag, and Au nanoclusters using the universal neural network potential The H<sub>2</sub>/O<sub>2</sub> catalytic recombination reaction, essential for safely utilizing hydrogen energy, was simulated rapidly and accurately using the molecular dynamics (MD) simulation with a machine-learning potential known as the 'universal neural network potential' (UNNP). The catalytic activities of Pd, Pt, Cu, Ag, and Au nanoclusters were systematically investigated under a 100 bar H<sub>2</sub>/O<sub>2</sub> atmosphere at 500 K. This approach enables the investigation of the entirely dynamic catalytic reaction, rather than assembling a static configuration at 0 K. Throughout the simulation, the ability to catalyze H<sub>2</sub>O formation on the Pt surface was successfully reproduced, aligning with previous experimental findings. The simulations have revealed characteristic differences and the suitability of reactivity with H<sub>2</sub>/O<sub>2</sub> across the studied metals, demonstrating the time evolution of gas-surface interactions and the overall detailed reaction mechanism of the H<sub>2</sub>/O<sub>2</sub> recombination reaction. In particular, the reaction pathway O<sub>2</sub> → OOH → H<sub>2</sub>O was revealed to occur preferentially on the Pt nanocluster, while other metals exhibited unsuitability, such as Pd's intense H trapping and Cu's surface instability due to excessive O<sub>2</sub> dissociation. The two features of molecular O<sub>2</sub> adsorption and the facile surface diffusion of H atoms play a crucial role as necessary conditions for catalytic activity. The accelerated MD method enables the rapid and accurate elucidation of the atomic-level mechanisms and activity differences in the H<sub>2</sub>/O<sub>2</sub> recombination reaction, offering a fast and accurate workflow for both performance screening and mechanism discovery, thus accelerating catalyst design. Catalysis ; Nanoclusters ; Molecular dynamics ; Recombination ; Hydrogen ; Diffusion ; Adsorption ; Mechanism (biology) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies
Authors: Xiao Fu | Jing Xu | Qifan Yang | Xuhe Gong | Jingchen Lian | Liqi Wang | Zibin Wang | Ruijuan Xiao | hong li
Source: npj Computational Materials (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

High-entropy solid electrolytes discovery: a dual-stage machine learning framework bridging atomic configurations and ionic transport properties The rapid development of computational materials science powered by machine learning (ML) is gradually leading to solutions to several previously intractable scientific problems. One of the most prominent is machine learning interatomic potentials (MLIPs), which expedites the study of dynamical methods for large-scale systems. However, as a promising field, high-entropy (HE) solid-state electrolytes (SEs) remain constrained by trial-and-error paradigms, lacking systematic computational strategies to address their huge and high-dimensional composition space. In this work, we establish a dual-stage ML framework that combines fine-tuned MLIPs with interpretable feature-property mapping to accelerate the high-entropy SEs discovery. Using Li3Zr2Si2PO12 (LZSP) as a prototype, the fine-tuned CHGNet-based relaxation provides atomic structure for each configuration, the structure features - mean squared displacement (SF-MSD) model predicts the ionic transport properties and identifies critical descriptors. The theoretical studies indicate that the framework can satisfy the multiple requirements including computational efficiency, generalization reliability and prediction accuracy. One of the most promising element combinations in the quinary HE-LZSP space containing 4575 compositions is identified with a high ionic conductivity of 4.53 mS/cm as an application example. The framework contains generalizability and extensibility to other SE families. Bridging (networking) ; Ionic bonding ; Work (physics) ; Electrolyte Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; High Entropy Alloys Studies
Authors: F. Adams | Daiwei Zhu | David Steuerman | A. Gilad Kusne | Ichiro Takeuchi
Source: APL Quantum (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Quantum kernel machine learning for autonomous materials science Autonomous materials science, where active learning is used to navigate large compositional phase space, has emerged as a powerful vehicle to rapidly explore new materials. A crucial aspect of autonomous materials science is exploring new materials using as little data as possible. Gaussian process-based active learning allows effective charting of multi-dimensional parameter space with a limited number of training data and, thus, is a common algorithmic choice for autonomous materials science. An integral part of the autonomous workflow is the application of kernel functions for quantifying similarities among measured data points. A recent theoretical breakthrough has shown that quantum kernel models can achieve similar performance with less training data than classical kernel models. This signals the possible advantage of applying quantum kernel machine learning to autonomous materials discovery. In this work, we compare quantum and classical kernels for their utility in sequential phase space navigation for autonomous materials science. In particular, we compute a quantum kernel and several classical kernels for x-ray diffraction patterns taken from an Fe–Ga–Pd ternary composition spread library. We conduct our study on both IonQ’s Aria trapped ion quantum computer hardware and the corresponding classical noisy simulator. We experimentally verify that a quantum kernel model can outperform some classical kernel models. The results highlight the potential of quantum kernel machine learning methods for accelerating materials discovery and suggest that complex x-ray diffraction data are a candidate for robust quantum kernel model advantage. Kernel (algebra) ; Kernel method ; Kernel embedding of distributions ; Quantum ; Quantum machine learning ; String kernel ; Radial basis function kernel ; Quantum computer Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Pengfei Hou | Jingshan Luo | Jin-Cheng Liu
Source: ACS Catalysis (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Accelerating Catalytic Reaction Network Exploration via Local Fine-tuning with Universal Machine Learning Interatomic Potentials Theoretical exploration of complex catalytic reaction networks (CRNs) is limited by the trade-off between the cost of quantum mechanical calculations and the reduced accuracy of approximate methods. We introduce the LFT-CRN, an active learning framework combining pretrained universal machine learning interatomic potentials (MLIPs) with a local fine-tuning (LFT) algorithm for efficient CRN exploration. The LFT-CRN accelerates geometry optimization, transition-state search, and vibrational analysis while maintaining consistent performance across different exchange–correlation functionals and density functional theory (DFT) settings. Applied to methanol synthesis on CuZn catalysts, the LFT-CRN achieves over a 14-fold acceleration compared with the conventional DFT workflow, retaining chemical accuracy (<1 kcal/mol) for several energy metrics. Energetics and microkinetic simulation reveal that low-coordination Cu sites with moderate Zn doping maximize both Cu–Zn synergy and catalytic activity, whereas excessive Zn reduces performance. This generalizable workflow enables high-throughput CRN exploration, thereby supporting catalyst design and optimization of industrial processes. Density functional theory ; Catalysis ; Workflow ; Quantum chemical ; Acceleration ; Quantum ; Interatomic potential ; Molecular dynamics ; Artificial neural network Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Manuel Grumet | Takeru Miyagawa | Olivier Pittet | Paolo Pegolo | Karin S. Thalmann | Waldemar Kaiser | David A. Egger
Source: AI for Science (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Revealing fast ionic conduction in solid electrolytes through machine learning accelerated Raman calculations Revealing fast ionic conduction in solid electrolytes through machine learning accelerated Raman calculations, Grumet, Manuel, Miyagawa, Takeru, Pittet, Olivier, Pegolo, Paolo, Thalmann, Karin S, Kaiser, Waldemar, Egger, David A Raman spectroscopy ; Ionic bonding ; Thermal conduction ; Raman scattering ; Ionic conductivity ; Fast ion conductor Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Spectroscopy and Quantum Chemical Studies
Authors: Udari De Alwis | Benjamin E. Mayer | Tom Ashby | Maria Cecilia Barrera | Timon Evenblij | Joyjit Kundu
Source: Open MIND (preprint, 2026-03-04)
Field: Materials Science — Materials Chemistry

Characterizing Machine Learning Force Fields as Emerging Molecular Dynamics Workloads on Graphics Processing Units Molecular dynamics (MD) simulates the time evolution of atomic systems governed by interatomic forces, and the fidelity of these simulations depends critically on the underlying force model. Classical force fields (CFFs) rely on fixed functional forms fitted to experimental or theoretical data, offering computational efficiency and broad applicability but limited accuracy in chemically diverse or reactive environments. In contrast, machine learning force fields (MLFFs) deliver near quantum chemical accuracy at molecular-mechanics cost by learning interatomic interactions directly from high level electronic structure data. While MLFFs offer improved accuracy at a fraction of the cost of quantum methods, they introduce significant computational overhead, particularly in descriptor evaluation and neural network inference. These operations pose challenges for parallel hardware due to irregular memory access, minimum data reuse and inefficient kernel execution. This work investigates the hardware performance of such models using poly alanine chains, a novel benchmark molecule system(s) with controllable input size, which used as performance evaluation test cases highlighting the computational bottlenecks of the graphical processor units when scaling out MLFF simulations. The analysis identifies key bottlenecks in descriptor and force computation, memory handling, highlighting the opportunities for improvements in the emerging area of MLFF based MD in drug discovery, that has received limited attention from a computer architecture perspective. Kernel (algebra) ; Benchmark (surveying) ; Graphics ; Force field (fiction) ; Reuse ; Key (lock) ; Molecular dynamics ; Field (mathematics) ; Computational resource ; Computer graphics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Physical and Chemical Molecular Interactions
Authors: Valerii Andreichev | Ján Dušek | Markus Meuwly | Jeremy O. Richardson
Source: arXiv (Cornell University) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

High-Accuracy Molecular Simulations with Machine-Learning Potentials and Semiclassical Approximations to Quantum Dynamics Accurate simulations of molecules require high-level electronic-structure theory in combination with rigorous methods for approximating the quantum dynamics. Machine-learning approaches can significantly reduce the computational expense of this workflow without any loss of accuracy. We discuss various methods for constructing potential energy surfaces including transfer learning, which requires a minimal number of expensive training points. In this way, we can study chemical reactions at a high level but a low cost. In particular, as the potentials are smooth and differentiable, they enable the use of more advanced semiclassical approximations to quantum dynamics, such as perturbatively corrected instanton theory, which can capture both tunnelling and anharmonicity. Semiclassical physics ; Quantum tunnelling ; Instanton ; Quantum ; Quantum dynamics ; Molecular dynamics ; Potential energy ; Energy (signal processing) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Gaussian Processes and Bayesian Inference
Authors: Hanh An Nguyen | Vu Phong Dinh | Nguyen Tran Truc Phuong | Thi Ngoc Diep Trinh | Woo Ri Chae | NAE YOON LEE | Kieu The Loan Trinh
Source: Coordination Chemistry Reviews (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

MXenes and machine learning enable integrated multimodal biosensing of chemical and biological targets MXenes ; Biosensor ; Human–machine system ; Human health MXene and MAX Phase Materials ; Advanced biosensing and bioanalysis techniques ; 2D Materials and Applications
Authors: Kun Liu | Xin. Leng | Ting. Liu | Wangwang. Liao | Weipeng. Li | Xiangyun Long
Source: Nuclear Engineering and Technology (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

A data-efficient physics-informed neural network framework for reliable irradiation hardening assessment in nanocrystalline materials This paper proposes a novel Physics-Informed Neural Network (PINN) framework for assessing ion irradiation hardening in nanocrystalline materials. A key innovation lies in the deep integration of irradiation hardening mechanisms into the neural network via a composite loss function, which imposes​ physical laws as hard constraints. During network training, key parameters of the physical model are simultaneously optimized, enabling adaptive matching to the microstructural characteristics of different materials. This integration ensures​ that the model’s predictions are​ both consistent with experimental data and physically plausible. Compared with conventional methods, this framework significantly reduces data dependence, overcomes the limitations of pure physical models under ill-posed conditions, and addresses the poor generalizability of purely data-driven approaches. Validation through multi-condition irradiation experiments on Ni-Mo-Cr alloys with two different grain sizes demonstrates that the model achieves a prediction error of less than 5% for the nanocrystalline alloy and maintains an error of approximately 10% for conventional alloy, with robust stability in extrapolation tests. Its evaluation accuracy and robustness are significantly superior to those of traditional physical models and purely data-driven methods. This study provides a physically interpretable, data-efficient, and highly generalizable paradigm for the assessment of irradiation hardening. Nanocrystalline material ; Irradiation ; Artificial neural network ; Hardening (computing) ; Radiation hardening Fusion materials and technologies ; Nuclear Materials and Properties ; Machine Learning in Materials Science
Authors: Agnes Valenti | Ina Park | Antoine Georges | Andrew J. Millis | Olivier Parcollet
Source: arXiv (Cornell University) (preprint, 2026-03-16)
Field: Materials Science — Materials Chemistry

Neural-Network Quantum Embedding Solvers for Correlated Materials Quantum impurity solvers are the computational bottleneck of quantum embedding approaches to correlated materials, such as dynamical mean-field theory (DMFT). We show that neural networks trained on synthetic, material-agnostic data learn the impurity mapping from hybridization functions and local interactions to Green's functions with quantitative accuracy for both model systems and real materials, providing fast solvers for single- and multi-orbital models. Benchmarks against numerically controlled quantum Monte Carlo show that the method reproduces the Mott transition, multi-orbital phase diagrams of Hubbard-Kanamori models, and the electronic properties of SrVO$_3$ and SrMnO$_3$. The learned solvers achieve orders-of-magnitude speedup and can initialize controlled calculations, dramatically accelerating DMFT while preserving accuracy. Embedding ; Quantum ; Bottleneck ; Speedup ; Quantum Monte Carlo ; Monte Carlo method ; Quantum computer Machine Learning in Materials Science ; Quantum many-body systems ; Electronic and Structural Properties of Oxides
Authors: Jinming Fan | Chao Qian | Wilhelm T. S. Huck | William E. Robinson | Shaodong Zhou
Source: arXiv (Cornell University) (preprint, 2026-03-03)
Field: Materials Science — Materials Chemistry

ChemFlow:A Hierarchical Neural Network for Multiscale Representation Learning in Chemical Mixtures Accurate prediction of the physicochemical properties of molecular mixtures using graph neural networks remains a significant challenge, as it requires simultaneous embedding of intramolecular interactions while accounting for mixture composition (i.e., concentrations and ratios). Existing approaches are ill-equipped to emulate realistic mixture environments, where densely coupled interactions propagate across hierarchical levels - from atoms and functional groups to entire molecules - and where cross-level information exchange is continuously modulated by composition. To bridge the gap between isolated molecules and realistic chemical environments, we present ChemFlow, a novel hierarchical framework that integrates atomic, functional group, and molecular-level features, facilitating information flow across these levels to predict the behavior of complex chemical mixtures. ChemFlow employs an atomic-level feature fusion module, Chem-embed, to generate context-aware atomic representations influenced by the mixture state and atomic characteristics. Next, bidirectional group-to-molecule and molecule-to-group attention mechanisms enable ChemFlow to capture functional group interactions both within and across molecules in the mixture. By dynamically adjusting representations based on concentration and composition, ChemFlow excels at predicting concentration-dependent properties and significantly outperforms state-of-the-art models in both concentration-sensitive and concentration-independent systems. Extensive experiments demonstrate ChemFlow's superior accuracy and efficiency in modeling complex chemical mixtures. Embedding ; Artificial neural network ; Representation (politics) ; Molecule ; Group (periodic table) ; Feature (linguistics) ; Chemical process ; Intramolecular force ; Complex system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks