Allan Chain<p>Our review paper <b>Deep Learning Quantum Monte Carlo for Solids</b> is now alive!</p><p>Deep learning has revolutionized <i>ab initio</i> calculations, and this review dives into how neural networks are pushing the boundaries of electronic structure simulations for solids and other periodic systems. Starting from the basic theories, we explore recent methodological advancements, various applications (energy, polarization, stress), and future outlooks.</p><p>Click the link below to read more!</p><p><a href="https://venera.social/search?tag=deep-learning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>deep-learning</span></a> <a href="https://venera.social/search?tag=quantum" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>quantum</span></a> <a href="https://venera.social/search?tag=quantum-monte-carlo" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>quantum-monte-carlo</span></a> <a href="https://venera.social/search?tag=physics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>physics</span></a><br><a href="https://onlinelibrary.wiley.com/share/author/7QWVNKJEB2VYZHN9J7II?target=10.1002/wcms.70015" rel="nofollow noopener noreferrer" target="_blank">Deep Learning Quantum Monte Carlo for Solids</a></p>