Crystal– tag –
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Google DeepMind’s GNoME Discovers 2.2 Million New Crystals: Accelerating Materials Science by 800 Years, Offering Novel Superconductor and Battery Candidates
Facebook (DeepMind) UK Overview Google DeepMind's AI tool, GNoME, has reportedly discovered 2.2 million new inorganic crystals, accelerating materials science progress by 800 years. GNoME leveraged AI models trained on existing materials... -
ACS Paper: Modular ML Workflow Achieves High-Efficiency Hypothesis Generation for Lithium Solid Electrolytes, Predicting Ionic Conductivity
The Journal of Physical Chemistry C | ACS Publications USA Overview A research published in 'The Journal of Physical Chemistry C' (ACS Publications) presents a modular machine learning (ML) workflow for generating and prioritizing lithiu... -
MIT Unveils CrysVCD Framework, Boosting AI-Designed Material Stability by 70% and Dramatically Reducing Computational Costs
MIT News USA Overview MIT researchers have developed 'CrysVCD' (crystal generator with valence-constrained design), a framework that significantly improves the stability rate of AI-generated materials. This approach ensures designs satis... -
MIT’s CrysVCD Framework Achieves 70% Stability in AI Crystal Material Design at Generation Stage, Significantly Boosting Efficiency
Superpower Daily USA Overview MIT researchers have developed the CrysVCD framework, combining language and diffusion models to dramatically improve the chemical stability of AI-designed crystalline materials at the generation stage, sign... -
Mira Proposes “Fifth Paradigm” of AI in Materials Science: Integrating MatterGen, A-Lab, GNoME for Autonomous Discovery
Mira USA Overview Mira has proposed the "Fifth Paradigm" of AI in materials science, asserting that the integration of generative design (MatterGen), quantum chemistry simulations, and closed-loop laboratories (A-Lab) will accelerate aut... -
Accelerating Deep-Ultraviolet Nonlinear Optical Material Discovery: Integrated MLIP-First Principles Framework Demonstrated in LiB2O3F
ACS Publications USA Overview An integrated framework for accelerating the discovery of deep-ultraviolet (deep-UV) nonlinear optical materials has been established, demonstrating its efficacy in the LiB2O3F system. This framework combine... -
COMPACK Program Aids CSP-MACE-Å in Temperature-Dependent Polymorph Prediction via Distance-Based Crystal Structure Similarity Identification
ResearchGate International Overview A research paper introduces 'COMPACK,' a program for identifying crystal structure similarity using distances. This program supports organic crystal structure prediction (CSP), where generative models ... -
ArXiv Unveils ‘CrystalGRPO’: A Target-Aligned Reinforcement Learning Framework for Flow-Based Crystal Structure Prediction
arXiv International Overview The paper "CrystalGRPO" introduces a target-aligned and coverage-preserving reinforcement learning framework for flow-based generative crystal structure prediction (CSP). This framework combines MACE predicte... -
ArXiv Presents ‘ED-CSP’: A Machine Learning Framework for Crystal Structure Prediction from Sparse Electron Diffraction
arXiv International Overview The paper "ED-CSP" introduces a machine learning framework for predicting crystal structures from sparse electron diffraction (ED) observations. This model co-predicts lattice parameters and fractional atomic... -
OAE Publishing: Closed-Loop Integration of LLMs and AI Agents Drives Inorganic Materials Discovery, A-Lab Realizes 36 Compounds in 17 Days
OAE Publishing Inc. USA Overview OAE Publishing reports that the integration of large language models (LLMs) and AI agents is shifting inorganic materials discovery from prediction to experimental realization. LLMs orchestrate a closed-l...
