17 papers · ranked by Valyu relevance
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Authors not listed
Bridging AI and self-driving laboratories, we introduce the first fully-automated, closed-loop molecular discovery cycle, exemplified by the identification of novel JAK inhibitors. With minimal human intervention, we combined AI-driven molecular design and retrosynthesis with IBM’s synthesis automation system RoboRXN…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Authors not listed
Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…
Authors not listed
Recent advances in generative artificial intelligence have enabled in silico molecular design to become a powerful approach for exploring chemical space toward specific design goals across various domains. However, in actual design workflows, determining the appropriate generation conditions, including generative…
Authors not listed
This article presents an overview about the state of the art in the development of structured packings for distillation applications. The focus is on highlighting different approaches including heuristic development cycles, the development of new packing structures, 3D-printing as tool for manufacturing, and…
Authors not listed
The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition…
Authors not listed
Agentic artificial intelligence (AI) is poised to redefine how science is conducted, automating not just data analysis but the entire research lifecycle, from hypothesis generation to validation. Yet most current AI agents remain domain-bound, tailored to specific applications such as materials synthesis or quantum…
Authors not listed
Generating novel, drug-like molecules with realistic synthetic pathways is an essential goal in computer-aided drug discovery, yet generative models often lack synthesis awareness, resulting in compounds that are difficult or impossible to produce. To overcome this limitation, models must optimize not only molecular…
Authors not listed
Current methods for predicting molecular porous materials typically exploit prior knowledge of similar systems, which biases the final outcome to a limited exploration space. To design novel structures and materials, the community must be able to evaluate and model all candidates without any bias. In this paper, we…
Authors not listed
Exploring the potential energy surface to sample transition state regions is crucial to understand the atomic processes that govern chemical reactivity. Ideally, the exploration does not require any collective variables that are based on prior chemical domain knowledge. With this in mind, we adapt the stochastic saddle…
Authors not listed
Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…
Authors not listed
Predicting how chemical modifications affect drug binding is central to rational drug design. Free Energy Perturbation (FEP) calculations provide accurate estimates of these binding affinity changes, but existing methods often require substantial computational resources and expert knowledge. Here we present QligFEP…
Authors not listed
We describe a collaborative research project spanning the disciplines of quantum hardware, quantum algorithms, conventional computational chemistry, synthetic medicinal chemistry and life sciences. Our project seeks to demonstrate an impact of quantum computing on human health. It is one of several funded by Wellcome…
Authors not listed
Molecular generation for pharmaceutical and agrochemical development remains computationally expensive and statistically under-validated. We conducted a comprehensive benchmark of five molecular generation methods—Genetic Algorithm, Proximal Policy Optimization (PPO), REINFORCE, Transformer-based generation, and random…
Authors not listed
Chemistry curricula often separate “wet” experimental work from “dry” computation, yet modern discovery increasingly demands both. This Perspective offers an instructor-ready roadmap to train “hybrid chemists” within existing courses. We distill recent advances in machine learning, automation, and real-time analytics…
Authors not listed
Meta-GGA density functional theory (DFT) is an important method in ab initio materials modelling; however, its computational cost limits applicability for generating large datasets or simulating extended length and time scales, as necessary for modern materials discovery. Deorbitalization is a promising strategy to…