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A recent study shows that neural symbolic regression offers a route to automated discovery of governing equations for network dynamics across high-dimensional complex systems.
Digital twins of self-driving chemistry laboratories may help reduce reliance on costly real-world experimentation and enable the testing of hypothetical automated workflows in silico.
SynGFN integrates synthesis constraints directly into the chemical design process. The result is a generative framework that produces diverse, high-quality molecules that can be readily synthesized in the laboratory.
A recent study demonstrates the applicability of quantum computers for multi-objective optimization, bringing quantum computing a step closer towards practical applications.
Scouter, a deep learning approach, predicts transcriptional responses to genetic perturbations by integrating large language model (LLM)-based gene embeddings with a lightweight compressor–generator neural network, providing valuable insights into the application of LLMs to biological research.
A recent study demonstrates the efficiency of quantum-mechanical modeling of material properties by mapping the problem onto neuromorphic device architectures.
Mass spectrometry data analysis has long been limited to known molecules and exact matches. In a recent manuscript, a scalable search algorithm is proposed for uncovering both known compounds and novel molecular variants, enabling insights into natural product biosynthesis.
A framework called AUTOENCODIX benchmarks diverse autoencoder architectures in biological molecular profiling data, enabling insights from complex, multi-layered data.
Quantum computers are inching closer to practical deployment, but shielding fragile quantum information from errors is still very challenging. Now, a machine-learning-based decoder offers a strategy for rectifying errors in logic quantum circuits, hastening the advent of reliable and fault-tolerant quantum systems.
A recent study proposes efficient numerical algorithms to reduce the required computational resources for solving the edge states in large-scale photonic or acoustic structures.
Research now suggests that large language models (LLMs) are viable in silico models of human language processing. By examining multi-participant high-quality brain responses, researchers were able to break new ground in the validation of this proposal, which could dramatically reduce the barrier to studying how language is processed in the human brain.
A systematic comparison of large language models suggests that larger models align better with both human behavior and brain activity during natural reading. Instruction tuning, however, does not yield a similar benefit.
A recent study highlights how data changes not only how we can assess the performance of legal firms in the US, but more broadly how computational science is expanding beyond its traditional scope and into the legal field.
A recent study proposes using a single neural network to model and compute a wide range of solid-state materials, demonstrating exceptional transferability and substantially reduced computational costs — a breakthrough that could accelerate the design of next-generation materials in applications from efficient solar cells to room-temperature superconductors.
The Large Perturbation Model (LPM) is a computational deep learning framework that predicts gene expression responses to chemical and genetic perturbations across diverse contexts. By modeling perturbation, readout, and context jointly, LPM enables in silico hypothesis generation and drug repurposing.
The recent computational model ‘BRyBI’ proposes that gamma, theta, and delta neural oscillations can guide the process of word recognition by providing temporal windows for the integration of bottom-up input with top-down information.
Rapid identification of pathogenic viruses remains a critical challenge. A recent study advances this frontier by demonstrating a fully integrated memristor-based hardware system that accelerates genomic analysis by a factor of 51, while reducing energy consumption to just 0.2% of that required by conventional computational methods.
A recent study demonstrates the potential of using in-memory computing architecture for implementing large language models for an improved computational efficiency in both time and energy while maintaining a high accuracy.
A recent study proposed ZeoBind, an AI-accelerated workflow enabling the discovery and experimental verification of hits within chemical spaces containing hundreds of millions of zeolites.
