Matter Sciences

52 items

Microsoft Research Sep 21, 2026
This work presents RetroChimera, a retrosynthesis prediction framework that combines a Transformer-based de-novo model, R-SMILES 2, with a graph-neural-network model grounded in reaction templates, NeuralLoc, through a learned, rank-dependent ensembling strategy, so that their complementary strengths are leveraged to perform strongly across both common and rare reaction classes and, in blind tests, to produce disconnections of complex molecules that PhD-level chemists preferred over those from its constituent sub-models, from more established approaches, and even from the test set itself.
This work presents RetroChimera, a retrosynthesis prediction framework that combines a Transformer-based de-novo model, R-SMILES 2, with a graph-neural-network model grounded in reaction templates, NeuralLoc, through a learned, rank-dependent ensembling strategy, so that their complementary strengths are leveraged to perform strongly across both common and rare reaction classes and, in blind tests, to produce disconnections of complex molecules that PhD-level chemists preferred over those from its constituent sub-models, from more established approaches, and even from the test set itself.
RetroChimera: Improving Small-Molecule Retrosynthesis Prediction with a Learned Two-Model Ensemble
This work presents RetroChimera, a retrosynthesis prediction framework that combines a Transformer-based de-novo model, R-SMILES 2, with a graph-neural-network model grounded in reaction templates, NeuralLoc, through a learned, rank-dependent ensembling strategy, so that their complementary strengths are leveraged to perform strongly across both common and rare reaction classes and, in blind tests, to produce disconnections of complex molecules that PhD-level chemists preferred over those from its constituent sub-models, from more established approaches, and even from the test set itself.
This work presents RetroChimera, a retrosynthesis prediction framework that combines a Transformer-based de-novo model, R-SMILES 2, with a graph-neural-network model grounded in reaction templates, NeuralLoc, through a learned, rank-dependent ensembling strategy, so that their complementary strengths are leveraged to perform strongly across both common and rare reaction classes and, in blind tests, to produce disconnections of complex molecules that PhD-level chemists preferred over those from its constituent sub-models, from more established approaches, and even from the test set itself.
bioRxiv Sep 18, 2026
Using the tabular foundation model TabPFN informed by a small library of living hydrogels, this work predicts macroscopic material properties of Escherichia coli-produced living hydrogels containing CsgA-based fibres fused to genetically encoded PEG-like biopolymers from genetic and process parameters, achieving the strongest prediction for storage modulus G' (R2 = 85.1%) on an independent validation set with a 48.0% RMSE reduction versus linear regression, and further enabling property-guided design to identify parameters for desired properties.
Using the tabular foundation model TabPFN informed by a small library of living hydrogels, this work predicts macroscopic material properties of Escherichia coli-produced living hydrogels containing CsgA-based fibres fused to genetically encoded PEG-like biopolymers from genetic and process parameters, achieving the strongest prediction for storage modulus G' (R2 = 85.1%) on an independent validation set with a 48.0% RMSE reduction versus linear regression, and further enabling property-guided design to identify parameters for desired properties.
Data-driven predictive design of engineered living hydrogels
Using the tabular foundation model TabPFN informed by a small library of living hydrogels, this work predicts macroscopic material properties of Escherichia coli-produced living hydrogels containing CsgA-based fibres fused to genetically encoded PEG-like biopolymers from genetic and process parameters, achieving the strongest prediction for storage modulus G' (R2 = 85.1%) on an independent validation set with a 48.0% RMSE reduction versus linear regression, and further enabling property-guided design to identify parameters for desired properties.
Using the tabular foundation model TabPFN informed by a small library of living hydrogels, this work predicts macroscopic material properties of Escherichia coli-produced living hydrogels containing CsgA-based fibres fused to genetically encoded PEG-like biopolymers from genetic and process parameters, achieving the strongest prediction for storage modulus G' (R2 = 85.1%) on an independent validation set with a 48.0% RMSE reduction versus linear regression, and further enabling property-guided design to identify parameters for desired properties.
Nanoscale Sep 17, 2026
This review systematically outlines host-guest composites using metal-organic frameworks (MOFs) as hosts and metal halide perovskites as guests, summarizing three synthetic strategies (ship-in-a-bottle, bottle-around-ship, and one-pot synthesis), noting that nanoconfinement, interfacial passivation, and electronic coupling of MOFs inhibit ion migration, defects, and degradation to improve photoluminescence quantum yield and stability, that emission wavelength and exciton dynamics can be tuned via MOF pore size, ligand functionalization, and nucleation kinetics, and outlining applications in photovoltaics, sensing, information encryption, anti-counterfeiting, and light-emitting devices along with integrating artificial intelligence with theoretical simulation for design optimization.
This review systematically outlines host-guest composites using metal-organic frameworks (MOFs) as hosts and metal halide perovskites as guests, summarizing three synthetic strategies (ship-in-a-bottle, bottle-around-ship, and one-pot synthesis), noting that nanoconfinement, interfacial passivation, and electronic coupling of MOFs inhibit ion migration, defects, and degradation to improve photoluminescence quantum yield and stability, that emission wavelength and exciton dynamics can be tuned via MOF pore size, ligand functionalization, and nucleation kinetics, and outlining applications in photovoltaics, sensing, information encryption, anti-counterfeiting, and light-emitting devices along with integrating artificial intelligence with theoretical simulation for design optimization.
Confinement-Guided Performance Enhancement of Perovskites within Metal-Organic Frameworks: A Review
This review systematically outlines host-guest composites using metal-organic frameworks (MOFs) as hosts and metal halide perovskites as guests, summarizing three synthetic strategies (ship-in-a-bottle, bottle-around-ship, and one-pot synthesis), noting that nanoconfinement, interfacial passivation, and electronic coupling of MOFs inhibit ion migration, defects, and degradation to improve photoluminescence quantum yield and stability, that emission wavelength and exciton dynamics can be tuned via MOF pore size, ligand functionalization, and nucleation kinetics, and outlining applications in photovoltaics, sensing, information encryption, anti-counterfeiting, and light-emitting devices along with integrating artificial intelligence with theoretical simulation for design optimization.
This review systematically outlines host-guest composites using metal-organic frameworks (MOFs) as hosts and metal halide perovskites as guests, summarizing three synthetic strategies (ship-in-a-bottle, bottle-around-ship, and one-pot synthesis), noting that nanoconfinement, interfacial passivation, and electronic coupling of MOFs inhibit ion migration, defects, and degradation to improve photoluminescence quantum yield and stability, that emission wavelength and exciton dynamics can be tuned via MOF pore size, ligand functionalization, and nucleation kinetics, and outlining applications in photovoltaics, sensing, information encryption, anti-counterfeiting, and light-emitting devices along with integrating artificial intelligence with theoretical simulation for design optimization.
Faraday Discussions Sep 17, 2026
This work combines the SISSO symbolic-regression approach with a gradient-based partial-effects (PE) sensitivity analysis to analyze 539 ethylene-selectivity measurements for nine supported palladium-based bimetallic alloy nanoparticles in the selective hydrogenation of concentrated acetylene streams, selecting eight materials genes out of twenty candidate primary features and using global and per-material sensitivity scores to identify the average d-band center, the surface and subsurface hydrogen binding energies, and the experimentally measured mean particle diameter as the most influential genes, thereby providing a statistical description of ethylene selectivity without explicitly modeling all underlying physical processes.
This work combines the SISSO symbolic-regression approach with a gradient-based partial-effects (PE) sensitivity analysis to analyze 539 ethylene-selectivity measurements for nine supported palladium-based bimetallic alloy nanoparticles in the selective hydrogenation of concentrated acetylene streams, selecting eight materials genes out of twenty candidate primary features and using global and per-material sensitivity scores to identify the average d-band center, the surface and subsurface hydrogen binding energies, and the experimentally measured mean particle diameter as the most influential genes, thereby providing a statistical description of ethylene selectivity without explicitly modeling all underlying physical processes.
Rethinking catalysis with interpretable AI and materials genes: SISSO symbolic regression combined with partial-effects sensitivity analysis
This work combines the SISSO symbolic-regression approach with a gradient-based partial-effects (PE) sensitivity analysis to analyze 539 ethylene-selectivity measurements for nine supported palladium-based bimetallic alloy nanoparticles in the selective hydrogenation of concentrated acetylene streams, selecting eight materials genes out of twenty candidate primary features and using global and per-material sensitivity scores to identify the average d-band center, the surface and subsurface hydrogen binding energies, and the experimentally measured mean particle diameter as the most influential genes, thereby providing a statistical description of ethylene selectivity without explicitly modeling all underlying physical processes.
This work combines the SISSO symbolic-regression approach with a gradient-based partial-effects (PE) sensitivity analysis to analyze 539 ethylene-selectivity measurements for nine supported palladium-based bimetallic alloy nanoparticles in the selective hydrogenation of concentrated acetylene streams, selecting eight materials genes out of twenty candidate primary features and using global and per-material sensitivity scores to identify the average d-band center, the surface and subsurface hydrogen binding energies, and the experimentally measured mean particle diameter as the most influential genes, thereby providing a statistical description of ethylene selectivity without explicitly modeling all underlying physical processes.
Faraday Discussions Sep 17, 2026
Using the hydrogen evolution reaction (HER) as a prototypical case, this study analyses the limitations of current first-principles approaches, particularly those based on the computational hydrogen electrode (CHE), for predicting single-atom catalyst (SAC) activity, identifying factors such as the sensitivity of reaction thermodynamics to the local atomic environment, the often-unknown experimental structure of SACs, neglected SAC-specific reaction intermediates, solvent effects, catalyst evolution under operating conditions, material instability, and intrinsic density functional theory approximations as sources of discrepancy between theory and experiment, and proposing that integrating these chemical complexities and uncertainties, potentially through artificial intelligence and data-dr
Using the hydrogen evolution reaction (HER) as a prototypical case, this study analyses the limitations of current first-principles approaches, particularly those based on the computational hydrogen electrode (CHE), for predicting single-atom catalyst (SAC) activity, identifying factors such as the sensitivity of reaction thermodynamics to the local atomic environment, the often-unknown experimental structure of SACs, neglected SAC-specific reaction intermediates, solvent effects, catalyst evolution under operating conditions, material instability, and intrinsic density functional theory approximations as sources of discrepancy between theory and experiment, and proposing that integrating these chemical complexities and uncertainties, potentially through artificial intelligence and data-dr
Critical assessment of theoretical modelling of single-atom catalysts
Using the hydrogen evolution reaction (HER) as a prototypical case, this study analyses the limitations of current first-principles approaches, particularly those based on the computational hydrogen electrode (CHE), for predicting single-atom catalyst (SAC) activity, identifying factors such as the sensitivity of reaction thermodynamics to the local atomic environment, the often-unknown experimental structure of SACs, neglected SAC-specific reaction intermediates, solvent effects, catalyst evolution under operating conditions, material instability, and intrinsic density functional theory approximations as sources of discrepancy between theory and experiment, and proposing that integrating these chemical complexities and uncertainties, potentially through artificial intelligence and data-dr
Using the hydrogen evolution reaction (HER) as a prototypical case, this study analyses the limitations of current first-principles approaches, particularly those based on the computational hydrogen electrode (CHE), for predicting single-atom catalyst (SAC) activity, identifying factors such as the sensitivity of reaction thermodynamics to the local atomic environment, the often-unknown experimental structure of SACs, neglected SAC-specific reaction intermediates, solvent effects, catalyst evolution under operating conditions, material instability, and intrinsic density functional theory approximations as sources of discrepancy between theory and experiment, and proposing that integrating these chemical complexities and uncertainties, potentially through artificial intelligence and data-dr
arXiv Sep 17, 2026
The work identifies an ambiguity in passive syndrome records that obstructs recovery selection and shows that in an odd-distance square toric code, opposite coherent X rotations produce identical passive syndrome-history distributions while a fixed phase correction helps at one sign and harms at the other; a terminal logical measurement on known encoded calibration states supplies the missing sign information, and a separate evaluator accepts an update only when calibration uncertainty and a justified drift bound certify improvement over the current recovery, thereby rejecting harmful proposals while retaining beneficial updates under honest advice in simulated advice attacks without assuming the adviser recommends correctly.
The work identifies an ambiguity in passive syndrome records that obstructs recovery selection and shows that in an odd-distance square toric code, opposite coherent X rotations produce identical passive syndrome-history distributions while a fixed phase correction helps at one sign and harms at the other; a terminal logical measurement on known encoded calibration states supplies the missing sign information, and a separate evaluator accepts an update only when calibration uncertainty and a justified drift bound certify improvement over the current recovery, thereby rejecting harmful proposals while retaining beneficial updates under honest advice in simulated advice attacks without assuming the adviser recommends correctly.
Securing Quantum Error Correction Against Misleading Advice from AI Agents
The work identifies an ambiguity in passive syndrome records that obstructs recovery selection and shows that in an odd-distance square toric code, opposite coherent X rotations produce identical passive syndrome-history distributions while a fixed phase correction helps at one sign and harms at the other; a terminal logical measurement on known encoded calibration states supplies the missing sign information, and a separate evaluator accepts an update only when calibration uncertainty and a justified drift bound certify improvement over the current recovery, thereby rejecting harmful proposals while retaining beneficial updates under honest advice in simulated advice attacks without assuming the adviser recommends correctly.
The work identifies an ambiguity in passive syndrome records that obstructs recovery selection and shows that in an odd-distance square toric code, opposite coherent X rotations produce identical passive syndrome-history distributions while a fixed phase correction helps at one sign and harms at the other; a terminal logical measurement on known encoded calibration states supplies the missing sign information, and a separate evaluator accepts an update only when calibration uncertainty and a justified drift bound certify improvement over the current recovery, thereby rejecting harmful proposals while retaining beneficial updates under honest advice in simulated advice attacks without assuming the adviser recommends correctly.
arXiv Sep 17, 2026
The work presents VyPER, a geometric learning framework that represents collider events as hypergraphs with a physics-inspired topology, combining supervised classification of hyperedges for assigning measured jets and charged leptons to parent particles with a diffusion model for predicting unmeasured neutrino kinematics, optimized jointly through a joint loss function within a unified framework, and compares its performance to existing analytical and machine-learning-based reconstruction techniques across several proton-proton collision processes, indicating that accurate event reconstruction is achievable across a diverse range of Standard Model physics processes.
The work presents VyPER, a geometric learning framework that represents collider events as hypergraphs with a physics-inspired topology, combining supervised classification of hyperedges for assigning measured jets and charged leptons to parent particles with a diffusion model for predicting unmeasured neutrino kinematics, optimized jointly through a joint loss function within a unified framework, and compares its performance to existing analytical and machine-learning-based reconstruction techniques across several proton-proton collision processes, indicating that accurate event reconstruction is achievable across a diverse range of Standard Model physics processes.
Comprehensive Reconstruction of Collider Events with Hypergraph Representation Learning and Graph-Conditioned Diffusion
The work presents VyPER, a geometric learning framework that represents collider events as hypergraphs with a physics-inspired topology, combining supervised classification of hyperedges for assigning measured jets and charged leptons to parent particles with a diffusion model for predicting unmeasured neutrino kinematics, optimized jointly through a joint loss function within a unified framework, and compares its performance to existing analytical and machine-learning-based reconstruction techniques across several proton-proton collision processes, indicating that accurate event reconstruction is achievable across a diverse range of Standard Model physics processes.
The work presents VyPER, a geometric learning framework that represents collider events as hypergraphs with a physics-inspired topology, combining supervised classification of hyperedges for assigning measured jets and charged leptons to parent particles with a diffusion model for predicting unmeasured neutrino kinematics, optimized jointly through a joint loss function within a unified framework, and compares its performance to existing analytical and machine-learning-based reconstruction techniques across several proton-proton collision processes, indicating that accurate event reconstruction is achievable across a diverse range of Standard Model physics processes.
MIT Technology Review Sep 16, 2026
This MIT Technology Review Insights conversation produced in partnership with Syensqo records the views of Mike Finelli, Syensqo's chief technology and innovation officer and chief North America officer: AI is pushing semiconductors and data centers toward physical limits, which piles up more simultaneous requirements on advanced materials, and Syensqo is responding by developing materials for high-voltage data center architectures, advanced sealing materials for semiconductor manufacturing, and thermal-management solutions including direct immersion cooling fluids, while working with Microsoft on AI agents that digitally synthesize millions of candidate molecules, predict their performance through physics-based simulation, and rank them down to roughly a hundred candidates for laboratory
This MIT Technology Review Insights conversation produced in partnership with Syensqo records the views of Mike Finelli, Syensqo's chief technology and innovation officer and chief North America officer: AI is pushing semiconductors and data centers toward physical limits, which piles up more simultaneous requirements on advanced materials, and Syensqo is responding by developing materials for high-voltage data center architectures, advanced sealing materials for semiconductor manufacturing, and thermal-management solutions including direct immersion cooling fluids, while working with Microsoft on AI agents that digitally synthesize millions of candidate molecules, predict their performance through physics-based simulation, and rank them down to roughly a hundred candidates for laboratory
Building the materials foundation for AI: Syensqo on advanced materials and AI as a two-way driver
This MIT Technology Review Insights conversation produced in partnership with Syensqo records the views of Mike Finelli, Syensqo's chief technology and innovation officer and chief North America officer: AI is pushing semiconductors and data centers toward physical limits, which piles up more simultaneous requirements on advanced materials, and Syensqo is responding by developing materials for high-voltage data center architectures, advanced sealing materials for semiconductor manufacturing, and thermal-management solutions including direct immersion cooling fluids, while working with Microsoft on AI agents that digitally synthesize millions of candidate molecules, predict their performance through physics-based simulation, and rank them down to roughly a hundred candidates for laboratory
This MIT Technology Review Insights conversation produced in partnership with Syensqo records the views of Mike Finelli, Syensqo's chief technology and innovation officer and chief North America officer: AI is pushing semiconductors and data centers toward physical limits, which piles up more simultaneous requirements on advanced materials, and Syensqo is responding by developing materials for high-voltage data center architectures, advanced sealing materials for semiconductor manufacturing, and thermal-management solutions including direct immersion cooling fluids, while working with Microsoft on AI agents that digitally synthesize millions of candidate molecules, predict their performance through physics-based simulation, and rank them down to roughly a hundred candidates for laboratory
Chemical Society reviews Sep 16, 2026
This review addresses active lithium loss during the initial cycling and long-term operation of lithium-ion batteries by establishing a classification framework for cathode lithium-rich compensators (LRCs) based on lithium compensation mechanisms, covering binary, ternary, over-lithiated, sacrificial lithium salt, and sustained-release types; it systematically summarizes their working principles, typical charge compensation pathways, and practical performance, discusses key challenges including air instability, gas evolution, high delithiation voltage, and processing issues, highlights mitigation strategies involving nanoscale engineering, surface coating, defect and doping design, and electrolyte optimization, and further proposes a synergistic multimodal lithium-compensation strategy int
This review addresses active lithium loss during the initial cycling and long-term operation of lithium-ion batteries by establishing a classification framework for cathode lithium-rich compensators (LRCs) based on lithium compensation mechanisms, covering binary, ternary, over-lithiated, sacrificial lithium salt, and sustained-release types; it systematically summarizes their working principles, typical charge compensation pathways, and practical performance, discusses key challenges including air instability, gas evolution, high delithiation voltage, and processing issues, highlights mitigation strategies involving nanoscale engineering, surface coating, defect and doping design, and electrolyte optimization, and further proposes a synergistic multimodal lithium-compensation strategy int
Cathode lithium-rich compensators for next-generation lithium-ion batteries: classification framework, challenges, and synergistic prelithiation strategies
This review addresses active lithium loss during the initial cycling and long-term operation of lithium-ion batteries by establishing a classification framework for cathode lithium-rich compensators (LRCs) based on lithium compensation mechanisms, covering binary, ternary, over-lithiated, sacrificial lithium salt, and sustained-release types; it systematically summarizes their working principles, typical charge compensation pathways, and practical performance, discusses key challenges including air instability, gas evolution, high delithiation voltage, and processing issues, highlights mitigation strategies involving nanoscale engineering, surface coating, defect and doping design, and electrolyte optimization, and further proposes a synergistic multimodal lithium-compensation strategy int
This review addresses active lithium loss during the initial cycling and long-term operation of lithium-ion batteries by establishing a classification framework for cathode lithium-rich compensators (LRCs) based on lithium compensation mechanisms, covering binary, ternary, over-lithiated, sacrificial lithium salt, and sustained-release types; it systematically summarizes their working principles, typical charge compensation pathways, and practical performance, discusses key challenges including air instability, gas evolution, high delithiation voltage, and processing issues, highlights mitigation strategies involving nanoscale engineering, surface coating, defect and doping design, and electrolyte optimization, and further proposes a synergistic multimodal lithium-compensation strategy int
Journal of Nondestructive Evaluation Sep 16, 2026
This study conditioned concrete prisms of four compressive strengths (f′c = 32–56 MPa) and two alkali-silica reaction (ASR) damaged specimens by compressive loading, monitored the subsequent velocity recovery with coda wave interferometry (CWI), and applied a self-referencing temperature correction to remove drift from ambient fluctuations of about ±0.2°C; for intact specimens the recovery rate mv and velocity drop magnitude |c| increased with strength (|c| from 3.08×10⁻⁴ to 6.02×10⁻⁴, mv from 6.77×10⁻⁵/s to 13.76×10⁻⁵/s) and recovery time shortened from 9.9 h to 6.
This study conditioned concrete prisms of four compressive strengths (f′c = 32–56 MPa) and two alkali-silica reaction (ASR) damaged specimens by compressive loading, monitored the subsequent velocity recovery with coda wave interferometry (CWI), and applied a self-referencing temperature correction to remove drift from ambient fluctuations of about ±0.2°C; for intact specimens the recovery rate mv and velocity drop magnitude |c| increased with strength (|c| from 3.08×10⁻⁴ to 6.02×10⁻⁴, mv from 6.77×10⁻⁵/s to 13.76×10⁻⁵/s) and recovery time shortened from 9.9 h to 6.
Slow Dynamics in Concrete: Effects of Temperature, Strength Variation, and Microcracking Damage
This study conditioned concrete prisms of four compressive strengths (f′c = 32–56 MPa) and two alkali-silica reaction (ASR) damaged specimens by compressive loading, monitored the subsequent velocity recovery with coda wave interferometry (CWI), and applied a self-referencing temperature correction to remove drift from ambient fluctuations of about ±0.2°C; for intact specimens the recovery rate mv and velocity drop magnitude |c| increased with strength (|c| from 3.08×10⁻⁴ to 6.02×10⁻⁴, mv from 6.77×10⁻⁵/s to 13.76×10⁻⁵/s) and recovery time shortened from 9.9 h to 6.
This study conditioned concrete prisms of four compressive strengths (f′c = 32–56 MPa) and two alkali-silica reaction (ASR) damaged specimens by compressive loading, monitored the subsequent velocity recovery with coda wave interferometry (CWI), and applied a self-referencing temperature correction to remove drift from ambient fluctuations of about ±0.2°C; for intact specimens the recovery rate mv and velocity drop magnitude |c| increased with strength (|c| from 3.08×10⁻⁴ to 6.02×10⁻⁴, mv from 6.77×10⁻⁵/s to 13.76×10⁻⁵/s) and recovery time shortened from 9.9 h to 6.

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